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International Journal of Informatics, Information System and Computer Engineering (INJIISCOM)
Published by Universitas Komputer Indonesia
ISSN : 28100670     EISSN : 27755584     DOI : https://doi.org/10.34010/injiiscom
Core Subject : Science, Engineering,
Computer Science & IT Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Mechanical Engineering
FOCUS AND SCOPE INJIISCOM cover all topics under the fields of Computer Engineering, Information system, and Informatics. Informatics and Information system IT Audit Software Engineering Big Data and Data Mining Internet Of Thing (IoT) Game Development IT Management Computer Network and Security Mobile Computing Security For Mobile Decision Support System Web and Cloud Computing Accounting Information system Electrical and Computer Engineering Sensors and Trandusers Signal, Image, Audio and Video processing Communication and Networking Robotic, Control and Automation Fuzzy and Neural System Artificial Intelligent
Arjuna Subject : Ilmu Komputer (semua kategori) - Sistem Informasi
Articles 146 Documents
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Testing Deep Learning Methods to Predict Ransowmare Activity from Hybrid Analysis Alexander M. Veach; Munther Abualkibash
International Journal of Informatics, Information System and Computer Engineering (INJIISCOM) Vol. 7 No. 1 (2026): INJIISCOM: VOLUME 7, ISSUE 1, JUNE 2026 (ONLINE FIRST)
Publisher : Universitas Komputer Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34010/injiiscom.v7i1.14803

Abstract

This study applies deep learning methods to predict ransomware using hybrid analysis samples. To understand current detection methods, prior research was analyzed, guiding the creation of an experiment that tests a model built from ransomware hybrid analysis. A training dataset of over 500 samples, encompassing 38 ransomware families and benign Windows programs, was utilized. The resulting model was subsequently evaluated against a testing dataset containing novel ransomware families not represented during training, revealing a notable performance decrease. Comparing these findings with existing literature highlights potential flaws in how artificial intelligence models are tested and reported. Consequently, this paper advocates for more complex prediction methods and alternative strategies to guarantee models maintain external effectiveness.
Visualizing Global Energy Transition using Tableau Punyisa Phumipho; Priyadarshini pattanaik; Sanchit Sarhadi
International Journal of Informatics, Information System and Computer Engineering (INJIISCOM) Vol. 7 No. 1 (2026): INJIISCOM: VOLUME 7, ISSUE 1, JUNE 2026 (ONLINE FIRST)
Publisher : Universitas Komputer Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34010/injiiscom.v7i1.15358

Abstract

The global transition to renewable energy presents both challenges and opportunities. This study uses data storytelling and Tableau’s visualization tools to explore energy trends from 2000 to 2019. Animated charts reveal rising renewable capacity and carbon emissions; notably, Brazil maintained low emissions despite economic growth. Geospatial analysis highlights China’s high emissions alongside rapid GDP growth, underscoring the vital need to balance development with sustainability (Mete, 2023). Furthermore, orbit charts expose regional gaps: Africa lags in wind and bioenergy, Asia leads in hydropower, and Europe excels in solar. These interconnected insights are crucial for policymakers to craft targeted strategies. Transforming complex data into visual stories supports informed decisions and drives innovative solutions toward a sustainable global energy future
Image Denoising Method Based on 3D Block Matching with Harmonic Filtering in Transform Domain Mizanur Rashid; Abdullah Ibne Sayed; Md Masud Rana
International Journal of Informatics, Information System and Computer Engineering (INJIISCOM) Vol. 7 No. 1 (2026): INJIISCOM: VOLUME 7, ISSUE 1, JUNE 2026 (ONLINE FIRST)
Publisher : Universitas Komputer Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34010/injiiscom.v7i1.15615

Abstract

Digital images are highly susceptible to noise during acquisition and transmission, requiring efficient preprocessing methods. Traditional denoising techniques often suffer from high computational complexity. To address this challenge, this paper presents a novel and efficient denoising algorithm that integrates wave-domain harmonic filtering with 3D block matching (BM3D). Similar 2D image blocks are grouped into a 3D array using the Euclidean distance approach for joint filtering. Following inverse transformation, wavelet decomposition isolates high-frequency noise. To prevent edge blurring and distortion, a Laplacian-Gaussian algorithm is incorporated to refine the diffusion model. Experimental results demonstrate that the proposed model significantly improves information protection, edge preservation, and computational speed
Utilizing Machine Learning Algorithms and SMOTE for Analyzing and Predicting Homicides: AI Hayder, Israa M.; Abdulnabi, Ghazwan; Younis, Hussain A.
International Journal of Informatics, Information System and Computer Engineering (INJIISCOM) Vol. 7 No. 1 (2026): INJIISCOM: VOLUME 7, ISSUE 1, JUNE 2026 (ONLINE FIRST)
Publisher : Universitas Komputer Indonesia

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

Abstract

This study analyzes homicide data in the United States from 1980 to 2014 using machine learning techniques to predict crime resolution and classify victim gender. The dataset, obtained from the FBI Supplementary Homicide Report, contains 638,454 records. Data preprocessing involved cleaning, converting categorical features to numerical values, and addressing class imbalance using SMOTE (Synthetic Minority Oversampling Technique).Various classification algorithms were applied, including Decision Tree and Naïve Bayes. The results showed that the Decision Tree model achieved 95% accuracy in predicting crime resolution and 85% accuracy in classifying victim gender, while Naïve Bayes reached 92% accuracy in crime resolution prediction. The findings highlight the effectiveness of machine learning in crime pattern analysis and prediction, aiding law enforcement in making more informed investigative decisions.
A Time-Adaptive Ensemble Framework for Multi-Year University Ranking Prediction Integrating Outlier-Aware Scoring and Hybrid Feature Selection Muhammad Aria Rajasa Pohan; Bagus Abdul Muiz
International Journal of Informatics, Information System and Computer Engineering (INJIISCOM) Vol. 7 No. 1 (2026): INJIISCOM: VOLUME 7, ISSUE 1, JUNE 2026 (ONLINE FIRST)
Publisher : Universitas Komputer Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34010/injiiscom.v7i1.16682

Abstract

University ranking prediction requires adaptive models to capture temporal dynamics and handle data anomalies. This study develops a time-adaptive ensemble framework integrating outlier-aware scoring and hybrid feature selection. Using Times Higher Education data (2011–2024), we applied windowed outlier detection with clipping and masking, alongside ANOVA, permutation importance, and SHAP values for dynamic feature selection. The framework ensembles linear moving-average, temporal Random Forest, and LSTM models via optimized weights. Rolling forecasts (2016–2024) yielded a low mean rank deviation of 1.2 positions and a Top-1000 classification accuracy of 0.96, outperforming single baselines. This robust, interpretable framework effectively supports strategic decision-making and resource allocation in higher education
Implementation of Local Binary Pattern Histogram for Automatic Locker System Tri Rahajoeningroem; Jana Utama; Bobi Kurniawan; Rodi Hartono; Afra Haniv Imtyramdhan; Suci Aulia
International Journal of Informatics, Information System and Computer Engineering (INJIISCOM) Vol. 7 No. 1 (2026): INJIISCOM: VOLUME 7, ISSUE 1, JUNE 2026 (ONLINE FIRST)
Publisher : Universitas Komputer Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34010/injiiscom.v7i1.16958

Abstract

This research presents the design and evaluation of an automatic locker system employing Local Binary Pattern Histogram (LBPH)-based facial recognition for secure, keyless access. The system integrates a webcam, Arduino Uno, relay module, and solenoid door lock, managed via a laptop interface. Performance was assessed through single-user variability testing under different facial accessories (66.7% recognition rate) and multi-user discrimination testing (50% accuracy across diverse profiles). Results indicate LBPH’s robustness against moderate occlusions but a notable decline with heavy facial coverage like helmets and masks. These findings emphasize the critical role of facial visibility in biometric reliability, offering practical insights for real-world security applications. 
Artificial Intelligence Project Management Methodologies: Insights from Field Experts in Saudi Arabia Walaa Alsumari; Omer Alrwais
International Journal of Informatics, Information System and Computer Engineering (INJIISCOM) Vol. 6 No. 2 (2025): INJIISCOM: VOLUME 6, ISSUE 2, DECEMBER 2025
Publisher : Universitas Komputer Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34010/injiiscom.v6i2.16127

Abstract

This study investigates the impact of project management (PM) methodologies on Artificial Intelligence (AI) project execution in Saudi Arabia, while addressing global AI PM challenges. Utilizing an exploratory approach combining literature reviews, multi-company case studies, and surveys with 31 local AI practitioners, this research analyzes operational workflows. Locally, Agile dominates, being applied in 60% of projects primarily focused on applied AI. Globally, projects grapple with unclear leadership, role ambiguity, inconsistent hybrid setups, and non-linear risks. These insights highlight the critical need for purpose-built frameworks tailored to AI’s inherent complexity, technical uncertainty, and ethical demands, paving the way for optimized regional AI initiatives.
Development of Mobile and Computer Application Based on Philippine Electrical Code (2017) for Single and Three-Phase Electrical Designing Jonas Martin; Russel Isaac Kabigting; Glend Jr. Lingad; Nathaniel Magtoto; Cenon Jr. III Yutuc; Louie G. Serrano
International Journal of Informatics, Information System and Computer Engineering (INJIISCOM) Vol. 6 No. 2 (2025): INJIISCOM: VOLUME 6, ISSUE 2, DECEMBER 2025
Publisher : Universitas Komputer Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34010/injiiscom.v6i2.16496

Abstract

This paper presents a mobile and computer application designed to automate electrical design and load scheduling, strictly complying with the 2017 Philippine Electrical Code (PEC). Developed to assist engineers, electricians, and students, the tool streamlines the planning of single-phase and three-phase systems for both residential and commercial buildings. It features vital computational resources, including voltage drop analysis, load calculation, and safe branch circuit distribution, which are critical for preventing overloads and ensuring energy efficiency. By bridging theoretical engineering principles with practical code compliance, this intuitive application significantly reduces manual calculation errors and enhances overall safety in electrical installations.
Cloud Computing Development of PT Divistant Teknologi Indonesia CRM System Using Microsoft Azure Husni Rofiq Muttaqin; Angga Setiyadi
International Journal of Informatics, Information System and Computer Engineering (INJIISCOM) Vol. 5 No. 2 (2024): INJIISCOM: VOLUME 5, ISSUE 2, DECEMBER 2024
Publisher : Universitas Komputer Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34010/injiiscom.v5i2.17363

Abstract

Cloud computing technology offers modern solutions by providing integrated virtual resources, including storage and infrastructure. Based on problem identification at PT. Divistant Teknologi Indonesia, this research aims to implement cloud computing using Microsoft Azure services for the company's CRM application. The implementation includes backup and recovery integration to enhance the application's availability, security, and scalability. Research steps involve problem identification, system requirements analysis, implementation, and testing. Results show that Microsoft Azure supports backup and recovery while significantly improving performance. The case study at PT. Divistant Teknologi Indonesia demonstrates that migrating the CRM application to cloud computing meets the need for improved data availability and security, addressing issues previously encountered with shared hosting. This research positively impacts CRM performance and minimizes the risk of critical data loss, supporting smooth business operations and maintaining strong customer relationships.
Implementation of Clusters Utilizing Resources Through High-Performance Computing Daffa Surya Mahardhika; Angga Setiyadi
International Journal of Informatics, Information System and Computer Engineering (INJIISCOM) Vol. 5 No. 2 (2024): INJIISCOM: VOLUME 5, ISSUE 2, DECEMBER 2024
Publisher : Universitas Komputer Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34010/injiiscom.v5i2.17365

Abstract

The growing data volumes due to technological advancements and digitalization challenge single computing systems in terms of scalability. These systems often struggle with increasing workloads, leading to reduced performance and processing limitations. Additionally, electronic waste (e-waste) is a rising concern, as many functional computers are underutilized, contributing to environmental issues. This study proposes the use of cluster computing and High-Performance Computing (HPC) as solutions. Cluster computing aggregates computational power across multiple nodes to enhance capacity, while HPC optimizes performance for tasks requiring extensive data processing. Through a systematic approach focused on implementing PelicanHPC, the research demonstrates that cluster computing and HPC improve scalability and performance, particularly in Virtual Machines (VMs), and promote more efficient resource management. These solutions also help reduce environmental impact and cost inefficiencies. However, tests reveal that cluster performance remains suboptimal compared to physical computers, primarily due to network and hardware limitations. Future improvements should focus on enhancing hardware, network infrastructure, and software optimization

Page 11 of 15 | Total Record : 146

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