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All Journal Jurnas Nasional Teknologi dan Sistem Informasi Jurnal Gentala Pendidikan Dasar Jurnal Teknik dan Informatika JURNAL ILMIAH EDUKATIF Bioscientist : Jurnal Ilmiah Biologi Jurnal Ekonomi Bisnis, Manajemen dan Akuntansi (JEBMA) Instal : Jurnal Komputer INTERNATIONAL JOURNAL OF CULTURAL AND SOCIAL SCIENCE Bulletin of Information Technology (BIT) Jurnal Budgeting Tabayyun : Journal of Islamic Studies Bulletin of Engineering Science, Technology and Industry Prosiding Seminar Nasional Keguruan dan Pendidikan (SNKP)
Khairul
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Religion Agriculture, Biological Sciences & Forestry Humanities Biochemistry, Genetics & Molecular Biology Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Immunology & microbiology Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Mathematics Social Sciences Transportation Other

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Journal : Bulletin of Engineering Science, Technology and Industry

 1 
EVALUATION OF INFORMATION TECHNOLOGY GOVERNANCE E-KINERJA SYSTEMS IN ASSESSING EMPLOYEE PERFORMANCE USING THE MODEL COBIT 2019 AT THE DISTRICT COMMINFO OFFICE WAS REALLY FUN Eswin Syahputra; Khairul; Muhammad Iqbal; Rian Farta Wijaya; Darmeli Nasution
Bulletin of Engineering Science, Technology and Industry Vol. 2 No. 3 (2024): September
Publisher : PT. Radja Intercontinental Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59733/besti.v2i3.60

Abstract

The Department of Communication and Information Technology (Kominfo) in developing an e-performance information system needs to carry out a governance evaluation. The aim of this research is to evaluate information technology governance in the E-Kinerja system in assessing employee performance at the Bener Meriah Regency Communication and Information Service using the COBIT 2019 model using the Action Research method. Data collection techniques use two sources of primary data and secondary data. The results of this research are three main domains in COBIT 2019, namely EDM02 (Ensured Benefits Delivery), APO10 (Managed Vendors), and BAI11 (Managed Projects). This evaluation is carried out to measure the targeted capability level (to-be), the current capability level (as-is), as well as the gaps (GAP) that exist between the two. So the capability level in the EDM02 domain is at level 4, the APO10 domain is at level 2 and the BAI11 domain is at level 2. These findings provide an overview of areas that require further improvement and development to achieve more effective and efficient information technology governance. Thus, it is hoped that this research can contribute to improving the quality of information technology governance at the Bener Meriah Regency Communication and Information Service, as well as becoming a reference for other government agencies in implementing the COBIT 2019 model for evaluating information technology systems.
MACHINE LEARNING ANALYSIS IN IMPROVING THE EFFICIENCY OF THE STUDENT ADMISSION DECISION MAKING PROCESS NEW AT PANCA BUDI MEDAN DEVELOPMENT UNIVERSITY M. Rasyid; Zulham Sitorus; Rian Farta Wijaya; Muhammad Iqbal; Khairul
Bulletin of Engineering Science, Technology and Industry Vol. 2 No. 3 (2024): September
Publisher : PT. Radja Intercontinental Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59733/besti.v2i3.62

Abstract

The decision-making process in admitting new students is a crucial aspect that can influence the quality and efficiency of academic administration in higher education. This research aims to analyze the role of Machine Learning methods, especially Support Vector Machines (SVM), in increasing the efficiency of the decision-making process for new student admissions at the Panca Budi Development University, Medan. The data used in this research includes information from the student admissions process for the odd semester of the 2022/2023 academic year, which includes various variables such as Registration Number, School of Origin, Registration Payment, and others. The data is divided into a training set (70%) and a testing set (30%). The Support Vector Machine (SVM) model that was built was evaluated using metrics such as accuracy, precision, recall, and F1-Score. The research results show that the SVM model achieves an accuracy of 100%, with high precision and recall for both classes. Precision for both classes reached 1.00, while recall for the minority class (class 1) reached 0.91, indicating excellent model performance in classification. The conclusion of this research is that the Support Vector Machine (SVM) model can significantly increase efficiency and accuracy in the decision-making process for new student admissions at the Panca Budi Development University in Medan compared to conventional methods. These findings indicate that the application of Machine Learning methods can provide substantial benefits in the context of academic administration.
COMPARATIVE ANALYSIS OF NAIVE BAYES ALGORITHM AND C4.5 ALGORITHM IN SELECTING TYPES OF UMKM PRODUCTS IN BBPSDMP KOMINFO MEDAN TRAINING Alex Siregar; Leni Marlina; Khairul; Muhammad Iqbal; Andysah Putera Utama Siahaan
Bulletin of Engineering Science, Technology and Industry Vol. 2 No. 3 (2024): September
Publisher : PT. Radja Intercontinental Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59733/besti.v2i3.64

Abstract

This study compares the accuracy of the Naïve Bayes and C4.5 algorithms in determining the most suitable product types for Micro, Small, and Medium Enterprises (MSMEs) participating in the Digital Entrepreneurship Academy (DEA) training program at BBPSDMP Kominfo Medan. This study uses a dataset from DEA participants between 2021 and 2022. The analysis shows that the C4.5 algorithm has a higher accuracy compared to Naïve Bayes, indicating its better effectiveness in helping MSMEs choose product types. These findings suggest that C4.5 is more suitable for applications that require a high level of accuracy, especially in the context of this study. This study provides valuable insights into the selection of algorithms to support decision making in the MSME sector.
Co-Authors Abdul Pandi Adim, Saiful Alex Siregar Alviona Marsya Andi Ernawati Andysah Putera Utama Siahaan Annisa Asrul Abdullah Aulia, Ananda Darmeli Nasution Devani Dewi Triana Eka Putra Eswin Syahputra Farta wijaya, Rian Fatimah Fitriani Gusmi Harry Wiryatama Hasmi Syahputra Harahap, Hasmi Syahputra Ilmiah, Putri Khairil Irwan Syahputra Jantianus, Jantianus Leni Marlina M. Rasyid Mentari, Risca Sri Muhammad Iqbal Muhammad Iqbal Muhammad Irsyad Naibaho, Dameria Nasution, Darmeli Ndona, Yakobus Ofta Sari, Ayu Putra Nauli Harahap, Sutan Rian Farta Wijaya RINI INDAHWATI Rosmaida Tambun Rumiyatun Samsuddin Sari, Novi Fitriandika Sholihin siti fadilah Sitorus, Zulham Situmorang, Zenfitri Sofyan, Siti Nurhaliza Sri Yunita Sucipto Sucipto Suhardiansyah Syafi’I Syahputra, Dicky Ananda Syifa Triyadi, M Dico Tuti Andriani Wiryatama, Harry Wulan, Nawang Zalukhu, Anzas Ibezato