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All Journal JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH)
Muhammad Bagir
Sekolah Tinggi Teknologi Informasi NIIT, Jakarta
Computer Science & IT Decision Sciences, Operations Research & Management

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Sistem Pendukung Keputusan Pemilihan Aplikasi Belajar Online Menggunakan Metode Additive Ratio Assessment Muhammad Bagir; Jefri Rahmadian; Ahmad Fatih Zahir; Irsyad Purbha Irwansyah
Journal of Information System Research (JOSH) Vol 4 No 4 (2023): Juli 2023
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josh.v4i4.3782

Abstract

In the digital era, there are many educational platforms or learning applications that offer easy learning. However, with so many existing online learning applications, care is needed to choose one that suits your needs. To select an online learning application, users must look for information one by one on the profiles and features of existing online learning applications. This results in the difficulty and duration of making choices. This study aims to build a decision support system through the application of the Additive Ratio Assessment (ARAS) method for selecting online learning applications so that it can facilitate the selection and does not require a long time. The ARAS method looks for the best solution by comparing the utility function of each option with the optimum utility function value. Based on the existing case studies through the implementation of the ARAS method, the best alternative is Ruangguru (A4) with a score of 0.8819, followed by Kelaskita (A2) with a score of 0.8469, Zenius (A3) with a score of 0.8397, and Udemy (A1) with a value of 0.6282. The built-in decision support system produces valid calculations because the calculation results obtain the same value as the manual calculation results. Then, the usability test results produced an average value of 88.33%. This means that the system that is built is able to facilitate users in every functional area.
Klasifikasi Risiko Diabetes Mellitus Menggunakan K-Nearest Neighbors dengan Peningkatan Performa Melalui Teknik Oversampling ADASYN Muhammad Bagir; Hendra Mayatopani; Umbar Riyanto; Dedy Alamsyah
Journal of Information System Research (JOSH) Vol 6 No 4 (2025): July 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josh.v6i4.7237

Abstract

Diabetes mellitus is a chronic metabolic disease with a continuously increasing global prevalence. Early detection of diabetes risk is crucial to reduce long-term health complications and the associated healthcare costs. However, a major challenge in applying machine learning models to medical data is the issue of class imbalance, which can lead to model bias toward the majority class. This study aims to develop a diabetes risk classification model by integrating the K-Nearest Neighbors (KNN) algorithm with the Adaptive Synthetic Sampling (ADASYN) technique to address the class imbalance problem. The dataset used was obtained from the Kaggle platform, containing 2,000 patient samples with nine predictive features. Data preprocessing was performed through missing value imputation, outlier handling using winsorizing, and feature normalization using StandardScaler. ADASYN was applied to generate adaptive synthetic samples for the minority class, and the KNN model was trained and evaluated using confusion matrix, precision, recall, F1-Score, accuracy, and ROC-AUC metrics. The results indicate that the implementation of ADASYN improved the ROC-AUC Score by 5.48% (from 91.34% to 96.82%) and the overall accuracy by 2.50% (from 81.50% to 84.00%). The F1-Score for the Diabetes class also increased by 0.40%. The integration of KNN and ADASYN has proven effective in enhancing model performance for detecting high-risk diabetes patients and improving sensitivity toward the minority class.
Co-Authors Ahmad Fatih Zahir Dedy Alamsyah Hendra Mayatopani Irsyad Purbha Irwansyah Jefri Rahmadian Umbar Riyanto