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M Rhifky Wayahdi
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INDONESIA
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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Decision Support System to Determine the Best Student at MAS Islamic Center in Class XI using a Simple Additive Weighting Method Thania, Sheila Try; Wayahdi, M. Rhifky; Mughnyanti, Mayang
Journal of Technology and Computer Vol. 1 No. 4 (2024): November 2024 - Journal of Technology and Computer
Publisher : PT. Technology Laboratories Indonesia (TechnoLabs)

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Abstract

This research aims to develop a Decision Support System (DSS) in determining the best students at MAS ISLAMIC CENTRE by using the Simple Additive Weighting (SAW) method. The SAW method was chosen because of its ability to calculate the number of performance weights on each alternative effectively, allowing comparison of various options based on specified criteria. This research uses a quantitative approach with system development that follows the Waterfall model, starting from the needs analysis stage to implementation and maintenance. The results show that the designed system is able to process student data accurately and display student rankings quickly and efficiently. This provides an advantage for schools in making decisions that are more objective and supported by solid data. The implementation of SAW-based DSS is expected to be a solution that supports transparency and effectiveness in determining the best students in the educational environment.
Application of the Chi-Square Automatic Interaction Detection (CHAID) Method in Analysing Variables That Influence Drug Abuse in Province North Sumatera Yuda, Muhammad Wira; Widyasari, Rina; Aprilia, Rima
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 aim of this research is to classify transmission variables in North Sumatera Province using the Chi-Square Automatic Interaction Detection (CHAID) method. This method is used to determine which factors are the most dominant that cause people in North Sumatra Province to use drugs. This research began with collecting data on drug users in North Sumatra Province taken from BNN PROVSU. After the data was collected, it was processed again using MS Excel and divided into the 6 most dominant factors in the data that trigger drugs. Then, using the SPSS application, calculate which factors are the most dominant that cause people in North Sumatra to use drugs. Based on research conducted by the author on drug abuse in North Sumatra Province using the CHAID method, conclusions can be drawn as follows: The most dominant factor causing people in North Sumatra Province to use drugs is the educational factor. Of the total values that have been examined using the chi-square method, they are X3 (educational factor) with a value = χ² count (13,018) > χ² Table (9,488). Education Factors of Drug Abuse in North Sumatra Province Associated with the Ratio of Narcotics Users.
Analysis of the Use of Fake GPS Applications on Drivers of PT GoTo Gojek Tokopedia Tbk (GOTO) Alhafidz, Muhammad Fikri; Hasibuan, Eka Hayana
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 purpose of this study was to determine the use and system used to detect the fake GPS application on PT Goto Gojek Tokopedia Tbk driver partners in the competition between Gojek drivers in Surabaya. The research method used is a qualitative method, which is obtained based on the results of field research (field research). The data required in this study were collected using observation, interview, and literature study techniques, which were then analysed using descriptive techniques in describing data about the use of the Fake GPS application on PT Goto Gojek Tokopedia Tbk partners. The results of this study conclude that the use of the Fake GPS application by PT Goto Gojek Tokopedia Tbk drivers is carried out without coercion, without fraud, and without oversight, but the driver violates company rules regarding the use of additional applications in the form of fake GPS. The system used to detect fake GPS applications in this research is based on ensemble learning using the AdaBoost and XGBoost algorithms. The learning ensemble is able to detect fake GPS applications very well.
Impact of Techno-corp Investment on Technology Startup Growth in Asia: Case Study of Several Portfolio Startups Khairunnisa; Zakaria, Mahmud
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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This study examines the impact of Techno-corp's investments on the growth of technology startups in Asia through a qualitative case study approach. Analyzing five to seven portfolio startups across fintech, AI, e-commerce, and SaaS sectors, the research evaluates four key growth indicators: financial performance, market expansion, technological innovation, and operational efficiency. Findings reveal that startups experienced an average revenue growth of 45% within two years post-investment, with significantly improved profit margins and operational efficiency. Beyond financial support, Techno-corp's strategic mentorship and industry networks proved instrumental in facilitating market expansion—enabling startups to enter new geographical markets and secure enterprise partnerships. The study highlights how Techno-corp-backed startups accelerated innovation cycles, exemplified by AI-driven product enhancements (e.g., a 30% improvement in customer service response times) and blockchain-based security solutions. Operational efficiencies were achieved through automation, reducing costs by up to 25%. These results demonstrate that corporate investment, when combined with strategic guidance, addresses critical scaling challenges more effectively than traditional venture capital alone. The research contributes to academic discourse on corporate venture capital while offering practical insights for investors, policymakers, and entrepreneurs seeking to optimize startup growth strategies in Asia’s competitive tech landscape. Limitations include the focus on a single corporate investor, suggesting avenues for future comparative studies.
Augmented Reality (AR) as a Learning Tool for Computer Engineering Technical Skills Lubis, Junaidi H.; Maimunah, Dewi
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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Augmented Reality (AR) has emerged as a transformative tool in technical education, particularly in computer engineering. This study evaluates AR’s effectiveness in enhancing practical skills through a mixed-methods approach involving 60 students divided into an AR-trained experimental group and a control group using traditional methods. Quantitative analysis revealed a significant improvement in post-test performance for the AR group (M = 88.1, SD = 6.3) compared to the control group (M = 75.3, SD = 8.1), with a large effect size (Cohen’s *d* = 3.14). Qualitative findings highlighted reduced anxiety, deeper conceptual understanding, and higher engagement among AR users. Despite challenges such as hardware requirements and content development, the study suggests that AR integration, coupled with instructor training, can significantly enhance technical education. Recommendations include institutional investment in AR tools and further research on long-term skill retention and scalability. The findings affirm AR’s potential as an immersive and interactive learning medium, bridging the gap between theory and practice in engineering education.
Implementation of Cloud-based Supply Chain Management Information System for Logistics Optimization Fauzi, Ahmad; Rahmawati, Siti
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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This research aims to implement a cloud-based Supply Chain Management Information System (SCMIS) to optimize the logistics process at PT Wijaya Badja Global. The company faces challenges in managing an efficient supply chain, including inter-departmental coordination, inventory tracking, and timely delivery of goods. By utilizing cloud technology, this system is designed to improve visibility, speed, and accuracy in supply chain management. The research methods used include requirements analysis, system design, implementation, and performance evaluation. The results show that the implementation of cloud-based SCMIS is able to reduce logistics costs, improve operational efficiency, and accelerate response time to changes in market demand. In addition, the system also enables better data integration between suppliers, distributors, and customers. Thus, this research contributes to the application of cloud technology for supply chain optimization in the logistics sector, especially at PT Wijaya Badja Global.
Development of IoT-based Ship Maintenance Information System at PT. Sera Jaya Kesuma Santoso, Budi; Wijaya, Ani
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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This study presents the development and implementation of an IoT-based Ship Maintenance Information System at PT. Sera Jaya Kesuma, designed to enhance maritime maintenance operations through real-time monitoring and predictive analytics. The system integrates industrial-grade sensors with edge-cloud architecture to monitor critical ship components, utilizing LSTM neural networks for anomaly detection. Results from a six-month trial demonstrated significant improvements, including 93.7% accuracy in fault prediction, a 35.9% reduction in unplanned downtime, and 28% lower maintenance costs ($12,500 monthly savings). Operational efficiencies were achieved through automated work orders (saving 17 hours/week) and prevented environmental incidents (100% oil spill prevention). Despite challenges in tropical marine conditions, the solution proved robust through adaptive data handling and durable sensor packaging. While currently limited to mechanical systems, the framework provides a scalable model for IoT adoption in mid-sized shipping companies, particularly in developing maritime economies. The study concludes that IoT-driven predictive maintenance transforms traditional reactive approaches, offering both immediate operational benefits and long-term strategic advantages for the maritime industry. Future work should expand monitoring scope to navigational systems and enhance edge computing capacity for fleet-wide deployment.
Evaluation of the Effect of Decision Support Systems on the Quality of Managerial Decisions in MSME Companies in Indonesia Kartini, Dewi; Hermawan, Rudi
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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This study evaluates the impact of Decision Support Systems (DSS) on managerial decision quality in Indonesian Micro, Small, and Medium Enterprises (MSMEs), focusing on retail, food production, and textile sectors. Through a mixed-methods approach combining quantitative analysis of 150 MSMEs and qualitative interviews with 75 business owners, the research demonstrates significant improvements post-DSS implementation. Key findings include a 59.6% reduction in decision latency for micro-enterprises, 22.4% higher inventory turnover in retail businesses, and 18.3% improved margin stability in food production. The study identifies critical success factors such as Bahasa Indonesia interface localization (adoption rate 88%) and mobile-first design (SUS score 82.4/100), while highlighting infrastructure and digital literacy as persistent barriers. Comparative analysis reveals the solution outperforms previous implementations in developing markets, achieving break-even 40% faster. The research contributes both practical frameworks for DSS deployment in resource-constrained environments and theoretical extensions to technology acceptance models, emphasizing "localization readiness" as a novel adoption dimension. These findings provide policymakers and business support organizations with evidence-based strategies for accelerating digital transformation in Indonesia's MSME sector, which constitutes 99% of the nation's businesses. Future research directions include longitudinal impact assessment and AI-voice integration for ultra-micro enterprises.
Performance analysis of classification algorithms in Decision Support Systems for early detection of chronic diseases Syahputra, Andika; Antoni, Steven
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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Early detection of chronic diseases is a critical step in effective prevention and treatment. Decision Support Systems (DSS) based on classification algorithms have become an increasingly important tool in helping medical personnel accurately and efficiently identify chronic disease risks. This study aims to analyze the performance of various classification algorithms in SPK for early detection of chronic diseases, focusing on accuracy, precision, recall, and F1-score as evaluation metrics. The research method involves the collection of health datasets that include clinical and demographic variables of patients. Classification algorithms evaluated include Decision Tree, Random Forest, Support Vector Machine (SVM), K - Nearest Neighbors (KNN), and Neural Network. The Dataset was divided into training data and test data, with a proportion of 80:20, and cross-validation was carried out to ensure the reliability of the results. Algorithm performance evaluation was conducted using accuracy, precision, recall, and F1-score metrics. The results showed that Random Forest achieved the highest accuracy of 92.5%, followed by Neural Network with 90.8% accuracy. Decision Tree and KNN showed quite good performance with accuracy of 88.3% and 86.7%, respectively, while SVM had the lowest accuracy of 84.2%. In terms of precision and recall, Random Forest also excelled with values of 91.8% and 92.0%, respectively, showing its good ability to identify positive cases and reduce false positives.
Comparison of WASPAS and TOPSIS Methods in Decision Support Systems Antonio, Rey; Putri, Cahaya Intan; Andini , Xavier
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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Decision Support Systems (DSS) are essential tools in assisting decision-makers to choose the most optimal alternative from a set of options based on multiple criteria. In the field of Multi-Criteria Decision-Making (MCDM), various methods have been developed to enhance the quality and objectivity of decisions. This research focuses on a comparative analysis between two widely used MCDM techniques: the Weighted Aggregated Sum Product Assessment (WASPAS) method and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method. The objective of this study is to evaluate the effectiveness, accuracy, and suitability of each method in supporting decision-making processes within a DSS framework. The research adopts a quantitative approach by applying both methods to the same decision-making problem scenario, which involves selecting the best alternative based on a set of weighted criteria. Data were collected through a simulation case study involving predetermined alternatives and criteria relevant to real-world decision contexts, such as supplier selection and project prioritization. Both methods were implemented using Microsoft Excel and Python-based tools to ensure accuracy in calculation and ease of comparison. The results from each method were then analyzed and compared in terms of ranking outcomes, computational complexity, sensitivity to weight variations, and ease of interpretation. Findings show that both WASPAS and TOPSIS produced consistent and logical rankings of alternatives, but each method offers distinct advantages. WASPAS, which integrates both additive and multiplicative aggregation models, demonstrated higher flexibility and robustness in handling variations in weight assignments. 

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