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Kampus Selatan Gedung Aula M. A. Jangka Fakultas Ilmu Komputer Universitas Almuslim Bireuen - Aceh. Jl. Al-Muslim, Peusangan, Kabupaten Bireuen, Aceh 24261.
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Jurnal Ilmu Komputer Aceh
Published by Universitas Almuslim
ISSN : -     EISSN : 29867797     DOI : -
Core Subject :
Jurnal Ilmu Komputer Aceh (ILKA) merupakan jurnal berbasis OJS 3 yang dikelola oleh program studi Informatika Fakultas Ilmu Komputer Universitas Almuslim Bireuen - Aceh dengan e-ISSN 2986-7797 (online). Artikel yang diterbitkan pada jurnal ini merupakan hasil penelitian dosen dan mahasiswa di bidang ilmu komputer. Jurnal ILKA menerbitkan tiga volume setiap tahun, pada bulan Fabruari, Juni dan Oktober tiap tahunnya. Setiap jurnal yang diterbitkan melalui proses double-blind review dalam proses review suatu artikel yang akan disajikan di mana suatu artikel di nilai oleh reviewer yang tidak mengetahui identitas penulis. Jurnal ILKA yaitu memuat artikel dalam bentuk hasil penelitian, dan artikel konseptual yang mencakup bidang ilmu komputer, antara lain: Sistem Informasi, Informatika, Artificial Intelligence, Jaringan Komputer, Data Science, Rekayasa Perangkat Lunak, Internet of Thing, Teknik Komputer dan Multimedia
Arjuna Subject : -
Articles 40 Documents
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Perancangan Sistem Pengolahan Citra Digital Klasifikasi Jenis Ikan Laut Menggunakan Model Logisitic Regression Mifzal; Iqbal Iqbal; Dasril Azmi
Jurnal Ilmu Komputer Aceh Vol 3 No 1 (2026): Jurnal Ilmu Komputer Aceh
Publisher : Fakultas Ilmu Komputer Universitas Almuslim

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51179/ilka.v3i1.37

Abstract

This research develops a web-based marine fish classification system by applying digital image processingtechniques and the Logistic Regression algorithm. The system is intended to recognize four marine fish species, namely milkfish, mackerel tuna, yellowstripe scad, and threadfin bream, through the combination of color and texture feature representations. Color characteristics are extracted using HSV color histograms, while texture information is obtained using the Local Binary Pattern (LBP) method. The experimental dataset consists of 4,000 fish images, with 3,200 images allocated for model training and 800 images used for testing. The evaluation results indicate that the proposed approach achieves an overall accuracy of 89%, with precision, recall, and f1-score values exceeding 0.85 for most fish categories. The system enables automatic image uploading, feature extraction, and classification via a Flask-based web interface, including the capability to detect images that do not belong to the trained classes. Despite achieving promising results, the system is still affected by limitations related to dataset size and visual similarities among fish species. Future work may focus on increasing data diversity and performing evaluations in real-world environments to enhance system reliability and generalization.
Klasifikasi Nilai Nominal Uang Logam Indonesia Menggunakan Support Vector Machine Triee Salsabila; Riyadhul Fajri; Heri Gustami
Jurnal Ilmu Komputer Aceh Vol 3 No 1 (2026): Jurnal Ilmu Komputer Aceh
Publisher : Fakultas Ilmu Komputer Universitas Almuslim

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51179/ilka.v3i1.39

Abstract

This study focuses on the classification of Indonesian Rupiah coin denominations using the Support Vector Machine (SVM) method based on digital image processing. The research objects consist of Rp100, Rp200, Rp500, and Rp1,000 coins issued from 2016 to the present. The pre-processing stage includes resizing the images to 128×128 pixels and converting them into grayscale to ensure data uniformity. Feature extraction is performed by combining shape features, Haralick texture, Local Binary Pattern (LBP), and HSV color features to represent the main characteristics of each coin. The classification model is developed using an SVM with a Radial Basis Function (RBF) kernel, with 80% of the data used for training and 20% for testing. The experimental results show an accuracy of 75%, indicating that the proposed approach is reasonably effective in distinguishing Indonesian coin denominations. However, further improvements can be achieved through parameter optimization and dataset expansion in future studies.
Penerapan Role-Based Access Control (RBAC) untuk Mengelola Hak Akses pada Sistem Informasi Sekolah Taman Kanak-Kanak Berbasis Multiuser: Implementasi Mekanisme Kontrol Akses Berbasis Peran pada Lingkungan Multiuser Sri Ramadhani; Dedy Armiady; Riyadhul Fajri
Jurnal Ilmu Komputer Aceh Vol 3 No 1 (2026): Jurnal Ilmu Komputer Aceh
Publisher : Fakultas Ilmu Komputer Universitas Almuslim

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

Abstract

This study aims to design and implement a multiuser information system based on Role-Based Access Control (RBAC) in a kindergarten (TK) environment to manage user access rights in a structured and secure manner. In multiuser-based school information systems, improper access control can lead to unauthorized data access, role conflicts, and security vulnerabilities. To address this issue, this research applies RBAC as the main authorization mechanism, where access rights are granted based on predefined user roles. The system is developed using a software engineering approach with a descriptive methodology, following stages of requirements analysis, system design, implementation, and testing. Three primary roles are defined in the system: Super Admin, School Admin, and Operator, each with different access privileges. The system is implemented using PHP with the CodeIgniter framework and MySQL as the database management system. To enhance responsiveness and accuracy in access control enforcement, a real-time mechanism using Pusher.js is integrated to synchronize access right changes without requiring manual page refresh. System testing is conducted using black-box testing to verify access restrictions, menu visibility, and data isolation between schools. The results show that the RBAC mechanism functions effectively in restricting access according to user roles, preventing unauthorized actions, and ensuring data separation across institutions. This research demonstrates that RBAC can be reliably applied as a foundational access control model for multiuser school information systems, particularly in early childhood education environments, and can be further developed for real-world implementation.
Implementasi Metode MAUT dalam Sistem Pendukung Keputusan Penentuan Bibit Udang Vaname Unggul Mustafa Kamal; Iskandar Zulkarnaini; Sriwinar
Jurnal Ilmu Komputer Aceh Vol 3 No 2 (2026): Jurnal Ilmu Komputer Aceh
Publisher : Fakultas Ilmu Komputer Universitas Almuslim

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51179/ilka.v3i2.44

Abstract

The success of whiteleg shrimp cultivation depends heavily on the selection of high-quality seeds. However, the seed selection process in the field is often subjective and based solely on intuition. This study aims to design and implement a Decision Support System (DSS) using the Multi-Attribute Utility Theory (MAUT) method to help farmers objectively determine superior whiteleg shrimp seeds. The MAUT method was chosen because of its ability to convert the values of various criteria into a uniform utility scale that can be compared mathematically. The criteria used in the assessment include body color, active movement, body shape, full intestines, fluffy tail, and bright eyes. The results showed that the developed system can provide the best seed recommendations based on the highest preference value. Based on testing, the SPF Global Gen Vannamei Shrimp Seed alternative obtained the highest score (1.0000) and became the main recommendation in seed selection.
Klasifikasi Tingkat Kematangan Buah Pisang Menggunakan Algoritma Support Vector Machine Besra Laoli; Imam Muslem; Fitri Rizani
Jurnal Ilmu Komputer Aceh Vol 3 No 1 (2026): Jurnal Ilmu Komputer Aceh
Publisher : Fakultas Ilmu Komputer Universitas Almuslim

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51179/ilka.v3i1.45

Abstract

An This study aims to develop an automatic classification system for determining the ripeness level of bananas using digital image processing and the Support Vector Machine (SVM) algorithm. Banana ripeness is commonly assessed visually based on skin color, which is subjective and prone to inconsistency. To address this issue, a computer-based classification approach is proposed to improve accuracy and objectivity. The dataset used in this study consists of banana images categorized into three ripeness levels: unripe, ripe, and overripe. The images were obtained from direct acquisition using a smartphone camera and an online dataset platform. The preprocessing stage includes image resizing, color space conversion, and normalization. Feature extraction is performed using color features in the HSV color space combined with texture features extracted using the Histogram of Oriented Gradients (HOG) method. The extracted features are then classified using the Support Vector Machine algorithm with a Radial Basis Function (RBF) kernel. Model performance is evaluated using accuracy, precision, recall, and F1-score metrics. Experimental results show that the proposed SVM-based approach is able to classify banana ripeness levels effectively with satisfactory performance. The results indicate that the integration of digital image processing and SVM has strong potential to support automatic and consistent banana ripeness classification, which can be applied in agricultural and post-harvest quality control systems.
Analisis Prediktif Tingkat Kematangan Alpukat Menggunakan Algoritma Logistic Regression Natasya Aulia; Iqbal Iqbal; Hannan Asrawi
Jurnal Ilmu Komputer Aceh Vol 3 No 1 (2026): Jurnal Ilmu Komputer Aceh
Publisher : Fakultas Ilmu Komputer Universitas Almuslim

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51179/ilka.v3i1.46

Abstract

Determining the ripeness level of avocado fruit is an important factor in distribution, marketing, and consumption processes. Conventional ripeness assessment is often subjective and dependent on human experience, which can lead to inconsistent results. This study aims to develop an avocado ripeness prediction system using the Logistic Regression algorithm based on physical and visual fruit characteristics. The dataset consists of 1,250 avocado samples with features including firmness, color attributes, tapping sound, weight, and fruit size. Data preprocessing involved cleaning, normalization of numerical features using StandardScaler, and categorical feature transformation using one-hot encoding. The experimental results show that the proposed model achieved an accuracy of approximately 77% in classifying avocado ripeness into ripe and unripe categories, indicating that Logistic Regression is a lightweight and efficient approach for numerical-based ripeness prediction systems.
Implementasi Game Edukasi 3D dalam Pembelajaran Bahasa Jepang pada Mahasiswa Universitas Almuslim dengan Unity Engine Menggunakan Metode Multimedia Development Life Cycle (MDLC) Sarah Nadia; Riyadhul Fajri; Sriwinar
Jurnal Ilmu Komputer Aceh Vol 3 No 2 (2026): Jurnal Ilmu Komputer Aceh
Publisher : Fakultas Ilmu Komputer Universitas Almuslim

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51179/ilka.v3i2.49

Abstract

This study aims to develop a 3D educational game as an interactive learning medium for Japanese language learning at Universitas Almuslim using the Multimedia Development Life Cycle (MDLC) method and Unity Engine. The research employs six MDLC stages: concept, design, material collecting, assembly, testing, and distribution. The game, entitled "Nihongo Quest," presents N5-level Japanese vocabulary and characters (Hiragana and Katakana) within an interactive 3D environment. The development utilized Blender for 3D asset modeling, Visual Studio Code for C# scripting, and Universal Render Pipeline (URP) for visual optimization. System testing was conducted using black box testing across 10 functional features. The results show that all tested features passed successfully, demonstrating that the game functions as intended. The game provides an interactive and engaging learning experience, making it a viable alternative learning medium for students who struggle with traditional Japanese language instruction.  
Pemodelan Aset 3D Interior Kamar Tidur Minimalis Dengan Penerapan Texturing PBR Untuk Meningkatkan Visualisasi Realistis Sultan Alam Khalil; Riyadhul Fajri; Zulkifli Zulkifli
Jurnal Ilmu Komputer Aceh Vol 3 No 1 (2026): Jurnal Ilmu Komputer Aceh
Publisher : Fakultas Ilmu Komputer Universitas Almuslim

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51179/ilka.v3i1.50

Abstract

Visualization quality is an important aspect in presenting interior design concepts realistically. Conventional texturing methods often produce flat and unrealistic appearances due to inaccurate light interaction. This study aims to implement Physically Based Rendering (PBR) techniques in modeling a minimalist bedroom interior using Blender software to improve visual realism. The development process follows the Multimedia Development Life Cycle (MDLC) method, which includes concept, design, material collecting, assembly, testing, and distribution stages. PBR materials are created by combining texture maps such as Albedo, Roughness, Metallic, and Normal through the Principled BSDF shader. The Cycles render engine is used to generate realistic lighting and shadows. The results show that PBR implementation significantly improves surface detail, light reflection accuracy, and overall realism compared to conventional texturing methods. This research proves that PBR-based texturing is effective for producing realistic interior visualizations for design presentation
Klasifikasi Kelayakan Penerimaan Beasiswa Kip Menggunakan Metode Decision Tree Nafira; Iqbal Iqbal; Hannan Asrawi
Jurnal Ilmu Komputer Aceh Vol 3 No 1 (2026): Jurnal Ilmu Komputer Aceh
Publisher : Fakultas Ilmu Komputer Universitas Almuslim

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

Abstract

The KIP Kuliah program aims to improve access to higher education for students from underprivileged families. The scholarship selection process, which is often conducted manually, tends to be inefficient and prone to subjectivity. This study aims to develop a classification model using the Decision Tree C4.5 algorithm based on a data mining approach. The dataset includes parents’ income, parents’ occupation and education, number of dependents, home ownership and condition, electricity capacity, house size, and achievement data. The research stages include data collection, preprocessing, model development, and evaluation using a confusion matrix and performance metrics such as accuracy, precision, recall, and f1-score. The results show that the model achieves an accuracy of 98%. Parents’ income and number of dependents are the most influential factors in determining eligibility. The resulting model has a simple structure, making it easy to interpret and useful for supporting a more objective and efficient selection process.
Klasifikasi Spesies Ikan Koi Berdasarkan Citra Menggunakan Metode YOLOv3-Tiny Dan OpenCV Rauzi Saputra; Imam Muslem; Riyadhul Fajri
Jurnal Ilmu Komputer Aceh Vol 3 No 1 (2026): Jurnal Ilmu Komputer Aceh
Publisher : Fakultas Ilmu Komputer Universitas Almuslim

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51179/ilka.v3i1.52

Abstract

Identification of koi fish (Cyprinus carpio) varieties in aquaculture and ornamental fish industries is commonly performed manually through visual observation, making the process subjective, inconsistent, and inefficient, particularly at large production scales. This study aims to develop an automated image-based detection and classification system for koi varieties using the YOLOv3-Tiny algorithm integrated with OpenCV, capable of operating in real-time conditions. The dataset consists of 3,154 images of six koi varieties—Asagi, Bekko, Hikarimono, Kohaku, Sanke, and Showa—which were expanded to 6,360 images through data augmentation techniques. Image labeling and annotation were conducted using Roboflow, while model training was implemented with the Darknet framework in a Google Colab environment supported by GPU acceleration. System performance was evaluated using mean Average Precision (mAP), loss function analysis, and both static image and real-time video testing. Experimental results demonstrate that the YOLOv3-Tiny model is capable of accurately detecting and classifying koi varieties with stable inference speed suitable for real-time applications. The proposed system enhances objectivity, consistency, and efficiency in koi variety identification and shows strong potential for practical implementation in technology-driven ornamental fish farming and trading industries

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