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All Journal Journal of Information Systems and Informatics International Journal Of Computer, Network Security and Information System (IJCONSIST)
Azizah, Nabila Wafiqotul
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Computer Science & IT

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Analyzing the Relationship Between Meteorological Parameters and Electric Energy Consumption Using Support Vector Machine and Cooling Degree Days Algorithm Azizah, Nabila Wafiqotul; Puspaningrum, Eva Yulia; Mas Diyasa, I Gede Susrama Susrama
Journal of Information System and Informatics Vol 6 No 2 (2024): June
Publisher : Universitas Bina Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51519/journalisi.v6i2.719

Abstract

Nowadays, electricity is increasing rapidly. This increase is caused by several factors, one of which is meteorological factors. Meteorological parameters have various types, but this research uses three types in the form of temperature, humidity, and wind speed. The selection of these three types is due to the fact that they have a very close relationship with human life. In line with that, this research uses datasets obtained from the official websites of BMKG (Meteorology, Climatology and Geophysics Agency) and PLN (State Electricity Company). On this occasion, researchers used several methods, namely Cross-Industry Standard Process for Data Mining (CRISP-DM), Cooling Degree Days (CDD), and Support Vector Machine (SVM). The CRISP-DM method is useful for describing the data mining cycle so that the process can be more organized. The SVM algorithm is useful for predicting electricity consumption based on meteorological parameters in January to April 2024, while the CDD method is useful for knowing the correlation of meteorological parameters to electricity consumption in winter. In line with this, this research produces predictions of electricity consumption based on meteorological parameters in January 2024 to April 2024 with an average range of 20.9 Watts per day. In addition, trends and predictions during model evaluation obtained a precision value of 0.796, recall of 0.793, F1 score of 0.793, MAPE of 17.2%, RMSE of 0.41, MAE of 0.167 and accurate of 0.98. These values indicate that the performance of the accuracy model is very high.
Explainable Artificial Intelligence (xAI) for Reliable Financial Decision-Making in Credit Scoring System Azizah, Nabila Wafiqotul; Ajizah, Imroatul; Muhammad Reza Pahlawan; Mohammad Al Hafidz
IJCONSIST JOURNALS Vol 7 No 1 (2025): September
Publisher : International Journal of Computer, Network Security and Information System

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33005/ijconsist.v7i1.161

Abstract

Finance plays a vital role as one of the key elements necessary for sustaining life. Since financial stability is closely linked to overall well-being, many individuals resort to borrowing from financial institutions. As a result, the increasing number of loan applications has led to a rise in financial burdens and fund congestion within these institutions. To mitigate such risks, credit scoring has become an essential predictive approach widely adopted in financial institutions to evaluate customer creditworthiness. Through credit scoring, institutions can determine whether a customer is eligible to receive a loan. This study employs an open-source dataset obtained from Kaggle and follows the CRISP-DM methodology, which consists of six phases: Business Understanding, Data Understanding, Data Preparation, Modeling, Evaluation, and Deployment. The research implements a classification approach by comparing two algorithms—Random Forest Regression and XGBoost. The results show that the Random Forest Regression model performs better, achieving the highest accuracy, recall, and precision, with an AUC value of 0.796 and a Coefficient of Variation (CV) of 0.712
Co-Authors Ajizah, Imroatul Eva Yulia Puspaningrum Mas Diyasa, I Gede Susrama Susrama Mohammad Al Hafidz Muhammad Reza Pahlawan