Journal of Engineering Technology and Science Integration (JETSI)
Journal of Engineering Technology and Science Integration (JETSI) is a peer-reviewed academic journal dedicated to publishing high-quality research in the fields of engineering, technology, and interdisciplinary scientific integration. The journal aims to provide a platform for researchers, academics, and practitioners to disseminate innovative ideas, theoretical advancements, and practical applications that contribute to sustainable industrial and technological development. JETSI covers a broad range of topics including engineering, applied systems, digital transformation, artificial intelligence, sustainable technology, innovation management, and interdisciplinary scientific integration. The journal promotes interdisciplinary approaches that integrate engineering principles with emerging technologies, data-driven innovation, and sustainability perspectives. The scope includes, but is not limited to, the following areas: 1. Engineering and Applied Systems Industrial and systems engineering, manufacturing systems, optimization, operations research, engineering design, simulation, automation systems, smart manufacturing, logistics and supply chain systems, ergonomics, quality engineering, and engineering management. 2. Digital Technology and Intelligent Systems Artificial intelligence, machine learning, Internet of Things (IoT), cyber-physical systems, digital twins, information systems, software engineering, intelligent computing, digital innovation, and emerging technologies for industrial and societal applications. 3. Data Science and Decision Analytics Data analytics, big data systems, predictive modeling, decision support systems, business intelligence, computational analysis, statistical modeling, optimization analytics, and data-driven decision making. 4. Sustainability and Environmental Systems Sustainable technology, cleaner production, circular economy, environmental management, green innovation, renewable energy systems, sustainability assessment, environmental engineering, and sustainable industrial development. 5. Innovation and Interdisciplinary Engineering Innovation management, interdisciplinary engineering research, technology integration, socio-technical systems, engineering education, knowledge management, technological convergence, and collaborative solutions for industrial and societal challenges. JETSI encourages submissions that highlight interdisciplinary integration, methodological innovation, and real-world applications. All submitted manuscripts undergo a rigorous double-blind peer review process to ensure the quality, originality, and relevance of the published works. JETSI is committed to maintaining high academic standards and supporting the global dissemination of knowledge.
Articles
13 Documents
<![CDATA[Evaluating the Performance Efficiency of the Banyuwangi Tourism App Using ISO 25010 and PLS-SEM Approach ]]>
<![CDATA[Fandi Achmad]]>;
<![CDATA[Ibnu Zulkarnain]]>;
<![CDATA[Ifrando Juandika]]>
<![CDATA[ Journal of Engineering Technology and Science Integration (JETSI) Vol. 1 No. 1 (2026): Journal of Engineering Technology and Science Integration ]]>
Publisher : <![CDATA[PT. Nordeo Scholar Publishing ]]>
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DOI: 10.68627/jetsi.v1i1.2
<![CDATA[The Banyuwangi Tourism App serves as a key digital platform for providing tourism-related information and services in Banyuwangi, East Java, Indonesia, including destinations, attractions, accommodations, culinary offerings, events, and supporting facilities. However, users have reported that the application's performance has not been optimal and tends to decline over time. Therefore, a comprehensive evaluation of application performance is essential to ensure optimal user experience and improve the effectiveness of tourism services. This study aims to analyze the performance quality of the Banyuwangi Tourism App using the ISO 25010 quality model, focusing on performance efficiency attributes. A quantitative research approach was employed. Hypothesis testing was conducted using Partial Least Squares Structural Equation Modeling (PLS-SEM) based on data collected from 166 users of the Banyuwangi Tourism App. The results indicate that out of eight proposed hypotheses, seven variables have a positive and significant effect on the application's performance efficiency, while one variable does not show a significant influence. These findings provide empirical insights into the key determinants of application performance from the user perspective. This study contributes by offering a structured and theory-driven evaluation of tourism application performance using ISO 25010 integrated with PLS-SEM analysis. The results can assist developers and stakeholders in prioritizing improvements on critical quality attributes, thereby enhancing application efficiency and delivering a better user experience.]]>
<![CDATA[Mapping the Performance of Knowledge Management in Crafts SMEs Using Clustering ]]>
<![CDATA[Bagas Diropati]]>;
<![CDATA[Cindy Joselyn Lim]]>;
<![CDATA[Marsha Livia]]>
<![CDATA[ Journal of Engineering Technology and Science Integration (JETSI) Vol. 1 No. 1 (2026): Journal of Engineering Technology and Science Integration ]]>
Publisher : <![CDATA[PT. Nordeo Scholar Publishing ]]>
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DOI: 10.68627/jetsi.v1i1.3
<![CDATA[Knowledge management (KM) is widely recognized as a strategic resource for Small and Medium Enterprises (SMEs), yet its implementation in craft SMEs is often heterogeneous and fragmented. Most existing studies measure the average effect of KM on firm outcomes, which may obscure differences in KM maturity among individual SMEs and limit the design of differentiated interventions. This study aims to map craft SMEs based on their KM performance using an unsupervised learning approach. Primary data were collected from 70 bag and luggage craft SMEs in Sidoarjo Regency, East Java, through structured questionnaires using a six-point Likert scale. The study employed purposive sampling, with respondents selected based on specific criteria: the SMEs had to operate in the bag and luggage craft sector, be active during the data collection period, meet SME characteristics, and be represented by owners, managers, or SME leaders directly involved in daily business activities and decision-making. Therefore, the sample provides an analytical representation of active bag and luggage craft SMEs in the observed context, although it is not intended for statistical generalization to all craft SMEs in Sidoarjo. The questionnaire measured four KM dimensions: knowledge creation, knowledge acquisition, knowledge sharing, and knowledge application. The K-Means clustering algorithm was applied with z-score normalization, while the optimal number of clusters was determined using the Elbow Method and validated through the Silhouette Coefficient. The analysis identified two optimal clusters (k = 2) with a silhouette score of 0.441. Cluster 1 represents SMEs with consistently strong KM performance, while Cluster 0 represents SMEs with relatively weak KM performance. Knowledge acquisition and knowledge sharing emerged as the most differentiating dimensions. The novelty of this study lies in providing an empirical, data-driven map of KM maturity among craft SMEs as a basis for targeted KM development programs.]]>
<![CDATA[Multidimensional Factors Affecting Senior High School Graduation Rates in Indonesia ]]>
<![CDATA[Reza Aditya]]>;
<![CDATA[Christ Williams Billy]]>;
<![CDATA[Nazwah Santi Fauziyah]]>;
<![CDATA[Thesi Martin]]>;
<![CDATA[Inky Pramudia Ramdhani]]>
<![CDATA[ Journal of Engineering Technology and Science Integration (JETSI) Vol. 1 No. 1 (2026): Journal of Engineering Technology and Science Integration ]]>
Publisher : <![CDATA[PT. Nordeo Scholar Publishing ]]>
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DOI: 10.68627/jetsi.v1i1.4
<![CDATA[This study aims to analyze the influence of the number of high school teachers, the student-teacher ratio, regional education expenditure, the Human Development Index (HDI), and school infrastructure on high school student graduation rates in Indonesia. The study used a quantitative approach with a descriptive-inferential design based on secondary data from the Indonesian Central Statistics Agency (BPS) in 2023 for 34 provinces with complete data. The analysis was conducted using multiple linear regression to examine the multidimensional relationships between variables. The results show that the number of high school teachers, regional education expenditure, the HDI, and school infrastructure have a significant positive effect on student graduation rates, while the student-teacher ratio has a significant negative effect. Among all variables, the HDI has the strongest contribution to secondary education attainment. The regression model has a fairly strong explanatory power with an R² value of 0.50. These findings indicate that improving the quality of secondary education depends not only on the number of educators but also requires support from socio-economic development, educational investment, proportional teacher distribution, and strengthening of school infrastructure. Academically, this study strengthens the development of quantitative studies of education through a more comprehensive multivariate approach. Practically, the research results can serve as an empirical basis for the government in formulating national education equality policies that are more effective, equitable, and data-based.]]>
<![CDATA[The Design of a Web-Based 360° Assessment Information System for AKHLAK Core Values at PT XYZ ]]>
<![CDATA[Thifal Adelia Ghifara]]>;
<![CDATA[Bagas Diropati]]>;
<![CDATA[Aghni Ridfalah]]>
<![CDATA[ Journal of Engineering Technology and Science Integration (JETSI) Vol. 1 No. 1 (2026): Journal of Engineering Technology and Science Integration ]]>
Publisher : <![CDATA[PT. Nordeo Scholar Publishing ]]>
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DOI: 10.68627/jetsi.v1i1.5
<![CDATA[The rapid advancement of digital technology has increasingly compelled organizations to transform human resource management practices through the adoption of integrated Human Resource Information Systems (HRIS). Among the critical dimensions of HR management, employee behavioral assessment occupies a pivotal role in fostering competency development and sustaining organizational effectiveness. However, PT XYZ currently administers its 360° Core Values AKHLAK assessments through semi-manual processes relying on Google Forms and spreadsheet files, resulting in operational inefficiencies, fragmented data management, constrained monitoring capabilities, and elevated risks of administrative error. This study aims to design a web-based 360° Core Values AKHLAK assessment information system integrated with the company's existing HRIS infrastructure, with the objective of enhancing assessment process efficiency and strengthening organizational monitoring capabilities. The research employs the Prototype Model within the System Development Life Cycle (SDLC) framework, supported by observational data collection, stakeholder interviews, systematic literature review, and UML-based system modeling. The proposed system encompasses automated evaluator assignment, weighted score computation, real-time monitoring dashboards, centralized data management, automated notification mechanisms, and analytical visualization features. Evaluation findings indicate that the proposed system design is capable of substantially reducing manual administrative workload, improving assessment monitoring effectiveness, mitigating data inconsistency risks, and supporting data-driven decision-making in the domain of employee behavioral evaluation. The primary contribution and novelty of this study reside in the comprehensive integration of 360° feedback mechanisms, Core Values AKHLAK evaluation, and dashboard-based analytical visualization within a unified centralized platform]]>
<![CDATA[The Adoption of Generative AI in Academic Information Systems: Impacts on User Satisfaction and Learning Efficiency ]]>
<![CDATA[Dybio Dompu Hot Asih]]>;
<![CDATA[Erbin Sitorus]]>;
<![CDATA[Jasael Simanullang]]>;
<![CDATA[Jevantree Bate'e]]>
<![CDATA[ Journal of Engineering Technology and Science Integration (JETSI) Vol. 1 No. 1 (2026): Journal of Engineering Technology and Science Integration ]]>
Publisher : <![CDATA[PT. Nordeo Scholar Publishing ]]>
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DOI: 10.68627/jetsi.v1i1.7
<![CDATA[The rapid advancement of Generative Artificial Intelligence (Generative AI) has significantly transformed academic information systems by enhancing learning processes, academic services, and user interaction. This study aims to analyze the adoption of Generative AI in academic information systems and examine its impacts on user satisfaction and learning efficiency in higher education environments. The research employs a quantitative approach using survey data collected from university students and academic users who actively utilize AI-based academic platforms. Structural Equation Modeling (SEM) is used to evaluate the relationships between Generative AI adoption, system usability, user satisfaction, and learning efficiency. The findings indicate that the adoption of Generative AI positively influences user satisfaction through improved accessibility, personalization, and responsiveness of academic information systems. Furthermore, Generative AI significantly enhances learning efficiency by supporting faster information retrieval, adaptive learning experiences, and academic task automation. The study contributes to the growing literature on artificial intelligence adoption in educational information systems and provides practical implications for universities seeking to implement AI-driven digital transformation strategies. The results also highlight the importance of system quality, trust, and user readiness in maximizing the effectiveness of Generative AI technologies in higher education.]]>
<![CDATA[Improving Excavator Productivity and Loading-Hauling Efficiency Through Double Side Loading in Open-Pit Mining Operations ]]>
<![CDATA[Yuni Sudarwanto]]>;
<![CDATA[Rulyenzi Rasyid]]>;
<![CDATA[Yenny Widianty]]>;
<![CDATA[Miftahul Fadillah]]>
<![CDATA[ Journal of Engineering Technology and Science Integration (JETSI) Vol. 1 No. 1 (2026): Journal of Engineering Technology and Science Integration ]]>
Publisher : <![CDATA[PT. Nordeo Scholar Publishing ]]>
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DOI: 10.68627/jetsi.v1i1.8
<![CDATA[Open-pit mining operations require efficient loading and hauling systems to achieve production targets while minimizing operational delays and equipment idle time. Conventional Single Side Loading systems often result in high queuing time, hanging time, and cycle time, which reduce excavator productivity and overall operational efficiency. This study evaluates the effectiveness of Double Side Loading (DSL) implementation in improving excavator productivity and loading-hauling performance in an open-pit mining operation in East Kalimantan, Indonesia. A quantitative before-and-after operational analysis was conducted using actual field data collected from 250-ton and 300-ton excavator fleets, comprising more than 1,000 observed loading cycles. The analyzed parameters included excavator productivity, queuing time, hanging time, cycle time, and match factor. Descriptive statistical analysis and paired sample t-tests were employed to assess the significance of operational changes following DSL implementation. The results indicate that DSL significantly improved excavator productivity by 20% for the 250-ton fleet and 17% for the 300-ton fleet. Queuing time decreased by 25% and 29%, while hanging time was reduced by 34% and 26% for the 250-ton and 300-ton fleets, respectively. In addition, cycle time decreased by 12.46% and 13.04%, whereas match factor improved from 0.96 to 1.00 and from 0.98 to 1.02. Statistical testing further confirmed the significance of the observed improvements. The paired sample t-test indicated significant productivity gains for both excavator fleets, with the 250-ton fleet yielding a t-value of 4.12 and a 95% confidence interval of 124.5–211.5 BCM/h, while the 300-ton fleet yielded a t-value of 3.89 and a 95% confidence interval of 145.2–256.8 BCM/h.]]>
<![CDATA[Data-Driven Assessment Engineering Model Based on Log Automation and Predictive Analytics for the Transformation of Science Evaluation Systems ]]>
<![CDATA[Muhammad Gibran Alif Prasetya]]>;
<![CDATA[Muhammad Rizqi Muflihuddin]]>
<![CDATA[ Journal of Engineering Technology and Science Integration (JETSI) Vol. 1 No. 1 (2026): Journal of Engineering Technology and Science Integration ]]>
Publisher : <![CDATA[PT. Nordeo Scholar Publishing ]]>
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DOI: 10.68627/jetsi.v1i1.9
<![CDATA[The transformation of science evaluation systems toward intelligent assessment is a critical urgency in the realm of digital technology, utilizing digital exhaust as a massive source of behavioral data. However, conventional monolithic Learning Management Systems (LMS) lack the architectural capacity to process real-time time-series logs, functioning merely as static score repositories rather than proactive diagnostic tools. This study aims to develop and validate a Data-Driven Assessment Engineering (DDAE) model to address this infrastructural limitation. Utilizing the Design Science Research Methodology (DSRM), the computational architecture was engineered using a distributed microservices approach integrating Apache Kafka, Polyglot Persistence (PostgreSQL, MongoDB, InfluxDB), and Machine Learning algorithms (XGBoost and DBSCAN). The prototype was deployed for junior high school science evaluations involving 312 eighth-grade students across four public schools (936 examination sessions; 487,592 raw log events), and user acceptance was quantitatively assessed using the Technology Acceptance Model (TAM) via Multiple Linear Regression. Load testing demonstrated the DDAE architecture's stability, maintaining an optimal latency of 42.1 ms under a peak load of 1,000 concurrent users. Furthermore, the XGBoost predictive model, optimized via grid-search hyperparameter tuning with stratified 5-fold cross-validation, achieved an outstanding recall of 95.1% in classifying learning mastery, while DBSCAN precisely isolated rapid-guessing anomalies as spatial noise. Consequently, the automated processing of exam logs successfully converts passive raw data into actionable intelligence. This shifts the science evaluation paradigm into a proactive Early Warning System (EWS), heavily driven by its perceived usefulness among educational stakeholders.]]>
<![CDATA[The Use of Machine Learning and Ensemble Stacking for Classifying Rainfall Intensity in Central Jakarta in 2025 ]]>
<![CDATA[Damar Januar Jorgie Marseno]]>;
<![CDATA[Maysya Amelia Putri]]>;
<![CDATA[Muhammad Favian Gustaf Ahnaf]]>;
<![CDATA[Muhammad Jawahir Asyrof]]>;
<![CDATA[Kania Laksono]]>;
<![CDATA[Faridhatun Nikmah]]>
<![CDATA[ Journal of Engineering Technology and Science Integration (JETSI) Vol. 1 No. 1 (2026): Journal of Engineering Technology and Science Integration ]]>
Publisher : <![CDATA[PT. Nordeo Scholar Publishing ]]>
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DOI: 10.68627/jetsi.v1i1.10
<![CDATA[High rainfall intensity in Central Jakarta can trigger inundation and flooding that disrupt infrastructure, mobility, and public activities. This condition highlights the need for a reliable rainfall classification model. This study develops a next-day rainfall classification approach using a Stacking Ensemble that combines Random Forest, Gradient Boosting, XGBoost, LightGBM, and CatBoost as base models, with LassoCV as the meta-model. The dataset was obtained from BMKG meteorological records for Central Jakarta in 2025. The research process follows the CRISP-DM framework, covering data understanding, data preparation, feature engineering, modeling, and evaluation. From 365 daily observations, the feature engineering stage produced 34 derived features. The dataset was then split into 80% training data and 20% testing data according to chronological order, while model validation was conducted using 5-fold Time Series Cross Validation. The evaluation results indicate that Stacking Lasso achieved the best performance, with an F1-Score of 0.774 and a recall of 0.878. During the weighting process, LassoCV retained only LightGBM as the active contributor, with a coefficient of 0.2671. SHAP interpretation showed that average relative humidity was the most influential variable in predicting rainfall. These findings suggest that the Stacking Ensemble approach has potential as a foundation for developing an urban hydrometeorological disaster mitigation support system.]]>
<![CDATA[Development of a GPS and QR Code Based Mobile Attendance Application to Improve Employee Discipline at PT Merpati Mahardika ]]>
<![CDATA[Toto Hermanto]]>;
<![CDATA[Gulo Monika]]>
<![CDATA[ Journal of Engineering Technology and Science Integration (JETSI) Vol. 1 No. 1 (2026): Journal of Engineering Technology and Science Integration ]]>
Publisher : <![CDATA[PT. Nordeo Scholar Publishing ]]>
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DOI: 10.68627/jetsi.v1i1.12
<![CDATA[Information technology serves as a driver for software innovation at PT. Merpati Mahardika, which faces challenges regarding employee attendance tracking. Existing flaws in the system allow employees to manipulate QR codes; for instance, absent employees can still "clock in" by submitting a photo of the QR code via the app or sharing it through WhatsApp. Furthermore, the Global Positioning System (GPS) functionality is unreliable, allowing employees to use the attendance system from remote locations, such as their homes. Monitoring attendance is crucial for the company, given the vital role employees play in organizational growth and the implementation of innovations for corporate advancement. This research aims to develop a smartphone-based attendance application integrated with GPS and QR code scanning capabilities. The development process follows the Waterfall methodology, while data collection involved observation, interviews with the Personnel Manager, a literature review, and the distribution of online questionnaires. The system was designed using Use Case Diagrams and implemented with a main interface featuring registration, login, a dashboard, check-in/check-out menus, and a camera scanning function. Research results indicate that the GPS accurately determines employee locations while the system is in use. Consequently, the company has innovated by developing a mobile attendance system based on GPS and QR code technology, a solution highly anticipated by the organization. The system is expected to improve attendance discipline, enable real-time recording, and provide accurate attendance data, ultimately enhancing operational efficiency and employee discipline. The development process encompassed requirements analysis, workflow mapping, and User Interface (UI) design for PT. Merpati Mahardika’s attendance application.]]>
<![CDATA[Systems Engineering Approaches for AI-Integrated STEM Learning in Primary Education ]]>
<![CDATA[Jastin Martino]]>;
<![CDATA[Wiputra Cendana]]>
<![CDATA[ Journal of Engineering Technology and Science Integration (JETSI) Vol. 1 No. 1 (2026): Journal of Engineering Technology and Science Integration ]]>
Publisher : <![CDATA[PT. Nordeo Scholar Publishing ]]>
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DOI: 10.68627/jetsi.v1i1.13
<![CDATA[The operational integration of artificial intelligence within primary STEM education is currently stalled by persistent data silos, high pedagogical latency, and opaque algorithmic governance. To resolve this structural misalignment, this study develops and validates a formal Systems Engineering framework the Architect Model to establish a robust, human-governed data flow within cyber-physical learning environments. Utilizing an engineering-informed systematic literature review protocol, this investigation analyzed 30 high-maturity, peer-reviewed studies (2021–2026) using the Mixed Methods Appraisal Tool (MMAT) and rigorous thematic synthesis. The structural analysis reveals that current platforms frequently induce an efficiency fallacy and severe semantic degradation. These systemic failures are fundamentally driven by proprietary API lock-ins and heuristic limitations that actively disrupt cognitive load management and destabilize instructional stability. To rectify these deficiencies, the finalized Architect Model functions as a definitive system specification, re-engineering the classroom hierarchy by establishing the educator as the Primary Controller. This control architecture mandates a localized metadata registry to guarantee systemic interoperability, enforces explicit operating thresholds to maintain a high signal-to-noise ratio in student telemetry, and deploys modular adaptive feedback controllers to regulate cognitive throughput via closed-loop mechanisms. By subordinating machine autonomy to strict human governance, this framework provides the exact engineering tolerances required to sustain optimal, highly interoperable, and pedagogically secure STEM learning ecosystems.]]>
