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Rizky Jumansyah
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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 145 Documents
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Testing Deep Learning Methods to Predict Ransowmare Activity from Hybrid Analysis Veach, Alexander M.; Abualkibash, Munther
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

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Abstract

This article focuses on using deep learning methods to predict ransomware from hybrid analysis samples. Other similar research is analysed to understand the common methods of detection used to predict ransomware using various methods of analysis. By using this knowledge an experiment is created which tests the performance of a model created from hybrid analysis of ransomware samples. The training dataset used is made up of more than five hundred samples containing 38 different ransomware families and benign Windows program samples. The resultant model was then tested against a dataset include ransomware families not represented in the training dataset, which showed a decrease in performance. These results were then compared to other research’s reported results which highlights potential issues in the way artificial intelligence models are tested and reported. The paper then proposes a focus on more complex methods of prediction, and other potential methods to ensure the models created are externally as effective as they report.
Visualizing Global Energy Transition using Tableau pattanaik, Priyadarshini; Phumiphol, Punyisa; Sarhadi, Sanchit
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

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Abstract

The global shift towards renewable energy sources presents complex challenges and opportunities for sustainability. However, conventional energy development often overlooks critical factors such as renewable energy capacity, carbon emissions, and economic indicators. This study employs a problem-oriented, solution-driven framework that leverages data storytelling and visualization techniques, utilizing Tableau's features and live connections for real-time data integration. We aim to comprehensively understand the global energy landscape, facilitating informed decision-making for a sustainable future. Analysis of global sustainable energy trends from 2000 to 2019 reveals significant insights. The animated line chart depicts a consistent rise in both renewable energy capacity and carbon emissions, with Brazil demonstrating stable emissions alongside increased renewable energy capacity. Geospatial analysis highlights China's persistent high carbon emissions and GDP growth, underscoring the importance of balancing economic growth with environmental responsibility. Additionally, the four-orbit charts illustrate regional disparities, with Asia leading in hydropower and solar energy, Europe in bioenergy and wind, and Africa lagging. The insights are invaluable for policymakers and business leaders seeking to make strategic decisions for a more sustainable future. Through well-designed data visualization, stakeholders gain diverse perspectives on complex global patterns, enabling them to formulate tailored strategies and allocate resources effectively. This approach fosters enhanced understanding, collaboration, and innovation in pursuing sustainability.
Image Denoising Method Based on 3D Block Matching with Harmonic Filtering in Transform Domain Rashid, Mizanur; Sayed, Abdullah Ibne; Rana, Md Masud
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

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Abstract

Today, I will explain an image denoising method based on 3D block matching with harmonic filtering in the transform domain. This topic is important because digital images are susceptible to noise during acquisition, storage, and transmission. Image denoising is crucial in pre-processing and is a key research area in digital image processing and computer vision. Traditional denoising techniques face limitations such as high computational complexity, so combining multiple methods is more effective. The integration of wave-domain harmonic filtering and 3D block matching (BM3D) introduces a new and efficient denoising algorithm. The Euclidean distance approach is used to group similar 2D image blocks into a 3D array. The inverse transformation reconstructs the image, followed by wavelet decomposition to filter high-frequency noise. To prevent edge blurring, the Laplacian-Gaussian algorithm is applied to refine the diffusion model. Finally, wavelet reconstruction is performed to approximate the original image. Experimental results demonstrate that this approach improves information protection and processing speed, making it highly effective in practice.
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

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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 Pohan, Muhammad Aria Rajasa; Muiz, Bagus Abdul
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

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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. We collected data from Times Higher Education from 2011 to 2024, applying windowed outlier detection with clipping and masking, and using ANOVA F-tests, permutation importance, and SHAP values to select dynamic feature subsets. The framework trains linear moving-average, temporal Random Forest, and LSTM models, then ensembles their forecasts using dynamically optimized weights. Experimental results on rolling forecasts (2016–2024) demonstrate a mean rank deviation of 1.2 positions, Top-1000 classification accuracy of 0.96, and reduced MAE and RMSE compared to single-model baselines. SHAP-based analyses reveal evolving feature importance across time windows, highlighting the impact of changing indicators. The findings indicate that integrating outlier handling, dynamic feature selection, and ensemble learning enhances prediction robustness and interpretability. This framework can support strategic decision-making, policy formulation, and resource allocation in higher education
Implementation of Local Binary Pattern Histogram for Automatic Locker System Rahajoeningroem, Tri; Utama, Jana; Kurniawan, Bobi; Hartono, Rodi; Imtyramdhan, Afra Haniv; Aulia, Suci
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

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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 control. The system integrates a Logitech C270 webcam, Arduino Uno microcontroller, relay module, and solenoid door lock, with processing performed via a laptop interface. Performance was assessed through two protocols: a single-user variability test under different facial accessories, achieving a 66.7% recognition rate, and a multi-user discrimination test, which yielded 50% accuracy across diverse facial profiles. Results indicate LBPH’s robustness against moderate occlusions but notable decline with heavy facial coverage such as helmets and masks. These findings highlight the critical role of facial visibility in biometric reliability and offer practical insights for implementing facial recognition systems in real-world security applications
Artificial Intelligence Project Management Methodologies: Insights from Field Experts in Saudi Arabia Alsumari, Walaa; Alrwais, Omer
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

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Abstract

This study investigates the impact of project management (PM) methodologies on the execution of Artificial Intelligence (AI) projects within Saudi Arabia, while also addressing challenges related to AI PM on a global scale. It explores unique difficulties in managing AI projects both locally and internationally. An exploratory research approach is used, drawing on case studies and literature to analyze AI project phases in leading companies. Surveys with AI experts and project managers in Saudi Arabia further illustrate current practices. Agile emerged as the most widely used methodology locally, applied in 60% of projects, most of which focused on applied AI. Globally, AI projects face issues such as unclear leadership, role ambiguity, inconsistent hybrid approaches, and nonlinear risks. These findings highlight the need for frameworks tailored to the complexity, uncertainty, and ethical dimensions of AI projects. This research contributes fresh insights into the field, supporting the development of targeted methodologies for managing AI initiatives and identifying areas for future work.
Development of Mobile and Computer Application Based on Philippine Electrical Code (2017) for Single and Three-Phase Electrical Designing Martin, Jonas; Kabigting, Russel Isaac; Lingad , Glend Jr.; Magtoto, Nathaniel; Yutuc, Cenon Jr. III; Serrano, Louie G.
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

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Abstract

This paper gives the design and development of an overall application for Electrical Design and Load Scheduling using the 2017 edition of the Philippine Electrical Code (PEC). The software is specially developed to aid engineers, electricians, and students in automating and streamlining the task of designing electrical systems of residential and commercial buildings. It offers essential computational resources that assist in PEC compliance as well as improved efficiency and precision in system planning. The program accommodates both Single-Phase and Three-Phase electrical systems and features an extensive array of capabilities, such as voltage drop analysis, load calculation, demand factor consideration, and safe branch circuit distribution. These capabilities are essential in avoiding overload, ensuring energy efficiency, and enhancing safety in electrical installations. The user interface is intuitive and easy to use, making it possible for even inexperienced individuals to carry out complicated electrical calculations with ease. Through the combination of smart computation and code compliance, the app combines theoretical knowledge with actual practice in electrical engineering.
Cloud Computing Development of PT Divistant Teknologi Indonesia CRM System Using Microsoft Azure Muttaqin, Husni Rofiq; Setiyadi, Angga
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

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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 Mahardhika, Daffa Surya; Setiyadi, Angga
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

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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 : 145

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