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All Journal JOIV : International Journal on Informatics Visualization Sinkron : Jurnal dan Penelitian Teknik Informatika Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Jurnal Penelitian Pendidikan IPA (JPPIPA) Jurnal Sains dan Teknologi: Jurnal Keilmuan dan Aplikasi Teknologi Industri Jurnal Teknologi Indonesian Journal of Electrical Engineering and Computer Science Jurnal Inovasi Teknik Informatika Journal of Applied Data Sciences Jurnal Abdimas ADPI Sains dan Teknologi Jurnal Ipteks Terapan : research of applied science and education Jurnal Sains Informatika Terapan (JSIT) Jurnal Komtekinfo Jurnal Teknologi Jurnal Teknik dan teknologi tepat guna
Irzal Arif Wisky
Universitas Putra Indonesia YPTK Padang
Aerospace Engineering Agriculture, Biological Sciences & Forestry Arts Astronomy Automotive Engineering Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Health Professions Immunology & microbiology Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Library & Information Science Materials Science & Nanotechnology Mathematics Medicine & Pharmacology Neuroscience Nursing Physics Public Health Veterinary Other

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Analisis Kepuasan Pelayanan Publik Menggunakan Metode Naïve Bayes Pada Dinas Kependudukan Dan Pencatatan Sipil Kabupaten Agam Wisky, Irzal Arief; Rianti, Eva; Syahira, Afika
Jurnal Sains Informatika Terapan Vol. 4 No. 3 (2025): Jurnal Sains Informatika Terapan (Oktober, 2025)
Publisher : Riset Sinergi Indonesia (RISINDO)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62357/jsit.v4i3.784

Abstract

This study aims to analyze public satisfaction with services provided by the Department of Population and Civil Registration (Disdukcapil) of Agam Regency using the Naïve Bayes method. The research data were collected from 252 respondents through a public satisfaction survey covering nine service indicators based on the Ministry of Administrative Reform Regulation No.16 of 2014. The Naïve Bayes algorithm was applied to classify satisfaction levels into four categories: very dissatisfied, dissatisfied, satisfied, and very satisfied. The results indicate that the developed web-based system can accurately predict public satisfaction levels, with the highest probability value of 0.1127 falling under the “very satisfied” category. These findings demonstrate that the service quality at Disdukcapil Agam Regency has been well implemented, and the application of the Naïve Bayes method is effective in supporting the evaluation and continuous improvement of public service performance.
Robust Predictive Model for Heart Disease Diagnosis Using Advanced Machine Learning Techniques Sovia, Rini; Anam, M. Khairul; Wisky, Irzal Arief; Permana, Randy; Rahmi, Nadya Alinda; Zain, Ruri Hartika
Journal of Applied Data Sciences Vol 7, No 1: January 2026
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v7i1.1092

Abstract

This study presents a hybrid ensemble learning framework designed to enhance the predictive accuracy, robustness, and generalizability of heart disease classification models. The framework integrates three base classifiers: Decision Tree (DT), Gaussian Naive Bayes (GNB), and K Nearest Neighbor (KNN), which are combined using a stacking ensemble method with Logistic Regression (LR) as the meta learner. Each classifier contributes a distinct analytical perspective: DT models nonlinear relationships, GNB provides probabilistic reasoning, and KNN captures similarity-based patterns. Logistic Regression aggregates their outputs to produce a unified predictive decision. To mitigate class imbalance commonly observed in clinical datasets, the Synthetic Minority Oversampling Technique (SMOTE) is applied to generate synthetic samples of the minority class, improving the model’s ability to recognize underrepresented cases. Hyperparameter optimization is performed using the Optuna framework, which applies the algorithm to efficiently explore parameter configurations. The proposed model was evaluated on a publicly available heart disease dataset and achieved an accuracy of 99.61%, precision of 99.62%, recall of 99.59%, F1 score of 99.60%, and specificity of 99.58%, corresponding to a false positive rate of only 0.42 percent. These results demonstrate the framework’s strong ability to accurately identify heart disease cases while minimizing misclassification. The integration of SMOTE, stacking, and Optuna optimization contributes to its superior performance and robustness. Consequently, this approach shows strong potential for integration into clinical decision support systems to assist healthcare professionals in reliable and timely diagnosis.
Machine Learning Predicts the Level of Disease Spread Dhio Saputra; Irzal Arief Wisky; Sarjon Defit
Jurnal Penelitian Pendidikan IPA Vol 10 No 4 (2024): April
Publisher : Postgraduate, University of Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/jppipa.v10i4.7070

Abstract

The aim of the research is predictive analysis of the spread of disease. Variable analysis at the population level in a region and the total disease events detected in the community. These variables can show the accuracy and certainty of the status of the resulting analysis. The concept of Machine Learning analysis is proposed to develop previous analysis models. The methods used include the K-Means cluster, Naïve Bayes, and Decision Tree (DT). There are two stages in the analysis process: pre-processing and classification. The discussion presented by K-Means provides a classification analysis pattern. The patterns obtained will be passed on to the classification process using Naïve Bayes and DT. Naïve Bayes results provide quite significant results with an accuracy rate of 83.33%. DT can also describe the results of information and knowledge analysis in the form of decision trees. DT produces decision trees that can provide knowledge and information analysis. The DT results provide an accuracy rate of 91.76% so these results can be used as consideration in decision making. The resulting information and knowledge can be used as a guide in making policies for handling health in the community.
Co-Authors Anam, M Khairul Aqil Ramadhan, Muhammad Dhio Saputra Dinul Akhiyar Eva Rianti Febri Hadi Gunadi Widi Nurcahyo Hadi Syahputra Heri Pratama Marfalino, Hari Mutiana Pratiwi Permana, Randy Purnama, Ayu Widya Radiyan Rahim Rahim, Radiyan Rahmi, Nadya Alinda Riandana Afira Riki Iskandar Ruri Hartika Zain Sarjon Defit Sofika Enggari Sovia, Rini Sumijan Syahira, Afika Teri Ade Putra Yanto, Musli Yogi Wiyandra