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INDONESIA
Journal of Computers and Digital Business
Published by PT Delitekno Media Mandiri
ISSN : -     EISSN : 28303121     DOI : 10.56427
Core Subject : Science,
Computer Science & IT Decision Sciences, Operations Research & Management
Journal of Computers and Digital Business is an interdisciplinary and open access journal covering Computers and Digital Business. The Journal of Computers and Digital Business is open to submission from experts and scholars in the wide areas of Information System, Security, Artificial Intelligent , Cloud Computing, Machine Learning, Digital Business Technology and other areas listed in the focus and scope of this journal. Focus and Scope Information System Information Security Information Retrieval Geographic Information System Fuzzy Logics Genetic Algorithms Neural Networks Machine Learning Decision Support System Data Mining Cloud Computing E-Learning E-Goverment E-Commerce E-Business Digital Business Management Digital Business Technology Digital Business Analysis & Design Big Data & Business Intelligence Cyber Security for Digital Business
Arjuna Subject : Ilmu Komputer (semua kategori) - Aplikasi Ilmu Komputer
Articles 6 Documents
 1 
Search results for , issue "Vol. 4 No. 3 (2025)" : 6 Documents clear
Securing Private Text Messages Using a Modified ASCII-256 Caesar Cipher and Avalanche Effect Assessment Maghfira Aida; Yulia Alfi Sinaga; Afthar Kautsar
Journal of Computers and Digital Business Vol. 4 No. 3 (2025)
Publisher : PT. Delitekno Media Madiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56427/jcbd.v4i3.765

Abstract

Cryptography is a scientific discipline used to protect information by transforming readable messages into forms that are unintelligible to unauthorized parties. One of the earliest and simplest cryptographic techniques is the Caesar Cipher, which remains relevant for academic exploration, particularly in understanding fundamental concepts of substitution ciphers. This study proposes a modified version of the Caesar Cipher by incorporating the full ASCII-256 character set, thereby expanding the substitution space and increasing the complexity of the encryption process. To evaluate the effectiveness of this modification, two measurement techniques were applied: the Avalanche Effect, which assesses the sensitivity of the cipher to small input changes, and the Character Error Rate (CER), which examines the accuracy and distortion level during decryption. The experimental results demonstrate that the modified cipher achieves an average Avalanche Effect exceeding 10% and a CER value above 50%, indicating enhanced resistance to simple cryptanalytic approaches and improved confidentiality of encrypted data. The implementation and simulations were performed using MATLAB R2013a to provide a controlled environment for testing and analysis. This study offers a deeper conceptual understanding of how classic ciphers can be strengthened through structural modifications and serves as a reference for introductory cryptographic research as well as educational demonstrations.
The Hybrid Deep Learning ANN-CNN Model for Enhancing Diabetes Prediction Amira Hassan Abed
Journal of Computers and Digital Business Vol. 4 No. 3 (2025)
Publisher : PT. Delitekno Media Madiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56427/jcbd.v4i3.766

Abstract

Diabetes mellitus is a global chronic metabolic disease that poses a serious threat to human health. Accurate and early prediction of diabetes is essential for effective medical treatment and long-term disease management. In this study, we propose a deep learning–based framework as a novel approach for diabetes prediction using a large-scale dataset containing more than 6,000 patient records. Several deep learning architectures, including Artificial Neural Networks (ANN) and Convolutional Neural Networks (CNN), are examined to determine the most effective model for prediction tasks. Building on the strengths of both methods, this research introduces a hybrid ANN–CNN architecture designed to leverage ANN’s capability in learning nonlinear relationships and CNN’s efficiency in extracting high-level feature patterns. Extensive data preprocessing and feature extraction were conducted to enhance model performance and ensure reliable outcomes. Experimental results demonstrate that the hybrid ANN–CNN model achieved the highest prediction accuracy of 91.4%, surpassing standalone ANN (86.2%) and CNN (88.9%) models. These findings highlight the potential of hybrid deep learning frameworks in improving clinical decision support systems, enabling more accurate risk assessment and early intervention for diabetes. The results further indicate that integrating complementary neural network structures can significantly enhance predictive performance in complex medical datasets.
An Enhanced U-Net-based Approach for Sinhala Document Layout Analysis Hulathdoowage S.K.D; Kumara B.T.G.S
Journal of Computers and Digital Business Vol. 4 No. 3 (2025)
Publisher : PT. Delitekno Media Madiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56427/jcbd.v4i3.767

Abstract

Document layout analysis plays a critical role in the digitization pipeline by identifying, segmenting, and classifying structural elements within documents to support accurate information extraction. This task becomes increasingly challenging when dealing with heterogeneous layouts that contain paragraphs, tables, figures, mathematical expressions, and other visual components. For Sinhala, a low-resource language with limited annotated datasets and specialized models, research in this area remains sparse. To address this gap, this study proposes an enhanced U-Net architecture that integrates convolutional neural networks with vision transformer blocks to improve semantic segmentation performance. The model leverages convolutional layers to capture fine-grained local features while employing transformer components to model long-range dependencies and global contextual relationships across document regions. A manually annotated dataset of 750 Sinhala document images covering 14 distinct element categories was developed to train and evaluate the model. Experimental results demonstrate that the proposed architecture significantly outperforms standard U-Net and attention U-Net variants, achieving 93.06% pixel accuracy, 64.37% mean IoU, and 77.32% mean F1-score. This research represents the first comprehensive document layout analysis framework tailored specifically for Sinhala documents and provides a strong foundation for future digitization, archival, and text processing initiatives within Sri Lankan academic, governmental, and cultural institutions.
Rancangan Sistem Prediksi Penyakit Jantung Berbasis Framingham Risk Score: Konsistensi Teoritis dan Implementasi Web Hania Ayu Karin; Timothy Christian; Gabriel Putra; Muhammad Fahad; Muhammad Dhaffa Nugroho; Ilham Yusuf Maulana; Bambang Irawan
Journal of Computers and Digital Business Vol. 4 No. 3 (2025)
Publisher : PT. Delitekno Media Madiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56427/jcbd.v4i3.775

Abstract

Penyakit jantung merupakan penyebab kematian tertinggi di dunia. Deteksi dini risiko penyakit jantung dapat dilakukan menggunakan algoritma Framingham Risk Score (FRS). Penelitian ini merancang sistem prediksi risiko penyakit jantung berbasis web dengan mengadaptasi algortima Framingham Risk Score (FRS) yang dimodifikasi untuk lingkungan digital. Sistem dirancang menggunakan HTML, CSS, dan JavaScript sengan antarmuka form input gejala subjektif (nyeri dada, sesak napas, diabetes, dll) dan faktor demografis (usia, gender, riwayat keluarga). Parameter skoring dimodifikasi dari pedoman American Heart Association dan European Society of Cardiology dengan penyesuaian bobot gejala klinis seperti nyeri dada “berat” (+4 poin) dan diabetes (+3 poin). Hasil simulasi teoritis terhadap 5 skenario kasus menunjukkan konsistensi 95-97% dengan perhitungan manual FRS menggunakan kalkulator standar MDCalc. Sistem mengklasifikasikan output menjadi tiga kategori risiko: rendah(<10%), sedang(10-20%), dan tinggi(>20%), disertai rekomendasi tindak lanjut. Keunggulan rancangan ini terletak pada kemudahan akses sebagai alat skrining mandiri, namun memiliki keterbatasan utama yakni tidak mencakup parameter laboratorium (kolesterol, LDL) dan belum diuji dengan data pasien nyata. Simpulan Studi menekankan bahwa sistem ini bersifat prepanduan (pre-screening) dan setiap hasil prediksi harus dikonfirmasi melalui pemeriksaan media lengkap.
Applying Random Forest Algorithm for Phishing URL Identification Kautsar, Afthar; Aida, Maghfira; Yulistia , Anita
Journal of Computers and Digital Business Vol. 4 No. 3 (2025)
Publisher : PT. Delitekno Media Madiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56427/jcbd.v4i3.782

Abstract

Phishing attacks continue to be one of the most pervasive cybersecurity threats, particularly through malicious URLs designed to mimic legitimate websites and steal sensitive user information. To address this challenge, this study employs the Random Forest algorithm for automated phishing URL detection using a publicly available dataset from Kaggle. The dataset contains diverse structural, technical, and popularity-based features that capture behavioral and lexical characteristics of each URL. Following data preprocessing and an 80/20 train–test split, the Random Forest classifier achieved strong predictive performance, attaining an accuracy of 94.94%, a precision of 95.19%, and a recall of 96.94%. The model further demonstrated robust classification capability with an F1-score of 96.06% and an ROC AUC value of 0.985, indicating excellent discrimination between phishing and legitimate URLs. Feature importance analysis shows that factors such as the URL’s presence in Google’s index, page rank metrics, and specific structural patterns significantly influence prediction outcomes. Additionally, performance visualizations including ROC and Precision–Recall curves reinforce the model’s reliability and stability. Overall, the findings suggest that Random Forest provides an effective and efficient solution for phishing URL detection, offering promising potential for integration into real-world cybersecurity systems.
Organizational Determinants and Project Performance: Mediating Roles of AI and Environmental Responsiveness in Malaysia’s Oil & Gas Budit, George anak; Mohd Zainal Munshid Harun; Rahmat Aidil Djubair
Journal of Computers and Digital Business Vol. 4 No. 3 (2025)
Publisher : PT. Delitekno Media Madiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56427/jcbd.v4i3.788

Abstract

Project performance remains a persistent challenge in Malaysia’s oil and gas industry, where cost overruns, schedule delays, and operational inefficiencies continue to occur despite extensive prior research. Although earlier studies have examined organizational, technological, and environmental determinants, limited attention has been given to how these dimensions interact to influence project outcomes. This conceptual paper addresses this gap by proposing a dual-mediation framework in which Artificial Intelligence (AI) adoption and environmental responsiveness function as mechanisms through which organizational factors enhance project performance. Drawing on the Technology–Organization–Environment (TOE) framework, the Resource-Based View (RBV), and Diffusion of Innovations (DOI) theory, the study reconceptualizes five organizational factors—top management support, organizational culture, communication, change management, and training—as strategic resources that facilitate digital transformation. AI adoption is theorized as an internal capability that translates managerial commitment and cultural readiness into improved efficiency, accuracy, and project timeliness. In parallel, environmental responsiveness reflects the organization’s adaptive capacity to meet regulatory requirements, sustainability expectations, and evolving stakeholder pressures. The integration of these mediating mechanisms produces a comprehensive model that explains how internal competencies and external pressures jointly shape performance in a high-risk, capital-intensive sector. The paper contributes theoretically by extending the TOE framework through a dual-mediation perspective and offers practical implications for managers seeking to leverage AI and adaptive capabilities to achieve sustainable improvements in project performance.

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