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All Journal Journal of Artificial Intelligence and Engineering Applications (JAIEA) Jurnal Manajemen Informatika dan Bisnis Digital
Ravensca Matatula
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Automotive Engineering Computer Science & IT Control & Systems Engineering

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Strategi Retensi Nasabah Perbankan Lokal Berbasis Machine Learning: Analisis Perbandingan Algoritma Klasifikasi dan Teknik Resampling Ravensca Matatula; Marchello Gefan Salenussa; Riyoly, Marvelous Marvin
JUMINTAL: Jurnal Manajemen Informatika dan Bisnis Digital Vol. 4 No. 2 (2025): November 2025
Publisher : Yayasan Literasi Sains Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55123/jumintal.v4i2.7090

Abstract

Customer retention has become an increasingly important strategic challenge for local banking institutions amid intensifying competition and the acceleration of digital transformation, making an understanding of customer loyalty patterns essential for designing effective and data-driven retention strategies. This study aims to analyze and compare the performance of machine learning algorithms in predicting customer loyalty in a local banking context, as well as to evaluate the impact of class imbalance handling techniques on model performance. Three classification algorithms—Decision Tree, Random Forest, and Logistic Regression—are employed in this study, with methodological stages including data preprocessing, model development, and performance evaluation. To address class imbalance in customer data, three approaches are applied, namely class weight adjustment, up sampling, and down sampling. Model performance is evaluated using the F1-Score and the Area Under the Receiver Operating Characteristic Curve (AUC-ROC). The results indicate that the Random Forest algorithm combined with the up sampling technique demonstrates the most consistent performance compared to the other algorithms tested, particularly in handling the minority class. The model achieves an F1-Score of 60% and an AUC-ROC value of 84%, indicating a good balance between precision and recall as well as adequate class discrimination capability. These findings suggest that ensemble-based machine learning models, supported by appropriate class imbalance handling techniques, can serve as effective decision-support tools for customer retention strategies in the context of local banking.
Visualization of Lecturer Teaching Evaluation Data Using K-Means Clustering and Tableau Methods Golda Tomasila; Marchello Gefan Salenussa; Maryo Indra Manjaruni; Ravensca Matatula; Paul Rio Pelupessy; Julius Chrisostomus Aponno
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 5 No. 2 (2026): February 2026
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59934/jaiea.v5i2.1920

Abstract

In the process, the results of monitoring and evaluating lecturers in each semester are usually only presented in the form of tables and descriptive explanations, but have not yet visualized the data for further analysis. The purpose of this study is to visualize the results of lecturer teaching evaluation using the K-Means Clustering and Tableau algorithms, and is expected to help the faculty and university monitor and evaluate lecturers in each semester in a more objective and informative manner. The results of the study found that the k-means clustering algorithm succeeded in finding the pattern of student clustering on the evaluation of lecturer teaching and based on the visualization of the results of k-means with a tableau it was found that most students gave a positive response to lecturer teaching and only a small number of students gave a poor assessment of lecturer teaching by emphasizing on improving the teaching process, namely consistently carrying out RPS, punctuality and so on
Co-Authors Golda Tomasila Julius Chrisostomus Aponno Marchello Gefan Salenussa Maryo Indra Manjaruni Paul Rio Pelupessy Riyoly, Marvelous Marvin