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All Journal Jurnal Sains dan Teknologi TELKOMNIKA (Telecommunication Computing Electronics and Control) Jurnal Teknologi Informasi dan Ilmu Komputer Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Jurnal Pilar Nusa Mandiri Jurnal Ekobistek Sebatik Jurnal Teknologi Informasi dan Pendidikan Indonesian Journal of Electrical Engineering and Computer Science Bubungan Tinggi: Jurnal Pengabdian Masyarakat Community Development Journal: Jurnal Pengabdian Masyarakat Majalah Ilmiah UPI YPTK Jurnal Informatika Terpadu Journal of Computer Scine and Information Technology Bulletin of Computer Science Research Jurnal Ipteks Terapan : research of applied science and education Jurnal Komtekinfo SATIN - Sains dan Teknologi Informasi Journal Of Informatics And Busisnes Jurnal Teknik dan teknologi tepat guna The Indonesian Journal of Computer Science TECHNOMEDIA : Informatics and Computer Science Jurnal TAM (Technology Acceptance Model)
Syafri Arlis
Universitas Putra Indonesia YPTK
Aerospace Engineering Agriculture, Biological Sciences & Forestry Arts Humanities Astronomy Automotive Engineering Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Immunology & microbiology Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Library & Information Science Materials Science & Nanotechnology Mathematics Medicine & Pharmacology Neuroscience Nursing Physics Public Health Social Sciences Veterinary Other

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Implementation of K-Means Algorithm and C4.5 Classification in the Analysis of Determinants of Student Timely Graduation Rahma Yanti; Musli Yanto; Syafri Arlis
Jurnal KomtekInfo Vol. 13 No. 1 (2026): Komtekinfo
Publisher : Universitas Putra Indonesia YPTK Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

This study was motivated by the importance of timely graduation as a key parameter affecting program accreditation. The timely graduation rate reflects the effectiveness of academic management and serves as an indicator of program quality. The purpose of this study was to apply the concept of data mining using the K-means and Decision Tree C4.5 methods to analyze the timely graduation of students in the Information Technology and Computer Education Study Program at UIN Bukittinggi. The research methods used are the K-Means and Decision Tree C4.5 methods. The K-Means algorithm is used to cluster student graduation data, which will then be processed in the next method. The Decision Tree C4.5 algorithm is used to classify student graduation data. The research data was sourced from the 2017 batch of the Information Technology and Computer Education Study Program at UIN Bukittinggi, with a total of 158 data points. The results of this study produced a model that was able to achieve an accuracy rate of 96% in the validation process. The accuracy results were relatively high, so the model produced can be used by the study program to improve academic quality. Based on the results of this study, it contributes as a basis for evaluating student academic performance, monitoring the risk of study delays, and supporting academic decision-making. In addition, this information contributes to maintaining and improving academic quality and supports the achievement and maintenance of the accreditation status of the PTIK UIN Sjech M. Djamil Djambek Bukittinggi Study Program.
Deteksi Perokok Menggunakan Algoritma You Only Look Once (YOLO) dan Convolutional Neural Network (CNN) Gevindo, Aprilian; Arlis, Syafri
Jurnal Informatika Terpadu Vol 12 No 1 (2026): Maret, 2026
Publisher : LPPM STT Terpadu Nurul Fikri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54914/jit.v12i1.2783

Abstract

Image processing technology continues to advance and is widely used for visual identification of human activities, including monitoring smoking behavior in no-smoking areas. This study develops an automated smoking activity detection and recognition system based on digital image processing, combining YOLO (You Only Look Once) for object detection and a CNN (Convolutional Neural Network) as an image classifier. YOLO detects and crops human objects, while the CNN classifies smoking and non-smoking activities based on visual features. The preprocessed dataset contains 560 valid images per class (smoking and not smoking). Training results show 96.09% accuracy on the training set and 94.44% on the validation set, with stable loss, while model evaluation yields 94.44% accuracy, 92.55% precision, 96.67% recall, 94.57% F1-score, and Average Precision (AP), indicating excellent classification performance. The model can also detect smoking activities in real-time images and camera feeds, demonstrating the effectiveness of combining YOLO and a CNN for automated detection, with potential applications in no-smoking areas.
Decision Support System for Selecting Casual Daily Workers to Become Permanent Employees Using the Profile Matching Method Eggy Febyanti Edwar; Yuhandri; Syafri Arlis
Journal of Computer Scine and Information Technology Volume 10 Issue 4 (2024): JCSITech
Publisher : Universitas Putra Indonesia YPTK Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35134/jcsitech.v10i4.109

Abstract

Information is the result of processing data from one or more sources, which is then processed to provide value, meaning and benefits. In modern times, the use of technology plays a very important role as a means of information and promotion, especially in the field of websites in delivering information. Technological advances in the field of computers are very helpful in the current decision-making process. One method of decision support systems is profile matching. This method is used to determine the assessment in selecting daily employees to become employees. Profile matching is broadly a process of comparing individual competition in job competition so that the difference in competition (also called gap) can be known, the smaller the gap produced, the greater the weight of the value which means that there is a greater chance for employees to occupy the position. After the calculation using the Profile Matching method, the ranking value that meets the requirements is in the alternative with the name of the worker, namely Bakhtiar with a score of 4.535 and is recommended to become a permanent employee. By applying this method, it is very helpful in determining the selection of casual laborers to become permanent employees.
Co-Authors Andrian, Reski Aswandi, Nopan Candra Putra Darma Syahrullah Ekajaya Dila, Rahmah Dodi Guswandi Doli Raharjo, Tio Eggy Febyanti Edwar Fachriqi Naldes Fajrul Islami Febri Hadi Feri Pratama Gevindo, Aprilian Gunadi Widi Nurcahyo Gunadi Widi Nurcahyo, Gunadi Guslendra Guslendra Hanum, Luthfia Harkamsyah Andrianof Hendrik, Billy Johan Harlan Jufriadif Na`am, Jufriadif Larissa Navia Rani Larissa Naviarani M.Iqbal, M.Iqbal Mardison Mardison Marfalino, Hari Muhammad, Abulwafa Musli Yanto Nabila Frisca Oktavia Nur, Rofil M Nurcahyo, Yuhandri Olivia, Ladyka Febby Permana, Nabilah Putri Prayuda, Muhammad Hozi Purnama, Ayu Widya Putra, Deri Marse Putra, Muhammad Reza Rachman, Bayu Rahma Yanti Ramadhanu, Agung - Ridwan, Ridwan Rini Sovia Rofil M. Nur Roza, Yesi Betriana Rusydi, Rezki S. Sumijan Sahari Sahari Sahari Sahari Sahari Sarjon Defit Sarjon Defit SIGIT SANJAYA, SIGIT Sofika Enggari Sumijan Sumijan Sumijan Sumijan Sumijan, Sumijan Sumijan Supperianto, Bambang Syahputra, Afriadi Utama Putra, Kharisma Wahyu, Fungki Waruwu, Kalfinus Winda Sutra Amanda Yanto, Musli Yuhandri Yuhandri Yuhandri Yuhandri Yunus Yuhandri, Y Yulasmi Yullys Helsa Zainil, Melva Zulnaidi, Hutkemri