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All Journal JSI: Jurnal Sistem Informasi (E-Journal) Annual Research Seminar JPSriwijaya Sistemasi: Jurnal Sistem Informasi JURNAL MEDIA INFORMATIKA BUDIDARMA Jurnal Sains dan Informatika The IJICS (International Journal of Informatics and Computer Science) JOISIE (Journal Of Information Systems And Informatics Engineering) Jurnal Tekno Kompak Building of Informatics, Technology and Science Jutisi: Jurnal Ilmiah Teknik Informatika dan Sistem Informasi Jurnal Pengabdian Masyarakat Bumi Raflesia Indonesian Journal of Electrical Engineering and Computer Science Mejuajua Administrative and Environmental Law Review Jurnal PETISI (Pendidikan Teknologi Informasi)
Fathoni -
Information Systems Universitas Sriwijaya Palembang, Indonesia
Agriculture, Biological Sciences & Forestry Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Mathematics Neuroscience Public Health Social Sciences Other

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Journal : building of informatics technology and science

 1 
Segmentasi Pelanggan E-Commerce Berbasis Integrasi Text Mining dan RFM untuk Deteksi Dini Churn Violin Juneyla Nandita; Juseia Wulandari; Apriyadi Apriyadi; Ali Ibrahim; Fathoni Fathoni
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.v8i1.9687

Abstract

The growth of transactions on e-commerce platforms generates a massive volume of unstructured customer review data. However, traditional Customer Relationship Management (CRM) models such as RFM often only focus on quantitative transaction data and ignore the emotional dimension contained in customer reviews. This study aims to analyze the relationship between purchase frequency and customer comment polarity through the integration of Text Mining and CRM Analytics approaches. The novelty offered is the development of a hybrid method that combines Lexicon Refinement-based sentiment extraction with the Random Forest algorithm to overcome rating bias in global e-commerce platform data (Kaggle). The proposed method includes the use of Natural Language Processing (NLP) techniques, topic modeling based on Latent Dirichlet Allocation (LDA), and sentiment analysis to extract polarity scores. The test results show that the initial lexicon model has limitations with an accuracy of 52.14% due to noise in neutral reviews (3-star rating). However, after optimization using the Random Forest algorithm and neutral data filtering, the classification accuracy increased significantly to 74.62%. These results prove that sentiment integration is able to provide more accurate loyalty mapping and help e-commerce management detect potential churn in the At-Risk customer segment.
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 Disparitas Data Kebencanaan Indonesia sebagai Dasar Mitigasi Berbasis K-Means Vanisa Amalia Putri; Fathoni Fathoni; Yadi Utama
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.v8i2.9667

Abstract

Indonesia experiences a high frequency of disasters; however, differences in characteristics between national and global datasets create challenges for consistent disaster analysis. Previous studies generally rely on a single data source, limiting the ability to capture disparities in data representation comprehensively. This study aims to analyze disparities in disaster data representation and identify regional disaster patterns using a data mining approach. A Disaster Severity Index was constructed using Min-Max normalization, and the K-Means algorithm was applied to cluster regions based on disaster index and event frequency. The optimal number of clusters was determined using the Elbow Method and validated using the Silhouette Score, while a global dataset was used for comparison. The results indicate that the optimal model is achieved at K=4 with a Silhouette Score of 0.610883, indicating good cluster separation. Most regions exhibit moderate disaster characteristics, while a small number show extreme patterns with disproportionate relationships between frequency and severity. Differences between national and global datasets suggest variations in reporting mechanisms and data coverage. These findings demonstrate that relying on a single data source may lead to biased interpretations, highlighting the importance of multi-source data integration to improve the accuracy of disaster analysis.
Co-Authors Abdiansah, Abdiansah Ahmad Fali Oklilas Ahmad Rafid Rizqullah Ahmad Rifai Al Farissi Ali Ibrahim Alif Baidhawi Alisia Silver Stone Allan Nugraha Allsela Meiriza Allsela Meiriza, Allsela Alzaini, Akbar Apriansyah Putra Apriansyah Putra Apriyadi Apriyadi Ari Wedhasmara Arvhi Randita Setia Cantika Aulia Dedy Kurniawan Deni Agus Hendrawan Dian Febriansyah Eka Saputra Endang Lestari Ruskan Ermatita - Erwin, Erwin Febrian Febrian Firman Muntaqo Juseia Wulandari Ken Ditha Tania Ken Ditha Tania Leiden Fauzi Yoka Surya Lyvia Valentina M Bintang Naufal Riansyah M Ihsan Jambak Maharani Maharani Mira Afrina Mohammad Nizar Muhammad Fadhil Rahman Muhammad Fakhri Nadrota Acta Muhammad Rafif AR Muhammad Rezky Naretha Kawadha Pasemah Gumay Pacu Putra Purwita Sari Putri Mutiara Arinie Rahmat Fadli Isnanto Rahmat Izwan Heroza Rahmat Maulana Rezqe, Beriadi Agung Nur Rio Abdi Sucipta Rizka Dhini Kurnia Sanjaya, M. Rudi Siti Nur Arafah Sultan Arba'i Suluh Arif Wibowo Syamsuryadi Syamsuryadi Tika Octri Dieni Vanisa Amalia Putri Violin Juneyla Nandita Yadi Utama Yadi Utama Yadi Utama Zikri Firmansyah