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All Journal JURIKOM (Jurnal Riset Komputer) JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) Jurnal Sistem Komputer dan Informatika (JSON)
Umbar Riyanto
Universitas Muhammadiyah Tangerang, Tangerang
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering

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Implementasi Data Mining dengan Algoritma Naïve Bayes Untuk Klasifikasi Kelayakan Penerima Bantuan Sembako Amat Damuri; Umbar Riyanto; Hengki Rusdianto; Mohammad Aminudin
JURIKOM (Jurnal Riset Komputer) Vol 8, No 6 (2021): Desember 2021
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v8i6.3655

Abstract

Poverty is one of the fundamental problems that is the center of attention of the government in a country. One of the important aspects to support the Poverty Reduction Strategy is the availability of accurate and targeted poverty data. Naïve Bayes is one method that can be used to classify data. The results of the classification carried out will later help aid managers to make decisions regarding the classification of determining the recipients of basic food assistance. There are two classes of predictions for the recipients of the basic food assistance, namely eligible and not eligible. The data used for prediction is sample data from XYZ village. In this research, the nave Bayes algorithm is implemented and analyzed using a web-based application. From the results of the evaluation using the confusion matrix, the resulting accuracy for 135 training data with 40 testing data and seven attributes used resulted in an accuracy of 86%, recall of 85%, and precision of 88%.
Implementasi Metode Perbandingan Eksponensial (MPE) Pada Sistem Pendukung Keputusuan Pemilihan Internet Protocol Camera Umbar Riyanto; Nurdiana Handayani; Mohammad Imam Shalahudin
Jurnal Sistem Komputer dan Informatika (JSON) Vol 4, No 1 (2022): September 2022
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/json.v4i1.4875

Abstract

The development of video surveillance has given rise to various types of surveillance cameras, one of which is the Internet Protocol Camera (IP Camera). The number of IP Camera brands in the market, makes people who want to buy IP Cameras have to find their own information about the specifications and capabilities of the IP Camera to be purchased. It takes time and effort to choose an IP Camera, because you have to learn one by one which IP Camera to buy. This study aims to build a decision support system for choosing an IP Camera with a website-based Exponential Comparison Method (MPE) to make it easier to determine the right IP Camera. MPE can sort the priority of decision alternatives on existing criteria and is able to distinguish the value of each alternative in contrast. Based on the case study, the best alternative is Xiaomi Mi 360 with a value of 386, followed by Yi Home Camera 3 getting a value of 369, Ezviz C6N getting a value of 350, Imilab EC4 getting a value of 343 and Cleverdog Egg Cam getting a value of 110. The results of the MPE calculation generated by the system shows the same value as the manual calculation, then the MPE calculation on the system is declared valid. In addition, the test results with black-box testing show that the system can run well.
Implementasi Metode Perbandingan Eksponensial (MPE) Pada Sistem Pendukung Keputusuan Pemilihan Internet Protocol Camera Umbar Riyanto; Nurdiana Handayani; Mohammad Imam Shalahudin
Jurnal Sistem Komputer dan Informatika (JSON) Vol. 4 No. 1 (2022): September 2022
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/json.v4i1.4875

Abstract

The development of video surveillance has given rise to various types of surveillance cameras, one of which is the Internet Protocol Camera (IP Camera). The number of IP Camera brands in the market, makes people who want to buy IP Cameras have to find their own information about the specifications and capabilities of the IP Camera to be purchased. It takes time and effort to choose an IP Camera, because you have to learn one by one which IP Camera to buy. This study aims to build a decision support system for choosing an IP Camera with a website-based Exponential Comparison Method (MPE) to make it easier to determine the right IP Camera. MPE can sort the priority of decision alternatives on existing criteria and is able to distinguish the value of each alternative in contrast. Based on the case study, the best alternative is Xiaomi Mi 360 with a value of 386, followed by Yi Home Camera 3 getting a value of 369, Ezviz C6N getting a value of 350, Imilab EC4 getting a value of 343 and Cleverdog Egg Cam getting a value of 110. The results of the MPE calculation generated by the system shows the same value as the manual calculation, then the MPE calculation on the system is declared valid. In addition, the test results with black-box testing show that the system can run well.
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 Amat Damuri Dedy Alamsyah Hendra Mayatopani Hengki Rusdianto Mohammad Aminudin Mohammad Imam Shalahudin Muhammad Bagir Nurdiana Handayani