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All Journal Dinamik Sinkron : Jurnal dan Penelitian Teknik Informatika JURNAL MEDIA INFORMATIKA BUDIDARMA Jiko (Jurnal Informatika dan komputer) JURNAL TEKNOLOGI DAN ILMU KOMPUTER PRIMA (JUTIKOMP) METIK JURNAL Jurnal Mantik Jusikom: Jurnal Sistem Informasi Ilmu Komputer BIOS : Jurnal Teknologi Informasi dan Rekayasa Komputer Jurnal Algoritma Data Sciences Indonesia (DSI) AT-TAKLIM: Jurnal Pendidikan Multidisiplin Jurnal Publikasi Sistem Informasi dan Manajemen Bisnis
Bayu Angga Wijaya
Universitas Prima Indonesia
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Languange, Linguistic, Communication & Media Physics Other

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Perbandingan Algoritma Decision Tree dan K-Nearest Neighbor untuk Klasifikasi Penyakit ISPA Marchell William Putra Pakpahan; Bayu Angga Wijaya; Marsaulina Lumbantoruan; Ambarsius Samosir; Mikhael Rafael
BIOS : Jurnal Teknologi Informasi dan Rekayasa Komputer Vol 7 No 2 (2026): September (In Progress)
Publisher : Puslitbang Sinergis Asa Professional

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37148/bios.v7i2.215

Abstract

Acute Respiratory Infection (ARI) is one of the most common respiratory diseases with diverse and overlapping clinical symptoms, making initial identification challenging and necessitating a systematic, data-driven classification approach. This study aims to compare the performance of the Decision Tree and K-Nearest Neighbor (KNN) algorithms in classifying ARI-related disease categories. The novelty of this research lies in the specific construction of ARI labels into five distinct categories from the Pediatric Respiratory Infections dataset, coupled with a rigorous feature selection process to handle mixed data types and address class imbalance using weighted evaluation metrics. The dataset consisted of 801 patient records with 91 initial attributes. The classification label was constructed from the Main diagnostic column and grouped into five categories: Asthma/Bronchospasm/Wheezing, Pneumonia/Pneumopathy, Bronchiolitis, Laryngeal/Upper Respiratory, and Other. After feature selection to remove noise and redundancy, 54 features were used, consisting of 24 numerical and 30 categorical features. The research stages included preprocessing, label construction, missing value handling, categorical encoding, KNN normalization, 80:20 train-test splitting, and model evaluation. The results show that Decision Tree achieved higher performance with 67.08% accuracy and 69.12% weighted F1-score, while KNN achieved 65.84% accuracy and 64.18% weighted F1-score. Thus, Decision Tree demonstrates superior performance and interpretability for this specific dataset.
Desain dan Implementasi Smart Contract untuk Pengelolaan Persetujuan Akses Data Pasien Berbasis Blockchain Anggie Ciecilia Saragih; Bayu Angga Wijaya; Jon Kevin Sihombing; Febryco Rives; Soeli Yanto Rotua Marbun
Jurnal Algoritma Vol 23 No 1 (2026): Jurnal Algoritma
Publisher : Institut Teknologi Garut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33364/algoritma/v.23-1.3421

Abstract

This study aims to design and implement blockchain-based smart contracts for secure, transparent, and patient-oriented patient data access consent management. The research method employed a systems approach combining qualitative and quantitative methods through waterfall development stages. The system was developed using the Ethereum Sepolia Testnet blockchain and Solidity-based smart contracts. The implementation results demonstrate that blockchain technology is capable of permanently and transparently recording all patient data consent transactions. The smart contract successfully implemented a Role-Based Access Control (RBAC) mechanism, allowing patients to grant and revoke access permissions for doctors or healthcare institutions. The testing results indicate that the access validation mechanism functioned properly, although there are limitations related to scalability and gas costs on public blockchains. Security evaluation was limited to functional testing and access validation, indicating the need for further testing such as penetration testing and smart contract vulnerability analysis. Overall, this study proves that blockchain technology and smart contracts are capable of improving security and trust in digital healthcare data management, while also supporting the future development of artificial intelligence-based Decision Support Systems.
Implementasi Algoritma Clustering dan Classification dalam Data Mining: Systematic Literature Review terhadap Tren dan Tantangan Terkini Jon Kevin Sihombing; Wijaya, Bayu Angga
Jurnal Publikasi Sistem Informasi dan Manajemen Bisnis Vol. 4 No. 3 (2025): September : Jurnal Publikasi Sistem Informasi dan Manajemen Bisnis
Publisher : Pusat Riset dan Inovasi Nasional

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55606/jupsim.v4i3.5240

Abstract

This study conducts a systematic literature review on the implementation of clustering and classification algorithms in data mining to identify methodological trends and contemporary challenges during the 2021-2025 period. The research methodology employs the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) approach. Analysis was performed on eight relevant studies from IEEE Xplore, ScienceDirect, Springer, and ACM Digital Library databases. Narrative synthesis was used to comprehensively organize research findings. The results demonstrate the dominance of classification algorithms at 50%, with Random Forest achieving optimal accuracy of 98.35% through Particle Swarm Optimization. Clustering techniques demonstrate effectiveness in data segmentation, with K-means producing optimal configuration through Davies-Bouldin Index of 0.47. Application domains are diversified with the healthcare sector dominating 37.5% of implementations. Applications include diabetes prediction and COVID-19 epidemiological analysis. Hybrid approaches integrate various techniques for comprehensive knowledge extraction, particularly in social media user behavior analytics. Major challenges include computational complexity, methodological transparency deficiency in 66.67% of studies, and algorithm scalability limitations. Practical implications indicate a paradigm transformation in organizational decision-making from reliance on subjective intuition toward objective data-based formulation. Business intelligence technology penetration reaches 31.18% for dashboards and 10.75% for clustering techniques in small and medium enterprise ecosystems, marking substantial evolution in contemporary managerial practices.
PERBANDINGAN ALGORITMA CONVOLUTIONAL NEURAL NETWORK (CNN) DAN SUPPORT VECTOR MACHINE (SVM) UNTUK KLASIFIKASI PENYAKIT KANKER TULANG BERDASARKAN DATA CITRA Bayu Angga Wijaya; Edoart Joel Pardede; Muhammad Reza; Daniel B.P Sihombing; Gian Juno Pabaha Panjaitan
JIKO (Jurnal Informatika dan Komputer) Vol 9 No 2 (2026)
Publisher : Program Studi Teknik Informatika Universitas Khairun

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33387/jiko.v9i2.12121

Abstract

This research is motivated by the high urgency of early diagnosis in bone cancer cases to reduce patient mortality rates. This study comparatively analyzes the performance of the Convolutional Neural Network (CNN) algorithm with ResNet50 architecture, Support Vector Machine (SVM), and their integration in a Hybrid CNN-SVM model for medical image classification. The research methodology involved a dataset of 8,814 radiological images processed through normalization and augmentation stages. In single-model testing, the end-to-end ResNet50 architecture achieved an accuracy of 87%, but showed limitations in generalizing microscopic textures at the softmax classification layer. On the other hand, the SVM algorithm supported by manual Histogram of Oriented Gradients (HOG) feature extraction demonstrated significant stability with an accuracy of 93.58%, proving the superiority of the optimal margin method in handling specific feature dimensions in medical images. The crucial finding in this study shows that the Hybrid CNN-SVM model—which utilizes ResNet50 as an automatic feature extractor and SVM as the final classifier—achieved peak performance with an accuracy of 95.18%, Precision value of 0.98, Recall of 0.96, and AUC of 0.98. These results confirm that the synergy between CNN hierarchical feature extraction and SVM classification robustness can significantly minimize the risk of false negatives, making it highly recommended as a reliable Computer-Aided Diagnosis (CAD) instrument to assist medical practitioners in early detection of bone cancer.
Co-Authors Adrian Julio Manalu Alvianus Sembiring Amar Nugraha Ambarsius Samosir Anggie Ciecilia Saragih Bayu Andreas Tarigan Berlian Fransisco Jhon Wau Brutu, Mahendra Christian Mitro Septiano Hutagalung Daniel B.P Sihombing Desniari Gea Dharsinni, N P Edoart Joel Pardede Febryco Rives Gea, Areta Delano Gian Juno Pabaha Panjaitan Halawa, Nestina Harahap, M Hulu, Mesrawati Insidini Fawwaz Jimy Okinawa Jon Kevin Sihombing Jon Kevin Sihombing Jovan Kinoto Juandry Juandry Juliansyah Putra Tanjung Juliansyah Putra Tanjung K. Dilpani Laksmi Khairia, Anisa Lase, Yelita Kristiani Lovely Septian Silitonga Marchell William Putra Pakpahan Marsaulina Lumbantoruan Mikhael Rafael Monalisa Monalisa Monalisa Muhammad Reza Muhammad Ridho Muhammad Syahputra Novelan N P Dharshinni Nainggolan, Johannes Kristian Nazara, Leni Wati Nicholas Alexander Nicholas Alexander Perisman Jaya Gea Purba, Risky Pradipa Resel, Resel Rico Wijaya Dewantoro Siboro, Sarwando Sihotang, Dohardo Dulisep Sinaga, Ferdinand Siregar, Afrahul Hidayah Siregar, Saut Dohot Siti Aisyah Sitorus, Andriani M. Soeli Yanto Rotua Marbun Tarigan, Angki Angkota Utami, Sekar Suryati Vijay Kumar Yennimar, Yennimar