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All Journal TEKNIK INFORMATIKA Jurnal Indo-Islamika TELKOMNIKA (Telecommunication Computing Electronics and Control) Jurnal Manajemen Pendidikan Sinkron : Jurnal dan Penelitian Teknik Informatika Jurnal CoreIT Applied Information System and Management MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer Jurnal Sosiologi Agama Building of Informatics, Technology and Science Journal of Applied Data Sciences
Nashrul Hakiem
Universitas Islam Negeri Syarif Hidayatullah Jakarta
Religion Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Social Sciences

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Journal : Journal of Applied Data Sciences

 1 
Modeling Ramadan Hilal Classification with Image Processing Technology Using YOLO Algorithm Anggraini, Nenny; Zulkifli, Zulkifli; Hakiem, Nashrul
Journal of Applied Data Sciences Vol 5, No 3: SEPTEMBER 2024
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v5i3.311

Abstract

This research aims to create a model for classifying hilal using the YOLO algorithm. The determination of the beginning of the month of Ramadan is an important aspect of the Islamic calendar that has an impact on the implementation of fasting. With technological advances, especially in image processing, there is potential to overcome the limitations of conventional methods currently used in hilal detection for determining the beginning of Ramadan. This research uses the prototyping method in its implementation. The dataset in this research comes from videos on the BMKG Youtube channel and images from various sources such as NASA Planetary Data System and Google Images. YOLOv5 and YOLOv8 algorithms are used to develop the object detection model. The novelty of this research is the use of the YOLO algorithm with video datasets to detect hilal to determine the beginning of the month of Ramadan and Shawwal. The best-performing model, YOLOv5m with 100 epochs and a batch size of 30, achieved a precision of 0.838 and a mAP of 0.5-0.95 of 0.735. The results indicate that YOLOv5m is more effective in hilal detection, providing a novel approach to determine the beginning of Ramadan and Shawwal with greater accuracy and consistency. This integration of advanced object detection technology with religious practice offers a significant improvement over traditional method.
Acceptance and Success Model for AI Use in Higher Education: Development, Instrument Decomposition, and Its Triangulation Testing Subiyakto, Aang; Huda, Muhammad Q; Hakiem, Nashrul; Suseno, Hendra B; Arifin, Viva; Azmi, Agus N; Sani, Asrul; Yuniarto, Dwi; Hartawan, Muhammad S; Suryatno, Agung; Muji, Muji; Kurniawan, Fachrul; Kusumawati, Ririen; Balogun, Naeem A; Ahlan, Abd. Rahman
Journal of Applied Data Sciences Vol 6, No 4: December 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v6i4.619

Abstract

Prior social computing studies described that the performance of technology products is about how the product use benefits the users, including Artificial Intelligence (AI). To have an impact, ensuring how AI is used is a prerequisite after the development. Furthermore, its use is also influenced by how users accept AI. This study aimed to develop an acceptance and success model of AI use in the higher education world from the user perspective, to decompose the model into its instrument level, and to test the validity and reliability of the research instrument. The researchers developed the model by adopting and combining the Technology Acceptance Model (TAM) and the Information System Success Model (ISSM) and adapting the proposed model in the context of AI use in higher education learning. The measurement items were derived from definitions of the variables and indicators of the model. The instrument was tested sequentially using triangulation methods. The quantitative testing was online survey with about 51 respondents and the qualitative one was interview involving five experts. This study may contribute methodologically as one of the guidance for novice scholars in similar works. It may relate to the clarity of the research procedure and the implementation of the mixed testing methods. Of course, the assumptions, samples, and data used in the study cannot be generalized for the other studies. Referring to the model development, the proposed model may not cover the other factors related to the ethical, cultural, and organizational barriers for adopting AI. These barriers may also affect its acceptance and success. Thus, the adoption of the factors related the barriers may also be interesting to study further.
Co-Authors AA Sudharmawan, AA Aang Subiyakto Abd. Rahman Ahlan Al Anziy, Misyal S Al Fath, Miftahul Kahfi Amin Rois Angga Kurniawan Anggraini, Nenny Arif, Farid Dhiya Ul Arini Arini Ariq Cahya Wardhana Asrul Sani Assyahid, Muhammad Hudzaifah Aulia, Atqia Azmi, Agus N Balogun, Naeem A Dana Indra Sensuse Dede Rosyada Denny Saputra Dwi Yuniarto Dzul Fadli Rahman Erma Suryani Evy Nurmiati Fachrul Kurniawan Faiza, Fikis Silmi Fetrina, Elvy Hartawan, Muhammad S Heni Pujiastuti Huda, Muhammad Q Huda, Muhammad Qomarul Imam M Shofi Khairani, Dewi Luh Kesuma Wardhani M. Tabah Rosyadi Meinarini Catur Utami Muhammad Yusuf Muji, Muji Nenny Anggraini, Nenny Nia Kumaladewi Ratnaningsih, Siti Ririen Kusumawati Rizal Broer Bahaweres Rohmawati, Hanung Sito Sangsawang, Thosporn Sarika Afrizal Sarika Afrizal Sarika Afrizal, Sarika Setyawan, Okky Bagus Shofi, Imam Marzuki Suryatno, Agung Suseno, Hendra B Syopiansyah Jaya Putra Viva Arifin Yudhanta, Satya Yuni Sugiarti, Yuni Zawawi, Khoirunnisya Zulkifli Zulkifli Zulkifli Zulkifli