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JURNAL TEKNOLOGI DAN OPEN SOURCE
Published by Universitas Islam Kuantan Singingi
ISSN : 26557592     EISSN : 26221659     DOI : 10.36378/jtos
Core Subject : Science,
Computer Science & IT
Jurnal Teknologi dan Open Source menerbitkan naskah ilmiah. yang berkaitan dengan sistem informasi, teknologi informasi dan aplikasi open source secara berkala (2 kali setahun). Jurnal ini dikelola dan diterbitkan oleh Program Studi Teknik Informatika Fakultas Teknik, Universitas Islam Kuantan Singingi. Tujuan penerbitan jurnal ini adalah sebagai wadah komunikasi ilmiah antar akademisi, peneliti dan praktisi dalam menyebarluaskan hasil penelitian.
Arjuna Subject : -
Articles 486 Documents
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The Impact of Agile Methodology Implementation on Software Quality in Information System Development A Case Study of Industry X Muhammad Galih Ramaputra; Hendri Purnomo; Seli Puri Andini
JURNAL TEKNOLOGI DAN OPEN SOURCE Vol. 9 No. 1 (2026): Jurnal Teknologi dan Open Source, June 2026
Publisher : Universitas Islam Kuantan Singingi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36378/jtos.v9i1.5627

Abstract

The rapid demand for high-quality software requires organizations to adopt adaptive and efficient development methodologies. This study examines the impact of Agile methodology adoption on software quality within information system development in Industry X. Utilizing a quantitative explanatory design, data were collected via questionnaires from 100 software development team members and analyzed using simple linear regression. Descriptive analysis indicates that both Agile implementation (mean = 4.14) and software quality (mean = 4.09) fall into the high category. Hypothesis testing confirms that Agile methodology has a positive and significant effect on software quality (β = 0.68, t-count = 11.33, p < 0.001), contributing 52% to its variance (R² = 0.52). While team collaboration and functional suitability emerged as the strongest contributors, customer engagement and performance efficiency received the lowest scores. These findings imply that robust Agile practices substantially elevate system quality. To maximize outcomes, organizations must strategically intensify user participation throughout the development cycles and reinforce rigorous performance testing.
Empirical Performance Analysis of BST and AVL Tree on Modern Computing Architectures: A Stress Test Study Under Varying Data Distributions Hazna At Thooriqoh; Ibnu Khoirul Anwar; Dimas Nugroho Dwi Seputro
JURNAL TEKNOLOGI DAN OPEN SOURCE Vol. 9 No. 1 (2026): Jurnal Teknologi dan Open Source, June 2026
Publisher : Universitas Islam Kuantan Singingi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36378/jtos.v9i1.5628

Abstract

Binary Search Tree (BST) and AVL Tree are fundamental data structures widely used for dynamic data management in performance-critical systems. Although both structures offer efficient theoretical complexity, their practical performance on modern systems is highly influenced by data distribution and workload characteristics. This study presents an empirical performance evaluation of BST and AVL Tree using a stress-test approach based on a game leaderboard system as a representative case study. Multiple workload patterns were simulated, including random, sequential (ascending and descending), and clustered data distributions, to reflect realistic high-frequency updates commonly observed in modern applications. Experimental results show that BST achieves slightly better performance under random data distributions due to the absence of balancing overhead. However, BST experiences severe performance degradation under sequential inputs, where it degenerates into an unbalanced structure. In contrast, the AVL Tree consistently maintains logarithmic height, achieving speedups of up to 32x compared to BST in worst-case scenarios.These findings indicate that while BST can be effective under controlled average-case conditions, AVL Tree provides superior robustness and predictable performance under non-uniform and adversarial workloads. For modern high-load systems such as game leaderboards, the balancing overhead of AVL Tree represents a minimal trade-off compared to the substantial stability and performance guarantees it offers.
Regession Model For Predicting Student Final Grades In Architecture And Computer Organization Courses Gunardi Hamza; Nofri Wandi Al-Hafiz; Helpi Nopriandi; Febri Haswan
JURNAL TEKNOLOGI DAN OPEN SOURCE Vol. 9 No. 1 (2026): Jurnal Teknologi dan Open Source, June 2026
Publisher : Universitas Islam Kuantan Singingi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36378/jtos.v9i1.5638

Abstract

The development of the digital era and the increasing need for human resources who are adaptable to information technology have prompted universities to utilize academic data to improve the quality of learning. This study aims to develop a multiple linear regression model to predict students’ final grades in the Computer Architecture and Organization course based on learning evaluation variables. The predictor variables used include Quiz 1 score, Quiz 2 score, assignment score, Midterm Exam (UTS) score, and Final Exam (UAS) score, while the students’ final grades are set as the dependent variable. The study employs a quantitative approach involving data collection, data preprocessing, splitting the dataset into training and testing sets, constructing the linear regression model, and evaluating model performance using Root Mean Squared Error (RMSE), Mean Squared Error (MSE), and the coefficient of determination (R²). The dataset was split into 75% training data and 25% testing data. The results indicate that adding predictor variables significantly improves model performance. The best model was obtained by combining quiz, assignment, and midterm exam variables, with an RMSE of 2.30, an MSE of 5.28, and an R² of 0.81. These findings indicate that multiple linear regression is capable of predicting students’ final grades with a high degree of accuracy and can explain the relative contribution of each academic variable to students’ learning outcomes. This study is expected to support the implementation of learning analytics and data-driven decision-making in the evaluation of learning at the university level.
Application of Backward Elimination Method for Optimization of Decision Tree C4.5 Algorithm in Employee Performance Prediction Ahmad Fauzi; Novita Indriyani; Andika Bayu Hasta Yanto
JURNAL TEKNOLOGI DAN OPEN SOURCE Vol. 9 No. 1 (2026): Jurnal Teknologi dan Open Source, June 2026
Publisher : Universitas Islam Kuantan Singingi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36378/jtos.v9i1.5639

Abstract

This study aims to analyze the application of the Backward Elimination method in optimizing the Decision Tree C4.5 algorithm in predicting employee performance. The main problem in this study is the employee performance evaluation process which is still done manually so that it has the potential to cause inconsistency in the assessment results. The study used a dataset of 500 employee data with several performance assessment attributes such as age, education level, work discipline, productivity, and superior assessment. The research method includes data preprocessing, feature selection using Backward Elimination, application of the Decision Tree C4.5 algorithm, and model evaluation using 10-Fold Cross Validation in the RapidMiner application. The test results show that the Decision Tree C4.5 algorithm without optimization obtained an accuracy value of 89.60% and an AUC of 0.944. After applying the Backward Elimination method, model performance increased with an accuracy value of 92.80% and an AUC of 0.972. This increase indicates that the Backward Elimination method is able to reduce less relevant attributes so that the classification process becomes more optimal. Thus, the application of the Backward Elimination method has proven effective in improving the performance of the Decision Tree C4.5 algorithm in predicting employee performance.
The Influence of Mobile Technology Adoption Rate on GenZ Productivity in the Digital Era Elvin Nury Khirdany; Wahyu Liana
JURNAL TEKNOLOGI DAN OPEN SOURCE Vol. 9 No. 1 (2026): Jurnal Teknologi dan Open Source, June 2026
Publisher : Universitas Islam Kuantan Singingi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36378/jtos.v9i1.5646

Abstract

The development of digital technology has brought significant changes to the lives of Generation Z, particularly in the use of mobile technology to support their daily activities. Mobile technology has now become a primary means of communication, learning, and accessing information in the digital age. This study aims to determine the effect of mobile technology adoption on Generation Z productivity in the digital age. The study was conducted on 100 Generation Z students aged 16–18 years using quantitative methods and questionnaires. The results show that the level of mobile technology use among Generation Z is relatively high and has a positive influence on student productivity, particularly in terms of ease of access to information, time efficiency, and daily learning activities. The results of the research test indicate that the level of mobile technology adoption has a significant effect on Generation Z productivity with an influence value of 61.8%. This indicates that the higher the use of mobile technology, the higher the productivity of Generation Z in the digital age. However, the use of mobile technology also needs to be controlled to avoid negative impacts such as distraction and digital addiction.
Prediction Model of Batam University Management Information System Based on Regression and Machine Learning Fendi Hidayat; Eisyaniah Desvazulinda; Syakinah Warrahmah
JURNAL TEKNOLOGI DAN OPEN SOURCE Vol. 9 No. 1 (2026): Jurnal Teknologi dan Open Source, June 2026
Publisher : Universitas Islam Kuantan Singingi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36378/jtos.v9i1.5648

Abstract

The implementation of Management Information Systems (MIS) in higher education is strongly influenced by the use of information technology (IT) and the digital competence of its users. This study aims to analyze the influence of IT use and digital competence on MIS at Batam University, as well as to develop the best prediction model by comparing conventional statistical methods and machine learning algorithms. Research data were collected through a Likert-scale-based questionnaire from respondents selected using a purposive sampling technique. Data analysis was performed using a combination of Multiple Linear Regression, Random Forest Regressor, and Gradient Boosting Regressor. The results of statistical tests indicate that the use of IT and digital competence simultaneously have a positive and significant effect on MIS with a coefficient of determination ($R^2$) of 87.40%. On the other hand, the results of the machine learning model evaluation show that Random Forest Regressor provides the best performance with the lowest MAE and RMSE values, and the highest prediction accuracy ($R^2$ Score) reaching 0.905072. This study concludes that the integration of statistical and machine learning approaches can produce an accurate and adaptive MIS prediction model to support data-driven decision making at Batam University
Digital Technology Utilization by Beginner Hikers: Safety and Technopreneurship Perspectives in Nature Tourism Muhamad Reza Tediantoro; Nanang Hoesen Hidroes Abbrori
JURNAL TEKNOLOGI DAN OPEN SOURCE Vol. 9 No. 1 (2026): Jurnal Teknologi dan Open Source, June 2026
Publisher : Universitas Islam Kuantan Singingi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36378/jtos.v9i1.5649

Abstract

This study analyzed digital technology utilization by beginner hikers to propose a safety-driven platform architecture integrating outdoor risk management and local technopreneurship. The research was motivated by recurring field vulnerabilities in beginner-friendly mountains, utilizing a multiple-case representation of Mount Gamkonora and Mount Prau. These locations represented contrasting profiles: unmapped, infrastructure-limited terrains versus highly accessible open-trip environments. Despite their differences, both destinations shared critical safety gaps, including navigational deficits and the dangerous bypass of pre-hike physical readiness assessments. A descriptive qualitative approach was employed through semi-structured interviews with eight beginner hikers. The thematic analysis revealed severe navigation difficulties, limited supporting facilities, managerial deficits in commercial trips, and an urgent need for offline-accessible information. Findings demonstrated that these hazards could be mitigated through smart tourism technologies, specifically utilizing offline maps, virtual basecamps, automated physical supply calculators, and verified local service marketplaces. This study concluded that localizing digital solutions effectively addressed immediate physical and psychological hazards while generating sustainable economic value for local communities.
Divorce Pattern Clustering Using The K-Prototype Ahsin Ilallah; Zaehol Fatah; Achmad Baijuri
JURNAL TEKNOLOGI DAN OPEN SOURCE Vol. 9 No. 1 (2026): Jurnal Teknologi dan Open Source, June 2026
Publisher : Universitas Islam Kuantan Singingi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36378/jtos.v9i1.5674

Abstract

Divorce is a complex social problem that continues to increase every year, including within the jurisdiction of the Banyuwangi Religious Court. Various factors such as early-age marriage, economic problems, prolonged conflict, and domestic violence are the main triggers of divorce. This study aims to classify the divorce patterns of the Banyuwangi community using the K-Prototype clustering method, which is capable of handling mixed numerical and categorical data. The data used are secondary data from the Case Tracking Information System (SIPP) of the Banyuwangi Religious Court in 2025, totaling 5,570 cases. The variables analyzed include the age of the plaintiff and defendant, number of children, length of marriage, type of case, occupation, education, and divorce factors. The clustering process was carried out through preprocessing stages, determining the gamma parameter, data encoding, and implementing the K-Prototype algorithm using the Python programming language and the Streamlit framework. The results of the study show the formation of two main clusters. Cluster 0 (59.7%) is dominated by young couples with an average plaintiff age of 29.8 years, a short marriage duration of 6.3 years, and an average of 0.7 children. Cluster 1 (40.3%) is dominated by mature couples with an average plaintiff age of 45.5 years, a long marriage duration of 17.4 years, and an average of 1.2 children. PCA visualization and scatter plots further emphasize the distinction between the two clusters based on age. The developed SKPP application successfully facilitates users in uploading data, preprocessing, clustering, and exporting results. Therefore, the K-Prototype method is effective for analyzing divorce patterns and can serve as a supporting tool for the Religious Court in formulating more targeted prevention policies.
Design and Development of an Automatic Clothes Drying System Based on Microcontroller Rosmiati Jamiah; Riswandi
JURNAL TEKNOLOGI DAN OPEN SOURCE Vol. 9 No. 1 (2026): Jurnal Teknologi dan Open Source, June 2026
Publisher : Universitas Islam Kuantan Singingi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36378/jtos.v9i1.5685

Abstract

The conventional clothes drying process is highly dependent on weather conditions and requires continuous human supervision, which reduces efficiency. This study aims to design and implement an automatic clothes drying system based on a microcontroller that operates independently according to environmental conditions. The system uses a microcontroller as the main controller integrated with temperature and humidity sensors to detect air conditions and a light sensor to identify sunlight intensity. DC motors and relays are employed as actuators to control the drying mechanism. The research method includes hardware design, software development, system implementation, and performance testing. The results show that the system can automatically determine the drying process based on sensor inputs and respond effectively to environmental changes. This system is expected to improve efficiency and provide a practical solution for automatic household clothes drying.
Development of a Vehicle Detection and Classification System Using YOLO and Real-Time API With The Rapid Application Development (RAD) Method Rizqo Sahala Putra; Muhammad Asep Subandri
JURNAL TEKNOLOGI DAN OPEN SOURCE Vol. 9 No. 1 (2026): Jurnal Teknologi dan Open Source, June 2026
Publisher : Universitas Islam Kuantan Singingi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36378/jtos.v9i1.5716

Abstract

This study developed a real-time, computer vision-based traffic monitoring system to address safety concerns regarding uncontrolled public vehicle access within the Politeknik Negeri Bengkalis campus environment. As the existing CCTV infrastructure lacked intelligent analytical capabilities, an automated solution was required to extract objective traffic data. The system utilized the YOLOv8n model for vehicle detection and the ByteTrack algorithm for multi-object tracking to prevent duplicate counting across frames. To overcome classification inaccuracies inherent in pre-trained models without relying on resource-intensive retraining, a rule-based post-processing method was implemented. This method evaluated bounding box geometries, including pixel area, aspect ratio, and a vertical camera perspective factor, to filter raw detections. Evaluation results demonstrated that the rule-based approach significantly minimized false positives, achieving a total precision of 93.48% and an overall accuracy of 86.00%, which vastly outperformed both baseline and fine-tuned configurations. Furthermore, comparative tests across model variants confirmed that YOLOv8n provided the most stable CPU execution, maintaining the highest frame rate and lowest inference latency. The integrated system successfully tracked vehicle counts and estimated relative speeds, providing a reliable, GPU-independent analytical tool to support data-driven campus traffic management policies.

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