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The IJICS (International Journal of Informatics and Computer Science)
Published by Sekolah Tinggi Manajemen Informatika dan Komputer Budi Darma
ISSN : 25488449     EISSN : 25488384     DOI : https://doi.org/10.30865/ijics
Core Subject : Science, Engineering,
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering
The The IJICS (International Journal of Informatics and Computer Science) covers the whole spectrum of intelligent informatics, which includes, but is not limited to : • Artificial Immune Systems, Ant Colonies, and Swarm Intelligence • Autonomous Agents and Multi-Agent Systems • Bayesian Networks and Probabilistic Reasoning • Biologically Inspired Intelligence • Brain-Computer Interfacing • Business Intelligence • Chaos theory and intelligent control systems • Clustering and Data Analysis • Complex Systems and Applications • Computational Intelligence and Soft Computing • Cognitive systems • Distributed Intelligent Systems • Database Management and Information Retrieval • Evolutionary computation and DNA/cellular/molecular computing • Expert Systems • Fault detection, fault analysis and diagnostics • Fusion of Neural Networks and Fuzzy Systems • Green and Renewable Energy Systems • Human Interface, Human-Computer Interaction, Human Information Processing • Hybrid and Distributed Algorithms • High Performance Computing • Information storage, security, integrity, privacy and trust • Image and Speech Signal Processing • Knowledge Based Systems, Knowledge Networks • Knowledge discovery and ontology engineering • Machine Learning, Reinforcement Learning • Memetic Computing • Multimedia and Applications • Networked Control Systems • Neural Networks and Applications • Natural Language Processing • Optimization and Decision Making • Pattern Classification, Recognition, speech recognition and synthesis • Robotic Intelligence • Rough sets and granular computing • Robustness Analysis • Self-Organizing Systems • Social Intelligence • Soft computing in P2P, Grid, Cloud and Internet Computing Technologies • Stochastic systems • Support Vector Machines • Ubiquitous, grid and high performance computing • Virtual Reality in Engineering Applications • Web and mobile Intelligence, and Big Data
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 12 Documents
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Search results for , issue "Vol. 9 No. 2 (2025): July" : 12 Documents clear
Artificial Intelligence Analysis of Recommendations for Granting Business Licenses to Determine the Priority of Business Supervision and Control Using the DBSCAN Method (Case Study: DPMPTSP Langkat Regency) diansyah, Suhar; Sitorus, Zulham; Iqbal, Muhammad
The IJICS (International Journal of Informatics and Computer Science) Vol. 9 No. 2 (2025): July
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/ijics.v9i2.8900

Abstract

In facing the challenges of limited resources and business complexity, the Investment and One-Stop Integrated Services Office (DPMPTSP) of Langkat Regency requires a data-driven approach to determine priorities for business supervision and enforcement. This study applies the DBSCAN (Density-Based Spatial Clustering of Applications with Noise) algorithm to cluster business entities based on three main parameters: risk level, business scale, and licensing status. Secondary data from 3,748 companies were collected, processed through label encoding and normalization, and analyzed in a three-dimensional space (X1_Risk, X2_Scale, X3_License). The clustering results revealed the formation of clusters and a Silhouette Score value, indicating optimal cluster structure and separation between groups. Each cluster was interpreted as a representation of recommendation categories such as Routine Monitoring and Evaluation, Intensive Monitoring and Evaluation, Administrative Warning, Temporary Operational Suspension, and Permanent Operational Termination. The resulting visualizations enhanced the understanding of spatial mapping and clustering patterns comprehensively. This demonstrates that DBSCAN is effective as a decision-support tool for automated and objective priority mapping in business supervision, and capable of detecting business entities that deviate from general norms (outliers). This approach significantly contributes to improving the efficiency and accuracy of decision-making in business license supervision and enforcement at the regional level.
ROI and SNA Analysis in Testing the Effectiveness of New Student Admission Promotion: A Case Study at MAS Al Washliyah Gedung Johor Angkat, Chairul Indra; Sitorus, Zulham; Iqbal, Muhammad
The IJICS (International Journal of Informatics and Computer Science) Vol. 9 No. 2 (2025): July
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/ijics.v9i2.8901

Abstract

Globalization and intense competition in the education sector, especially among private high schools, require institutions such as MAS Al Washliyah Gedung Johor to continue optimizing their new student admission promotion strategies. Although the school has implemented multi-channel promotions that include social media (Instagram, TikTok), conventional methods (brochures), and financial incentives (alumni tuition fee discounts), there has been no in-depth analysis of the effectiveness of each variable. The problem of less than optimal promotion results due to inappropriate media selection often results in inefficient allocation of promotion costs with minimal student recruitment results. This study aims to analyze the effectiveness of various promotion variables used by MAS Al Washliyah Gedung Johor, in order to support a more appropriate and efficient allocation of funding sources. Data were collected through a questionnaire given to new students regarding their sources of promotional information. To achieve this goal, this study uses a two-method approach: Return on Investment (ROI) to measure financial efficiency and return on funds, and Social Network Analysis (SNA) to visualize interaction patterns, reach, and identify the most influential communities or promotions in the student exposure network. By combining ROI and SNA analysis, it is hoped that this study can provide clear information regarding promotion costs and the most efficient and effective types of promotion, as a basis for improving the school promotion system in the future.
Performance Analysis of CNN (Convolutional Neural Network) in Nominal Classification of Rupiah Emissions 2022 Sahputra, Fajar; Sitorus, Zulham; Iqbal, Muhammad; Marlina, Leni; Nasution, Darmeli
The IJICS (International Journal of Informatics and Computer Science) Vol. 9 No. 2 (2025): July
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/ijics.v9i2.8903

Abstract

This study aims to analyze the performance of Convolutional Neural Network (CNN) algorithm in classifying the nominal of Rupiah banknotes issued in 2022. Three test models are developed, namely two CNN architectures with different optimizers (Adam and RMSprop), and one transfer learning model using VGG16. The dataset used consists of 1,848 banknote images of seven denominations: Rp1,000, Rp2,000, Rp5,000, Rp10,000, Rp20,000, Rp50,000, and Rp100,000. The data was collected using a smartphone camera and processed through augmentation, normalization, and classification stages. The model was evaluated using accuracy, precision, recall, and F1-score metrics. The results show that CNN with Adam's optimizer achieves a validation accuracy of 98.97%, while CNN with RMSprop reaches 99.59%. Meanwhile, the VGG16 model achieved perfect validation accuracy of 100%, with precision, recall, and F1-score values of 1.00 each. These results show that the transfer learning approach provides the best performance compared to conventional CNN models. This research supports the development of an accurate and efficient banknote recognition automation system for digital finance applications.
Implementation of Deep Learning Method Using BERT Model in Career Choice Analysis of Gen Z Ramadhani, Silvia; Hasugian, Abdul Halim
The IJICS (International Journal of Informatics and Computer Science) Vol. 9 No. 2 (2025): July
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/ijics.v9i2.8917

Abstract

The development of digital technology has significantly influenced how individuals, particularly Generation Z (born between 1997 and 2012), make career decisions. Faced with an abundance of digital information, many individuals in this cohort experience difficulties in selecting career paths that align with their interests, abilities, and labor market demands. This study analyzes the career preferences of Generation Z using a deep learning approach through the Bidirectional Encoder Representations from Transformers (BERT) model, specifically the IndoBERT variant, which is pre-trained on Indonesian-language data. The research data were collected from textual responses to Google Form questionnaires, focusing on four digital career paths: Software Engineer, Content Creator, Digital Marketing, and Entrepreneur. From 601 data samples, sentiment analysis revealed that 57.85% of the responses were positive, while 42.15% were negative. Classification results indicated that Content Creator was the most preferred career, followed by Entrepreneur, Digital Marketer, and Software Engineer. Model evaluation showed a test accuracy of 51.24%, with better performance in categories that had larger data volumes. These findings demonstrate that IndoBERT is effective in capturing opinions and career tendencies from unstructured text and provides a scientific basis for educational institutions, industries, and policymakers to design more relevant career development strategies in the digital era.
Analysis of Food Menu Purchasing Patterns in Campus Canteens Using the Apriori Algorithm in Data Mining Sianturi, Lince Tomoria; Murdani, Murdani; Fadlina, Fadlina
The IJICS (International Journal of Informatics and Computer Science) Vol. 9 No. 2 (2025): July
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/ijics.v9i2.8919

Abstract

This study aims to identify purchasing patterns of food menus in the campus cafeteria of STMIK Mulia Darma by applying the Apriori algorithm within the data mining framework. The background of this research is based on the increasing volume of transaction data that remains underutilized in supporting managerial decision-making. The Apriori algorithm is employed to uncover associations between items frequently purchased together by calculating their support and confidence values. A dataset of 20 daily digital transactions was used as the basis for analysis. The results revealed a single valid association rule that met the minimum threshold: Nasi Goreng,Teh Manis with a support value of 15% and a confidence value of 60%. This finding indicates a strong tendency in student consumption behavior, which can be leveraged for marketing strategies such as menu bundling and predictive inventory management. The study demonstrates that the Apriori algorithm can offer practical and strategic insights in the context of a campus cafeteria and holds potential for further development using larger datasets and more advanced analytical methods.
Implementation of a Decision Support System in Determining Scholarship Recipients Using the MOORA Method at STMIK Mulia Darma Sianturi, Chandra Frenki
The IJICS (International Journal of Informatics and Computer Science) Vol. 9 No. 2 (2025): July
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/ijics.v9i2.8920

Abstract

The scholarship recipient selection process often faces challenges such as subjectivity, time constraints, and a lack of transparency in decision-making. To address these issues, this study developed a web-based decision support system that implements the Multi-Objective Optimization on the Basis of Ratio Analysis (MOORA) method. This system is designed to assist scholarship administrators in determining recipients objectively and efficiently by considering several criteria, namely GPA, parental income, number of dependents, and non-academic achievements. The research was conducted using a quantitative approach that included the stages of problem identification, data collection, criterion weight determination, data normalization, calculation of Yi preference values, and alternative ranking. The results of the system implementation indicate that the MOORA method can provide accurate rankings based on the combination of criterion weights and the value of each alternative. This system not only accelerates the selection process but also enhances transparency and accountability in decision-making. In conclusion, the application of the MOORA method in the scholarship decision support system contributes significantly to a fair, structured, and accountable selection process.
Comparison of Elias Delta and Interpolative Coding Algorithms in Video File Compression Khairani, Nani; Aripin, Soeb; Siagian, Meryance Viorentina
The IJICS (International Journal of Informatics and Computer Science) Vol. 9 No. 2 (2025): July
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/ijics.v9i2.8921

Abstract

Large video file sizes can burden storage capacity and slow down data transmission, making effective compression methods essential. WebM, a commonly used video format supported by platforms such as YouTube and Skype, often results in large file sizes that demand storage efficiency. This study compares two lossless compression algorithms—Elias Delta Code and Interpolative Coding—for compressing WebM video files. Interpolative Coding applies a non-linear approach based on the entire content of the message, while Elias Delta Code is an integer-based algorithm efficient for encoding positive numbers. This research is motivated by the lack of direct comparative studies between these two algorithms in the context of video compression. The objective is to evaluate their performance based on compression ratio, processing time, and storage efficiency. The results are expected to provide recommendations on the most suitable compression algorithm for high-complexity video files.
Comparative Analysis of Sequencing Methods and Markov Models for Predicting High-Achieving Students at Budi Darma University Sinambela, Sugi Hartono; Iqbal, Muhammad; Khairul, Khairul; Darmeli Nasution; Zulham Sitorus
The IJICS (International Journal of Informatics and Computer Science) Vol. 9 No. 2 (2025): July
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/ijics.v9i2.8964

Abstract

The prediction of high-achieving students is a strategic step in supporting the development of academic quality within higher education institutions. This study aims to compare two data mining approaches, namely the Sequencing method and the Markov Model, in predicting high-achieving students at Universitas Budi Darma Medan. The Sequencing method is used to identify patterns in the sequence of academic grades and non-academic activities of students from semester to semester, while the Markov Model is used to calculate the probability of transitions in students' academic status based on historical data. The research adopts a quantitative approach involving 100 active students with complete academic and non-academic data. The data analyzed include semester GPA, participation in organizations, seminars, and achievements in competitions. Both methods were evaluated using metrics such as accuracy, precision, recall, and F1-score. The evaluation results show that the Sequencing method achieved an accuracy of 87%, precision of 85%, recall of 88%, and an F1-score of 86%, while the Markov Model recorded an accuracy of 81%, precision of 79%, recall of 83%, and an F1-score of 81%. Based on these results, the Sequencing method is considered superior in detecting patterns and providing more accurate predictions of students’ achievement potential. The comparison of these two methods provides a foundation for institutions to develop more accurate, objective, and comprehensive student achievement prediction systems. Thus, universities can implement early and well-targeted interventions and guidance.
Naïve Bayes and Bidirectional Algorithm Analysis: Encoder Representations From Transformers (BERT) to Teachers' Learning Services to Students Based on the Website of SMK Multi Karya School Sianturi, Ismail; Iqbal, Muhammad; Sitorus, Zulham
The IJICS (International Journal of Informatics and Computer Science) Vol. 9 No. 2 (2025): July
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/ijics.v9i2.8968

Abstract

This study analyzes the comparison of two algorithms, namely Naive Bayes and Bidirectional Encoder Representations From Transformers (BERT), for the evaluation of the performance of education personnel at SMK MULTI KARYA This study uses manual calculation methods and the Python application. The results showed that the Naive Bayes algorithm gave very consistent results with accuracy, precision, and recall values of 76.67% both in manual calculations and with Pyton. This indicates that the Naive Bayes algorithm is effective in grouping data on the performance of education personnel. Meanwhile, the Bidirectional Encoder Representations From Transformers (BERT) algorithm shows mixed results, while with Python it reaches 12.00%. There are significant differences in recall values and precision between these two calculation methods. Nevertheless, the performance category "Good Performance Staff" remains the most dominant. The difference in results between manual and python calculations is that Naive bayes is a more stable and consistent method across different platforms, whereas Bidirectional Encoder Representations From Transformers (BERT) shows flexibility but with smaller variation in results. Therefore, in the context of education performance evaluation, NAive bayes are more reliable to produce consistent performance categories, while Bidirectional Encoder Representations From Transformers(BERT) can be an alternative with a fairly high level of accuracy but require further consideration in the interpretation of the results..
Academic Chatbot Based on Natural Language Processing for Student Services at STMIK Mulia Darma Batubara, Muhammad Iqbal; Panjaitan, Muhammad Iqbal; Rajagukguk, Denni M; Sihombing, Monang Juanda Tua
The IJICS (International Journal of Informatics and Computer Science) Vol. 9 No. 2 (2025): July
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/ijics.v9i2.8970

Abstract

The increasing demand for efficient and accessible academic services has led higher education institutions to adopt innovative digital solutions. At STMIK Mulia Darma, students often experience delays and limited access to academic information due to manual service systems and limited staff availability. To address these challenges, this research proposes the development of an academic chatbot using Natural Language Processing (NLP) to automate and enhance student services. The chatbot is designed to understand and respond to student inquiries in Bahasa Indonesia, providing real-time information related to course schedules, registration procedures, tuition deadlines, and other academic matters. By integrating NLP with the institution’s academic information system, the chatbot delivers personalized and context-aware responses. The system was developed using a rule-based NLP model enhanced with intent classification and entity recognition techniques. Testing results indicate that the chatbot successfully answered more than 90% of user queries with acceptable response time and accuracy. This solution demonstrates the potential of NLP-powered chatbots to improve service efficiency, reduce administrative workload, and support the implementation of a smart campus ecosystem.

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