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All Journal INTI Nusa Mandiri
M. Rangga Ramadhan Saelan
Nusamandiri University
Computer Science & IT

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K-BEST SELECTION UNTUK MENINGKATKAN KINERJA ARTIFICIAL NEURAL NETWORK DALAM MEMPREDIKSI RANGE HARGA PONSEL M. Rangga Ramadhan Saelan; Agus Subekti
INTI Nusa Mandiri Vol. 19 No. 1 (2024): INTI Periode Agustus 2024
Publisher : Lembaga Penelitian dan Pengabdian Pada Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/inti.v19i1.5554

Abstract

Determining the price of a mobile phone that will be released to the market cannot be based on assumptions alone. This problem can be overcome by utilizing machine learning. In this study, what is predicted is not the exact price, but rather the price range of a cellphone based on the specifications that are its attributes. In machine learning, the Deep Learning ANN model will be used to predict the price range of a mobile phone. To understand the relationship between features and labels, the Univariate feature selection method SelectKBest is used which will calculate the correlation value between features and labels. In this study, the best performance was obtained from the ANN model with feature selection and hyperparameter tuning, the evaluation of performance metrics obtained the highest accuracy of 97.5%. Experiments were conducted by building several models to compare until there was one model that performed well in processing training and validation data. Model evaluation is presented using confusion metrics with various types of performance metrics: accuracy, precision, recall and f1-score. This study also aims to evaluate the effectiveness of the SelectKBest feature selection method in improving model accuracy and testing various hyperparameter configurations to obtain the best performance.
OPTIMALISASI PREDIKSI JARAK TEMPUH KENDARAAN LISTRIK MENGGUNAKAN XGBOOST DAN FEATURE SELECTION RANDOM FOREST M. Rangga Ramadhan Saelan; Riyan Latifahul Hasanah; Siti Fauziah
INTI Nusa Mandiri Vol. 20 No. 2 (2026): INTI Periode Februari 2026
Publisher : Lembaga Penelitian dan Pengabdian Pada Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/zahvvq98

Abstract

Electric vehicle (EV) adoption is increasing with increasing awareness of clean energy and environmental concerns. However, range anxiety, the uncertainty in estimating driving range, remains a major barrier. This study aims to develop a predictive model for EV range based on technical specifications to provide more accurate estimates and alleviate user concerns. A Machine Learning approach is applied, using Random Forest for feature selection and XGBoost as the primary prediction algorithm. The dataset consists of 478 EV records with 22 attributes, including battery capacity, efficiency, dimensions, and speed. Key features affecting range prediction include battery_capacity_kWh, efficiency_wh_per_km, and height_mm. The XGBoost model demonstrates strong predictive performance with an R² of 0.978, an MAE of 10.555, and an RMSE of 15.180. These results suggest that combining Random Forest and XGBoost offers a promising solution to improve the accuracy of EV range estimation, potentially reducing range anxiety and supporting wider EV adoption
Co-Authors Agus Subekti Riyan Latifahul Hasanah Siti Fauziah