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INOVTEK Polbeng - Seri Informatika
Published by Politeknik Negeri Bengkalis
ISSN : 25279866     EISSN : -     DOI : https://doi.org/10.35314
Core Subject : Science,
Computer Science & IT
The Journal of Innovation and Technology (INOVTEK Polbeng—Seri Informatika) is a distinguished publication hosted by the State Polytechnic of Bengkalis. Dedicated to advancing the field of informatics, this scientific research journal serves as a vital platform for academics, researchers, and practitioners to disseminate their insightful findings and theoretical developments. Scope and Focus: INOVTEK Polbeng - Seri Informatika focuses on a broad spectrum of topics within informatics, including but not limited to Web and Mobile Computing, Image Processing, Machine Learning, Artificial Intelligence (AI), Intelligent Systems, Information Systems, Databases, Decision Support Systems (DSS), IT Project Management, Geographic Information Systems, Information Technology, Computer Networks and Security, and Wireless Sensor Networks. By covering such a wide range of subjects, the journal ensures its relevance to a diverse readership interested in both the practical and theoretical aspects of informatics.
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 256 Documents
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A Study of DevOps Implementation to Accelerate Software Development in Start-Up Industry Angeline; Eryc; Deu, Indasari
INOVTEK Polbeng - Seri Informatika Vol. 10 No. 3 (2025): November
Publisher : P3M Politeknik Negeri Bengkalis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35314/35yd1802

Abstract

This study explores how implementing DevOps influences perceived productivity in software development within startup companies in Batam, Indonesia. A quantitative method was used through a structured survey involving 261 respondents from ten software houses. Data were analyzed using Structural Equation Modeling (SEM) with AMOS to assess relationships among seven constructs adapted from the Unified Theory of Acceptance and Use of Technology (UTAUT). Results show that Social Influence and Perceived Feasibility significantly affect the Intention to Adopt DevOps, which in turn has a positive impact on Perceived Productivity. Conversely, Performance Expectancy, Facilitating Conditions, and Perceived DevOps Practices show no significant effect. These findings highlight the importance of social support, collaborative culture, and technical readiness in shaping adoption intentions in resource-limited startups. The model demonstrates good fit (CFI = 0.924, TLI = 0.914, RMSEA = 0.065), explaining 74.2% of variance in perceived productivity, with the strongest path between Intention to Adopt DevOps and Perceived Productivity (β = 1.032, p = 0.001).
Implementation of Appsheet-Based Queue Management at Public Electric Vehicle Charging Stations SPLKU Muhammad Ridhan Fadli Rahman; Dian Permata Sari
INOVTEK Polbeng - Seri Informatika Vol. 10 No. 3 (2025): November
Publisher : P3M Politeknik Negeri Bengkalis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35314/cftr2425

Abstract

A significant increase in electric adoption in Indonesia has created high demand at Public Electric Vehicle Charging Stations (SPKLU). Reliance on manual queue management methods at these stations often results in vehicular congestion and unstructured recording processes. This study implements and evaluates "Antridaya," an application designed to streamline queue recording at SPKLUs, developed using the AppSheet platform. Employing a prototyping methodology, the research conducted two testing phases: functional validation by five experts across 32 system components and a feasibility assessment using the System Usability Scale (SUS) with 21 SPKLU officers. The results indicate that Antridaya is functionally valid, securing an average score of 3.9 on a 4.0 scale. Moreover, the system demonstrated high end-user feasibility with an average SUS score of 85.7. This score stability was confirmed via a 95% Confidence Interval (78.63–92.79), placing the application in the "acceptable" category with an "excellent" rating. This study confirms that AppSheet provides a viable solution for digitizing queuing processes, offering a valid, user-friendly tool that establishes a foundation for standardized data recording systems.
Implementation of Web-Based Inventory System With Email Notifications for Stock Efficiency and Project Management at CV Bonanza Naufal Meidhana Kenmahdy; Ahmad Jazuli; Esti Wijayanti
INOVTEK Polbeng - Seri Informatika Vol. 10 No. 3 (2025): November
Publisher : P3M Politeknik Negeri Bengkalis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35314/nggzee18

Abstract

Material inventory management at CV Bonanza is still conducted manually, leading to report delays and data inaccuracies. This research aims to design a web-based inventory information system to address these problems. This study employs the Waterfall model for the system development life cycle (SDLC). The requirements analysis phase (as the requirements baseline) was conducted qualitatively from February to April 2025 through structured interviews with 4 stakeholders (owner, admin, foreman, and project executor) and workflow observation (analysis of 50 transactions). The system was implemented using PHP Native and MySQL, with an innovation focus on stock-project management integration, event-driven email notifications (PHPMailer), and a project-check transparency feature. System testing used the Black Box Testing method, covering 55 test scenarios (encompassing 100% of functional requirements). Initial testing found 2 defects (a 3.6% failure rate), which were subsequently fixed, resulting in 100% of scenarios being declared "Valid" upon re-testing. The main outcome of this research is a system prototype that is functionally valid. Claims of "efficiency" and "accuracy" are limited to design targets, as this study was restricted to functional testing and did not include non-functional testing (such as performance, usability/SUS, or security) or quantitative post-implementation impact evaluation.
Optimization of Services Through the Development of a Website-Based Online Queueing System Iqbal, Arifal Muhamad; Sari, Dian Permata
INOVTEK Polbeng - Seri Informatika Vol. 10 No. 3 (2025): November
Publisher : P3M Politeknik Negeri Bengkalis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35314/pdx5hg42

Abstract

This study developed a web-based online queue system using the SDLC Prototype model to improve administrative service efficiency at PT. XYZ. System performance was evaluated by comparing the manual process with the new system using M/M/1 queueing theory parameters, including waiting time, service time, no-show rate, and service capacity. Observations indicated significant improvements: service time decreased from 8 minutes to 6 minutes, waiting time reduced from 28–32 minutes to 5–9 minutes, queue length decreased, and service capacity increased from approximately 35 to 45 participants per day. The no-show rate also dropped from 23–28% to 4–7% after participants could monitor the queue in real time. Usability testing was conducted using the System Usability Scale (SUS) with 23 respondents (15 participants and 8 staff). Scores were calculated using the standard odd-even item transformation, yielding an average of 84.89, categorized as “Good.” Participants generally gave higher scores than staff due to the complexity of the admin dashboard. These findings indicate that the system not only functions properly but also provides a measurable impact on improving service efficiency.
The Impact of Ephemeral Content on Digital Marketing Strategies: Efforts to Increase Consumer Engagement and Trust Christina, Lidya; Aklani, Syaeful Anas; Prasetyo, Stefanus Eko
INOVTEK Polbeng - Seri Informatika Vol. 10 No. 3 (2025): November
Publisher : P3M Politeknik Negeri Bengkalis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35314/y1nkwf45

Abstract

This study examines the influence of ephemeral content on consumer engagement and trust by integrating quantitative and qualitative evidence through a sequential explanatory mixed-method approach. Quantitative data were collected from 201 active social media users using validated measurement scales for ephemeral content, engagement, and trust (Cronbach’s α = 0.882). The statistical analysis consists of Pearson correlation, validity testing, and simple regression. Results show that ephemeral content significantly predicts consumer engagement (β = 0.570, p < 0.001; R² = 0.32) and consumer trust (β = 0.620, p < 0.001; R² = 0.38). These R² values indicate moderate explanatory power, consistent with the correlation coefficients. The qualitative phase involved semi-structured interviews with 20 participants to explain the mechanisms underlying the statistical relationships. Thematic analysis reveals three psychological factors that shape consumer responses: (1) real-time urgency that triggers FoMO-driven attention, (2) authenticity generated through minimally edited and spontaneous content, and (3) interactive features that foster a sense of participation. Integration of both datasets shows that ephemeral content is effective not merely because of its temporary nature, but because it combines immediacy, emotional proximity, and interactive cues that enhance perceived credibility.
Application of KNN Voting Classification and Naive Bayes for Classification of Type II Diabetes Mellitus Agung, I Gusti Agung Made Suparta Yasa; Muntina Dharma, Eddy; Widya Utami, Nengah
INOVTEK Polbeng - Seri Informatika Vol. 10 No. 3 (2025): November
Publisher : P3M Politeknik Negeri Bengkalis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35314/tynbfz55

Abstract

Type II diabetes mellitus (Type II DM) is a public health burden that requires fast and accurate early detection, particularly in primary care settings. Single machine-learning classifiers such as K-Nearest Neighbor (KNN) and Naive Bayes (NB) are widely used but have limitations, including the computational cost of KNN and the strong feature-independence assumption of NB. This study applies an ensemble Voting Classifier (VC) that combines KNN and NB to classify Type II DM using clinical data from 2,390 patients at Mengwi 1 Health Center. Following the CRISP-DM process, we evaluate the models under 80:20 and 70:30 train–test splits using accuracy, precision, recall, F1-score, and ROC/AUC. Compared with the KNN baseline, soft voting consistently improves performance: on the 80:20 split, accuracy increases from 80.33% to 81.59% (+1.26 percentage points) and F1-score from 79.52% to 80.91% (+1.39%); on the 70:30 split, accuracy increases from 80.47% to 82.01% (+1.54%) and F1-score from 79.65% to 81.24% (+1.59%). The soft-voting ensemble also yields higher AUCs, reaching 0.8138 (80:20) and 0.8213 (70:30), and outperforms both single models and hard voting. The novelty of this work lies in demonstrating that a lightweight KNN–NB soft-voting ensemble, designed for the computational constraints of a primary health center and evaluated with repeated cross-validation, can provide small but consistent gains over single classifiers on real DM data. These findings indicate that such an ensemble is a promising building block for clinical decision support in resource-limited primary care, although further calibration, external validation, and prospective testing are still required.
Development of a Project-Based Learning Model in a Learning Management System using an Iterative Incremental Approach Rice Novita; Medyantiwi Rahmawita M; Raudah Islamiah
INOVTEK Polbeng - Seri Informatika Vol. 10 No. 3 (2025): November
Publisher : P3M Politeknik Negeri Bengkalis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35314/dh4xc832

Abstract

Technological innovation in education has driven the adoption of Learning Management Systems (LMS) as a primary platform for digital learning. However, current LMS implementations remain focused on knowledge transfer and have not fully supported Project-Based Learning (PjBL), a model that emphasizes active learner engagement in producing concrete outputs. This study aims to develop a project-based learning model within a learning management system using an iterative incremental approach in the software engineering course. The development method refers to the ADDIE model (Analysis, Design, Development, Implementation, and Evaluation) to design project-based learning syntax, which is then integrated into the LMS using the iterative incremental approach and system design based on Object-Oriented Analysis and Design (OOAD). The resulting product consists of a project-based learning module and web-based learning media, both validated by education and information technology experts. Validation results show that the learning module obtained a validity score of 0.83, while the learning media (LMS) obtained a score of 0.84, both categorized as valid. These findings indicate that the initial design of the PjBL-integrated LMS aligns with pedagogical and technical requirements, although its effectiveness and practicality have not yet been tested and remain areas for further research. The contribution of this study lies in integrating RPL-specific PjBL syntax into LMS features developed using the iterative incremental model, providing a foundation for more adaptive and collaborative PjBL-oriented LMS development in digital learning.
Subject Selection Decision Support System Using the Weighted Aggregated Sum Product Assessment Method Setiawan, Mhd. Liandra; Sriani
INOVTEK Polbeng - Seri Informatika Vol. 10 No. 3 (2025): November
Publisher : P3M Politeknik Negeri Bengkalis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35314/s2d8vn67

Abstract

High school subject selection is crucial for aligning with students' interests and goals, but manual processes are often time-consuming and prone to errors. This study developed a decision support system using the WASPAS method, which combines WSM and WPM to produce a more stable and consistent evaluation of alternatives. A total of 35 10th-grade students of SMAN 16 Medan were recruited through total sampling using a Likert-scale questionnaire as the basis for the calculation. The system evaluation was verified on the entire data set, not just three samples like the previous version, to ensure the algorithm's suitability. The results show that the system generates interest recommendations based on the highest Qi score and is consistent with manual calculations, although its accuracy cannot yet be fully concluded. The distribution of student preferences is also presented, along with explanations of potential instrument bias and response bias as limitations of the study. Overall, this WASPAS-based system is considered capable of helping provide more objective and efficient subject selection recommendations.
Improving Internet of Things Cyber Attack Detection with Information Gain and Decision Tree Alvin Mufidha Ahmad; Fauzi Adi Rafrastara
INOVTEK Polbeng - Seri Informatika Vol. 10 No. 3 (2025): November
Publisher : P3M Politeknik Negeri Bengkalis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35314/p2c33t87

Abstract

The rapid proliferation of Internet of Things (IoT) devices within digital ecosystems has enhanced efficiency and availability but has also expanded the attack surface for cyber threats. This study aims to improve intrusion detection accuracy in IoT environments by addressing two key challenges: class imbalance and high feature dimensionality. Random Undersampling (RUS) is employed to mitigate data imbalance in the CIC IoT 2023 dataset, while feature selection is performed using the filter-based Information Gain method. A decision tree classifier is implemented and validated using k-fold cross-validation to ensure result reliability. Experimental results demonstrate that the proposed approach achieves an accuracy of 88.7%, outperforming a wrapper-based method, which attained 87.3%. These findings confirm that an appropriately designed filter-based feature selection strategy can effectively enhance the performance of intrusion detection systems for IoT security.
Computational Analysis of Student Stress on Social Media using Support Vector Machine and Latent Dirichlet Allocation Fauzan, Mochammad; Ashaury, Herdi; Ramadhan, Edvin
INOVTEK Polbeng - Seri Informatika Vol. 10 No. 3 (2025): November
Publisher : P3M Politeknik Negeri Bengkalis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35314/8jcvxk45

Abstract

This study develops a two-stage machine-learning framework to identify academic stressors among Indonesian university students using Twitter data. A Support Vector Machine (SVM) classifier was trained on manually annotated tweets and benchmarked against Naïve Bayes, logistic regression, and random forest, achieving an accuracy of 0.91 and a macro F1-score of 0.914, outperforming all baselines. Tweets classified as stress-related with ≥75% confidence were subsequently analyzed using Latent Dirichlet Allocation (LDA), which generated six coherent stressor categories. The framework reveals both structural academic pressures and culturally specific patterns, including references to “dosen killer” and emerging mental-health vocabulary. Contributions include the first Indonesia-focused stressor map derived from large-scale social media discourse and the integration of confidence filtering to enhance topic quality. While results demonstrate the feasibility of social-media–based stress detection, limitations remain regarding temporal drift, annotation bias, and demographic representativeness. Future research should incorporate real-time streaming pipelines, multimodal annotation, and longitudinal evaluation to enhance robustness and early-warning potential.

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