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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 MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer Jurnal Teknologi Informasi dan Pendidikan Indonesian Journal of Electrical Engineering and Computer Science Community Development Journal: Jurnal Pengabdian Masyarakat Majalah Ilmiah UPI YPTK 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 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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Journal : Bulletin of Computer Science Research

 1 
Analisis Algoritma K-Means Clustering Dalam Pengelompokan Prestasi Belajar Siswa Menengah Atas (SMA) Dila, Rahmah; Defit, Sarjon; Arlis, Syafri
Bulletin of Computer Science Research Vol. 5 No. 5 (2025): August 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v5i5.751

Abstract

The increased use of social media among high school students has a positive and negative impact on academic achievement. This can be seen from changes in learning patterns, concentration levels, and students' motivation in participating in learning activities. This study aims to classify student learning achievement based on the level of social media use using the K-Means Clustering algorithm. K-Means Clustering is one of the main methods in data mining.  which is a technique of grouping data based on the similarity of its characteristics. The parameters used in analyzing this study are Social Media Duration (X1), Active Time (X2), Main Platform (X3), Main Goal (X4), Social Media Access Time While Learning (X5), Social Media Addiction (X6), Social Media Addiction Level (X7), Number of Study Groups (X8) and Academic Average (X9). Based on the K-Means Clustering method, it has been proven to be able to group students based on the level of social media use. These results can be seen from the cluster category C0 (High) with 46 students, C1 (medium) with 80 students, and C2 (Low) with 72 students. The contribution of this research benefits students by helping them understand the relationship between social media usage habits and learning achievement, so as to encourage more effective time management.
Analisis Cluster Algoritma K-Means Untuk Pengelompokan Kondisi Gizi Balita Pada Posyandu Roza, Yesi Betriana; Defit, Sarjon; Arlis, Syafri
Bulletin of Computer Science Research Vol. 5 No. 5 (2025): August 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v5i5.752

Abstract

Toddler health is a crucial indicator of community and national development. Integrated Service Posts (Posyandu) play a key role in monitoring the nutritional status of toddlers through routine weight and height checks. This study aims to analyze toddler nutritional status using the K-Means Clustering algorithm, a non-hierarchical method that groups data based on centroid proximity. The data came from 98 toddlers at the Posyandu in Manggung Village, North Pariaman District, Pariaman City, including weight, height, weight-for-age, height-for-age, weight-for-height, and weight gain. The K-Means results showed a distribution of three clusters: C0 (undernourished) with 37 toddlers, C1 (severely malnourished) with 17 toddlers, and C2 (well-nourished) with 44 toddlers. The majority of toddlers were categorized as well-nourished. This research contributes to the rapid identification of toddler nutritional problems, enabling Posyandu staff to take appropriate preventive and corrective measures.
Identifikasi Varietas Kopi Berdasarkan Analisis Warna dan Tekstur Menggunakan Metode Convolutional Neural Network Utama Putra, Kharisma; Ramadhanu, Agung; Arlis, Syafri
Bulletin of Computer Science Research Vol. 5 No. 5 (2025): August 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v5i5.759

Abstract

Coffee is a plantation commodity with high economic value in Indonesia, with various varieties such as Arabica, Robusta, and Liberica. Differences in coffee varieties can generally be identified through the physical characteristics of the beans, especially color and texture. Based on this, this study aims to develop a digital image-based coffee variety identification system using the Convolutional Neural Network (CNN) method with color and texture analysis as the main features. The research stages include coffee bean image acquisition, pre-processing including color segmentation and image conversion to grayscale, and color and texture feature extraction. This research dataset comes from images of unroasted coffee beans, commonly called green beans, taken using a high-resolution smartphone camera and also using secondary data taken from the Kaggle site. Both types of datasets have the same characteristics and resolution to maintain data consistency. The image dataset is divided into training data and test data, then used to train and test the Convolutional Neural Network (CNN) model. Based on this study, the Convolutional Neural Network (CNN) method can identify coffee varieties based on color and texture analysis. By using 210 training data and 90 test data of coffee bean images, the CNN method can produce an accuracy rate of 94,44%. This research contribution has the potential to be a supporting solution in the process of identifying coffee varieties quickly, accurately, and consistently, so that it can help the coffee industry in the sorting and quality control process.
Co-Authors Agung Ramadhanu Andrian, Reski Aswandi, Nopan Candra Putra Darma Syahrullah Ekajaya Dila, Rahmah Dodi Guswandi Doli Raharjo, Tio Edwar, Eggy Febyanti Fajrul Islami Febri Hadi Feri Pratama 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 Ramadhanu, Agung Ridwan, Ridwan Rini Sovia Rofil M. Nur Roza, Yesi Betriana Rusydi, Rezki 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 Winda Sutra Amanda Yanto, Musli Yuhandri Yuhandri Yunus Yuhandri, Y Yulasmi