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All Journal Journal of Mechanical Engineering Education (Jurnal Pendidikan Teknik Mesin) JOURNAL OF APPLIED INFORMATICS AND COMPUTING Jurnal Peternakan Nusantara EDUMATIC: Jurnal Pendidikan Informatika Jusikom: Jurnal Sistem Informasi Ilmu Komputer Jurnal Teknologi Dan Sistem Informasi Bisnis JIPIS Jurnal Informatika Ekonomi Bisnis JATI (Jurnal Mahasiswa Teknik Informatika) REMIK : Riset dan E-Jurnal Manajemen Informatika Komputer Jurnal Teknologi Informatika dan Komputer Bulletin of Computer Science Research Brilliance: Research of Artificial Intelligence Jurnal Ilmiah SIGMA: Informatics Engineering Journal of UPB Malcom: Indonesian Journal of Machine Learning and Computer Science Jurnal Lentera Pengabdian VIDHEAS: Jurnal Nasional Abdimas Multidisiplin
muhidin, asep
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Religion Agriculture, Biological Sciences & Forestry Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Mathematics Mechanical Engineering Social Sciences Veterinary Other

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Journal : Bulletin of Computer Science Research

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Optimasi Algoritma Random Forest untuk Prediksi Eksport Kelapa Sawit Global Danny, Muhtajuddin; Muhidin, Asep
Bulletin of Computer Science Research Vol. 5 No. 5 (2025): August 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v5i5.744

Abstract

Palm oil production is a strategic commodity in global trade, with a trend showing an increase from year to year. This study aims to optimize the Random Forest algorithm in predicting the amount of global palm oil production based on historical data. The dataset used consists of 12,458 observations with one dependent variable (Palm_Oil_00002577_) representing the amount of palm oil production, and four independent variables: country, Code, Year, and Palm_Oil_00002577_log. The data is divided into 80% for training (9,966 observations) and 20% for testing (2,492 observations). The model optimization process is carried out by adjusting the key parameters of Random Forest using Grid Search and Cross-Validation. The initial Random Forest model (without optimization) produces a Root Mean Squared Error (RMSE) value of 115.27 and an R-squared (R²) value of 0.9824 on the test data. After optimization using Grid Search and Cross-Validation on key parameters (n_estimators, max_depth, and max_features), the optimized model showed significant performance improvements, with the RMSE decreasing to 103.54 and the R² increasing to 0.9984. The decrease in the RMSE indicates a reduction in the model's average prediction error, while the increase in R² approaching 1 indicates the model's ability to explain almost all of the variation in global palm oil production data. These results indicate that parameter optimization in Random Forest can substantially improve prediction accuracy, enabling the model to be used as a production planning tool and strategic decision-making tool in the palm oil commodity trading sector.
Prediksi Kegagalan Perangkat Industri Menggunakan Random Forest dan SMOTE untuk Pemeliharaan Preventif Muhidin, Asep; Muhtajuddin Danny; Surojudin, Nurhadi
Bulletin of Computer Science Research Vol. 5 No. 5 (2025): August 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v5i5.745

Abstract

Preventive maintenance is an essential strategy to minimize losses due to industrial equipment failures. This study aims to develop an equipment failure prediction model using the Random Forest algorithm with the SMOTE technique to address class imbalance. The dataset used is the AI4I 2020 Predictive Maintenance Dataset with 10,000 entries and six main input variables. Preprocessing includes normalization of numerical features, one-hot encoding for categorical features, and handling of missing values. The Random Forest model was optimized using GridSearchCV and compared with K-Nearest Neighbors. Results show that Random Forest with SMOTE achieved 97% accuracy, 0.47 precision, 0.75 recall, and 0.58 F1-score on the failure class. This model outperforms KNN in detecting failures, particularly in imbalanced data. These findings contribute to the development of an early warning system to support preventive maintenance in industrial environments.
Co-Authors . Ermanto Abdul Halim Anshor Ahmad Syukron Ardhi, Hendra Yunianto Baehaqi Butsianto, Sufajar Dede Kardaya Deden Sudrajat Dendy K. Pramudito Dodit Ardiatma Dwi Prasetyo, Novian Edi Triwibowo Effendi, M. Makmun Eko Ardianto, Riski Eko Putra, Fibi Elkin Rilvani Ermanto Fikri, Chairul Forsia, Lastry Hidarini, Silviana Fitria Hutauruk, Basar Maringan Ibnu Mubarak Indah kurniawati, Indah Jamal, Akhiratul Latifah Nurhasanah, Reka Hani Miyanti, Violi Muhammad Noor, Ridwan Adam Muhtajuddin Danny Naya, Candra Nurhadi Surojudin Pamungkas, Panji Putra Purwani Setyaningrum, Retno Putra, Fibi Eko Ramadhan, Aldi Setyaningrum, Retno Purwani Setyaningrum Setyawan, Wisnu Siswandi, Arif Surojudin, Nurhadi Tri Ngudi Wiyatno Triwibowo, Edi Wisnu Setyawan Yusmadi, Yudies Zaenal Abidin, Zihan