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All Journal BIOEDUSAINS: Jurnal Pendidikan Biologi dan Sains Media Mahardhika Aptisi Transactions on Management Jurnal Gentala Pendidikan Dasar Jurnal Teknik dan Informatika JURNAL ILMIAH EDUKATIF Keraton: Journal of History Education and Culture 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 Nasional Teknologi Komputer Jurnal Budgeting Jurnal Abdimas Hawari Jurnal Komputer Teknologi Informasi Sistem Komputer (JUKTISI) Jurnal Masyarakat Indonesia Al-Hisbah Jurnal Ekonomi Syariah Ramatekno Jurnal Pembelajaran Bahasa dan Sastra Tabayyun : Journal of Islamic Studies Journal of Environmental and Development Studies Bulletin of Engineering Science, Technology and Industry Jurnal Sains dan Teknologi Jurnal Pendidikan Teknik Mesin Prosiding Seminar Nasional Keguruan dan Pendidikan (SNKP)
Khairul
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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 Ade Iskandar Ade Surya Bakti Pane Adim, Saiful Alex Siregar Alviona Marsya Andi Ernawati Andysah Putera Utama Siahaan Annisa Aradi Sebayang Ari Anshari Yuliansyah Siregar Astri Mutia Rahma Aulia, Ananda Ayu Husniyyah Baehaqi Boy Rizki Akbar Chelfina Utami Darlius Darmeli Nasution Dedy Rahman Harahap Devani Dewi Triana Diva, Krisna Eka Putra Erbin Sitorus Eswin Syahputra Farta wijaya, Rian Fatimah Fery Anugerah Fitriani Fitriany, Lestari Gusmi Hamzah, Iswadi Harahap, Ilham Hakiki Harmiati Bungsu Bangun Harry Wiryatama Hasmi Syahputra Harahap, Hasmi Syahputra Heryati Ilmiah, Putri Khairil Imam Syofii Irfan Nainggolan Irvan Malay Irwan Syahputra Iwan Purnama Jantianus - Laila Maghfirah Leni Marlina M. Azwan M. Rasyid Mentari, Risca Sri Mochamad Heru Riza Chakim Muhammad Azuan Muhammad Fahriza Muhammad Fuad Hafiz Muhammad Iqbal Muhammad Irsyad Muhammad Rachmat Mulyati Mulyati Mulyati Naibaho, Dameria Ndona, Yakobus Nurmatias, Faizal Ofta Sari, Ayu Permata Sari, Indah Prabowo, Toni Putra Nauli Harahap, Sutan Ramadani, Putri Amanda Rian Farta Wijaya RINI INDAHWATI Rio Septian Hardinata Rosmaida Tambun Rumiyatun Rusdi Machrizal Samsuddin Samuel Sampe Tuah Purba Sari, Novi Fitriandika Sholihin siti fadilah Sitorus, Zulham Situmorang, Zenfitri Sofyan, Siti Nurhaliza Sri Yunita Suhardiansyah Suseno, Doni Syafi’I Syahputra, Dicky Ananda Syifa Tegar Ramadhan Tengku Didi Ferdillah Triyadi, M Dico Tuahman Turnip Tuti Andriani Wahyungsih, Hesti Widodo Widodo Wijaya, Rian Farta Wiryatama, Harry Wulan, Nawang Yusinta Tia Rusdiana zalina, indah Zalukhu, Anzas Ibezato Zebua, Selamat Zulfahmi Syahputra