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All Journal International Journal of Informatics and Communication Technology (IJ-ICT) Jurnal Pengabdian Masyarakat Bidang Sains dan Teknologi Jurnal Masyarakat Indonesia
Ronald Belferik
Universitas Pelita Harapan
Aerospace Engineering Agriculture, Biological Sciences & Forestry Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Education Electrical & Electronics Engineering Engineering Health Professions Industrial & Manufacturing Engineering Mathematics Mechanical Engineering Medicine & Pharmacology Nursing Public Health Transportation Veterinary

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Upaya Peningkatan Kelola Keuangan di Kantin Dinas Pertanian Kabupaten Langkat Nurhayati; Arisman; Frans Mikael Sinaga; Ronald Belferik; Tuti Andriani; Irfan Nainggolan; Suhendra Simangunsong
Jurnal Masyarakat Indonesia (Jumas) Vol. 4 No. 03 (2025): Jurnal Masyarakat Indonesia (Jumas)
Publisher : Cattleya Darmaya Fortuna

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Abstract

The canteen is one of the key facilities that supports the activities of employees as well as visitors in government institutions. However, a frequent issue encountered is financial management, which is still recorded manually in notebooks and not properly documented. This community service program was conducted at the Canteen of the Department of Agriculture, Langkat Regency, with the aim of providing guidance in manual financial recording, cash flow management, and monthly financial reporting. The methods applied in this program included observation, socialization, training, and evaluation. The results indicated an improvement in the manager’s ability to prepare daily and monthly financial reports, understand income and expenditure flows, and implement a recording system using Microsoft Excel. Through this program, it is expected that the canteen managers will be able to maintain transparency and accountability, thereby ensuring the sustainability of the canteen’s operations effectively.
Pelatihan SAP Analytics Cloud dan Pengenalan Canva di SLB Karya Murni Medan Jepronel Saragih; Ronald Belferik; Evander Banjarnahor
ABDIKAN: Jurnal Pengabdian Masyarakat Bidang Sains dan Teknologi Vol. 4 No. 4 (2025): November 2025
Publisher : Yayasan Literasi Sains Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55123/abdikan.v4i4.6853

Abstract

This community service activity is a collaborative initiative between Universitas Pelita Harapan and SLB Karya Murni Medan, aimed at enhancing digital literacy and technology skills among deaf students. The program focuses on two main areas: Data Science training using SAP Analytics Cloud (SAC) and digital banner and poster design training using Canva. In the SAC training, participants were introduced to fundamental concepts of data analytics, data modeling, data exploration, information visualization, and key features such as Smart Discovery, Designer Mode, and Data Explorer Mode. A COVID-19 dataset was used as a case study to help students understand basic data analysis processes, including how to build models, run simple machine learning algorithms, and generate interactive dashboards. All materials were designed using visual, demonstrative, and applicative approaches to ensure accessibility for students with special needs and to support a gradual learning process. In addition, the Canva training was provided to foster students’ creativity in creating informative and visually appealing digital banners and posters. The results of the program show that the students were able to create simple data models, understand basic visualizations, and produce digital design works. This program provides a meaningful contribution to the empowerment of inclusive education and strengthens sustainable collaboration between academic institutions and special education schools.
Predicting student academic success using entry test, language, and spiritual formation data with ensemble learning Evander Banjarnahor; Budi Wibawanta; Ronald Belferik; Rijanto Purbojo
International Journal of Informatics and Communication Technology (IJ-ICT) Vol 15, No 3: September 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijict.v15i3.pp1322-1330

Abstract

Student academic success is influenced by various factors, both academic and non-academic. This study aims to examine the correlation between key student attributes and final grade point average (GPA), as well as to develop a machine learning model to predict academic success. The correlation analysis involved academic variables such as admission scores (Mathematics, English, Indonesian, and academic aptitude test/TPA), English ability test (EAT), spiritual formation (SF), and first-year GPA (GPA_1). The results indicate that GPA_1 has the highest correlation with final GPA (0.63), followed by SF (0.44), while other variables exhibit lower correlations. To enhance prediction accuracy, a machine learning approach using three primary models was employed: Naïve Bayes, support vector machine (SVM), and an ensemble learning method based on a stacking classifier that combines SVM and Naïve Bayes. The evaluation used five train-test split ratios and performance metrics, including accuracy, precision, recall, and F1-score. Experimental results reveal that the SVM model achieves the highest accuracy at 88.40%, followed by the ensemble model combining SVM and Naïve Bayes (88.00%) and the Naïve Bayes model (87.10%). These findings confirm that the machine learning approaches, could effectively predict student academic success, providing a foundation for academic decision-making and educational intervention strategies.
Co-Authors Arisman Banjarnahor, Evander Budi Wibawanta, Budi Frans Mikael Sinaga Irfan Nainggolan Jepronel Saragih Nurhayati Rijanto Purbojo Suhendra Simangunsong Tuti Andriani