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All Journal Jurnal Penelitian Saintek Teika Jurnal Ilmu Komputer dan Agri-Informatika Jurnas Nasional Teknologi dan Sistem Informasi ANDHARUPA Sistemasi: Jurnal Sistem Informasi JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING JOURNAL OF APPLIED INFORMATICS AND COMPUTING METHOMIKA: Jurnal Manajemen Informatika & Komputerisasi Akuntansi Jurnal Teknologi Sistem Informasi dan Aplikasi Jurnal Ilmiah Media Sisfo JURTEKSI Jurdimas (Jurnal Pengabdian Kepada Masyarakat) Royal JOISIE (Journal Of Information Systems And Informatics Engineering) Journal of Information Systems and Informatics Jurnal Teknologi Dan Sistem Informasi Bisnis JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) Jurnal Sistem Komputer dan Informatika (JSON) Jurnal Generic REKA ELKOMIKA: Jurnal Pengabdian kepada Masyarakat Jurnal Algoritma The Indonesian Journal of Computer Science Jurnal Komtika (Komputasi dan Informatika)
Sevtiyuni, Putri Eka
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Agriculture, Biological Sciences & Forestry Arts Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Social Sciences

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Penerapan Metode Machine Learning Dan Teknik SMOTE untuk Prediksi Diabetes Sembiring Depari, Alrayssa Davinka; Tania, Ken Ditha; Sevtiyuni, Putri Eka
Jurnal Sistem Komputer dan Informatika (JSON) Vol. 7 No. 2 (2025): Desember 2025
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/json.v7i2.9032

Abstract

Diabetes merupakan salah satu penyakit tidak menular yang prevalensinya terus meningkat secara global maupun nasional. Kondisi ini menimbulkan risiko komplikasi serius seperti penyakit jantung, stroke, hingga gagal ginjal apabila tidak terdeteksi sejak dini. Oleh karena itu, dibutuhkan metode prediksi berbasis data yang mampu membantu proses deteksi awal secara cepat, akurat, dan efisien. Penelitian ini bertujuan membandingkan kinerja empat algoritma pembelajaran mesin, yaitu Random Forest, XGBoost, Support Vector Machine (SVM), dan K-Nearest Neighbor (KNN) dalam memprediksi penyakit diabetes menggunakan dataset publik dari Kaggle. Penelitian dilakukan dengan mengacu pada kerangka Knowledge Discovery in Databases (KDD) yang terdiri dari tahapan seleksi data, pra-pemrosesan (data cleaning, transformasi, dan normalisasi), penyeimbangan kelas menggunakan Synthetic Minority Over-sampling Technique (SMOTE), pembagian data latih dan data uji dengan rasio 80:20, implementasi algoritma, serta evaluasi performa model. Evaluasi dilakukan menggunakan metrik Accuracy, Precision, Recall, dan F1-Score untuk memastikan kualitas prediksi secara menyeluruh. Hasil penelitian menunjukkan bahwa Random Forest dan XGBoost memberikan performa terbaik dengan nilai Accuracy, Precision, Recall, dan F1-Score sebesar 0,97. Model KNN menunjukkan performa cukup baik dengan skor 0,94, sementara SVM memperoleh nilai terendah sebesar 0,89. Temuan ini menegaskan bahwa penerapan kerangka KDD dengan teknik SMOTE mampu menghasilkan model prediksi yang optimal. Random Forest dan XGBoost direkomendasikan sebagai algoritma unggulan pada penelitian serupa, terutama pada dataset dengan karakteristik kelas yang tidak seimbang.
Integrating Aspect-Based Sentiment Analysis with CSI–IPA for Telecommunications App Development Marshella, Siti Hariza; Kurniawan, Dedy; Oktadini, Nabila Rizky; Sanjaya, Rudi; Sevtiyuni, Putri Eka; Gumay, Naretha Kawadha Pasemah
Journal of Information System and Informatics Vol 7 No 4 (2025): December
Publisher : Asosiasi Doktor Sistem Informasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63158/journalisi.v7i4.1363

Abstract

This study integrates Aspect-Based Sentiment Analysis with Customer Satisfaction Index (CSI) and Importance Performance Analysis (IPA) to determine priority development features in digital service applications in the telecommunications sector. A total of 10,000 MyIM3 user reviews were analyzed using sentiment analysis with Pseudo-labeling and Fine-Tuned IndoBERT, then from the results of the analysis, negative sentiment was mapped into several topics using LDA. The topic is used to compile question indicators based on the five dimensions of SERVQUAL. After the questionnaire data is declared valid and reliable, CSI and IPA analysis is carried out. A CSI value of 79.22% indicates that user satisfaction is in the "borderline" category, but several aspects still need to be improved, especially system updates (RS3), application attention to user needs (E2), and feature personalization (E3) which are in quadrant I (Concentrate Here). This hybrid approach offers novelty by demonstrating how ABSA and LDA can be systematically integrated with CSI and IPA to provide more comprehensive and user-oriented insights. The limitations of this study include focusing on negative sentiment data for feature exploration, as it is most relevant for identifying problems and opportunities for improvement and development of telecommunication digital services.
Co-Authors Ahmad Rifai Akbaroka, Leo Ali Bardadi Ali Ibrahim Allsela Meiriza, Allsela Amanda, Bella Rizkia Ari Wedhasmara Arnan, Sefian Ayu, Nabila Riska Az Zahra, Cindy Putri Bayu Wijaya Putra Danendra, Devano Dedy Kurniawan Dedy Kurniawan Dinna Yunika Hardiyanti Endang Lestari Ruskan Faizah, Ovie Nur Firda, Hiliah Fitriani, Lesa Gumay, Naretha Kawadha Pasemah Idpal, Idpal Irmawati Irmawati Ken Ditha Tania Larasati, Salsabila M, Nys Marliza Tiara Marjusalinah, Anna Dwi Marshella, Siti Hariza Meiriza, Viola Miftahul Falah Mira Afrina Muhammad Ihsan Mulyadi Mulyadi Nadia Anggraini Oktadini, Nabila Oktadini, Nabila Rizky Pacu Putra Padlefi, Muhamad Riza Putri, Amelia Rizki Putri, Nyayu Dwi Tarisa Ramadhan, Kumara Aditya Ramadhan, Muhammad Gilang Rezeki, Yunika Tri Sanjaya, Rudi Saputri, Sonia Dwi Sembiring Depari, Alrayssa Davinka Seprina, Iin Susanti, Helen Wahyudi, Muhammad Iqbal