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All Journal Building of Informatics, Technology and Science KLIK: Kajian Ilmiah Informatika dan Komputer
Ken Ditha Tania
Universitas Sriwijaya, Palembang
Computer Science & IT

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Journal : building of informatics technology and science

 1 
Classification of School Students Lifestyle Risks Based on Smoking Behavior Using Naïve Bayes Oktaria Dwi Cahyani; Deltari Balka; Dinni Rezky Amelia; Rainda Cintari Aulya; Ken Ditha Tania; Allsela Meiriza; Zaqqi Yamani
Building of Informatics, Technology and Science (BITS) Vol 8 No 1 (2026): June 2026
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v8i1.9668

Abstract

This study aims to classify students' lifestyle risks based on smoking behavior using the Naïve Bayes algorithm within a knowledge management framework. The research was conducted on students at a vocational high school within the coverage area of a local community health center. The dataset consisted of 277 valid records after undergoing data selection, cleaning, and transformation stages. The modeling process was carried out using RapidMiner software with an 80:20 data split for training (221 students) and testing (56 students). The evaluation metrics used included accuracy, precision, recall, and confusion matrix. The experimental results demonstrate that the Naïve Bayes model achieved an accuracy of 85.92%, precision of 86.12%, and recall of 92.86% for the unhealthy class. Furthermore, the classification results were integrated into a knowledge management framework to support decision-making processes in schools and community health centers. This study contributes to the application of predictive data mining in adolescent health and demonstrates how classification models can serve as effective tools for early detection, preventive interventions, and evidence-based policy formulation in educational and health settings.
Komparasi Model Ensemble dan Algoritma Machine Learning Untuk Memprediksi Penyakit Jantung Muhammad Syarief Albani; Dedy Kurniawan; Ken Ditha Tania
Building of Informatics, Technology and Science (BITS) Vol 7 No 4 (2026): March 2026
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v7i4.8346

Abstract

This study compared the performance of nine machine learning algorithms in predicting heart disease using a dataset dating back to 1988 and consisting of four databases: Cleveland, Hungary, Switzerland, and Long Beach totaling 1025 data. The dataset used includes medical features that reflect physiological states, clinical examination results, and cardiovascular risk factors, namely age, gender, type of chest pain, resting blood pressure, serum cholesterol levels, fasting blood sugar levels, resting electrocardiography results, maximum heart rate, chest pain during physical activity, ST segment depression, ST segment slope, number of major blood vessels visible by fluoroscopy, and thalassemia status. The stages of this study include data cleaning, data transformation, and evaluation carried out using the data splitting method for training and testing as well as K-fold cross-validation with metrics of accuracy, precision, recall, F1 score, and AUC-ROC. The algorithms used in this study are Decision Tree, Random Forest, Support Vector Machine, MLP Classifier, Bagging Classifier, Gradient Boosting, CatBoost, XGBoost, and LightGBM with ensemble-based models, such as CatBoost, Random Forest, XGBoost, and LightGBM, showing consistent performance on various evaluation metrics when compared to non-ensemble models. Among all models tested, CatBoost showed the best performance, with an accuracy reaching 98%, an F1-Score of 0.980, and a Recall of 0.9875 then followed by other ensemble algorithms such as Random Forest, XGBoost and LightGBM. The results of this study indicate that ensemble models are proven to be more effective in predicting heart disease. This study aims to present an in-depth comparative study of the performance of ensemble algorithms and modern machine learning in predicting heart disease, as well as enriching the literature related to the application of Knowledge Discovery in the health sector and providing a basis for selecting more reliable prediction algorithms to support clinical decision making and the development of machine learning-based heart disease diagnosis support systems.
Analisis Segmentasi Pelanggan Menggunakan RFM dan K-Means Clustering sebagai Dasar Penyusunan Aturan Pendukung Keputusan Meisya Dwi Andini; Rafa Nadira Catra; Weli Ratri Homausyah; Haaniyah Aurelia; Allsela Meiriza; Ken Ditha Tania; Zaqqi Yamani
Building of Informatics, Technology and Science (BITS) Vol 7 No 4 (2026): March 2026
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v7i4.9511

Abstract

One of the important methods in supporting data-driven Customer Relationship Management (CRM) initiatives is customer segmentation. However, in practice, segmentation results are often limited to descriptive analysis and are not further utilized in decision-support processes. This study aims to utilize customer segmentation results based on the Recency, Frequency, Monetary (RFM) approach and the K-Means algorithm as a basis for developing decision-support recommendations. The research stages include data preprocessing, RFM value calculation, normalization using the Min-Max Scaling method, and determining the optimal number of clusters using the Elbow Method and Silhouette Score. The evaluation results indicate that the optimal number of clusters is four, with a Silhouette Score of 0.61, which reflects a moderately good level of cluster separation. The segmentation results classify customers into four categories: High Value/VIP Customers, Loyal Customers, Potential Customers, and Low Value/Dormant Customers, each exhibiting distinct transactional behavior characteristics. These characteristics are then interpreted into decision rules using IF–THEN logic; for example, customers with low Recency, high Frequency, and high Monetary values are recommended strategies such as loyalty rewards and upselling. The findings suggest that customer segmentation can be extended beyond descriptive analysis and utilized as a practical basis for marketing decision-making, although the approach remains relatively simple and heuristic-based. The contribution of this study is to integrate RFM-KMeans segmentation results with IF–THEN decision rules to generate more applicable marketing strategy recommendations in supporting data-driven decision making.
Co-Authors Adya Salwa Dyani Allsela Meiriza Ari Wedhasmara Dedy Kurniawan Deltari Balka Dinni Rezky Amelia Haaniyah Aurelia Meisya Dwi Andini Muhammad Syarief Albani Oktaria Dwi Cahyani Rafa Nadira Catra Rainda Cintari Aulya Weli Ratri Homausyah Zaqqi Yamani