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All Journal JURIKOM (Jurnal Riset Komputer) Building of Informatics, Technology and Science JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) KLIK: Kajian Ilmiah Informatika dan Komputer
Allsela Meiriza
Universitas Sriwijaya, Palembang
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering

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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.
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.
Klasifikasi Status Gizi Bayi sebagai Upaya Deteksi Dini Masalah Gizi Menggunakan Algoritma Naive Bayes Muhammad Rafif AR; Fathoni Fathoni; Ken Ditha Tania; Allsela Meiriza; Ali Ibrahim
Building of Informatics, Technology and Science (BITS) Vol 8 No 2 (2026): September 2026
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v8i2.9490

Abstract

The classification of infant nutritional status remains a significant challenge in the utilization of health data, particularly in developing predictive models that are accurate and reliable for supporting decision-making at the primary healthcare level. This study aims to implement the Naïve Bayes algorithm to classify infant nutritional status based on anthropometric indicators, namely weight-for-age (W/A) and height-for-age (H/A). The data used in this study were obtained from Posyandu activity reports at Citra Medika Public Health Center covering the period from 2023 to 2025, with a total of 10,654 infant records. The class distribution in the dataset includes normal, well-nourished, undernourished, severely undernourished, overnourished, and at risk of overnutrition categories. Since the class proportions are relatively balanced, no oversampling technique was applied. The research process involved several stages, including data cleaning, category normalization, class label transformation, and dataset splitting using stratified sampling with a composition of 80% training data and 20% testing data. The evaluation results indicate that the Naïve Bayes model achieved an accuracy of 82%. The precision values were 0.92 for the normal class, 0.39 for the malnutrition class, and 0.14 for the overweight class. The recall values were 0.89 for the normal class, 0.63 for the malnutrition class, and 0.08 for the overweight class. Meanwhile, the F1-scores were 0.83 for the normal class, 0.48 for the malnutrition class, and 0.11 for the overweight class. These findings suggest that the model demonstrates fairly good performance in classifying infant nutritional status based on anthropometric data. Therefore, the Naïve Bayes algorithm can be considered effective for classifying infant nutritional conditions using W/A and H/A indicators. This study is expected to contribute to the development of data-driven systems to enhance analytical quality and support decision-making in primary healthcare services.
Analisis Perbandingan Random Forest dan XGBoost dalam Prediksi Pembatalan Pesanan Shopee Menggunakan Interpretasi SHAP Muhammad Bayu Samudra; Mira Afrina; Allsela Meiriza; Rizka Dhini Kurnia; Ardina Ariani
Building of Informatics, Technology and Science (BITS) Vol 8 No 2 (2026): September 2026
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v8i2.10960

Abstract

Order cancellation is a significant issue for e-commerce platforms because it can result in revenue loss, increased operational costs, and reduced transaction management efficiency. This study aims to compare the performance of Random Forest and Extreme Gradient Boosting (XGBoost) in predicting customer order cancellations on Shopee and to interpret the factors contributing to the prediction outcomes using an Explainable Artificial Intelligence approach based on SHapley Additive exPlanations (SHAP). The study employed the Shopee Consumer Behaviour Cancellation Order Analysis dataset obtained from Kaggle. The research process consisted of data preprocessing, dataset partitioning using the Stratified Train-Test Split method with an 80:20 ratio, implementation of both algorithms, model evaluation using Accuracy, Precision, Recall, F1-Score, and Receiver Operating Characteristic–Area Under Curve (ROC-AUC), followed by model interpretation using SHAP. The evaluation results indicate that Random Forest achieved better performance across most evaluation metrics, while XGBoost obtained a slightly higher ROC-AUC value. SHAP analysis identified Total Discount, Buyer-Paid Shipping Cost, Estimated Shipping Cost, and Estimated Shipping Fee Deduction as the variables contributing most to order cancellation predictions.These findings indicate that combining predictive algorithms with an Explainable Artificial Intelligence approach not only supports the classification of order cancellations but also provides insights into the factors influencing prediction outcomes, which can serve as a consideration for reducing order cancellations on e-commerce platforms.
Co-Authors Adya Salwa Dyani Ali Ibrahim Ardina Ariani Ari Wedhasmara Danar Feriano Dedy Kurniawan Deltari Balka Dinni Rezky Amelia Endang Lestari Ruskan Fathoni - Fika Febrika Haaniyah Aurelia Helmalia Sandy Ken Ditha Tania Letty Latifani Arifah Meisya Dwi Andini Mira Afrina Muhammad Bayu Samudra Muhammad Rafif AR Nabilla Ajeng Ningtyas Oktadini, Nabila Rizky Oktaria Dwi Cahyani Pacu Putra Putri Eka Sevtiyuni Rafa Nadira Catra Rainda Cintari Aulya Rizka Dhini Kurnia Weli Ratri Homausyah Zaqqi Yamani