Temukan karya ilmiah dan simpan jejak pengetahuan komunitas.

Cash waqf is an Islamic financial instrument that has the potential to fund social projects, particularly in addressing inequality and poverty issues. However, the traditional management still faces challenges, such as limited transparency and public participation reach. Digitization through fintech with crowdfunding modeling was considered capable of increasing donor participation while improving the effectiveness of management and distribution. This research aimed to provide a comprehensive overview of the development of cash waqf, mapping the trends, identifying the main factors influencing its development, and exploring gaps, particularly in the context of integration with crowdfunding models. The process led to the adoption of performance analysis and science mapping methods. Performance analysis was conducted descriptively to measure productivity and impact through bibliometric indicators. Science mapping was used to review the research landscape by analyzing the relationships and structures of topics, authors, and relevant institutions. It was also used to identify collaboration patterns, thematic focuses, including under-researched areas, and integration with crowdfunding platforms. The results showed that regulatory aspects, contemporary digital technology innovations, and the role of Islamic financial institutions were factors influencing the development of cash waqf research. Topic modeling analysis proved that the exploration on the integration of cash waqf and crowdfunding was limited. This led to a gap in research on the governance of digital waqf platforms, as well as strategies to enhance public participation through technology. The research provided a map that served as a reference, including used by regulators and platform developers in designing policies and innovations for digital waqf products.

<jats:p>This study examines factors influencing library visit frequency in the digital era, where declining reading interest and changing user behavior pose significant challenges. It analyzes the relationship between on-site reading, book circulation by classification, and visit intensity, while identifying the most influential factors driving visits. A quantitative correlational design with a non-parametric approach was applied using secondary data from the Inlislite system of the Metro City Library and Archives Service, covering January 2023 to August 2024. Spearman correlation was employed to assess the strength and direction of relationships among variables. The findings reveal that on-site reading activity has a very strong positive correlation with visit frequency. Collections in literature, social sciences, and religion show moderate correlations, whereas pure and applied sciences exhibit no significant relationship. These results highlight the strategic importance of reading spaces and popular collections in fostering user engagement. This study provides practical implications for library management, including optimizing reading areas, aligning collections with user interests, and promoting underutilized materials. It contributes to the literature by integrating reading, circulation, and visitation dimensions into a unified data-driven framework to support adaptive and user-centered library services.</jats:p>

<jats:p>This study analyzes the effect of transparent digital nudging on digital consumer loyalty through perceived autonomy and consumer trust on digital platforms in Indonesia. An explanatory quantitative study was conducted on 400 digital consumers using SEM-PLS. The analysis results show that transparent digital nudging has a significant effect on perceived autonomy with a large effect size (f2 0.375), and perceived autonomy subsequently strengthens consumer trust with a small effect size (f2 0.100). Consumer trust was found to be the strongest predictor of loyalty with a large effect size (f2 0.742), while nudging and autonomy do not directly affect loyalty. Serial mediation testing was proven significant with a moderate mediation effect (v 0.092). These results confirm that long-term digital consumer loyalty is formed through trust-based relational mechanisms, not through instant digital design interventions. This study expands the relationship marketing literature by positioning digital choice architecture design as a consumer relationship management practice.</jats:p>

<jats:p>This paper seeks to explain the problem of widespread consumer data theft and the significance of online consumer data privacy in a review of Islamic values-based digital ethics. This research employs a qualitative approach by reviewing a variety of books, journals, papers, and other reputable sources, which are then examined using content analytic tools. The findings of this research indicate that first and foremost, effective cybersecurity will secure customer data. Second, ethical conduct derived from Islamic beliefs will be able to deter criminal activity and data breaches against consumers. Data privacy is a fundamental right that must be maintained collectively, according to Islam. The key to combating crime and breaches in the digital environment is hence the precautionary principle of stakeholders.==========================================================================================================ABSTRAK – Etika Digital Berbasis Islam: Fenomena Keamanan Data Konsumen Online. Penelitian ini bertujuan untuk menjelaskan fenomena maraknya pencurian data konsumen serta melihat posisi penting privasi data konsumen online dalam tinjauan etika digital berbasis nilai Islam. Penelitian ini menggunakan metode kualitatif, dengan menelaah berbagai literatur baik buku, jurnal, laporan dan sumber reliable lainnya yang kemudian dianalisis menggunakan teknik content analysis. Hasil penelitian ini menemukan bahwa, pertama, keamanan siber yang baik akan mampu melindungi data konsumen. kedua, perilaku etis yang bersumber dari nilai Islam akan mampu mencegah kejahatan dan pelanggaran data konsumen. Ketiga, dalam Islam data privasi merupakan hak dasar yang harus dihormati secara bersama-sama. Untuk itu prinsip kehati-hatian daripara stakeholder menjadi kunci melawan kajahatan dan pelanggaran diruang digital.</jats:p>

Papuan Batik motifs began to appear in 1984 and are only known in general with one designation, namely Papuan batik, even though these various motifs can be classified according to the area of origin of manufacture, culture and flora and fauna that have meaning. local wisdom of the community. The ability to recognize every batik motif from Papua requires experience and knowledge from certain circles, so that knowledge and meaning are maintained, a tool is needed that can classify various batik motifs from Papua. This study uses four classes of datasets, namely Cendrawasih Motifs, Raja Ampat Motifs, Tifa Honai Motifs and Asmat Motifs which are non-geometric. It is proposed to classify these motifs using deep learning using Vgg16 and Resnet50 architectures with fine tuning, to add data with various combinations in order to obtain better performance. The test results showed that without data augmentation on the VGG16 architecture, an accuracy of 78.79% was obtained and the Resnet50 architecture obtained an accuracy of 81.82% with several combinations of augmentation techniques giving the same better results without data augmentation with the results on the VGG16 architecture giving 84 results., 85% and on the Resnet50 architecture it gives a yield of 87.88%.

Tropical fungal skin infections, including Tinea corporis, Tinea versicolor, Tinea pedis, and Tinea nigra, are common health problems in tropical countries such as Indonesia. Although not life-threatening, these diseases can cause discomfort, reduce self-confidence, and interfere with daily activities. Conventional diagnostic methods still rely on subjective visual observation, which is often inaccurate—especially in regions with limited infrastructure and scarce access to specialists. Moreover, existing studies rarely provide a comparative evaluation of deep learning architectures for tropical fungal infections using small and diverse datasets. Therefore, this study aims to address these challenges by conducting a systematic comparative evaluation of three pre-trained models—MobileNetV3, EfficientNet-B2, and SE-ResNet101—to determine the most accurate and computationally efficient architecture for multi-class classification of tropical fungal skin diseases. In this study, a dataset of 660 clinical skin images sourced from the Kaggle repository was used, covering four tropical fungal infection classes. The dataset consisted of 165 images per class, which were divided into training, validation, and testing subsets. Experimental results demonstrated that MobileNetV3 achieved the best performance, with a validation accuracy of 95.08%, a test accuracy of 97.34%, and the shortest training time of 15 minutes, compared to EfficientNet-B2 (16 minutes) and SE-ResNet101 (22 minutes). The main contribution of this study is to provide a systematic comparative evaluation of deep learning models for the classification of tropical fungal skin infections, while recommending MobileNetV3 as the most suitable model for practical implementation of automated image-based diagnosis in primary healthcare services with limited resources.

This study develops an AI-based chatbot to provide education and recommendations for using Papuan herbal tea. The goal is to raise awareness and dispel traditional knowledge about Papuan herbal tea, which has historically been weak in local communities. The methods used include problem analysis, literature review, data collection, system testing using Large Language Models (LLM), and implementation using Google Generative AI (Gemini-Pro). This system is designed to analyze text queries and provide relevant answers using text-based query syntax. Pengujian is carried out using the BERTScore method to assess the system's adherence to the reference data. The study's results indicate very good performance, with a rata-rata Precision of 0.58357/58 %, a Recall of 0.66781/66%, and an F1-Score of 0.62205/62. %. More precise recall indicates that the system can capture a lot of pertinent information, even when there are performance differences between questions. This study demonstrates the potential of AI technology to improve understanding between local communities and the general public as well as to support local Papuan kearifan events

Deteksi kematangan tomat sangat penting untuk pertanian dan industri pertanian. Pendekatan pembelajaran mendalam baru-baru ini menunjukkan bahwa mereka dapat menangani masalah yang melibatkan deteksi objek, termasuk deteksi buah. Untuk menentukan tingkat kematangan tomat, algoritma You Only Look Once (YOLOv5) akan digunakan dalam penelitian ini. Teknik ini menggunakan satu tahap yang menyatukan proses lokalisasi dan deteksi. Dataset yang kami gunakan untuk pelatihan dan pengujian algoritma YOLOv5 berisi gambar tomat pada berbagai tahap kematangan. 981 total foto untuk data train, 121 data validasi, dan 64 data test. Temuan pengujian menunjukkan akurasi yang sangat baik dengan mana algoritma YOLOv5 dapat mengidentifikasi dan mengkategorikan kematangan tomat. Studi ini memajukan teknik untuk mendeteksi kematangan buah dan dapat diterapkan pada kontrol kualitas tomat sektor pertanian. Temuan penelitian ini ditunjukkan oleh nilai akurasi deteksi maksimum, yaitu 73%.

Playing music through a digital platform that has a large database of songs requires automated classification of music genres, highlighting the need to develop a model for music genre classification that is more efficient and accurate. This study evaluated the hyperparameters in the music genre classification process using CNN in the GTZAN dataset with 30-second duration data optimized using MFCC feature extraction. The model that is formed with a time of 3 (three) seconds classifies music genres in the first 3 seconds of music. This model has a high potential for error because the first 3 seconds of initial music are varied and cannot be used as a benchmark in determining music genres. This study performed hyperparameters on batch size, epoch, and split data set variables with various scenarios. The highest precision result was obtained at 72% with a data split of 85%:15%, 32 batch sizes, and 500 epochs.

Nowadays, humans cannot be separated from information technology; this significantly impacts various fields, especially educational administration. Academic services that were initially manual have now transformed into digital, where academic data, both information and other administration, can now be handled by information systems. This study aimed to determine the effectiveness of implementing the integrated academic information system (SIAT) that has been running in Islamic universities. Data collection was carried out using a descriptive qualitative method concerning the field research approach combined with data triangulation; data collection was obtained by distributing questionnaires, qualitative interviews, and observations. The findings were that SIAT could increase the effectiveness and efficiency of academic services at the Faculty, ease of accessing information, especially in KRS filling activities, KHS checking, lecture monitoring, lecturer grade input, and distribution of lecture schedules can be done in one system. The obstacles felt were few, especially if the system was undergoing maintenance, data loading was hampered, and some students did not understand how to operate the information system. The solution that can be done is to hold periodic socialization regarding the implementation of SIAT and to update the system so that it remains optimal in its operation.

Media sosial telah menjadi bagian penting dalam kehidupan masyarakat, namun peningkatan penggunaannya sering dikaitkan dengan dampak negatif terhadap kesehatan mental seperti stres, adiksi, FoMo, dan insomnia. Upaya prediksi risiko penggunaan media sosial dapat membantu menjaga kesehatan mental dengan memanfaatkan teknik data mining. Penelitian ini menggunakan metodologi CRISP-DM sebagai kerangka utama serta Exploratory Data Analysis (EDA) untuk mengidentifikasi tren dan anomali yang mendukung proses pemodelan. Beberapa algoritma supervised learning seperti C4.5, k-NN, dan Naïve Bayes diterapkan untuk memprediksi dampak negatif penggunaan media sosial terhadap kesehatan mental. Hasil eksperimen menunjukkan bahwa Naïve Bayes memberikan kinerja terbaik dengan akurasi tertinggi sebesar 92,5%, melampaui C4.5 dan k-NN. Integrasi EDA dan CRISP-DM terbukti menghasilkan model prediksi yang akurat, meskipun penerapan EDA memerlukan waktu tambahan dalam analisis. CRISP-DM berperan penting dalam menyediakan kerangka kerja yang sistematis sehingga membantu peneliti bekerja lebih terstruktur dan mengurangi risiko kesalahan. Selain itu, temuan memperlihatkan bahwa semakin lama seseorang menggunakan media sosial, semakin besar dampak negatif yang dialami, terutama bagi mereka yang menghabiskan waktu lebih dari lima jam per hari. Secara keseluruhan, hasil penelitian ini memberikan kontribusi terhadap pengembangan model prediksi berbasis data mining dan dapat menjadi landasan bagi upaya pencegahan gangguan kesehatan mental akibat penggunaan media sosial. Abstract Social media has become an integral part of modern life, enabling users to express feelings and opinions. However, its increasing use has been linked to negative impacts on mental health, such as stress, addiction, FoMo, and insomnia. Predicting the risks associated with social media use can help maintain mental well-being, and this can be achieved through data mining techniques. This study applies the CRISP-DM methodology as the main framework, complemented by Exploratory Data Analysis (EDA) to identify trends and anomalies that support the modeling process. Several supervised learning algorithms, including C4.5, k-NN, and Naïve Bayes, were employed to predict the negative impact of social media use on mental health. Experimental results show that Naïve Bayes achieved the best performance with the highest accuracy of 92.5%, outperforming both C4.5 and k-NN. The integration of EDA and CRISP-DM proved effective in producing accurate predictive models, although EDA required additional time for data analysis. CRISP-DM played a crucial role in providing a systematic framework, enabling researchers to work more structurally and minimizing the risk of errors. Furthermore, findings indicate that the longer individuals spend on social media, the greater the negative impact they experience, particularly among those using it for more than five hours per day. Overall, this study contributes to the development of predictive models based on data mining and provides insights that may support preventive efforts against mental health issues caused by excessive social media use.

Ornamental plants possess high aesthetic value and environmental benefits, yet identifying their species often poses a challenge, especially for beginners. This study aims to develop an Android-based application employing the You Only Look Once version 8 (YOLOv8) algorithm to detect ornamental plant species through leaf images in real-time. The dataset comprises 1,096 images of ornamental plant leaves, including snake plant (Sansevieria), aloe vera (Aloe vera), and coral cactus (Cereus peruvianus). The data were annotated using bounding box techniques, and the model was trained on Google Colab with an 80:20 split between training and testing datasets. The training resulted in an accuracy rate of 96% based on the mean Average Precision (mAP) metric. The application was developed using Android Studio with a user-friendly interface, enabling real-time detection on Android devices with a minimum RAM specification of 3 GB. Application testing involved black-box testing to ensure functionality and usability testing with 31 respondents, revealing a user satisfaction rate of 87%. Some challenges encountered included the impact of lighting on detection accuracy and result variability across different devices. This study contributes to the utilization of artificial intelligence technology for biodiversity education and supports environmental conservation efforts

The city of Sorong, as one of the largest cities in the Southwest Papua region, is facing serious problems due to potholes in various areas. This problem causes the risk of accidents and vehicle damage, disrupts the mobility of city residents, and hinders sustainable infrastructure development. Therefore, we need a system that is efficient and accurate in detecting potholes quickly. In this study, researchers used the "You Only Look Once" (YOLO v5) method to detect potholes in Sorong City. YOLO v5 is a real-time object detection algorithm that has been proven to have high speed and accuracy in recognizing objects. This approach allows for instant detection of potholes as vehicles pass by, so authorities can quickly take the necessary remedial action. This study aims to implement pothole detection technology using YOLO v5, with the hope of increasing efficiency and accuracy in overcoming potholes in Sorong City and improving traffic safety by detecting potholes in real-time. The results of this study obtained the highest training precision, namely 96.6% and the accuracy value of the pothole detection system test.

The wood processing industry in Indonesia, especially in the Southwest Papua region, faces ongoing challenges in accurately counting wooden logs, a task traditionally performed manually. Manual methods are time-intensive and prone to error, leading to inefficiencies in operations and weaknesses in resource management. This study addresses these challenges by applying a deep learning-based object detection approach, specifically the You Only Look Once version 8 (YOLOv8) algorithm, to automate the detection and counting of wooden beams in real time. YOLOv8 was chosen for its ability to perform high-speed and accurate detection even under varying environmental conditions, such as different lighting levels and camera angles. The model was trained on a custom dataset consisting of 265 annotated images of wooden beams, with a split of 70% for training, 20% for validation, and 10% for testing. Performance evaluation using a confusion matrix revealed a detection accuracy of 94%. These findings suggest that YOLOv8 is highly effective in supporting automation within wood processing workflows. By reducing dependency on manual labor and minimizing counting errors, the system contributes to more accurate inventory tracking and enhanced productivity. This research demonstrates the potential of integrating AI-driven models into mobile and industrial applications for improved efficiency in forestry-related sectors.

Merupakan warisan budaya Indonesia, batik Papua hadir dengan ragam motif, selain dikenal dengan motif daerah asal pembuatannya, motif corak budaya serta corak flora dan faunapun mewarnai keragaman motif batik dari Papua. Kemampuan untuk mengenal motif – motif tersebut, menjadi tantangan untuk menemukan model dalam mengklasifikasi motif batik dari Papua untuk melestarikannya. Penelitian ini menggunakan deep learning dalam mengklasifikasi motif batik dari Papua dengan melakukan transfer learning menggunakan arsitektur efficienNet dengan transfer learning dan teknik agumentasi data Hasil pengujian menggunakan empat kelas dataset memperoleh dengan arsitektur EfficientNet-B2 dengan fine tuning memberikan akurasi 72% dan ditambahkan teknik augmentasi tertinggi dengan menggunakan teknik ColorJitter dan Contrast dengan hasil 90%.

Keretakan permukaan gedung merupakan salah satu permasalahan yang sering terjadi pada bangunan, yang disebabkan oleh faktor-faktor seperti bencana alam, perubahan temperatur, kelembaban tinggi, dan penggunaan bahan bangunan berkualitas rendah. Permasalahan ini dapat mengancam keselamatan dan kenyamanan pengguna bangunan jika tidak segera ditangani. Metode konvensional dalam mendeteksi keretakan pada bangunan sering kali melibatkan inspeksi manual yang memakan waktu dan berisiko tinggi. Penelitian ini bertujuan untuk mengembangkan sistem deteksi keretakan permukaan gedung menggunakan algoritma YOLO (You Only Look Once) berbasis web. Sistem ini dirancang untuk meningkatkan efisiensi dan akurasi dalam mendeteksi keretakan dengan memanfaatkan teknologi pemrosesan citra dan pembelajaran mendalam (deep learning). Algoritma YOLO dipilih karena kemampuannya dalam melakukan deteksi objek secara dengan tingkat akurasi yang tinggi. Hasil evaluasi menunjukkan bahwa sistem deteksi keretakan permukaan gedung menggunakan algoritma YOLOv8 berbasis web berhasil mencapai tingkat akurasi sebesar 54,8%, dengan nilai precision sebesar 62,9%, recall sebesar 81,1%, F1-score sebesar 70,1%, dan nilai mAP sebesar 75,1%. Dengan demikian, sistem ini diharapkan dapat menjadi solusi yang efektif dan efisien dalam memantau kondisi bangunan, mengurangi risiko kecelakaan kerja, dan meningkatkan keselamatan serta kenyamanan pengguna gedung.

A marketplace is a place in cyberspace where there are commercial activities between buyers and sellers. Products offered from the marketplace have reviews to review. Shopee is the most visited marketplace by people and offers various products. Product reviews can provide benefits for other consumers in assessing the products offered. By utilizing NLP technology in particular, this study can classify positive sentiment and negative sentiment in product review data. The IndoBERT model is a model that can be used in NLP technology by utilizing the relationship between each input and output element as well as the weights to be calculated simultaneously. By utilizing this technology, sentiment analysis on Shopee product reviews provides maximum accuracy until 93% with different training conditions. This provide that IndoBERT model can show that the performance of the indoBERT model in this research is very good.

Abstrak— Keselamatan kerja di industri konstruksi merupakan hal yang sangat penting untuk mencegah kecelakaan dan cedera. Namun, masih sering terjadi pelanggaran dalam penggunaan helm keselamatan. Oleh karena itu, penelitian ini bertujuan untuk mengembangkan sistem deteksi penggunaan helm keselamatan di area konstruksi menggunakan metode YOLO (You Only Look Once). Dataset yang digunakan dalam penelitian ini terdiri dari 694 gambar yang diambil menggunakan smartphone dan sumber internet. Gambar-gambar tersebut menampilkan orang-orang yang menggunakan berbagai jenis penutup kepala dan model rambut, baik yang menggunakan helm keselamatan maupun yang tidak. Metode yang digunakan dalam penelitian ini adalah YOLO (You Only Look Once), sebuah teknologi deteksi objek real-time yang menggunakan deep neural network. YOLO digunakan untuk melakukan anotasi pada setiap gambar dataset dengan gambar objek, diikuti oleh beberapa lapisan yang terhubung sepenuhnya. Selanjutnya, kinerja model diukur menggunakan beberapa metode, yaitu Recall, Precision, F1, Intersection over Union, mean Average Precision, dan Accuracy. Hasil penelitian menunjukkan bahwa sistem deteksi penggunaan safety helmet yang dikembangkan dapat mencapai tingkat akurasi yang tinggi, dengan nilai Precision sebesar 0,95 dan Recall sebesar 0,93. Dengan demikian, sistem ini dapat membantu meningkatkan keselamatan kerja di area konstruksi dengan memberikan pengawasan yang lebih efektif terhadap penggunaan safety helmet oleh para pekerja.

Penyandang disabilitas, khususnya tunarungu, menghadapi tantangan besar dalam berkomunikasi, terutama di perguruan tinggi yang seringkali belum sepenuhnya inklusif. Penelitian ini bertujuan untuk mengembangkan sistem deteksi Bahasa Isyarat Indonesia (SIBI) berbasis Android menggunakan framework MediaPipe dan teknologi deep learning. Dataset penelitian mencakup 24 kelas huruf abjad statis yang diambil menggunakan kamera smartphone dan sumber daring. Proses pengembangan melibatkan ekstraksi fitur menggunakan MediaPipe dan integrasi ke platform Android. Pengujian menunjukkan model mencapai akurasi 90,85%, meskipun terdapat tantangan pada kelas dengan pola isyarat yang mirip. Hasil usability testing dengan 60 pengguna mencatat rata-rata kepuasan 83,51%, menunjukkan respons positif terhadap kemudahan penggunaan dan manfaat aplikasi. Aplikasi ini memungkinkan deteksi isyarat secara akurat dan cepat, serta stabil di berbagai kondisi. Hasil penelitian ini penting untuk meningkatkan apksesibilitas komunikasi penyandang tunarungu melalui solusi teknologi yang mudah diakses dan aplikatif.

Air merupakan kebutuhan dasar bagi kehidupan khususnya bagi manusia yang selama hidupnya selalu memerlukan air, namun demikian air dapat menjadi malapetaka bilamana tidak tersedia dalam kondisi yang benar. Kualitas air menyatakan tingkat kesesuaian air terhadap penggunaan tertentu dalam memenuhi kebutuhan hidup manusia, mulai dari air untuk memenuhi kebutuhan langsung seperti minum dan kebutuhan sehari-hari lainnya, air irigasi atau pertanian, peternakan, perikanan, rekreasi dan transportasi. Kualitas air dapat diketahui dengan melakukan pengujian tertentu terhadap air tersebut. Pengujian yang biasa dilakukan adalah uji kimia, dan fisika. Pada penelitian ini bertujuan untuk mengidentifikasi serta memetakan kualitas air sumur bor warga Kota Sorong berdasarkan parameter fisik dan kimia. Pengukuran parameter fisik terdiri dari turbidity (kekeruhan), salinitas, konduktivitas, TDS, ORP dan parameter kimia terdiri dari pH, DO, dan Fe. Sampel yang digunakan pada penelitian ini terdiri dari empat belas titik sumur bor warga yang tersebar di Kota Sorong, data hasil pengukuran sampel yang dilakukan secara in-situ (pengukuran langsung di lapangan). Dari hasil analisa 28,57 % titik sampel yang memenuhi baku mutu kelas II, III dan IV, 21,43 % yang memenuhi baku mutu kelas III, dan 50 % titik sampel yang hanya memenuhi baku mutu kelas IV. Adapun hasil kualitas air sumur bor warga Kota Sorong diolah dengan menggunakan aplikasi sofware untuk pemetaan arcgis dengan parameter TDS 100 % memenuhi baku mutu, pH 92,8 % memenuhi baku mutu, FE 50 % memenuhi baku mutu, dan DO untuk kelas II 25 % memenuhi baku mutu, kelas III 21,4% memenuhi baku mutu, dan kelas IV 50 % memenuhi baku mutu.

The global database underscores Indonesia's scant 0.47% global contribution, of which 7.7% is from humid tropical climates. However, existing infiltration studies have primarily focused on specific soil textures and limited research points, resulting in a lack of comprehensive data. This knowledge gap is particularly evident in the Papua region of Indonesia, which boasts many small watersheds with abundant water resources but limited hydrologic data, especially regarding infiltration rates. Previous studies indicated that Horton and Philip's models excelled in equatorial regions but were limited in the number of soil textures and watersheds analysed or focused mainly on larger watersheds. Therefore, this study aimed to address this research gap by conducting a comprehensive analysis of the performance of the Horton and Philip's models across different soil textures in small watersheds, using the Hydrologic Soil Group classification as a reference. A performance analysis was conducted to assess Horton and Philip's performance using the Moriasi technique (based on R, RSR, and NSE best values). Field observations were conducted at 95 points in eleven small watersheds in Papua as representatives of equatorial small watersheds globally. Observations were suspended for 48 hours if rainfall occurred; thus, ten months were needed to finish the observation. The results of this study demonstrated that the Horton model performed exceptionally well for six of nine soil textures, whereas the Philip's model showed excellent performance for five out of nine. The obtained research results were compared with similar studies from Ghana, Nigeria, and India, reinforcing the conclusion that globally, the Horton and Philip's model effectively describes infiltration rates in equatorial small watersheds. Further research was recommended in equatorial small watersheds with sand and loamy sand soil textures, two of the nine soil textures that were not covered in the conducted study.

Air merupakan sumber kehidupan manusia, binatang dan tanaman. Dalam kehidupan manusia, air dipergunakan dalam semua bidang kehidupan. Dalam kegiatan rumah tangga, air dimanfaatkan untuk minum, mencuci, mandi, dan kakus. Air sangat berfungsi dan berperan penting bagi kehidupan makhluk hidup di bumi ini. Penting bagi kita sebagai manusia untuk tetap selalu melestarikan dan menjaga agar air yang digunakan tetap terjaga kelestariannya.Dalam penelitian ini bertujuan untuk mengetahui gambaran kualitas air sumur bor warga Kabupaten Sorong dari Distrik Aimas – Distrik Mariyat berdasarkan parameter fisik, kimia dan mikrobiologi. Pengukuran parameter fisik terdiri dari turbidity (kekeruhan), salinitas, konduktivitas, TDS, ORP dan parameter fisika terdiri dari pH, DO dan Fe. Sampel yang digunakan dalam penelitian ini terdiri dari dua puluh titik sumur bor warga yang tersebar di Kabupaten Sorong dari Distrik Aimas – Distrik Mariyat, data hasil pengukuran sampel yang dilakukan secara in-site (pengukuran langsung di lapangan). Sedangkan parameter mikrobiologi terdiri dari E.coli dan Total bakteri coliform sampel yang digunakan dalam penelitian ini terdiri dari sembilan titik sampel yang mewakili, data hasil pengukuran sampel dilakukan di laboratorium PT.TIRTA REMU.Dari hasil analisa dua puluh titik sampel yang melalui uji parameter fisik dan kimia hanya 20% titik sampel yang memenuhi baku mutu kelas I, 80% memenuhi baku mutu kelas II,III dan IV, sedangkan sembilan titik sampel yang di uji parameter fisik, kimia dan mikrobiologi hanya 11 % memenuhi baku mutu kelas I, dan 89% yang memenuhi baku mutu kelas II, III dan IV. Adapun gambaran kualitas air sumur bor warga Kabupaten Sorong diolah dengan menggunakan aplikasi software arcgis dengan parameter pH 75% memenuhi baku mutu, TDS 100% memenuhi baku mutu, Fe 90% memenuhi baku mutu dan parameter DO untuk kelas I 20% memenuhi baku mutu, kelas II 65% memenuhi baku mutu, kelas III 15% memenuhi baku mutu.

This research is aimed to find out the profile of the mathematics student’ science process skill in the even semester of the academic year of 2017/2018. Class F students are the population and sample in this research. In data collection phase, evaluation was conducted to gather data on students’ science process skills in Physics by providing observation sheet and test questions on skills. The statistical analysis employed to process the research data was descriptive statistical analysis. The results indicated that students did not demonstrate good ability in doing practicum or they were not skillful in certain aspects. This is evidenced by the way the practicum process was conducted which showed that student still needs evaluation and many of them gained low score that they needed to improve. This indicates that learning physics in some way is worth the attention for the formation of students’ understanding of the learning material in the class in terms of defining variables, measuring and processing data. Students should constantly delve into these three aspects in order to gain a better understanding of the material given.

Kegiatan evaluasi tata ruang wilayah Distrik Mayamuk pada prinsipnya untuk menilai keselarasan antara tujuan, strategi, dan kebijakan yang termuat dalam dokumen rencana tata ruang wilayah (RTRW) Kabupaten Sorong. Tujuan penelitian in iadalah untuk 1)Mengetahui perbandingan luas menurut RTRW dengan hasil analisis, 2)Mengetahui kesesuaian rencana tata ruang pada dokumen rencana tata ruang wilayah (RTRW) Kabupaten Sorong Distrik Mayamuk dengan yang terlaksana di Distrik Mayamuk, 3)Menghasilkan produk peta pola ruang tiap kelurahan dan peta tata ruang serta peta administrasi Distrik Mayamuk tahun 2017. Metode penelitian adalah pengambilan data citra foto udara dengan drone atau pesawat tanpa awak, data yang dikumpulkan adalah data primer dari citra foto udara dan data sekunder dari dokumen RTRW dan BPS Kab. Sorong. Kesimpulan yang dihasilkan adalah 1)Hasil penelitian menunjukkan bahwa perbandingan luas Distrik Mayamuk adalah selisih 83,76 Ha. 2)Hasil analisis pola ruang Distrik Mayamuk kawasan lindung cagar alam sebesar 1,92 %, kawasan budidaya hutan produksi (HPK) sebesar 92,16%, permukiman sebesar 1,42%, pertanian lahan kering (PLK) sebesar 4,29%, kawasan industri dan pelabuhan terpadu sebesar 0,16% dan lahan kosong sebesar 0,02%, 3)Hasil peta pola ruang Kelurahan Makotyamsa, Makbalim, Yeflio, Makbusun, Arar, Klain, Klasmelek, Wen dan peta tata ruang serta peta administrasi Distrik Mayamuk.

In highway construction work requires a strong and stable subgrade. But in reality the subgrade that is often encountered is subgrade with low support, so it does not meet the requirements when used for road pavement construction. One way to overcome this problem is to do stabilization, namely by using an additive in the form of wood ash. The purpose of this study was to identify how much influence wood ash had on the CBR value of the soil. This research is expected to provide information regarding the feasibility of soil stabilization in Sorong City by using wood ash from wood waste so that it can be used appropriately. For this reason, it is necessary to conduct a study on the use of wood burning residues as a stabilizing material and is expected to increase the carrying capacity of the soil. This research will be carried out by means of stabilization with variations in the mixture content of 3%, 6%, 9%, 12% and 15%. The type of soil that will be used in this study comes from Jalan Moyo, East Sorong District, Sorong City. Next will be testing specific gravity, atterberg limit, compaction test and CBR on each variation of mixing. The results of this study indicate that stabilization of clay soil using wood ash can increase the bearing capacity of the soil, namely where the CBR value of the original soil is 7.32%, which increases with each addition of the percentage of wood ash content.

Stabilitas tanah merupakan salah satu usaha untuk meningkatkan kapasitas daya dukung tanah. Tanah lempung plastisitas tinggi berpotensi menjadi tanah yang bermasalah karena mempunyai kekuatan rendah dan penurunan yang tinggi. Salah satu metode untuk meningkatkan daya dukung adalah stabilisasi tanah lempung salah satunya menggunakan Portland Cement Type I. Penelitian ini diharapkan untuk mengetahui seberapa besar pengaruh perubahan semen terhadap nilai kuat geser tanah. Dalam penelitian ini tanah yang digunakan berasal dari Jalan Makam Kelurahan Malason, Distrik Aimas, Kabupaten Sorong. Pengujian tanah ini terdiri dari pengujian fisik dan mekanis, pengujian yang utama dilakukan adalah pengujian Direct Shear Test (Uji Kuat Geser Langsung) yang dilakukan di Laboratorium Mekanika Tanah Fakultas Teknik Universitas Muhammadiyah Sorong dan berpedoman pada SNI dengan variasi campuran semen sebesar 2%, 4%, 6%, 8% dan 10%. Dari hasil penelitian didapatkan bahwa semakin besar variasi semen maka tegangan geser, kohesi dan sudut geser semakin meningkat. Nilai tegangan geser, kohesi dan sudut geser yang tertinggi pada penambahan bahan stabilitas sebesar 10% dengan waktu pemeraman 3 hari dengan nilai tegangan geser 0,541 kg/cm² kohesi sebesar 0,172 Kg/cm² dan sudut geser sebesar 42,62°. Hal ini menunjukan bahwa semen dapat dijadikan bahan stabilisasi untuk meningkatkan kekuatan dan daya dukung tanah lempung ditinjau dari peningkatan nilai tegangan geser, kohesi tanah dan sudut geser yang dihasilkan.

Setiap proyek konstruksi lazimnya mempunyai rencana pelaksanaan dan jadwal pelaksanaan yang tertentu, kapan pelaksanaan proyek tersebut harus dimulai, kapan harus diselesaikan dan bagaimana proyek tersebut akan dikerjakan, serta bagaimana penyediaan sumberdayanya.Namun dalam proses pelaksanaan tersebut, sering terjadi hambatan - hambatan yang tidak diketahu isebelumnya. Untuk itu kiranya perlu dilakukan penelitian untuk mengetahui faktor-faktor penyebab keterlambatan penyelesaian proyek agar waktu penyelesaian proyek sesuai dengan rencana. Maksud dan tujuan penelitian ini Untuk mengetahui faktor-faktor penyebab keterlambatan pekerjaan konstruksi jembatan Klamalu Kabupaten Sorong, merangking faktor-faktor keterlambatan tersebut, serta mengetahui persepsi responden terhadap faktor-faktor penyebab keterlambatan. Metode yang digunakan dalam penelitian ini adalah metode penelitian kuantitatif dengan pengolahan data menggunakan SPSS. Peneliti mengambil sampel 11 responden yang terdiri dari penyedia jasa, pengguna jasa dan konsultan pengawas pada pekerjaan konstruksi Jembatan Klamalu Kabupaten Sorong. Hasil dari penelitan ini ialah faktor-faktor keterlambatan konstruksi Jembatan Klamalu Kabupaten Sorong setelah di rangking sebagai berikut Cuaca buruk (hujan deras / lokasi tergenang), Tidak memenuhi perencanaan awal proyek, Produktivitas tidak optimal oleh kontraktor, Mengalami gangguan diluar proyek,Keterlambatan pengiriman bahan. Penelitian ini juga menyimpulkan bahwa tidak ada perbedaan persepsi responden berdasarkan jabatan responden setelah dilakukan Uji Chi Square dimana nilai asymptotic signifikansi lebih besar dari 0.05 atau Ho diterima.

Abstract Research on soil water infiltration in equatorial climates like Indonesia is limited. Despite Papua accounting for 29% of Indonesia’s surface water, there’s a shortage of hydrological data. This study aimed to identify the best infiltration model tailored to the characteristics of basic infiltration rates in Papua’s small watersheds. Observations were made at 95 points across 11 small watersheds in Papua. Based on equatorial basic infiltration rate theories, analysis classified infiltration values against observation time. The performance of four infiltration models — Horton, Philip, Kostiakov, and Green Ampt — were compared. The results indicated the Philip model as superior, with average scores of 0.901 (R), 0.814 (NSE), and 0.424 (RSR), followed by the Horton and Green Ampt models. These three models showed excellent performance. However, the Kostiakov model was found lacking and needs modification. Further research on rapid and very rapid classifications is vital for enhancing infiltration rate predictions in small equatorial watersheds.

Eco-hydrological processes in watersheds are essential for policy development and social growth despite growing environmental concerns.These processes represent a dynamic interaction between the hydrological cycle and ecological elements, with infiltration as one of the important components.According to preliminary studies, the availability of infiltration rate data holds significant importance for irrigation planning, flood management, and water resource studies.Presently, there is a global decrease in hydrological data due to the abundance of water resources in Papua.Existing literature emphasizes the need to evaluate the Kostiakov and Green Ampt models in varied soil textures in Papua, particularly within small watersheds.Therefore, this research compared the performance of the two models on each soil texture using the Hydrological Soil Group classification.It also analyzed modifications for models with unsatisfactory performance through the use of RStudio software analysis with k-fold cross-validation.The results showed that the Green Ampt model performs better than the Kostiakov model for nine soil textures, with average performance values of 0.800 (R), 0.636 (NSE), and 0.588 (RSR).Although the Kostiakov model initially underperformed, it was enhanced to the Kostiakov-Small Watershed Model by integrating soil properties such as water content (w) and porosity (n).Further evaluation in other small watersheds is needed to confirm its robustness.In conclusion, this research filled hydrology data gaps in Papua, which resulted in an infiltration model tailored to the soil characteristics of small watersheds.

Pertumbuhan dan perkembangan industri Kota Sorong yang semakin maju menimbulkan dampak yang cukup besar pada siklus hidrologi yang berpengaruh terhadap sungai terkhusus Sungai Klagison yang merupakan sungai yang rentan terjadi banjir. Salah satu permasalahan yang terjadi pada sungai tersebut adalah sedimentasi, permasalahan diawali dengan adanya erosi dibagian hulu dan aktifitas pencucian pasir yang limbahnya dibuang pada saluran sungai tanpa adanya bak penampung limbah. Penelitian ini bertujuan menganalisis sedimentasi dengan memprediksi total muatan sedimen melalui karakteristik sedimen dengan pemodelan. Pemodelan menggunakan HEC-HMS untuk menghasilkan hidrograf debit banjir rancangan dan HEC-RAS untuk menganalisis sedimentasi. Dari hasil analisis sedimentasi diperoleh karakteristik sedimen dengan berat jenis sedimen rata-rata 2.32 g/cm3 dengan ukuran butiran d50 dan d90 pada hulu 0,7 mm dan 3 mm, hilir 0.022 mm dan 0.064 mm dan tengah sungai 0.062 mm dan 0.073 mm. Kapasitas total sedimen dengan waktu simulasi 5 tahun sebesar 124.007,647 ton dengan tinggi agradasi terbesar 64,9 cm.

Penambahan tingkat pencemaran Sungai Klawili diakibatkan oleh pembuangan sampah dan limbah yang langsung ke badan sungai. Dengan demikian maka dilakukan peninjauan kualitas air sungai untuk mengetahui status mutu, pengaruh tata ruang bantaran Sungai Klawili dan rekomendasi pengendalian pencemaran air. Penelitian ini dilakukan berdasarkan beberapa sifat fisika dan kimia sesuai dengan PP No 82 Tahun 2001 dengan penentuan status mutu menggunakan Metode Indeks Pencemaran dan pemetaan menggunakan software ArcGis 10.3. Hasil penelitian ini menunjukkan bahwa status mutu air Sungai Klawili di titik pantau 1 (hulu) masuk dalam kategori cemar ringan untuk baku mutu kelas I dengan nilai IP 1,70 dan status memenuhi untuk baku mutu kelas II-IV dengan nilai IP 0,73. Pada titik pantau 2 (tengah) masuk dalam kategori cemar ringan untuk baku mutu kelas I dengan nilai IP 3,71 dan status memenuhi untuk baku mutu kelas II-IV dengan nilai IP 0,60. Kemudian pada titik pantau 3 (hilir) masuk dalam kategori cemar ringan untuk baku mutu kelas I dengan nilai IP 3,77 dan status memenuhi untuk baku mutu kelas II-IV dengan nilai IP 0,54 berdasarkan Peraturan Pemerintah Nomor 82 Tahun 2001. Untuk pengaruh tata ruang bantaran sungai dengan menggunakan observasi dan pemetaan diperoleh pengaruh pencemaran air Sungai Klawili adalah limbah rumah tangga dan sampah organik maupun anorganik, kemudian rekomendasi pengendalian pencemaran air tersebut diperlukan pembuatan septictank karena adanya limbah cair rumah tangga yang langsung dialirkan ke sungai serta perlu adanya pembuatan bak sampah pada beberapa tempat disepanjang Sungai Klawili dan sirkulasi pengambilan sampah yang berkala.

Abstract : This paper reports on a study conducted to examine the development of cadets’ soft skills by implementing project-based learning in Moodle LMS during the COVID-19 pandemic. This study was classroom action research following the Kemmis & McTaggart’s model. It was conducted in three cycles, each consisting of plan, action, observation, and reflection. The participants of this study were the cadets from four different study programs at Sekolah Tinggi Maritim Yogyakarta currently enrolled in online classes. The data were collected through observations and focused-group discussions. The results showed that the implementation of project based learning in Moodle LMS contributed effectively to the development of the cadets’ soft skills including responsibility, discipline, creativity, and problem-solving. After implementing the projectbased learning for about 16 week-meetings, the researchers recorded the gradual improvement in the soft skills performed by the cadets as follows: 39.42%(low) in Pre-cycle, 52.71% (average) in Cycle I, 69.91% (high) in Cycle II, and 83.71% (very high) in Cycle III. The aforementioned skills were perceived as key fundamental skills required by the cadets to perform their jobs successfully in the 21st century workplace, particularly in the maritime industry. Keywords : Soft skill, project-based learning, Moodle LMS

This case study was a preliminary stage of an on-going research and development project on developing a business English textbook integrated with digital tools for students of English Education at Nahdlatul Ulama University of Yogyakarta. It investigated the students’ perceived needs on their business English course. To collect the data, a needs analysis questionnaire was administered to the students using Google form including the students’ necessities, lacks, and wants for a practical business English course design. Both quantitative and qualitative methods were used to analyse the collected data. It defined the context in which the students would use business English in their future profession. Results showed that (1) the students’ English proficiency was still in the middle, (2) The use of English in daily life communication was on occasion, (3) The English skills’ preferences should be learnt in the business context were speaking, writing, listening, and reading, (4) Topics included in the business contexts were interview, curriculum vitae, and presentation in the top three, (5) Digital tools’ preferences to be integrated into learning were Kahoot, Padlet, and VoiceThread in the top three, and (6) Learning methods implemented were group discussion and project-based learning in the top two.

Saat ini Indonesia sedang menghadapi era Revolusi Industri ke-4 atau Revolusi Industri 4.0. Maka dari itu revolusi industri 4.0 di sektor pelabuhan merupakan hal baik untuk menuju smart port dan smart supply chain. Seiring dengan revolusi industri 4.0 dan teknologi digital, persaingan bisnis dan pembangunan yang semula bertumpu pada pemanfaatan sumber daya alam akan bergeser pada penguasaan teknologi informasi dan kompetensi angkatan kerja. Di sinilah pentingnya investasi SDM. Era revolusi industri 4.0 membuka kesempatan bagi sumber daya manusia (SDM) di sektor manufaktur untuk memiliki keahlian yang sesuai dengan perkembangan teknologi terkini. Untuk itu, diperlukan pelaksanaan program peningkatan keterampilan (up-skilling) atau pembaruan keterampilan (reskilling) para tenaga kerja berdasarkan kebutuhan dunia industri saat ini.

This case study aimed to: (1) reveal how intercultural language learning occurred within a sister school partnership in an Indonesian public high school in Yogyakarta, and (2) describe student behaviours perceived to be indicative of intercultural language learning. Using an ethnographic approach through observations and dialogues, this study investigated real life interactions occurring among the research participants within the uniqueness of the partnership between an Indonesian public high school in Yogyakarta and its Australian school partner. The results showed that intercultural language learning occurred in the forms of learning experiences which engaged the students in interaction, observation and reflection with the target community. A number of indicators of intercultural language learning were revealed in the students behaviours within four authentic language experiences including exploring language and culture, noticing verbal and non-verbal cultural behaviours, making connections between home and the target language and culture, and reflecting on the development of a third or intermediate personal position between cultures.

The digital technology has turned out to be a revolution to gender equality and the socioeconomic empowerment of women due to its fast development. This paper discusses the digital innovation process as a radical platform in ensuring gender equity and inclusive development. Technology has given women a platform where they are able to speak out, become financially stable and be in frontline to fill up the geographical and social barriers. The case studies examined in the paper include the women-led start-ups, the digital literacy programs, and the e-governance programs that can increase inclusivity and reduce structural disadvantages. Finally, the paper provides the argument that innovation can trigger holistic development of women and transform socioeconomic systems, guided by inclusiveness and equity. The gender and technology intersect therefore is not only a place of usage but a place to change where women are not only consumers but creators of the online future.

Presentasi dalam pre-conference ICOSBE 2026, dilaksanakan di Binus University, kampus Alam Sutera, 14 Juli 2026

Higher education institutions in the era of digital disruption face challenges in integrating

Fajar belum sepenuhnya pecah di Tinanggea, Konawe Selatan.Di sebuah desa yang dikepung hutan tropis, seorang remaja sedang menatap layar ponselnya yang hanya menampilkan logo buffering.Di belahan bumi Indonesia yang lain, tepatnya di pesisir Sulawesi Tenggara, Seorang Putra baru saja merapikan jala ikannya sembari memikirkan pengumuman beasiswa universitas yang tak kunjung ia ketahui kelanjutannya karena sinyal telekomunikasi timbul tenggelam.Sementara di Bungku Selatan, garis batas provinsi yang hanya berjarak beberapa langkah dari rumah Fulan seolah menjadi dinding tebal yang memisahkan impiannya untuk mencicipi bangku kuliah.

Daftar Riwayat Hidup, terkait dengan kepakaran dalam pendidikan tinggi, dalam hal ini aspek pengelolaan konferensi dan penerbitan luaran konferensi pada prosiding dengan penerbit global.

Crowdsourcing is coined first by Jeff Howe and become more popular in recent years. It is concept of outsourcing in the crowd. Crowdsourcing can be useful to project managers when they need to perform tasks with low budget, massive participants, multi-disciplinary, expert or non-experts. Using 10 rules from Howe will help a project manager manage the crowd and achieve their project objective. By addressing some questions such as why should a project manager use crowdsourcing? What the benefits and risks of crowdsourcing. This articles explain the contribution of crowdsourcing in project and change management

Online reviews are an essential aspect of online shopping for both customers and retailers. However, many reviews found on the Internet lack in quality, informativeness or helpfulness. In many cases, they lead the customers towards positive or negative opinions without providing any concrete details (e.g., very poor product, I would not recommend it). In this work, we propose a novel unsupervised method for quantifying helpfulness leveraging the availability of a corpus of reviews. In particular, our method exploits three characteristics of the reviews, viz., relevance, emotional intensity and specificity, towards quantifying helpfulness. We perform three rankings (one for each feature above), which are then combined to obtain a final helpfulness ranking. For the purpose of empirically evaluating our method, we use review of four product categories from Amazon review 1 . The experimental evaluation demonstrates the effectiveness of our method in comparison to a recent and state-of-the-art baseline.

Research on fake reviews detection and review helpfulness prediction is prevalent, yet most studies tend to focus solely on either fake reviews detection or review helpfulness prediction, considering them separate research tasks.In contrast to this prevailing pattern, we address both challenges concurrently by employing a multi-task learning approach.We posit that undertaking these tasks simultaneously can enhance the performance of each task through shared information among features.We utilize pre-trained RoBERTa embeddings with a document-level data representation.This is coupled with an array of deep learning and neural network models, including Bi-LSTM, LSTM, GRU, and CNN.Additionally, we employ ensemble learning techniques to integrate these models, with the objective of enhancing overall prediction accuracy and mitigating the risk of overfitting.The findings of this study offer valuable insights to the fields of NLP and machine learning and present a novel perspective on leveraging multi-task learning for the twin challenges of fake reviews detection and review helpfulness prediction.

Online reviews have become critical in informing purchasing decisions, making the detection of fake reviews a crucial challenge to tackle.Many different Machine Learning based solutions have been proposed, using various data representations such as n-grams or document embeddings.In this paper, we first explore the effectiveness of different data representations, including emotion, document embedding, n-grams, and noun phrases in embedding format, for fake reviews detection.We evaluate these representations with various state-of-theart deep learning models, such as a BILSTM, LSTM, GRU, CNN, and MLP.Following this, we propose to incorporate different data representations and classification models using early and late data fusion techniques in order to improve the prediction performance.The experiments are conducted on four datasets: Hotel, Restaurant, Amazon, and Yelp.The results demonstrate that a combination of different data representations significantly outperforms any single data representation.

Our goal was to identify the practical usage of medication management apps and what they can offer a consumer especially in their old age, keeping in view the aspect of medication adherence.A search of FREE medication management apps was conducted on app store of iPhone/Android operating system. Apps were included if they had a medication management system which included a reminder setup option and had an easy to use interface.Of the 22 apps on IOS and 18 apps on Android identified by strategy all met the inclusion criteria. WebMD and Drugs.com rated the highest among the apps because of their basic medication reminder feature coupled with their enhanced levels of functionality. WebMD was rated best for its information and usability which included a symptom checker, medication reminder, list of disease conditions, drug list with Indications, dosage information, side effects, drug interactions, drug use in pregnancy and lactation, first aid information, local health facilities listings, tests and procedures listings, healthy living tips, food and recipes.Finding a medication management app is challenging if you are looking for a specific type of apps as most of the apps are not tested by any authorized drug regulatory authority for example FDA. Almost all have no reviews and are not affiliated to any healthcare organization. Despite being untested, medication apps represent a possible strategy that pharmacists, healthcare workers, carers and family members of elderly patients can recommend for helping nonadherent patients and incorporate into their practice.Â