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All Journal Teknika Sinkron : Jurnal dan Penelitian Teknik Informatika Bianglala Informatika : Jurnal Komputer dan Informatika Akademi Bina Sarana Informatika Yogyakarta Jurnal Teknologi Informasi MURA JOISIE (Journal Of Information Systems And Informatics Engineering) JUSIM (Jurnal Sistem Informasi Musirawas) Jurnal Teknologi Informasi dan Terapan (J-TIT) Jurnal Infortech Jurnal Sistem Komputer dan Informatika (JSON) Jurnal Sistem Informasi SPEED - Sentra Penelitian Engineering dan Edukasi Jurnal Teknik Informatika Paradigma Innovative: Journal Of Social Science Research Jurnal Teknologi Informasi Mura Jurnal ilmiah teknologi informasi Asia
Resti Lia Andharsaputri
Universitas Bina Sarana Infromatika
Humanities Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering Law, Crime, Criminology & Criminal Justice Library & Information Science Public Health Other

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K-Nearest Neighbors Optimization using Particle Swarm Optimization in Selection Digital Payments Ridwansyah Ridwansyah; Resti Lia Andharsaputri; Yudhistira Yudhistira; Irmawati Carolina; Suharjanti Suharjanti
Jurnal Teknologi Informasi dan Terapan Vol 12 No 1 (2025): June
Publisher : Jurusan Teknologi Informasi Politeknik Negeri Jember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25047/jtit.v12i1.442

Abstract

Fintech developments have increased the use of digital payment systems such as OVO and GoPay. However, selecting a payment method that suits user preferences is still a challenge. This research proposes a combination of K-Nearest Neighbors (KNN) and Particle Swarm Optimization (PSO) to improve the classification accuracy of digital payment systems. The dataset used comes from a survey of Fintech users with factors such as ease of application, data security, cashback and customer service. KNN is used as a classification method, while PSO is applied for feature selection to improve model efficiency. Evaluation is carried out using accuracy, precision, recall, and AUC. The research results show that accuracy increased from 94.00% to 95.47% after optimization with PSO. The most influential factors are customer service, user employment and cashback. However, the AUC value remains 0.500, which shows that the model still has limitations in optimally differentiating categories. Further research is recommended to explore other algorithms such as Random Forest and SVM, as well as developing a machine learning-based digital payment recommendation system
Comparative Analysis of Multi-Classifier Models with Resampling Techniques for Imbalanced Student Graduation Prediction Carolina, Irmawati; Lia Andharsaputri, Resti; Suharjanti, Suharjanti; Prihatin, Titin; Nurdin, Hafis
Paradigma - Jurnal Komputer dan Informatika Vol. 28 No. 1 (2026): March 2026 Period
Publisher : LPPM Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/p.v28i1.11976

Abstract

Student graduation prediction supports early academic intervention but commonly suffers from class imbalance, where on-time graduates dominate the dataset. This study evaluates five classifiers—Random Forest (RF), XGBoost, Logistic Regression (LR), k-Nearest Neighbors (k-NN), and Gaussian Naïve Bayes (GNB)—under five class-imbalance handling scenarios: Baseline (no resampling), Random Undersampling (RUS), SMOTE, ADASYN, and Borderline-SMOTE. Experiments were conducted on 796 student records (10 attributes) with an imbalanced distribution (634 on-time vs. 162 not on-time; ratio 1:3.9) using Stratified 5-Fold Cross-Validation. Performance was assessed using confusion-matrix metrics and AUC-ROC to reflect minority-class detection. Under baseline, RF achieved the highest accuracy (0.873) but limited minority recall (0.573), confirming majority-class bias. Resampling consistently improved minority recall across models; for example, LR recall increased to 0.802 with RUS, while GNB reached 0.833 with ADASYN, although accuracy decreased due to the sensitivity–specificity trade-off. Overall, RF and XGBoost showed the most stable discrimination across resampling scenarios based on AUC (RF: 0.870–0.883; XGBoost: 0.847–0.866). The main contribution is a systematic, reproducible comparative evaluation of classifier–resampling combinations for imbalanced graduation prediction, providing practical guidance for selecting robust models to identify students at risk of delayed graduation.  
Co-Authors - STMIK Nusa Mandiri, Adelia Alvi Yana Anus Wuryanto Astrilyana Astrilyana Desvia, Yessica Fara Edwin Syahputra Hafis Nurdin Ichbal Wedyo Prianto Imam Taopik Irmawati Carolina Minda Septiani Muhamad Iqbal Nurul Afni Nurul Afni Nurul Afni Nurwulandari, Nurwulandari Ridwansyah Ridwansyah Ridwansyah Ridwansyah Suhar Janti Suhardjono S suharjanti Suharjanti Suharjanti Tri Santoso Yudhistira Yudhistira