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All Journal Jurnal Edukasi dan Penelitian Informatika (JEPIN) Journal of Applied Computer Science and Technology (JACOST)
Yulvida, Donata
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Computer Science & IT Education

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Journal : journal of applied computer science and technology jacost

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Pendekatan Metode Ensemble Learning untuk Prakiraan Cuaca menggunakan Soft Voting Classifier Steven Joses; Yulvida, Donata; Rochimah, Siti
Journal of Applied Computer Science and Technology Vol 5 No 1 (2024): Juni 2024
Publisher : Indonesian Society of Applied Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52158/jacost.v5i1.741

Abstract

Weather conditions are one of the crucial factors that need attention. Changes in weather conditions significantly impact various activities. Weather condition changes are determined by numerous factors, often occurring within a relatively short period in the atmosphere, such as pressure, wind speed, rainfall, temperature, and other atmospheric phenomena. Issues in weather forecasting arise due to several factors, namely the fluctuating atmospheric conditions. This research proposes the development of a weather forecasting model using the ensemble learning method approach. The weather data used consist of 33746 records with attributes used after preprocessing, namely Temperature, Dew Point, Humidity, Wind Speed, Wind Gust, Pressure, Precipitation, and Condition. Testing in this research employs several single-machine learning methods such as K-Nearest Neighbor (KNN), Logistic Regression, Random Forest, Naive Bayes, and Multi-Layer Perceptron. The Naive Bayes method using default parameters achieves a high accuracy of 99.00%. In the ensemble method, combinations of three methods exhibit excellent accuracy for all combinations. The best combination methods are found in the Soft Voting Classifier method (Random Forest, MLP, Naive Bayes), Soft Voting Classifier (Logistic Regression, MLP, Naive Bayes), and Soft Voting Classifier (Random Forest, KNN, Naive Bayes) with an accuracy of 99.03%.
Klasifikasi Pemohon Pinjaman dengan Hyperparameter Tuning dan Teknik Penyeimbangan Data Yulvida, Donata; Quinevera, Stefanie; Mardianto, Ricky; Joses, Steven
Journal of Applied Computer Science and Technology Vol. 6 No. 2 (2025): Desember 2025
Publisher : Indonesian Society of Applied Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52158/krjtrh05

Abstract

Loan classification is a critical component of credit risk management, as it categorizes loans based on risk levels and supports the financial stability of banks, where loan-related income represents a substantial share of assets. Effective classification aims to ensure secure asset allocation, minimize credit risk, and prevent potential repayment issues. This study enhances loan classification performance through two strategies: hyperparameter optimization of Decision Tree and Random Forest algorithms, and data balancing techniques to address class imbalance. Experimental results show that the Decision Tree achieves 89.21% accuracy with an F1-Score of 70.17%, while the Random Forest demonstrates higher performance, reaching 94.04% accuracy and an F1-Score of 79.75%. Random Oversampling reduces bias toward majority classes by improving model sensitivity, while hyperparameter tuning with GridSearchCV identifies optimal parameter settings, thereby strengthening predictive performance. The findings highlight that combining data balancing with hyperparameter optimization effectively improves accuracy and F1-Scores. These approaches are not limited to the algorithms tested but can also be applied to other classification methods, offering broader potential for enhancing credit risk prediction in banking.
Co-Authors Ary Mazharuddin Shiddiqi Joses, Steven Mardianto, Ricky Quinevera, Stefanie Siti Rochimah Steven Joses