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Journal of Technology and Computer (JOTECHCOM)
Published by PT. Technology Laboratories Indonesia
ISSN : -     EISSN : 30480477     DOI : -
Core Subject : Science,
Computer Science & IT
The Journal of Technology and Computer (JOTECHCOM) brings together researchers, academics (faculty and students), and industry practitioners to develop the field, discuss new trends and opportunities, exchange ideas and practices, and promote cross-disciplinary and cross-domain collaboration. JOTECHCOM aims to integrate all scientific disciplines, such as computer science, information systems, informatics, information technology, data science, databases, artificial intelligence, data mining, decision support systems, expert systems, and other related disciplines. This journal is published by PT. Technology Laboratories Indonesia (TechnoLabs) Publisher division. Accepted papers will be available online (free open access).
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 73 Documents
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Development of Robot Control System Based on Machine Learning at Rumah Robot Indonesia Prayudha, Rendy Rizky; Renata , Silvia
Journal of Technology and Computer Vol. 2 No. 1 (2025): February 2025 - Journal of Technology and Computer
Publisher : PT. Technology Laboratories Indonesia (TechnoLabs)

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Abstract

The development of robotics and machine learning technology has opened up new opportunities in the development of smarter and more adaptive robot control systems. Rumah Robot Indonesia (Robonesia) as one of the robotics innovation centers in Indonesia requires a robot control system that is capable of operating autonomously and responsive to its environment. robot that is able to operate autonomously and responsive to its environment. This research aims to develop a machine learning-based robot control system that can improve the robot's ability to perform complex tasks, such as navigation, object recognition, and interaction with the environment. The research method involves collecting data from the robot's operational environment, training a machine learning model using algorithms such as the such as Convolutional Neural Network (CNN) for object recognition and Reinforcement Learning (RL) for navigation, and testing the system in simulated and real-world scenarios. The datasets used include images, sensor data, and relevant environmental information. System performance evaluation is performed based on the metrics of object recognition accuracy, response speed, and navigation success, and navigation success. The results show that the robot control system based on machine learning-based robot control system is able to achieve object recognition accuracy of 95.2% and navigation success rate of navigation success rate of 92.8% in a dynamic environment. The system also shows rapid response to environmental changes, with an average response time of 0.8 seconds. This success demonstrates that the integration of machine learning in robot control systems can improve the robot's ability to operate autonomously. improve the robot's ability to operate autonomously and adaptively.
The Influence of Understanding Level on the Development of Mobile-Based E-Money Technology Services (OVO Case Study) Jawa, Paimen; Syahputra, Dinur; Rambe, Aripin
Journal of Technology and Computer Vol. 2 No. 2 (2025): May 2025 - Journal of Technology and Computer
Publisher : PT. Technology Laboratories Indonesia (TechnoLabs)

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Abstract

This study examines the influence of understanding level (X₁) and perceived usefulness (X₂) on the adoption of mobile-based e-money services, using OVO in Sumbul District, Indonesia, as a case study. Employing a quantitative causal-comparative design grounded in the Unified Theory of Acceptance and Use of Technology (UTAUT2), the research surveyed 100 OVO users through stratified random sampling. Results indicate that both understanding (p=0.033, β=0.211) and usefulness (p=0.000, β=0.385) significantly enhance usage interest (Y), with their combined effect further validated by regression analysis (F-statistic=14.457). Demographic trends reveal higher adoption among tech-savvy males (52%) aged 36–45 (53%) with bachelor’s degrees (54%), underscoring the role of education and age in digital payment adoption. The study highlights practicality, time efficiency, and long-term benefits as key drivers, aligning with technology acceptance theories. However, barriers persist in rural areas and among less-educated populations, suggesting the need for targeted financial literacy programs and infrastructure improvements. These findings offer actionable insights for policymakers, fintech providers, and businesses aiming to accelerate Indonesia’s transition to a cashless economy. The research contributes to fintech adoption literature by contextualizing behavioral factors in emerging markets, emphasizing the interplay of cognitive, attitudinal, and demographic variables in e-money adoption.
Selection of Outstanding Students Using the WASPAS Method (Case Study: Battuta University) Umri, Chairil
Journal of Technology and Computer Vol. 2 No. 2 (2025): May 2025 - Journal of Technology and Computer
Publisher : PT. Technology Laboratories Indonesia (TechnoLabs)

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This study implements the WASPAS method to improve the selection process of outstanding students at Battuta University. Traditional evaluation methods often suffer from subjectivity and inconsistency when assessing multiple criteria, such as academic performance, research contributions, leadership, and community service. The WASPAS method addresses these limitations by systematically integrating the Weighted Sum Model and Weighted Product Model, ensuring a balanced and transparent ranking system. Using a quantitative descriptive approach, this research evaluates 10 shortlisted students based on five weighted criteria: GPA (0.35), research publications (0.25), leadership (0.20), community service (0.12), and competition achievements (0.08). The results show that WASPAS produces a reliable composite score (Qi), with the top-ranked student (S5) achieving a score of 0.888. Sensitivity analysis confirms the robustness of the rankings, as variations in criterion weights (±20%) only minimally affected the top candidates. Compared to Battuta University’s existing manual evaluation system, WASPAS enhances objectivity, traceability, and fairness by reducing human bias. The study highlights the potential of WASPAS as a decision-support tool in higher education, particularly for merit-based selections. Future research could expand this framework to scholarship allocations, faculty evaluations, or adaptive weighting systems using machine learning. By adopting WASPAS, universities can promote data-driven, transparent, and holistic student assessments, ultimately fostering academic excellence and institutional credibility.
AI for MSMEs: Smart Solutions to Optimize Operations and Marketing Manza, Yuke
Journal of Technology and Computer Vol. 2 No. 2 (2025): May 2025 - Journal of Technology and Computer
Publisher : PT. Technology Laboratories Indonesia (TechnoLabs)

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Abstract

This research investigates the transformative potential of Artificial Intelligence (AI) for Micro, Small, and Medium Enterprises (MSMEs) in optimizing operations and marketing. Employing a mixed-methods approach—combining quantitative surveys (n=100+) and in-depth qualitative interviews (n=15-20)—the study reveals a significant positive correlation between AI adoption and enhanced operational efficiency, evidenced by average reductions of 25% in data processing time and 15% in inventory management. Furthermore, AI substantially boosts marketing effectiveness, leading to a 30% increase in audience reach and an 18% rise in sales conversion rates. Despite these clear benefits, MSMEs face considerable barriers to AI adoption, primarily financial constraints (65% of respondents) and limited digital literacy (58%). To address these challenges, the research proposes an affordable and easy-to-implement AI framework emphasizing cloud-based solutions (SaaS) and comprehensive training programs. The findings underscore AI as a crucial driver for MSME competitiveness and recommend concerted efforts from government and industry stakeholders to foster a supportive ecosystem. This study bridges the digital divide, offering evidence-based recommendations for resilient, efficient, and sustainable MSMEs in the digital era.
Classification of Bank Syariah Indonesia (BSI) Customer Sentiments on Twitter Using Naive Bayes Algorithm Alfarisi, Muhammad; Munawir, Munawir; Samsuddin, Samsuddin
Journal of Technology and Computer Vol. 2 No. 2 (2025): May 2025 - Journal of Technology and Computer
Publisher : PT. Technology Laboratories Indonesia (TechnoLabs)

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Abstract

Bank Syariah Indonesia (BSI) is an active topic of conversation on Twitter, but customer sentiment patterns towards the bank's services have not been quantitatively analyzed. This study performs positive and negative sentiment classification on 24,401 Indonesian tweets collected on May 17, 2023. The preprocessing stage includes text cleaning, nonstandard word normalization, stopword removal, and stemming with the Sastrawi library. The data was labeled based on the affection dictionary and verified manually. Text representation is done with word frequency-based unigram-bigram method using CountVectorizer, then trained using Multinomial Naive Bayes algorithm. Evaluation of the model against test data resulted in an accuracy of 94%, with precision, recall, and F1-score of 93% each. Words that commonly appear in positive sentiments include easy and fast service, while negative sentiments are dominated by the words error and maintenance. These results show that the Naive Bayes-based approach and word frequency representation are effective for rapid analysis of public opinion towards BSI on social media.
Application of the Analytic Hierarchy Process (AHP) Method in Supporting Go Green Policies for Environmental, Economic, Social and Technological Sustainability Wahyuni, Dewi; Sridewi, Nurmala
Journal of Technology and Computer Vol. 2 No. 2 (2025): May 2025 - Journal of Technology and Computer
Publisher : PT. Technology Laboratories Indonesia (TechnoLabs)

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Abstract

The Go Green policy aims to create environmental, social and economic sustainability through better management of natural resources and technology. Decision making in implementing the Go Green policy requires a method that can handle various interrelated criteria. One effective method is the Analytic Hierarchy Process (AHP), which allows the selection of policy alternatives by taking into account the main factors systematically and objectively. This research applies the AHP method to evaluate Go Green policy alternatives based on four main criteria: environmental, economic, social and technological sustainability. The analysis results show that the use of renewable energy is the best policy that can support the implementation of Go Green with a score of 0.50.
Performance Evaluation of Information Technology Governance using the COBIT 5 Framework at PT. Global Bangunan Jaya Fajrillah, Fajrillah; Hasyim Syarif, Shamir; Hiya, Nirmadarningsih; Zulfa, Ira; Eliyin, Eliyin
Journal of Technology and Computer Vol. 2 No. 2 (2025): May 2025 - Journal of Technology and Computer
Publisher : PT. Technology Laboratories Indonesia (TechnoLabs)

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Abstract

PT. Global Bangunan Jaya Riau is a company focused on providing essential building materials in Riau Province. This is driven by the rapid development in the region, including office infrastructure and road networks. The company was established to support the significant growth of construction in Riau, and due to its high competitiveness, it must maintain strong performance both in terms of product quality and customer service. To ensure that the quality and services provided by PT. Global Bangunan Jaya Riau align with the company's vision and mission, it is essential to evaluate the extent to which the organization’s IT goals support its overall business objectives. This evaluation is carried out using the COBIT 5 framework, focusing on the EDM (Evaluate, Direct, Monitor) domain. The research process involves three stages of analysis: assessing the current IT capability level, identifying the desired capability level, and analyzing the gap between the two. Data for this study were collected through questionnaires. The results indicate that the IT processes at PT. Global Bangunan Jaya Riau have been effectively implemented, achieved, and well-managed.
Improving Information System Audit Security through Artificial Intelligence (AI) Technology Integration Fajrillah, Fajrillah; Hasyim Syarif, Shamir; Hiya, Nirmadarningsih
Journal of Technology and Computer Vol. 2 No. 2 (2025): May 2025 - Journal of Technology and Computer
Publisher : PT. Technology Laboratories Indonesia (TechnoLabs)

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In today's digital age, information systems are the backbone of various organizations' operations, yet they are vulnerable to increasingly complex cybersecurity threats. Information system auditing plays a crucial role in ensuring system security and reliability, but conventional audit methods are beginning to face various limitations, particularly in handling large volumes of data and detecting threats quickly. This study aims to analyze the role, benefits, and challenges of integrating artificial intelligence (AI) technology into the information system audit process. Using a literature review method, this research found that AI can enhance audit effectiveness through data analysis automation, real-time fraud detection, and optimizing auditors' roles in strategic analysis. However, the implementation of AI still faces issues such as data quality, algorithm transparency, potential bias, auditor readiness, and the need for strong regulation and governance. This study recommends the need for synergy between technology, policy, infrastructure, and human resource competencies to ensure the effective and responsible implementation of AI in information system auditing in modern business environments.
Text Classification Using TF-IDF and Naïve Bayes: Case Study of MyXL App User Review Data Nurhayati, Nurhayati; Hartimar, Lima; Manza, Yuke; Siregar, Kiki Putriani
Journal of Technology and Computer Vol. 2 No. 2 (2025): May 2025 - Journal of Technology and Computer
Publisher : PT. Technology Laboratories Indonesia (TechnoLabs)

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The MyXL application, developed by leading Indonesian operator XL Axiata, allows customers to independently manage their telecommunication services. However, a significant volume of negative user reviews necessitates a deeper analysis of user sentiment. This research classifies MyXL app reviews using the TF-IDF (Term Frequency-Inverse Document Frequency) method for feature extraction and the Naïve Bayes algorithm for sentiment classification, implemented via a Python-based GUI. The study's objective is to categorize reviews into positive, negative, and neutral sentiments. A dataset of 1000 user reviews from Kaggle underwent comprehensive preprocessing—including text cleaning, normalization, tokenization, stopword removal, and stemming—before conversion into a numerical representation using TF-IDF. The classification model, built with the Naïve Bayes algorithm, was evaluated using accuracy, precision, recall, and F1-score metrics. The model achieved an accuracy of 61.5%. This finding demonstrates that combining TF-IDF and Naïve Bayes is effective for classifying sentiment in Indonesian text reviews, particularly within the mobile app domain. Furthermore, the methodology shows clear potential for development into a large-scale and automated user opinion analysis system.
Application of IoT Technology and Data Science Prediction Models in Household Energy Consumption Efficiency Aditama, Fajar Satrya; Putri, Zaharatun Nisa; Faqihuddin, Faqihuddin
Journal of Technology and Computer Vol. 2 No. 2 (2025): May 2025 - Journal of Technology and Computer
Publisher : PT. Technology Laboratories Indonesia (TechnoLabs)

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The increasing demand for electrical energy in residential areas has highlighted the need for more intelligent and efficient energy management systems. This research explores the application of Internet of Things (IoT) technology integrated with data science prediction models to enhance the efficiency of household energy consumption. By deploying IoT-based smart sensors in various electrical appliances, real-time energy usage data was collected and transmitted to a centralized cloud-based system. The data was then processed and analyzed using predictive modeling techniques, including Linear Regression, Random Forest, and Long Short-Term Memory (LSTM) networks, to identify usage patterns and forecast future consumption. The research was conducted through a prototype implementation in selected households, where energy usage was monitored over a period of 30 days. The prediction models were trained using historical consumption data and validated with a testing dataset to evaluate their accuracy. Among the models used, the LSTM model demonstrated the highest prediction accuracy with a Mean Absolute Percentage Error (MAPE) of 5.3%, outperforming traditional regression-based methods. Additionally, a user-friendly dashboard was developed to visualize real-time consumption and provide personalized recommendations for energy-saving behavior. The results indicate that the integration of IoT and data science can significantly contribute to more informed decision-making in energy usage at the household level. 

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