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All Journal Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) J-SAKTI (Jurnal Sains Komputer dan Informatika) IJISTECH (International Journal Of Information System & Technology) JURTEKSI MIND (Multimedia Artificial Intelligent Networking Database) Journal JURNAL IT: Media Infromasi STMIK Handayani Makassar Building of Informatics, Technology and Science INFOKUM IJISTECH J-SAKTI (Jurnal Sains Komputer dan Informatika) Jurnal Teknik Informatika Journal of Artificial Intelligence and Engineering Applications (JAIEA) TAMIKA: Jurnal Tugas Akhir Manajemen Informatika & Komputerisasi Akuntansi BEduManageRs Journal : Borneo Educational Management and Research Journal
Pratiwi, Heny
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Humanities Automotive Engineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Languange, Linguistic, Communication & Media Mathematics Social Sciences Other

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Journal : INFOKUM

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Application of K-Means Algorithm for Segmentation Analysis of Youtube Viewers in Indonesia Halim, Ryan Artanto; Pratiwi, Heny; Azahari, Azahari
INFOKUM Vol. 13 No. 03 (2025): Infokum
Publisher : Sean Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58471/infokum.v13i03.2850

Abstract

The application of K-Means as a clustering method in segmentation analysis is common. However, academic research on YouTube audience segmentation in Indonesia is still limited. YouTube audiences in Indonesia are diverse, ranging from entertainment, education, to news, so more in-depth analysis is needed to identify user segments more specifically. YouTube audience segmentation can provide a deeper understanding of people's video consumption behavior. This understanding can help content creators and digital industry players develop more effective content strategies. K-Means was chosen as the clustering method in this study because it can group YouTube viewers in Indonesia based on their interaction patterns with YouTube content. In addition, K-Means' ability to handle large data is suitable for segmenting platforms with a large number of users such as YouTube. This research uses three main features, namely views, duration, and engagement rate to group viewers into five clusters. Cluster evaluation using Silhouette Score (0.3445), Davies-Bouldin Index (0.9576), and Calinski-Harabasz Index (481.4730) shows that the resulting segmentation is of good quality. The analysis shows that there are differences in video consumption patterns across clusters, reflecting variations in viewer preferences and engagement levels.
Eye Disease Classification Using Convolutional Neural Network (CNN) with Web-based MobileNetV2 Architecture Fahriawan, Muhammad; Pratiwi, Heny; Harpad, Bartolomius
INFOKUM Vol. 13 No. 03 (2025): Infokum
Publisher : Sean Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58471/infokum.v13i03.2851

Abstract

The high prevalence of preventable eye diseases, such as cataracts, glaucoma, and diabetic retinopathy, emphasizes the importance of accessible and efficient diagnostic solutions. This research aims to develop a web-based eye disease classification system using a lightweight Convolutional Neural Network (CNN) architecture, MobileNetV2, to overcome computational limitations in real-time applications. CRISP-DM methodology is applied, including dataset preparation, transfer learning with MobileNetV2 and VGG16, model evaluation, and implementation using Flask. The dataset from Kaggle consisting of 4,217 eye fundus images with four classes (cataract, glaucoma, diabetic retinopathy, and normal) was divided into 80% training, 10% validation, and 10% testing. Data augmentation and normalization were performed to improve model generalization. The results showed MobileNetV2 achieved the highest accuracy (90.14%) with low computational requirements, outperforming VGG16 (89.66%) and CNN (86.78%). MobileNetV2 displays balanced precision (89-99%), recall (74-96%), and F1-score (81-99%) across all classes, especially excelling in diabetic retinopathy detection. Its efficiency on resource-constrained environments makes it ideal for web integration. The developed Flask-based application allows users to upload images for instant classification, bridging the healthcare access gap. This research proves the effectiveness of MobileNetV2 in combining high accuracy and computational efficiency, offering a scalable solution for early screening of eye diseases, especially in remote areas.
Co-Authors Adeputra, James Angreani, Fadillah Anwar, Rafidan Azahari Azahari, Azahari Cembes, Yosefina Damaya, Filio Angga Daud, Jundro Dovist Calvino Ekawati, Hanifah Fahriawan, Muhammad Halim, Ryan Artanto Harianto, Kusno Harpad, Bartolomius Ita Arfyanti Junirianto, Eko Lamsi, Rahmadiansyah Zain Muhammad Ibnu Saad Saad NUR FITRIANI Putra, Muhammad Sadam Saktia SA'AD, MUHAMMAD IBNU Saad, Muhammad Ibnu Salmon Sa’ad, Muhammad Ibnu Suhariyadi, Yonatan Sururi, M Za’iem Syamsuddin Mallala W Wahyuni, W Wahyuni - Wahyuni Y Yunita Zakaria, Muhammad Alamsyah