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M. Khalil Gibran
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JITCoS : Journal of Information Technology and Computer System
Published by CV. Multimedia Teknologi Kreatif
ISSN : -     EISSN : 31096182     DOI : https://doi.org/10.65230/jitcos
Core Subject : Science, Engineering,
Computer Science & IT Control & Systems Engineering
JITCoS: Journal of Information Technology and Computer System is a peer-reviewed scholarly journal that aims to advance the theory and practice of information technology and computer systems. The journal seeks high-quality contributions from researchers, academics, and industry professionals that enrich the body of knowledge and deliver practical insights. The journal welcomes original articles, comprehensive reviews, and practical case studies in, but not limited to, the following areas: Information systems development and IT governance, Web and mobile application engineering, Big data analytics, data mining, and data science, Cybersecurity, digital forensics and privacy, Digital transformation, E-Government, and Smart Cities, Cloud and edge computing technologies, Geographic Information Systems (GIS), Decision Support Systems (DSS) and business intelligence, Computer architecture and hardware acceleration, Networking protocols and distributed systems, Embedded systems and Internet of Things (IoT), Operating systems and kernel-level development, Parallel, grid, and cloud-based computation, Control systems, robotics, and, intelligent automation, Artificial Intelligence (AI) and Machine Learning (ML). JITCoS encourages interdisciplinary approaches that merge engineering, computing, and data-driven insights to tackle contemporary challenges and foster innovation.
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 36 Documents
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Web-Based Binary Image Compression Using Thresholding and Run-Length Encoding Adli Rahman Harun Harahap; Fazila Nazifa Edilia; Andita Utami; Zianah Nafisah Simbolon; Faiz Akhtar Aqila
Journal of Information Technology and Computer System Vol. 2 No. 1 (2026): June
Publisher : CV. Multimedia Teknologi Kreatif

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.65230/jitcos.v2i1.116

Abstract

This study implements the Run-Length Encoding (RLE) algorithm for binary image compression in a web-based environment using thresholding as a preprocessing stage. The system was developed using JavaScript and the HTML5 Canvas API to perform grayscale conversion, thresholding, and image compression directly within a web browser. Thresholding was applied to transform RGB images into binary images with simpler and more repetitive pixel patterns suitable for RLE compression. The compression process encodes consecutive pixels with identical values into tuples consisting of run lengths and pixel values. Experiments were conducted using a test image and five threshold values (50,75,100,127,150) to evaluate their influence on compression performance. The results show that threshold selection affects pixel redundancy, the number of generated tuples, and compression efficiency. The highest performance was achieved at a threshold value of 127, resulting in storage savings of 63.6% and a compression ratio of 2.75:1. The findings indicate that RLE is effective for compressing binary images with repetitive pixel patterns. Furthermore, the RLE compression stage is lossless with respect to the binary image generated after thresholding, whereas the preprocessing stage from RGB to binary is lossy because color and intensity information are discarded.
Implementation of Color Feature Extraction and KNN Algorithm for Oil Palm Fruit Ripeness Classification Haidar Al-mutawalli; M. Miko Sahputra Sembiring; Khairul Arifin; Fadila Rahma Sari
Journal of Information Technology and Computer System Vol. 2 No. 1 (2026): June
Publisher : CV. Multimedia Teknologi Kreatif

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.65230/jitcos.v2i1.117

Abstract

Determining the ripeness level of oil palm fresh fruit bunches (FFB) is crucial for Crude Palm Oil (CPO) quality, but current manual inspection in the field remains highly subjective and inconsistent. This study aims to develop and evaluate an automatic classification system for oil palm fruit ripeness using global HSV color feature extraction and the K-Nearest Neighbor (K-NN) machine learning algorithm. The research utilized a comprehensive public dataset comprising 4,728 images categorized into five ordinal ripeness classes (unripe, underripe, ripe, overripe, and decayed). The dataset was split into 80% training and 20% testing data. Images were resized, and global average values of the Hue, Saturation, and Value channels were extracted without prior background segmentation. The classification was executed using the K-NN algorithm optimized with the Euclidean Distance metric. Experimental results demonstrated that the optimal model configuration was achieved at a neighbor value of k=7, yielding an overall accuracy of 54.86%. The system showed the highest reliability in identifying the ripe class with an F1-score of 64.66% due to its prominent red-orange characteristics. However, utilizing global color averages without image segmentation led to misclassifications, particularly in transition stages and due to background noise. Therefore, it is highly recommended for future implementations to apply automatic image segmentation techniques to eliminate background interference and to combine HSV features with texture descriptors like GLCM to significantly enhance classification accuracy.
Rupiah Banknote Denomination Classification Using CNN with MobileNetV2 Architecture fadil Mhd Fadil Ramadhana; Mahfuzhah Rahma Kesuma; Tiara Tiara Amanda Lestari; Dewi Dewi Aulia Tanjung; Dwi Dwi Mauliddya Irawan; Trivanny Trivanny Qimitha
Journal of Information Technology and Computer System Vol. 2 No. 1 (2026): June
Publisher : CV. Multimedia Teknologi Kreatif

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.65230/jitcos.v2i1.118

Abstract

The automatic identification of Rupiah banknote denominations using digital image processing and deep learning is a pivotal focus in financial technology, offering potential support for visually impaired individuals. While deep learning models achieve high accuracy, standard approaches often overlook computational variations and the challenges posed by the physical condition of banknotes in real-world scenarios. This study implements a Convolutional Neural Network (CNN) with the MobileNetV2 architecture to classify seven Rupiah banknote denominations. To enhance scientific novelty and address the limitations of basic replication studies, this research evaluates model performance through a multi-resolution framework (64×64, 128×128, 224×224, and 256×256 pixels) and validates the system against an external, non-ideal dataset. The dataset, comprising 2,398 images, was partitioned using a robust three-way split strategy: 64% for training, 16% for validation, and 20% for testing. Evaluations using a detailed confusion matrix indicate high multi-class classification stability. Furthermore, robustness tests demonstrate the model’s viability under realistic deployment constraints, such as folded, wrinkled, tilted, and unevenly illuminated banknotes. These findings establish a solid technical foundation for future mobile-based assistive applications designed to support the financial independence of visually impaired individuals.
A Comparative Analysis of the Performance of Decision Trees, Random Forests, and XGBoost in Classifying SAR Information Production Priorities Ravil Baadilahasan; Eka Putra Jaya
Journal of Information Technology and Computer System Vol. 2 No. 1 (2026): June
Publisher : CV. Multimedia Teknologi Kreatif

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.65230/jitcos.v2i1.137

Abstract

Advances in information technology have driven the need for fast, accurate, and targeted management of SAR information. In the information production process, prioritization is crucial so that urgent information with widespread impact can be produced first. This study aims to analyze and compare the performance of the Decision Tree, Random Forest, and XGBoost algorithms in classifying the priority of SAR information production. The data used consists of 500 synthetic data points organized based on SAR information production scenarios with three priority classes: low, medium, and high. The attributes used include information type, content type, publication platform, source material, operational status, contextual location, urgency level, public impact, deadline, completeness of material, production complexity, need for revision, estimated production duration, and number of available personnel. The research stages included collecting synthetic data, preprocessing the data, splitting the data at an 80:20 ratio, training the model, and evaluating the model. Evaluation was conducted using accuracy, precision, recall, F1-score, and a confusion matrix. The results showed that Random Forest achieved the best performance with an accuracy of 98.00%, precision of 98.03%, recall of 98.00%, and an F1-score of 98.00%. XGBoost achieved an accuracy of 97.00%, while Decision Tree achieved an accuracy of 93.00%. These results indicate that Random Forest is more effective at classifying SAR information production priorities using the synthetic data provided. The findings of this study can serve as a preliminary framework for utilizing machine learning to support a more objective classification of SAR information production priorities.
A Comparative Analysis of the Performance of Naive Bayes, SVM, and Random Forest in Classifying SAR Information Communication Responses Suwari Abdillah; Irhan Kasali
Journal of Information Technology and Computer System Vol. 2 No. 1 (2026): June
Publisher : CV. Multimedia Teknologi Kreatif

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.65230/jitcos.v2i1.142

Abstract

The development of information technology increases the need for fast, clear, and structured management of SAR information communication. Responses to SAR information may include appreciation, requests for information, and criticism; therefore, a text-based approach is needed to classify these responses more systematically. This study aims to analyze and compare the performance of Naive Bayes, Support Vector Machine (SVM), and Random Forest algorithms in classifying SAR information communication responses. The dataset used in this study consists of 500 synthetic records created based on SAR information communication response scenarios and does not represent actual public responses. The classification labels consist of three classes: appreciation, information request, and criticism. The research stages include synthetic data creation, text preprocessing, TF-IDF weighting, data splitting with an 80:20 ratio, model training, and model evaluation. The evaluation uses accuracy, precision, recall, F1-score, and confusion matrix. The results show that SVM and Random Forest achieve the highest accuracy of 95.00%, while Naive Bayes achieves 89.00%. Based on precision and F1-score, SVM provides the best performance with 95.66% precision and 95.04% F1-score. These findings indicate that machine learning algorithms can be used as an initial approach for classifying SAR information communication responses using synthetic data.
Text Readability Analysis of Digital Promotional Posters on Mobile Displays Using Otsu Thresholding and Canny Edge Detection M. Nugraha Aulia Akbar Hasibuan; Farhan Amar Pramudya; Ferdi Frans Dirga; Fahri Habibi Siregar; Raja Alfian Syah
Journal of Information Technology and Computer System Vol. 2 No. 1 (2026): June
Publisher : CV. Multimedia Teknologi Kreatif

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.65230/jitcos.v2i1.146

Abstract

This study aimed to develop and evaluate a rule-based system for analyzing the readability of text in digital promotional posters viewed on mobile displays. The proposed approach combined grayscale conversion, Gaussian blur, Otsu thresholding, Canny edge detection, morphological processing, and visual-feature analysis based on local contrast, equivalent mobile character height, and background complexity. A controlled dataset of 96 poster images was generated from 12 base designs under four conditions, namely readable, low contrast, small text, and complex background, at resolutions of 1080 × 1080 and 360 × 360 pixels. The dataset contained 960 ground-truth text regions, with 24 images used for calibration and 72 images for testing. On the test dataset, text-region detection achieved 95.81% precision, 66.67% recall, a 78.62% F1-score, and a mean Intersection over Union of 0.6576. Readability classification on 480 matched regions produced 63.96% accuracy and a macro F1-score of 64.28%. The system performed best on readable and complex-background posters, while low-contrast text produced the weakest detection result. Processing time averaged 0.0700 seconds for 1080 × 1080 images and 0.0253 seconds for 360 × 360 images. The findings indicate that the proposed method is suitable as an initial screening tool for identifying text-readability problems in digital promotional posters, although further development using real-world posters, human evaluation, and adaptive segmentation is required.

Page 4 of 4 | Total Record : 36

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