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SAFITRI JUANITA
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INDONESIA
Idealis : Indonesia Journal Information System
Published by Universitas Budi Luhur Jakarta
ISSN : -     EISSN : 26847280     DOI : -
Core Subject : Science,
Computer Science & IT Decision Sciences, Operations Research & Management
Jurnal Indonesia Journal Information System (Idealis) adalah jurnal penelitian Program Studi Informasi, Fakultas Teknologi Informasi, Universitas Budi Luhur. Topik pada Jurnal ini adalah Decision Support System, E-Commerce/E-Business, Datawarehouse/BI, Enterprise System, Data Mining, Sistem Penunjang Keputusan selamat membaca,  Admin Jurnal Idealis
Arjuna Subject : Ilmu Komputer (semua kategori) - Sistem Informasi
Articles 942 Documents
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Prediksi Non-Performing Loan untuk Analisis Pengajuan Kredit Menggunakan Seleksi Fitur dan Ensemble Methods David Jefri Aruan; Rusdah Rusdah; Ahmad Pudoli
IDEALIS : InDonEsiA journaL Information System Vol. 9 No. 2 (2026): Jurnal IDEALIS Juli 2026
Publisher : Universitas Budi Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36080/idealis.v9i2.3851

Abstract

Non-Performing Loans (NPL) are a fundamental indicator of a financial institution's asset health, reflecting loans that fail to meet interest or principal payment obligations as agreed. A high NPL ratio negatively impacts a bank's financial performance, such as decreased profitability as measured by Return on Assets (ROA) and decreased liquidity. Bank Indonesia sets an NPL tolerance limit of 5% of total credit provided by banking financial institutions. Therefore, a predictive model is needed that can detect the possibility of customers experiencing NPLs early. This study aims to identify relevant factors in predicting NPLs and create an NPL prediction model based on these factors. The contribution of this study lies in combining the results of three feature selection techniques: Chi-Square, Mutual Information, and Random Forest feature importance, using the average score eliminated by the Recursive Feature Elimination technique. Several ensemble algorithms, namely Random Forest, XGBoost, Gradient Boosting, and LightGBM, were explored to produce the best-performing model. Then, hyperparameter tuning was performed on the best model. The Random Forest model produced the best performance, with 92.17% accuracy, 78.1% precision, 98.1% recall, and 95.5% AUC. Hyperparameter tuning was shown to improve recall, thus improving the model's ability to measure how much positive data (Current class) was successfully predicted by the model. The results of this study can assist management in making credit decisions. Thus, it is hoped that it can help reduce the number of NPL cases.
Analisis Pola Asosiasi Gangguan Berulang Jaringan Distribusi PLN Menggunakan Algoritma Apriori Dwina Kuswardani; Muhamad Habibi
IDEALIS : InDonEsiA journaL Information System Vol. 9 No. 2 (2026): Jurnal IDEALIS Juli 2026
Publisher : Universitas Budi Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36080/idealis.v9i2.3853

Abstract

Repeated disturbances in power distribution networks can reduce system reliability and increase maintenance costs. However, maintenance activities are often performed reactively because recurring disturbance patterns have not been systematically identified from historical operational data. This study aims to identify recurring disturbance patterns in the distribution network of PT PLN (Persero) by applying the Apriori algorithm within the CRISP-DM framework. The dataset consisted of 29,446 historical disturbance records containing disturbance type, cause, and location. After the data preparation stage, which included data cleaning, standardization, and transformation, 10,274 valid transactions were obtained for association rule mining. The Apriori algorithm was implemented using a minimum support threshold of 0.5%, a minimum confidence threshold of 70%, and lift values greater than 1 as the evaluation criterion. The analysis produced 57 frequent 1-itemsets, 180 frequent 2-itemsets, 35 frequent 3-itemsets, and 64 association rules. The results indicate that Core FO Problem, FOC – Putus Core, and Putus Kabel FO are the most frequently occurring disturbance patterns. The strongest association rule, Banten and FOT – Software → Konfigurasi Di Router Problem, achieved a support of 0.993%, a confidence of 81.6%, and the highest lift value of 30.936, indicating a strong positive relationship between the antecedent and consequent. These findings demonstrate that the Apriori algorithm is effective in discovering recurring disturbance patterns from historical operational data and can provide maintenance-oriented knowledge to support inspection prioritization and preventive maintenance planning in power distribution networks.

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