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Journal of Technology Informatics and Engineering
Published by Universitas Sains Dan Teknologi Komputer
ISSN : 29619068     EISSN : 29618215     DOI : 10.51903
Core Subject : Science,
Computer Science & IT
Power Engineering Telecommunication Engineering Computer Engineering Control and Computer Systems Electronics Information technology Informatics Data and Software engineering Biomedical Engineering
Arjuna Subject : Ilmu Komputer (semua kategori) - Sistem Informasi
Articles 235 Documents
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Optimization of Operational Risk in Ecuadorian Mining Using FMEA and Weibull Distribution to Reduce Critical Equipment Failures José David Barros Enriquez; Carlos David Amaya-Jaramillo; Danny Alexander Rivas Sierra; Edgar Atilio Suárez Bardelline; Milton Iván Villafuerte López; Ángel Moisés Avemañay Morocho
Journal of Technology Informatics and Engineering Vol. 5 No. 2 (2026): AUGUST | JTIE : Journal of Technology Informatics and Engineering
Publisher : University of Science and Computer Technology

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51903/jtie.v5i2.489

Abstract

Designing and implementing a preventive maintenance plan in the Ecuadorian mining industry requires systematic methodologies to reduce failures in crushing and grinding machines, improving operational availability and productivity. The main problem identified is reliance on corrective maintenance, with no historical failure records, which causes unexpected shutdowns. This study applies the FMEA (Failure Mode and Effects Analysis) methodology to evaluate the criticality of equipment using the Risk Priority Number (NPR), complemented with the Weibull distribution to model useful lifetimes. Key equipment such as the ball mill and jaw crusher at a mining beneficiation plant were analyzed. The results indicate that 25% of the subcomponents have NPR > 300 (unacceptable risk), mainly concentrated in structural elements subjected to severe operating conditions. A scenario analysis based on the fitted Weibull models and on reductions reported in the literature projects that implementation of the proposed preventive plan would reduce unplanned shutdowns by 40–50%, raising operational availability from the current 73% to approximately 90%; these figures are model-based projections and not post-implementation measurements. Weibull integration provides a robust statistical framework for the transition from reactive maintenance to predictive schemes, optimizing intervention intervals and extending the life of critical assets.
Challenges in Intra-Data Sharing in Savings and Credit Cooperative Societies in Uganda: A Case of Wazalendo SACCO Shamusi Nakajubi; Michael Adelani Adewusi; Margaret Kareyo; Habib Shehu
Journal of Technology Informatics and Engineering Vol. 5 No. 2 (2026): AUGUST | JTIE : Journal of Technology Informatics and Engineering
Publisher : University of Science and Computer Technology

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51903/jtie.v5i2.509

Abstract

Savings and Credit Cooperative Societies (SACCOs) increasingly depend on effective internal data sharing to support transparent governance, efficient services, informed decision-making, and financial inclusion. However, fragmented information practices, limited technological infrastructure, and governance constraints continue to hinder reliable data exchange within many SACCOs in Uganda. This study investigates the types of data shared, the mechanisms used for intra-organizational data exchange, and the major challenges affecting data-sharing practices at Wazalendo SACCO. A qualitative single-case study was conducted involving 30 participants, comprising 10 management personnel and 20 ordinary members. Data were collected through semi-structured interviews and analyzed thematically using NVivo 14, generating 187 initial codes that were refined into 42 focused codes and three major thematic clusters. The findings identified transparency deficits as the most prominent challenge, reported by 80% of participants, followed by standardization gaps (75%) and security concerns (65%). Digital systems accounted for 42.3% of identified sharing methods, while physical/manual methods represented 38.9%, revealing continued dependence on fragmented hybrid practices. The study's novelty lies in interpreting these challenges not as isolated technical limitations but as an interconnected socio-technical governance problem. It introduces the Governance-Capacity Paradox, demonstrating that transparency failures may arise from limited institutional capacity to process and communicate data effectively, thereby providing a broader framework for improving SACCO data governance.
A Systematic Literature Review on Software Testing Prediction Models Job Onyinkwa Osoro; John Ndia
Journal of Technology Informatics and Engineering Vol. 5 No. 2 (2026): AUGUST | JTIE : Journal of Technology Informatics and Engineering
Publisher : University of Science and Computer Technology

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51903/jtie.v5i2.526

Abstract

Software testing prediction models play a critical role in improving software quality, reducing faults, and optimizing testing. Although past studies show that these models have improved over time, little focus has been given to them. Many studies rely on limited datasets that do not fully capture the complexity of real software systems, which limits how well the models can generalize. There is also insufficient evidence on how these models use existing datasets in practical, real-life settings, since most evaluations are conducted under controlled or experimental conditions. In addition, key aspects such as interpretability, generalizability, and practical usability are still not adequately addressed, which reduces trust and slows down adoption in practice. This study presents a systematic literature review of recent empirical research on predictive approaches in software testing, focusing on machine learning, deep learning, and hybrid techniques. A structured methodology was used, including clearly defined inclusion and exclusion criteria, systematic searches across major academic databases, quality assessment, and data extraction from 22 selected studies. The analysis considered model types, datasets, feature methods, evaluation metrics, and methodological approaches. The findings show a shift from traditional statistical models to advanced artificial intelligence techniques, including graph neural networks, contrastive learning, and deep fuzzy clustering. In addition, optimization and data balancing techniques improve predictive performance, while explainable artificial intelligence enhances model interpretability. However, challenges such as limited cross-project generalization and insufficient industrial validation still exist. 
A Critical Evaluation of IoMT Security Frameworks in Resource-Constrained Healthcare Systems Winfred Arinaitwe; Kareyo Margaret; Olusegun Ganiyu
Journal of Technology Informatics and Engineering Vol. 5 No. 2 (2026): AUGUST | JTIE : Journal of Technology Informatics and Engineering
Publisher : University of Science and Computer Technology

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51903/jtie.v5i2.528

Abstract

The increasing adoption of Internet of Medical Things (IoMT) technologies within healthcare systems has significantly improved real-time patient monitoring, clinical decision-making, and health information exchange. However, the integration of interconnected medical devices has simultaneously expanded the cybersecurity threat landscape, particularly within resource-constrained healthcare environments characterized by limited technical capacity, inadequate governance mechanisms, and insufficient cybersecurity expertise. This study critically evaluated existing cybersecurity frameworks for Information Sharing-Enabled IoMT systems, including the National Institute of Standards and Technology (NIST) Cybersecurity Framework, COBIT, Critical Information Infrastructure Protection (CIIP), and the IoT Security Foundation (IoTSF) Security Compliance Framework. The study employed a mixed-method exploratory research design combining systematic literature review, comparative framework analysis, and descriptive quantitative assessment conducted at St. Francis Hospital Nsambya and Uganda Martyrs Hospital Lubaga. Quantitative findings were analyzed using descriptive statistics, while qualitative findings were synthesized thematically. The findings revealed that existing frameworks provide fragmented security solutions that inadequately integrate technical security controls, governance mechanisms, healthcare operational requirements, and socio-behavioral determinants necessary for secure IoMT implementation in low-resource healthcare contexts. Based on the identified gaps, the study proposes an integrated healthcare-oriented IoMT cybersecurity framework that combines governance, technical safeguards, compliance mechanisms, and socio-behavioral dimensions to strengthen secure information sharing and cybersecurity resilience within resource-constrained healthcare institutions. The study concludes that context-specific and integrated cybersecurity frameworks are essential for enhancing secure information sharing and sustainable IoMT adoption in developing healthcare systems.
Bridging the Conceptual Understanding Gap in Calculus for Civil Engineering: The Role of AI in Mediating Theorems and Definitions Haris Jamaludin
Journal of Technology Informatics and Engineering Vol. 5 No. 2 (2026): AUGUST | JTIE : Journal of Technology Informatics and Engineering
Publisher : University of Science and Computer Technology

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51903/jtie.v5i2.533

Abstract

This study aims to analyze the role of Artificial Intelligence (AI) in bridging the conceptual understanding gap in calculus among civil engineering students, focusing on how AI mediates the relationship between theorems and formal definitions. A Systematic Literature Review (SLR) following the PRISMA 2020 protocol was conducted. Literature searches were performed on Scopus, ScienceDirect, Google Scholar, DOAJ, and SINTA for the period 2020–2025. From 187 initially identified articles, 29 articles met the inclusion criteria after full-text screening. The findings reveal that AI has significant potential to bridge the conceptual gap through three primary mechanisms: (1) providing instant feedback and step-by-step explanations, (2) personalizing learning according to student comprehension levels, and (3) offering interactive visualizations of abstract concepts. However, AI still has limitations in complex reasoning and theorem proving, as well as risks of epistemic offloading that can reduce students' independent problem-solving abilities. The implications emphasize the importance of a blended approach with clear instructional guidance and adaptive assessment redesign to support effective AI integration in calculus learning within civil engineering programs, particularly in the Indonesian context.
Intelligent IoT-based Alert System for Monitoring Drip of Intravenous Fluids (IV) and Reverse Blood Flow Douglas Omwenga Nyabuga; Nadia Iradukunda
Journal of Technology Informatics and Engineering Vol. 5 No. 2 (2026): AUGUST | JTIE : Journal of Technology Informatics and Engineering
Publisher : University of Science and Computer Technology

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51903/jtie.v5i2.550

Abstract

Intravenous (IV) therapy is widely used in hospitals for administering fluids, medications, and nutrients, yet conventional manual monitoring may lead to delayed intervention, fluid depletion, reverse blood flow, and other patient-safety risks. This study proposes an Intelligent IoT-Based IV Drip System (IBIVDS) for real-time monitoring of IV fluid levels and reverse blood flow. The system integrates a NodeMCU ESP8266 microcontroller, load-cell weight sensor, LCD, buzzer, and Node-RED platform, supported by Arduino IDE, FreeRTOS, Python, HTML, JavaScript, and Django. The system continuously monitors IV fluid status and automatically sends alerts to healthcare personnel when predefined thresholds are reached. Notifications are generated at 50% remaining fluid and again at 20%, with the latter accompanied by an audible buzzer alert. Experimental evaluation showed that the proposed system achieved an average notification delay of approximately 0.01 min, substantially lower than the approximately 1 min delay reported for the baseline Smart Infusion Pump System under comparable conditions. The system also provides room and patient identifiers to facilitate rapid clinical response. The novelty of this study lies in integrating real-time IV level monitoring, reverse blood-flow prevention, multi-threshold alerting, and remote IoT-based notification within a single low-cost monitoring framework. The findings indicate that IBIVDS can improve the responsiveness, reliability, and consistency of IV therapy monitoring while reducing dependence on continuous manual observation.
Enhancing Conversion Of Flared Natural Gas Into Liquefied Petroleum Gas For Industrial And Domestic Energy Use Godloves Tondie Nonju; Hezekiah-Braye Oritom
Journal of Technology Informatics and Engineering Vol. 5 No. 2 (2026): AUGUST | JTIE : Journal of Technology Informatics and Engineering
Publisher : University of Science and Computer Technology

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51903/jtie.v5i2.551

Abstract

Gas flaring remains a major environmental and economic challenge in Nigeria, resulting in the loss of valuable hydrocarbon resources and contributing to greenhouse gas emissions. This study evaluates the technical and economic feasibility of converting flared natural gas into Liquefied Petroleum Gas (LPG) for domestic and industrial energy utilization in the Niger Delta region of Nigeria. Process simulation was carried out using Aspen HYSYS Version 3.2 with the Peng–Robinson Equation of State to model gas dehydration, cryogenic Natural Gas Liquids (NGL) recovery, and hydrocarbon fractionation processes. A flare gas feed stream of 34.83 million standard cubic feet per day (MMScfd) was subjected to Triethylene Glycol (TEG) dehydration, cryogenic cooling, and separation through demethanizer, deethanizer, and depropanizer columns. Simulation results indicated that the proposed process could recover approximately 3.87 MMScfd of Liquefied Petroleum Gas, 4.01 MMScfd of ethane, and 25.10 MMScfd of methane. The recovered LPG contained propane and butane predominantly and corresponded to an annual production of approximately 74.96 million kilograms. Economic analysis showed an estimated annual revenue of ₦18.74 billion at an LPG selling price of ₦250 per kilogram. The results demonstrate that flare gas can be effectively converted into commercially valuable products while reducing environmental pollution associated with routine gas flaring. Although profitability depends on assumptions regarding market conditions and operating costs, the study confirms that LPG recovery from flared gas represents a technically feasible and economically attractive option for enhancing energy utilization and supporting sustainable development in Nigeria.
Development and Validation of a Pyrolyzer Model for Ethylene Production from Natural Gas Liquids Using Mass and Energy Balance Approaches Godloves Tondie Nonju; Braye Oritom
Journal of Technology Informatics and Engineering Vol. 5 No. 2 (2026): AUGUST | JTIE : Journal of Technology Informatics and Engineering
Publisher : University of Science and Computer Technology

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51903/jtie.v5i2.552

Abstract

Ethylene is one of the most important petrochemical feedstocks used in the production of plastics, synthetic fibers, solvents, and numerous industrial chemicals. The increasing global demand for ethylene has necessitated the development of efficient and sustainable production technologies. Natural gas liquids (NGLs), particularly ethane, have emerged as attractive feedstocks for ethylene production because of their high hydrogen-to-carbon ratio, availability, and favorable cracking characteristics. This study developed a pyrolyzer model for ethylene production from natural gas liquids using fundamental mass and energy balance approaches. The model was implemented in MATLAB to simulate the performance of a tubular pyrolysis reactor under varying operating conditions. Reactor performance was evaluated using fractional conversion, reactor temperature, reactor volume, pressure drop, space time, and space velocity as key performance indicators. The simulation results showed that reactor temperature increased from 894 K to 1063 K as conversion increased from 10% to 90%, while reactor volume, pressure drop, and space time exhibited corresponding increases. Conversely, space velocity decreased with increasing conversion due to longer residence time requirements. Model validation was conducted through comparison with published benchmark literature data. The validation results showed prediction deviations below 5% for major reactor performance parameters, indicating good agreement between model outputs and reported industrial operating conditions. The developed model provides a reliable preliminary engineering tool for pyrolysis reactor analysis, process evaluation, and future optimization studies involving ethylene production from natural gas liquids
Detection of Locust Breeding and Feeding Zones Using Hyperspectral Signatures and Environmental Variables in South Nyanza Region, Kenya Emmanuel Ochako Manyange; Juliana Kamaghe; Lilian Mutalemwa
Journal of Technology Informatics and Engineering Vol. 5 No. 2 (2026): AUGUST | JTIE : Journal of Technology Informatics and Engineering
Publisher : University of Science and Computer Technology

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51903/jtie.v5i2.554

Abstract

Desert locust infestations continue to threaten agricultural productivity, food security, and environmental sustainability across East Africa. Conventional locust surveillance approaches remain largely dependent on field-based monitoring systems that are reactive, labor-intensive, and constrained by limited spatial coverage. This study investigated the hyperspectral signatures and environmental variables associated with locust breeding and feeding zones in South Nyanza, Kenya, using an Artificial Intelligence (AI)-driven hyperspectral remote sensing framework. Environmental variables including vegetation indices, soil moisture, rainfall, temperature, vegetation stress indicators, and hyperspectral bands were analyzed using correlation analysis, binary logistic regression, Random Forest classification, and variable importance ranking. Correlation analysis revealed weak relationships between individual environmental indicators and historical locust occurrence, with all predictors demonstrating statistically insignificant relationships (p > 0.05). Logistic regression findings similarly indicated that no individual environmental variable independently predicted locust occurrence. However, Random Forest classification achieved a classification accuracy of 75.33% with an Out-of-Bag error rate of 24.67%, suggesting moderate predictive capability when multiple variables were integrated. Variable importance analysis identified rainfall, soil moisture, temperature, and vegetation-related indicators as dominant predictors. The findings suggest that locust breeding and feeding habitats are influenced by complex multidimensional interactions rather than isolated environmental indicators. The study demonstrates the practical potential of AI-driven hyperspectral frameworks for strengthening locust surveillance and early warning systems within emerging high-risk agricultural regions.
Adaptive Scalability Optimization for Blockchain-Powered Academic Credential Repositories Using Intelligent Caching and Metadata-Aware Sharding Blessing Emmanuel Oladele; Adekunle Olugbenga Ejidokun; Chukwuemeka Odi Agwu
Journal of Technology Informatics and Engineering Vol. 5 No. 2 (2026): AUGUST | JTIE : Journal of Technology Informatics and Engineering
Publisher : University of Science and Computer Technology

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51903/jtie.v5i2.555

Abstract

Academic credential verification remains difficult for institutions because manual checks are slow, fragmented, and vulnerable to fraud. Blockchain can improve trust by anchoring credential proofs, but repeated verification requests and growing off-chain repositories can still create performance bottlenecks. This study presents an adaptive blockchain-powered academic credential repository that combines off-chain MySQL storage, Solidity-based hash anchoring, Redis verification caching, and metadata-aware sharding. Full academic records are not stored on-chain or in Redis; only credential hashes, verification responses, and related metadata are used for trust validation and performance optimization. A CodeIgniter 4 prototype was evaluated using synthetic academic credential records and controlled workloads of 1,000, 5,000, and 10,000 verification requests under fresh, mixed, and repeated access patterns. The results show that Redis caching substantially reduced repeated blockchain queries, especially under mixed and repeated workloads, while metadata-aware sharding improved repository organization and supported more targeted credential retrieval. Sepolia testnet validation confirmed smart-contract feasibility, including issuance, verification, revocation, gas use, confirmation time, and event evidence, but was treated separately from scalability testing. The findings indicate that combining blockchain trust anchoring with cache-aware verification and metadata-based repository partitioning can improve the scalability of academic credential repositories, provided that cache consistency, revocation handling, and deployment limitations are carefully managed.

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