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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 134 Documents
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Web-Based E-Business Application with Customer Data Encryption Feature to Protect Privacy at CV Jasa Karya Rizki Siregar, Lola Kamal; Ikhwan, Ali
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.v9i2.8999

Abstract

This research presents the development of a web-based e-business application integrated with a data encryption feature using the Data Encryption Standard (DES) algorithm to protect customer privacy. The system is designed for CV Jasa Karya Rizki to manage customer and transaction data securely. DES encryption is implemented manually using PHP without relying on external libraries. The encryption process is applied during data input to ensure that only ciphertext is stored in the database. When data needs to be accessed, decryption is performed on-the-fly, allowing authorized users to view the original information securely. The application consists of core modules for managing customer data, transactions, products, and reports. Encryption and decryption functions are encapsulated within the system's main classes, enhancing maintainability and modularity. Testing results show that the DES implementation functions correctly, providing a secure data management workflow without negatively impacting system performance. This research demonstrates that classic encryption algorithms like DES can still be effectively applied in small-scale business environments to enhance data security and customer trust.
Management Information System for Maimun Palace Tourism Using Web Engineering and FAST Methods Zahrina, Nadhilah; Suendri
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.v9i2.9000

Abstract

This study aims to design and implement an event reservation and payment system to support tourism services at Istana Maimun, Medan. The system was developed to simplify the process of event booking, payment, and management for both users and administrators. The development method adopted was a web-based approach using the Laravel framework and MySQL database. System testing was carried out using the black-box method, focusing on functional requirements such as event creation, reservation processing, and payment confirmation. The testing scenarios included valid and invalid inputs to evaluate the system’s ability to handle different cases. The results showed that all core functionalities performed as expected, with correct status updates and validation messages. The payment module successfully processed transactions and displayed accurate order details, including transaction amounts and unique order IDs. In addition, the system effectively prevented duplicate payments and ensured data consistency between reservations and payment records. Based on these findings, the system is considered functionally ready for production use. It can improve operational efficiency and user experience in managing event reservations at Istana Maimun. Future development may focus on integrating additional features such as online ticket verification and mobile application support.
Book Tracking Methods In Libraries Using Online Public Access Catalog sipayung, liska; Megaria Purba; Abiomega Maria Manalu; Puji Nirwana
The IJICS (International Journal of Informatics and Computer Science) Vol. 9 No. 3 (2025): November
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Advances in information technology have encouraged libraries to transform from conventional service systems to digital-based services to improve the quality of information access for users. One problem still frequently encountered in libraries is the limited access for users to quickly and accurately search for books, especially in libraries with growing collections. This study examines the implementation of a book tracking method in libraries using the Online Public Access Catalog (OPAC) as the primary means of searching collections. The purpose of this study is to analyze the effectiveness of OPAC use in helping users find bibliographic information and book locations independently and efficiently. The research methods used included literature review, user needs analysis, digital catalog system design, and implementation of a web-based OPAC integrated with the library's collection database. The OPAC system is designed to support book searches based on various parameters, such as title, author, subject, and keywords, thus facilitating user access to relevant information. System testing was conducted through functional testing and usability evaluation to assess the accuracy of search results and user-friendliness of the interface. The results indicate that the implementation of OPAC can improve the speed and accuracy of the book tracking process, reduce search errors, and increase user satisfaction with library services. Furthermore, this system contributes to improving librarians' work efficiency and supporting more structured collection management. Therefore, the OPAC book tracking method can be a strategic solution to support the modernization of library services and the sustainable optimization of information access.
Development of a Social Communications Application Expert System for Youth Faith Analysis Purba, Megaria; Liskedame Yanti Sipayung; Puteri Fajar Addini; Tesalonika Pesta Siagian; Nurika Sari Siregar
The IJICS (International Journal of Informatics and Computer Science) Vol. 9 No. 3 (2025): November
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/ijics.v9i3.9437

Abstract

Monitoring adolescent faith development requires an objective and data-driven approach, whereas existing practices remain largely manual, subjective, and weakly documented. This study proposes a web-based expert system as a decision support tool for classifying levels of adolescent faith development. The novelty of this research lies in the integration of a rule-based inference engine using forward chaining with a structured, indicator-driven assessment framework. The system was developed using a structured software engineering approach, including UML-based functional modeling and an Entity Relationship Diagram (ERD) for database design. The expert system processes assessment data derived from four validated indicators: prayer practice, participation in communal activities, social attitudes, and faith reflection. Data were collected from 30 respondents through a web-based assessment module and analyzed using expert-defined inference rules. The classification results indicate that 33.3% of respondents were categorized as having good faith development, 46.7% moderate, and 20.0% low. Functional testing using black-box methods confirmed that all system features operated according to specifications, while expert validation confirmed the relevance and consistency of indicators and inference rules. These findings demonstrate that the proposed system produces measurable and consistent classification outcomes, contributing to the application of expert systems and web-based decision support technologies for objective adolescent development monitoring.
Analysis of Digital Image Forensics Authentication in Image Forgery Cases Dameria E Br Jabat; Megaria Purba; Mhd. Avin Winata; Sophia Widiana
The IJICS (International Journal of Informatics and Computer Science) Vol. 9 No. 3 (2025): November
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/ijics.v9i3.9440

Abstract

This document introduces a combined framework for validating digital images in forensic contexts by merging Error Level Analysis (ELA) with Convolutional Neural Networks (CNN). The innovation of this research resides in the direct integration of a conventional explainable forensic method alongside a datadriven deep learning approach to ensure both clarity and enhanced detection efficacy. ELA serves to identify JPEG compression irregularities as forensic indicators, whereas CNN is employed to extract significant hierarchical features for robust image categorization. Trials were performed on the CASIA v2.0 dataset, which comprises 10,002 authentic and altered images. The suggested two-stream architecture concurrently processes original images and ELA-generated maps, facilitating synergistic feature acquisition. The hybrid model secures an accuracy rate of 74.32%, illustrating a 7.2% enhancement over isolated ELA. Furthermore, the framework diminishes the false positive rate from 50.2% to 34.8% while maintaining high sensitivity (0.84) in identifying altered regions. From a machine learning angle, this research illustrates how manually crafted forensic attributes can boost CNN capabilities when merged at the input stage. From an image processing viewpoint, it confirms ELA as a potent preprocessing strategy for directing deep feature extraction. The proposed framework provides an equilibrium between precision and forensic transparency, making it ideal for real-world digital forensic practices, including application in environments with limited resources.
Implementation of K-Nearest Neighbor Algorithm for Scientific Determination of Aid Recipients at STM Agape Sinaga, Dedi Candro Parulian; Siahaan, R. Fanry; Tarigan, Nera Mayana Br; Lubis, Rodiah Hannum; Amallia, Dwi Novia
The IJICS (International Journal of Informatics and Computer Science) Vol. 9 No. 3 (2025): November
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/ijics.v9i3.9484

Abstract

Providing assistance to underprivileged families is an important social effort to enhance community welfare; however, the selection of aid recipients often encounters problems such as subjectivity, unstructured data, and time inefficiency when conducted manually. This study aims to develop and evaluate a decision support system for determining aid recipients at STM Agape using the K-Nearest Neighbor (KNN) algorithm to improve accuracy and objectivity in the selection process. The research methodology employed a quantitative classification approach, where data were collected from families based on predefined criteria, including family income, number of dependents, housing conditions, and the occupation of the head of the household. The dataset was divided into training and testing data, and all attributes were normalized prior to processing. The KNN algorithm was applied using Euclidean distance to measure similarity between data instances, classifying each family into “eligible” or “ineligible” categories. The results indicate that the proposed system achieved higher classification accuracy and more consistent decision outcomes compared to manual selection methods. Additionally, the implementation of KNN reduced processing time and minimized subjective bias in determining aid recipients. These findings demonstrate that the KNN-based system is effective as a decision support tool, enabling STM Agape to distribute social assistance in a more targeted, objective, transparent, and efficient manner.
Analysis of Data Security Resilience in Text Steganography on Indonesian Language Structure R. Fanry Siahaan; Dedi Candro P. Sinaga; Zanziqbar Alaydrus; Ikhwan Rafif Fadhil
The IJICS (International Journal of Informatics and Computer Science) Vol. 9 No. 3 (2025): November
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/ijics.v9i3.9487

Abstract

An in depth analysis of data security in text-based steganography is necessary to ensure the sustainability and security of the methodology used. The purpose of this study is to analyze the resilience of data security in text-based steganography. The analytical approach used involves identifying and assessing the vulnerabilities of text steganography methods using Indonesian sentence patterns. The initial stage of the research was to analyze previous works related to this field to understand previously identified vulnerabilities. The applied text embedding model is based on a dictionary consisting of 1,929 words grouped into seven word categories that correspond to sentence patterns in Indonesian, namely adj (adjective), adv (adverb), nom (noun), num (numeral), par (particle), pro (pronoun), and ver (verb). Each word class is arranged into a sentence structure and each has the same bit length, namely eight bits. The robustness analysis results show that single-word input data is still vulnerable to brute-force attacks or pattern analysis if the message embedding process uses a simple sentence structure. This is due to the relatively small search space, which makes it easier for attackers to guess the embedding pattern. Conversely, using sentence patterns consisting of more than two words significantly increases combinatorial complexity and expands the possibility space, making hacking attempts much more computationally difficult. Thus, the robustness of a steganographic system increases as the number of words in the sentence pattern increases, as the time and resources required to perform the attack become practically inefficient.
Implementation of the MARCOS Method in a Decision Support System for Foundation Scholarship Determination Sinambela, Sugi Hartono; Rajagukguk, Denni M; Pristiwanto; Muhammad Iqbal Batubara; R.L harmady Tamba
The IJICS (International Journal of Informatics and Computer Science) Vol. 9 No. 3 (2025): November
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/ijics.v9i3.9574

Abstract

The scholarship selection process often involves multiple criteria and is prone to subjectivity when conducted manually. This study aims to implement the Multi-Attributive Ideal-Real Comparative Analysis (MARCOS) method in a Decision Support System (DSS) to determine foundation scholarship recipients objectively and systematically. The research applies a quantitative approach by evaluating several student alternatives based on academic and non-academic criteria, including academic achievement, parents’ income, number of dependents, organizational activity, and social status. The MARCOS method is employed through decision matrix construction, normalization, weighting, utility value calculation, and ranking. The results indicate that the proposed system is able to generate clear and consistent rankings of scholarship candidates. Validation results show an accuracy of 80% when compared with the foundation’s manual decision process. These findings demonstrate that the MARCOS-based Decision Support System can improve accuracy, transparency, and efficiency in scholarship determination and can be adapted to other multi-criteria decision-making problems.
Quality of Service (QoS) Analysis of Campus Internet Networks: A Case Study at Universitas Islam Sumatera Utara Prayogi, Satria Yudha; Hulu, Delisman
The IJICS (International Journal of Informatics and Computer Science) Vol. 9 No. 3 (2025): November
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/ijics.v9i3.9582

Abstract

The increasing reliance on internet connectivity in higher education institutions requires campus networks to deliver reliable and high-quality services to support academic and administrative activities. However, high user density and traffic load often cause performance degradation, particularly during peak usage periods. This study aims to analyze the Quality of Service (QoS) performance of the campus internet network at Universitas Islam Sumatera Utara to determine whether the existing network infrastructure meets acceptable service quality standards. The research employs a quantitative descriptive approach by measuring key QoS parameters, including throughput, delay, packet loss, and jitter, under peak and non-peak usage conditions. Network performance data were collected through direct measurement and analyzed based on TIPHON and ITU-T standards. The results indicate that network performance degrades during peak hours due to increased traffic load, resulting in lower throughput and higher delay, packet loss, and jitter. Nevertheless, all measured parameters remain within acceptable QoS thresholds, indicating that the campus network is generally capable of supporting academic, administrative, and online learning activities. This study provides valuable insights into campus network performance and highlights the importance of bandwidth management and continuous QoS monitoring to maintain service quality as user demand increases
Integrating Random Forest And Forward-Chaining Inference For Automated Coffee Quality Classification Using Sensory Standards sari, ika yusnita; Khairunnisa, Khairunnisa; Rahmi, Elvika; Rangkuti, Siti Rafiah; Rachmadini, Haliza Suci
The IJICS (International Journal of Informatics and Computer Science) Vol. 9 No. 3 (2025): November
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/ijics.v9i3.9585

Abstract

The increasing consumption of coffee has driven the need for a fast and consistent coffee quality assessment process. The quality of specialty coffee is generally determined through cupping tests based on sensory attributes; however, this method still relies heavily on panelist subjectivity and requires considerable time and cost. This study aims to develop an automated system for specialty coffee quality classification by integrating the Random Forest algorithm and Forward Chaining inference logic. Random Forest is employed to perform initial classification and identify the importance level of sensory attributes, while Forward Chaining functions as a rule-based system to validate and explain the classification results. The study utilizes 207 coffee sensory profile data samples with 11 attributes based on the Specialty Coffee Association (SCA) cupping standards. The experimental results show that the Random Forest model achieves optimal performance with 100% accuracy, precision, recall, and F1-score, with Total Cup Points identified as the most dominant attribute. The integration of these two methods produces an accurate, consistent, and explainable coffee quality classification system in accordance with SCA standards.

Page 13 of 14 | Total Record : 134

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