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Asep Muhidin
Universitas Pelita Bangsa, Bekasi
Computer Science & IT

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Sistem Pengembangan Chatbot Whatsapp Untuk Monitoring Hasil Pembelajaran Siswa Sekolah Menngah Kejuruan Zihan Abidin; Muhtajuddin Danny; Asep Muhidin
Bulletin of Computer Science Research Vol. 3 No. 5 (2023): Agustus 2023
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v3i5.272

Abstract

Service is an activity or sequence that occurs in direct interaction between a person and another person or machine physically, and provides customer satisfaction. In serving or accessing information at SMK Negeri 3 Kuningan, they still use conventional methods, so it takes time to access them. As students have to do when they are going to ask about grades with certain subjects, they have to meet the teacher in question. The value recap carried out by the teacher also still uses manuals with the use of file books. Therefore, with this problem, a chatbot is made as a solution to this problem as a forum to solve problems in finding this information. In addition, the value recapitulation also uses a website system to facilitate data processing. The development of this chatbot uses the waterfall method. By developing a system in the form of a chatbot as a student tool and a website as a teacher tool, it is hoped that it will be able to speed up the process of monitoring grades. The results of the study were able to produce a whatsapp chatbot application that students could use to monitor grades and a website that teachers could use to recapitulate grades.
Optimasi Algoritma Random Forest untuk Prediksi Eksport Kelapa Sawit Global Muhtajuddin Danny; Asep Muhidin
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 Asep Muhidin; Muhtajuddin Danny; Nurhadi Surojudin
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 Muhtajuddin Danny Nurhadi Surojudin Zihan Abidin