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All Journal Jurnal Sains dan Teknologi TELKOMNIKA (Telecommunication Computing Electronics and Control) Explore: Jurnal Sistem Informasi dan Telematika (Telekomunikasi, Multimedia dan Informatika) Jurnal Teknologi Informasi dan Ilmu Komputer Sistemasi: Jurnal Sistem Informasi 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 Jurnal Informatika Terpadu Journal of Computer Scine and Information Technology Jurnal Ipteks Terapan : research of applied science and education COMSERVA: Jurnal Penelitian dan Pengabdian Masyarakat Jurnal Pustaka AI : Pusat Akses Kajian Teknologi Artificial Intelligence Jurnal Media Computer Science Jurnal Komtekinfo SATIN - Sains dan Teknologi Informasi Jurnal Teknik dan teknologi tepat guna The Indonesian Journal of Computer Science TECHNOMEDIA : Informatics and Computer Science Jurnal TAM (Technology Acceptance Model) Jurnal Komputer, Informasi dan Teknologi
Syafri Arlis
Universitas Putra Indonesia YPTK
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Journal : jurnal media computer science

 1 
Data-Based Health Insurance Premium Modeling at KUD Tirta Kencana Using a Machine Learning Approach Sukardi Sukardi; Syafri Arlis
Jurnal Media Computer Science Vol 5 No 1 (2026): Januari
Publisher : LPPJPHKI Universitas Dehasen Bengkulu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37676/jmcs.v5i1.9354

Abstract

The Village Unit Cooperative (KUD) plays a strategic role in improving the economic welfare of rural communities. However, attention to the health aspects of members is often not a priority. This study aims to analyze the health insurance costs required by members of the Tirta Kencana KUD in Kuantan Singingi District using a data-based approach and predictive modeling. The methods used include collecting membership and health claim data, processing the data using Principal Component Analysis (PCA), and applying predictive algorithms to estimate ideal and sustainable insurance costs. The analysis results indicate a significant correlation between age, membership status, and health history with the amount of premiums that should be covered. The predictive model successfully identified the optimal premium scheme with a predictive accuracy of 92%. These findings are expected to serve as a basis for policy-making in planning more efficient and equitable cooperative-based insurance. This research also opens opportunities for the application of data science in optimizing community-based microhealth systems.
Automated Medical Classification of Human Brain Tumors Leveraging the Xception Convolutional Neural Network Bambang Supperianto; Syafri Arlis
Jurnal Media Computer Science Vol 5 No 1 (2026): Januari
Publisher : LPPJPHKI Universitas Dehasen Bengkulu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37676/jmcs.v5i1.9356

Abstract

Brain tumors are among the most critical neurological disorders, marked by abnormal cell proliferation within the brain, either benign or malignant,adversely impacting cognitive, motor, and overall patient quality of life.Accurate and prompt diagnosis is pivotal for determining effective treatment and improving survival outcomes. While Magnetic Resonance Imaging (MIRI) remains the standard diagnostic tool due to its high soft-tissue contrast, manual interpretation is labor-intensive, expertise-dependent, and subject to observer bias. Consequently, deep learning approaches, particularly Convolutional Neural Networks (CNN), have garnered considerable attention for automating brain tumor classification with superior efficiency and accuracy. This study presents a medical classification model for human brain tumors based on the Xception CNN architecture. The model was developed using a publicly available MRI dataset comprising 2,875 images categorized into glioma, meningioma, and pituitary tumor classes. Preprocessing involved resizing, normalization, and data augmentation. The model was initialized with ImageNet weights and fine-tuned for the three-class classification task with softmax activation.The proposed model achieved robust performance, recording test accuracy of 98.4% and an average F1-score of 98.5%, indicating balanced precision and recall. Confusion matrix and error analysis revealed minimal and evenly distributed misclassifications, while training dynamics showed rapid convergence with no significant overfitting.These findings demonstrate the effectiveness and clinical feasibility of the Xception CNN for automated brain tumor diagnosis. Future research should validate the approach on larger, multi-institutional datasets and integrate interpretability techniques to strengthen clinical applicability.
Co-Authors Agung Ramadhanu Ahmad Khomsi Andrian, Reski Aprilian Gevindo Ariza Ikhlas Aswandi, Nopan Bambang Supperianto Billy Hendrik Candra Putra Darma Syahrullah Ekajaya Deri Marse Putra Dito Jurinaldo Dodi Guswandi Doli Raharjo, Tio Eggy Febyanti Edwar Fachriqi Naldes Fadhila Putri Sani Fadhila Putri Sani Fajrul Islami Febri Hadi Febri Hadi Feri Pratama Gunadi Widi Nurcahyo Gunadi Widi Nurcahyo, Gunadi Guslendra Hanum, Luthfia Harkamsyah Andrianof Ilsa hidayat Imam Fakhri Muhammad Iskandar Fitri Johan Harlan Jufriadif Na`am, Jufriadif Larissa Navia Rani, Larissa Larissa Naviarani M.Iqbal, M.Iqbal Mardison Mardison Marfalino, Hari Muhammad Reza Putra Muhammad, Abulwafa Nabila Frisca Oktavia Nabilah Putri Permana Olivia, Ladyka Febby Permana, Nabilah Putri Purnama, Ayu Widya Putra, Muhammad Reza Rahma Yanti Ridwan, Ridwan Rofil M. Nur Rusydi, Rezki Sahari Sahari Sahari Sahari Sahari Saputra, Randy Sarjon Defit SIGIT SANJAYA, SIGIT Sofika Enggari Sovia, Rini Sukardi Sukardi Sumijan Sumijan Sumijan, Sumijan Sumijan Supperianto, Bambang Syahputra, Afriadi Wahyu, Fungki Waruwu, Kalfinus Winda Sutra Amanda Winda Sutra Amanda Yanto, Musli Yanto, Musli Yesri Elva Yuhandri Yuhandri, Yuhandri Yulasmi