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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

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Penerapan Data Mining dengan Algoritma C4.5 dan K-nearest Neighbor untuk Prediksi Penjualan Bahan Bangunan Terlaris Surojudin, Nurhadi; Danny, Muhtajuddin
Jurnal Informatika Ekonomi Bisnis Vol. 7, No. 3 (September 2025)
Publisher : SAFE-Network

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37034/infeb.v7i3.1241

Abstract

The main problem faced by PT. Surya Kapuas Perkasa is the difficulty in accurately determining the types of building materials with the highest sales levels. Currently, stock determination still relies on manual estimates based on previous sales trends, which are prone to errors and inaccuracies. As a result, the company often faces the risk of overstocking products that are less in demand, or understocking products that are actually in high demand. This condition can impact the sales process, increase storage costs, and reduce customer satisfaction. To overcome this problem, a method is needed that can predict the sales of the best-selling building materials more objectively and based on historical data. This prediction will utilize sales data from the past three years by applying data mining classification techniques using the C4.5 algorithm and K-Nearest Neighbor (K-NN) through the RapidMiner application. With this approach, the company can accurately identify the types of building materials that are most in demand in the market, allowing for more precise and efficient stock management. Based on the research results, four types of building materials were found to be the best-selling out of a total of 16 types analyzed: Light Steel, Brick, Iron, and Cement, with a prediction accuracy rate of 87.16%.
Pelatihan Internet of Things (IoT) untuk Smart Home dan Smart School di SMK Garuda Nusantara Danny, Muhtajuddin; Arwan Sulaeman, Asep; Maringan Hutauruk, Basar; Damuri, Amat
VIDHEAS: Jurnal Nasional Abdimas Multidisiplin Vol. 3 No. 2 (2025): Desember 2025
Publisher : VINICHO MEDIA PUBLISINDO

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61946/vidheas.v3i2.157

Abstract

The rapid development of digital technology, particularly the Internet of Things (IoT), requires vocational education institutions to align graduate competencies with the demands of Industry 4.0. SMK Garuda Nusantara has significant potential in developing technology-based human resources; however, limitations remain in teachers’ and students’ practical IoT skills as well as in the availability of supporting learning facilities. This community service program aims to enhance teachers’ and students’ competencies through IoT training integrated with smart home and smart school concepts. The implementation method consists of preparation, socialization, theoretical training, hands-on workshops, mentoring, and program evaluation. The training focuses on the use of microcontrollers, sensors, and the development of applied IoT prototypes such as automatic lighting systems, RFID-based attendance systems, and classroom environmental monitoring. The expected outcomes include improved practical skills of teachers and students, the development of IoT learning modules based on project-based learning, and the creation of simple smart home and smart school prototypes applicable in the school environment. This program also supports the implementation of the Merdeka Belajar Kampus Merdeka (MBKM) policy and contributes to the achievement of higher education Key Performance Indicators (IKU) through sustainable collaboration between universities and school partners.
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.
Analisis Tingkat Sentimen Opini Publik Terhadap Kebijakan TV Digital di Platform X Menggunakan Multinomial Naïve Bayes Asep Arwan Sulaeman; Candra Naya; Muhtajuddin Danny; M. Makmun Effendi
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.
Implementasi Data Mining untuk Menentukan Pola Pembelian Obat Menggunakan Metode Apriori Muhtajuddin Danny; Isarianto Isarianto
Bulletin of Computer Science Research Vol. 6 No. 4 (2026): June 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v6i4.1133

Abstract

The development of information technology has increased the amount of drug sales transaction data in the pharmacy sector. However, transaction data are generally used only as administrative archives and have not been optimally utilized to produce strategic information. This study aims to implement data mining using the Apriori method to determine drug purchasing patterns based on pharmaceutical transaction data. This research employed a quantitative approach using the Pharmacy Transactional Dataset obtained from the Kaggle platform. The research stages were conducted using the Cross Industry Standard Process for Data Mining (CRISP-DM), including business understanding, data understanding, data preparation, modeling, evaluation, and deployment. The analysis process was carried out using the Python programming language with the assistance of the pandas and mlxtend libraries. The results showed that the purchasing relationship between Paracetamol and Vitamin C had the highest association value with a support value of 32% and a confidence value of 78%. These results indicate that the Apriori algorithm is capable of identifying relationships among drug products based on pharmaceutical transaction data. The resulting information can be utilized to support promotional strategies, drug inventory management, and business decision-making in the pharmaceutical sector.
Perancangan Sistem E-Parking Berbasis Arduino dengan Kartu RFID Muhammad Ferdi Herdiansyah; Muhtajuddin Danny; Retno Fitri Astuti
Journal of Information System Research (JOSH) Vol 6 No 2 (2025): January 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josh.v6i2.6467

Abstract

Security and efficiency of parking management are crucial aspects in the operations of companies and educational institutions. At Pelita Bangsa University, the conventional parking system still uses manual methods and paper-based tickets, which is inefficient and potentially creates security issues. In addition, the lack of integration between the parking system and vehicle identification increases the risk of theft. This research aims to design an RFID-based parking system that can be accessed using a Student Identity Card. The system uses RFID at low frequencies to ensure the security and accuracy of vehicle identification. The results show that the RFID system is able to efficiently replace conventional methods, reduce paper usage, and increase parking access speed. The system is also integrated with the student database, enabling better access control and automatic recording of vehicles. The implementation of the system in Pelita Bangsa University's parking area not only improves security but also user experience, with a faster payment process and structured vehicle data management. Hopefully, this system can be an innovative solution that can be applied in various institutions to face the challenges of parking security and efficiency in the digital age.
Building Transparent and Efficient Community Administration: Agile Development of a Neighborhood Information System at Kertamukti Sakti Residence Reza Riyaldi Irawan; Dendy K Pramudito; Muhtajuddin Danny
SISTEMASI Vol 15, No 1 (2026): Sistemasi: Jurnal Sistem Informasi
Publisher : Universitas Islam Indragiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v15i1.5784

Abstract

Community management in residential areas often relies on manual paper-based administration, leading to inefficiency, unclear financial records, data loss, and limited transparency, which undermine good governance and residents’ trust. This study aims to develop a web-based neighborhood (RT/RW) management information system to improve administrative effectiveness, financial transparency, and service quality. The system was built using CodeIgniter, PHP, MySQL, Bootstrap, and jQuery, applying the Agile development method to ensure flexibility and iterative improvement through continuous feedback between the developers and the community. The development process consisted of planning, design, coding, testing, and release stages, with flowcharts and wireframes supporting interface design and black box testing used for functional validation. The system was evaluated using a user-centered usability assessment (System Usability Scale – SUS), obtaining an average score of 82.5, which falls under the Excellent category. In addition, the financial reporting process time was reduced from three days to one hour, and data entry errors decreased by 90%, proving that the system significantly improves operational efficiency and transparency compared to manual methods. In conclusion, the combination of Agile methodology and lightweight frameworks such as CodeIgniter successfully delivers a responsive, transparent, and user-oriented information system that enhances trust and collaboration within the community. Future development will focus on integrating QRIS, e-wallets, and bank transfers to further streamline financial transactions and support sustainable digital transformation in community management.
Penggunaan Teknologi Artificial Intelligence Dalam Penulisan Buku Muhtajuddin Danny; Elkin Rilvani; Edora; Iwan Mulyana
VIDHEAS: Jurnal Nasional Abdimas Multidisiplin Vol. 2 No. 1 (2024): Juni 2024
Publisher : VINICHO MEDIA PUBLISINDO

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61946/vidheas.v2i1.77

Abstract

Training on the use of Artificial Intelligence (AI) technology in book writing is a response to the rapid development of information and communication technology which has influenced various aspects of life, including the publishing and writing industry. AI technology has developed rapidly and shown its potential in various fields, including data analysis, natural language processing (NLP), and machine learning (Machine Learning). AI algorithms can generate text, analyze writing style, and provide relevant recommendations for writers. AI can help writers in various stages of the writing process, from brainstorming ideas, creating initial drafts, to editing. Tools like GPT-4 can generate paragraphs or chapters based on specific instructions, which helps speed up the writing process and overcome creative barriers. The use of AI can improve the quality of writing by providing suggestions for editing and improvement. AI can also ensure consistency in writing style and use of terminology, which is especially important in writing technical books or series. In the digital era, speed and quality of content production are key factors in competition. Authors and publishers who are able to utilize AI technology can have a competitive advantage by producing books faster and with better quality.
Pelatihan Penggunaan Teknologi Dalam Mengelola Dan Mempromosikan Acara Asep Arwan; Muhtajuddin Danny; Andriani; Amat Damuri
VIDHEAS: Jurnal Nasional Abdimas Multidisiplin Vol. 2 No. 1 (2024): Juni 2024
Publisher : VINICHO MEDIA PUBLISINDO

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61946/vidheas.v2i1.82

Abstract

Dalam era digital yang semakin maju, penggunaan teknologi menjadi krusial dalam berbagai aspek kehidupan, termasuk dalam pengelolaan dan promosi acara. Pelatihan ini bertujuan untuk membekali peserta dengan pengetahuan dan keterampilan praktis dalam memanfaatkan berbagai alat dan platform teknologi guna meningkatkan efisiensi dan efektivitas manajemen acara serta strategi pemasaran yang lebih luas dan terarah. Materi pelatihan mencakup penggunaan perangkat lunak manajemen acara, teknik pemasaran digital, analitik media sosial, serta pemanfaatan teknologi interaktif untuk meningkatkan keterlibatan peserta. Dengan mengikuti pelatihan ini, peserta diharapkan mampu merancang, mengelola, dan mempromosikan acara dengan lebih profesional dan inovatif, sesuai dengan tuntutan zaman. Studi kasus dan praktik langsung akan menjadi bagian integral dari pelatihan untuk memastikan transfer pengetahuan yang aplikatif dan relevan.
Co-Authors . Ermanto Aditya Eka Sukma Agung Nugroho Agus Safarudin Agus Suwarno Agus Suwarno, Agus Ahmad Turmudi Zy Amali Amali, Amali Amat Damuri Amat Damuri Andri Firmansyah Andriani Arif Susilo Arwan Sulaeman, Asep Asep Arwan Sulaeman Asep Arwan Sulaeman Asep Muhidin Asep Muhidin Asep Muhidin Baehaqi Basar Maringan Hutauruk Butsianto, Sufajar Candra Naya Daimin Daimin Damuri, Amat Dendy K. Pramudito Donny Maulana Edi Tri Triwibowo Edi Tri Wibowo Edora Edora, Edora Edy Widodo Elkin Rilvani Ermanto Ermanto Ermanto Fikri, Chairul Hasidin Hendri Rahman Herdiana Herdiana Hidarini, Silviana Fitria Hutauruk, Basar Maringan Isarianto Isarianto Iwan Mulyana Jamal, Akhiratul Khoiriyah Khoiriyah Khris Phasarilla, Dutha Lestari M. Makmun Effendi Maringan Hutauruk, Basar Maulana, Donny Mugiarso Muhamad Fatchan Muhammad Ferdi Herdiansyah muhidin, asep Nadya Fadhilah Naya, Candra Nugroho, Agung Nur Hidayati Nurhadi Surojudin Nurhadi Surojudin Nurhadi Surojudin Pamungkas, Panji Putra Pratama, Suria Priana, Firzi Cahya Priyo, Basuki Edi Putri Nurhafenda Harun Rahman, M. Aufi Rasim Retno Fitri Astuti Reza Riyaldi Irawan Reza Suryadi Gunawan Rita Hardianti Saana Atmaja Sariman Rio Permadi Setyawan, Wisnu Subkhan Fadilah Suherman Suherman Surojudin, Nurhadi Suryadi, Dikky Syahlan Sugiarto Syaiful Umam Taufik Hidayat Triwibowo, Edi Wachid Hasyim, Wachid Wahyudi Wahyudi Wiyanto - Wiyanto Wiyanto Yoga Pratama, Evan Zaenal Abidin, Zihan Zihan Abidin