Article Per Year (5 Year)
p-Index From 2021 - 2026
7.56
P-Index
This Author published in this journals
All Journal Sistemasi: Jurnal Sistem Informasi Progresif: Jurnal Ilmiah Komputer Jurnal Teknologi Dan Sistem Informasi Bisnis Jurnal Informatika Ekonomi Bisnis REMIK : Riset dan E-Jurnal Manajemen Informatika Komputer JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) Jurnal Teknologi Informatika dan Komputer Journal of Computer Networks, Architecture and High Performance Computing Jurnal Ilmiah Wahana Pendidikan Bulletin of Computer Science Research Brilliance: Research of Artificial Intelligence Pelita Teknologi : Jurnal Ilmiah Informatika, Arsitektur dan Lingkungan Jurnal Ilmiah SIGMA: Informatics Engineering Journal of UPB Jurnal Pustaka Data : Pusat Akses Kajian Database, Analisa Teknologi, dan Arsitektur Komputer Jurnal Lentera Pengabdian Jurnal Informatika Ekonomi Bisnis VIDHEAS: Jurnal Nasional Abdimas Multidisiplin Jurnal Pelita Pengabdian SAINTEK Dedikasi : Jurnal Pengabdian Lentera Explore
Muhtajuddin Danny
Universitas Pelita Bangsa
Religion Aerospace Engineering Agriculture, Biological Sciences & Forestry Humanities Astronomy Automotive Engineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Mathematics Mechanical Engineering Social Sciences Other

Published : 70 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 4 Documents
Search
Journal : Bulletin of Computer Science Research

 1 
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 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.
Analisis Tingkat Sentimen Opini Publik Terhadap Kebijakan TV Digital di Platform X Menggunakan Multinomial Naïve Bayes Sulaeman, Asep Arwan; Naya, Candra; Danny, Muhtajuddin; Effendi, M. Makmun
Bulletin of Computer Science Research Vol. 6 No. 2 (2026): February 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v6i2.951

Abstract

The migration from analog to digital television broadcasting is part of the transformation of the broadcasting system aimed at improving broadcast quality and spectrum efficiency. However, the implementation of the digital television policy has generated diverse public responses, ranging from support to criticism. This study aims to analyze public opinion on the digital television policy in Indonesia using social media data from platform X. A quantitative approach was employed using text mining and supervised machine learning techniques. Data were collected through a crawling process using the keyword “tv digital”, resulting in 1,855 tweets. After data selection and cleaning, 789 tweets were obtained as the final dataset. The analysis stages included text preprocessing, feature extraction using Term Frequency–Inverse Document Frequency (TF–IDF), and sentiment classification using the Multinomial Naïve Bayes algorithm. The results indicate that positive sentiment dominates public opinion, with 478 tweets (60.58%), while negative sentiment accounts for 311 tweets (39.42%). Model performance evaluation shows an accuracy of 79.21%, precision of 82.45%, and recall of 85.06%, indicating that the model performs well and consistently in classifying sentiment. These findings demonstrate that social media–based sentiment analysis can serve as an empirical approach to understanding public perceptions of digital television policy.
Co-Authors . Ermanto Aditya Eka Sukma Agus Safarudin Agus Suwarno, Agus Ahmad Turmudi Zy Amali Amali, Amali Amat Damuri Andri Firmansyah Andriani Arif Susilo Arwan Sulaeman, Asep Arwan, Asep Asep Muhidin Asep Muhidin Asep Muhidin Astuti, Retno Fitri Baehaqi Butsianto, Sufajar Daimin Daimin Damuri, Amat Dendy K. Pramudito Donny Maulana Edi Tri Triwibowo Edi Tri Wibowo Edora Edora, Edora Edy Widodo Effendi, M. Makmun Elkin Rilvani Ermanto Ermanto Ermanto Fikri, Chairul Hasidin Herdiana Herdiana Herdiansyah, Muhammad Ferdi Hidarini, Silviana Fitria Hutauruk, Basar Maringan Irawan, Reza Riyaldi Jamal, Akhiratul Khoiriyah Khoiriyah Khris Phasarilla, Dutha Lestari Maringan Hutauruk, Basar Maulana, Donny Mugiarso Muhamad Fatchan muhidin, asep Nadya Fadhilah Naya, Candra Nugroho, Agung Nurhadi Surojudin Nurhadi Surojudin Pamungkas, Panji Putra Pratama, Suria Priana, Firzi Cahya Priyo, Basuki Edi Putri Nurhafenda Harun Rahman, M. Aufi Rasim Reza Suryadi Gunawan Rita Hardianti Saana Atmaja Sariman Rio Permadi Setyawan, Wisnu Subkhan Fadilah Suherman Suherman Sulaeman, Asep Arwan Surojudin, Nurhadi Suryadi, Dikky Syahlan Sugiarto Syaiful Umam Taufik Hidayat Triwibowo, Edi Wachid Hasyim, Wachid Wahyudi Wahyudi Wiyanto - Yoga Pratama, Evan Zaenal Abidin, Zihan Zihan Abidin