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@suhardiaras
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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.
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%.