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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 543 Documents
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Sentiment Analysis of LinkAja Digital Wallet Application Reviews on Google Play Store using Transfer Learning IndoBERT Sandy Sanjaya; Rangga Gelar Guntara; Syti Sarah Maesaroh
Journal of Innovation and Technology Polbeng Series on Informatics (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/afjx7b16

Abstract

The LinkAja digital wallet receives an average rating of 3.5 on the Google Play Store despite having a higher number of user reviews than its competitors, indicating a strong need for data-driven evaluation of user satisfaction. This study performs sentiment classification on LinkAja user reviews using the IndoBERT model implemented within the CRISP-DM framework. A total of 1,483 reviews posted from January 1 to May 31, 2025, were analyzed through automatic labeling using a pretrained IndoBERT sentiment model and validated using an 80:20 hold-out scheme. Model performance was evaluated using accuracy, the F1 score, and the Matthews Correlation Coefficient (MCC) to address class imbalance. The results show high classification performance with 95% accuracy, a macro F1-score of 0.92, a weighted F1-score of 0.94, and an MCC of 0.90. Sentiment distribution reveals a dominance of negative sentiments at 59.5%, followed by positive (26.1%) and neutral (14.4%) sentiments. Theoretically, this study reinforces the superiority of IndoBERT over conventional machine learning methods for Indonesian sentiment analysis. Practically, the findings provide actionable insights into service improvements, particularly regarding transaction stability and system reliability.
UTAUT2 Approach to Byond BSI Adoption in Padang: Mediation and Moderation Effects Nurul Mu'tamim; Vidyarini Dwita
Journal of Innovation and Technology Polbeng Series on Informatics (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/b0cv3709

Abstract

The use of Islamic mobile banking apps is still not optimal, even though people’s interest in digital finance is growing. This study examines how social influence, enjoyment (hedonic motivation), and user habits shape people’s intention to use these apps and how that intention affects actual usage. It also tests whether word of mouth (WOM) strengthens the link between intention and behavior. Using a quantitative approach, the study surveyed 105 active BYOND BSI users in Padang City through purposive sampling. Data were analyzed with the PLS-SEM method using SmartPLS 4.0 and bootstrapping with 5,000 samples to ensure reliable results. The findings show that enjoyment and habit strongly affect intention, and intention significantly impacts actual use. However, social influence has no significant effect on intention, and WOM does not moderate the relationship between intention and use. Moreover, intention mediates the effects of enjoyment and habit on use but not that of social influence. Overall, the results highlight that users’ positive experiences and consistent habits are key drivers behind their intention and actual use of Islamic mobile banking services.  
Legal Service Satisfaction Assessment Information System at the Tapaktuan District Attorney's Office Using the Rating Scale and MAUT Methods Laura Melsya; Muhammad Dedi Irawan
Journal of Innovation and Technology Polbeng Series on Informatics (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/wd05bk49

Abstract

Public satisfaction assessment of legal services is an important indicator for improving the quality of public service. The Tapaktuan District Attorney’s Office requires a system that can measure public satisfaction objectively and systematically. This study aims to design and develop an information system for evaluating legal service satisfaction using the Rating Scale and Multi-Attribute Utility Theory (MAUT) methods. The rating scale is used to assign scores to five criteria: service speed, service friendliness, staff competency, report handling process, and facility comfort. The assessment results are then analyzed using MAUT to obtain overall satisfaction rankings. The system is developed as a web-based application for easy access by staff and the public. Respondent samples are selected proportionally and representatively, including the general public, complainants/defendants, witnesses, lawyers, and employees who have received legal services, using accidental sampling during the survey period, with 30 respondents to ensure data representativeness. From five criteria, three alternatives, and 30 respondent samples, the final score obtained is 4.51, indicating a “very satisfied” level for Case Reporting Services. This system is expected to assist the Tapaktuan District Attorney’s Office in evaluating and improving the quality of legal services consistently.
Digital Record Classification Using SVM on Permissioned Blockchain Hyperledger Fabric for Regional Status Visualization Muhammad Azhar Rasyad; Widdy Chandra Permana; Mohammad Syafrullah
Journal of Innovation and Technology Polbeng Series on Informatics (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/dfyc4v87

Abstract

This paper proposes a simulation-based model for classifying public aspiration records using the Support Vector Machine (SVM) Linear algorithm integrated with a permissioned blockchain network, Hyperledger Fabric. A total of 1,000 simulated text entries were manually labeled into two categories, complaints and aspirations, and three urgency levels (high, medium, and low) by the researchers. Text preprocessing included case folding, stopword removal, stemming, and TF–IDF vectorization. The model was evaluated using 5-fold cross-validation with an 80:20 train-test split and random seed 42, producing an accuracy of 77.5%, an F1-score of 0.78, and AUC of 0.86 for category classification, and 35.5% accuracy with AUC 0.58 for urgency classification. Integration testing with Hyperledger Caliper achieved 128 transactions per second throughput, 182 ms latency, and 2.4 s block commit time with an average block size of 412 KB, demonstrating efficient and verifiable data management. Although based on simulated data, the proposed SVM Blockchain architecture provides an initial foundation for secure, transparent, and data-driven decision-making in digital government systems.
Development of a Project-Based Learning Model in a Learning Management System using an Iterative Incremental Approach Rice Novita; Medyantiwi Rahmawita M; Raudah Islamiah
Journal of Innovation and Technology Polbeng Series on Informatics (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.
Design and Prototype Implementation of a Web-Based Car Rental Information System using Agile Scrum Fitto Victorya Harissa
Journal of Innovation and Technology Polbeng Series on Informatics (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/wfaayr38

Abstract

This study designs and implements a web-based car rental information system prototype to overcome manual process inefficiencies at the case-study company. Development followed Agile Scrum across several sprints that delivered core features: authentication, online booking, real-time vehicle management, transaction handling, and an integrated payment gateway. The technology stack comprises Laravel, MySQL, and XAMPP in a test environment. An early-stage evaluation was conducted in the partner’s operational setting using black-box testing per use-case scenario, along with measurements of process performance, data-recording accuracy, and user satisfaction. Results show transaction time decreased from 20–30 minutes to 5–10 minutes, recording errors dropped from approximately 10% to <1%, 85% of respondents reported satisfaction with booking convenience, and 90% considered the integrated payment helpful. These findings indicate that the prototype effectively improves operational efficiency and service quality, while underscoring the relevance of Agile practices for dynamic rental business needs. Future work includes standardized usability testing, load testing, and a more comprehensive security assessment.
Optimization of Stock Trading Strategies Using a Hybrid Reinforcement Learning and Forecasting Model Rezha Ikhwan Hidayat; Anggit Dwi Hartanto
Journal of Innovation and Technology Polbeng Series on Informatics (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: 10.35314/9vzmbf06

Abstract

Stock price prediction is an interesting challenge in machine learning due to the non-linear nature of the market. Although forecasting models can predict prices, they often do not provide optimal trading strategies. Reinforcement learning (RL) has the potential to optimize strategies, but it is highly dependent on the input states. This study integrates two methods—a CNN-LSTM forecasting model and RL (A3C)—to develop an algorithmic trading strategy. The model is evaluated using historical INDF stock data (2016–2024) with a data-split validation protocol of 80% training and 20% testing. Backtesting simulations on the period (Feb 2023–Dec 2024) show that the hybrid model achieves a cumulative total return of 121.44%. This result was obtained using an all-in trading strategy (one full position at a time) and includes transaction costs: a trading fee of 0.01% per transaction and a borrow interest rate of 0.0003% per day for short positions. This performance significantly outperforms traditional strategies: Buy and Hold (23.45%), MA Crossover (51.13%), RSI (9.09%), and MACD (−29.08%). The hybrid model also achieves a Sharpe Ratio of 2.381 (annualized, assuming a 0% risk-free rate).
Integration of MCDM and GIS in Household Gas Network Development Strategy Planning Through DSS Yossy Kurniawan Suprapto; Retnowati
Journal of Innovation and Technology Polbeng Series on Informatics (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/y1kmay03

Abstract

This research develops a Decision Support System (DSS) that integrates the Fuzzy AHP (FAHP), VIKOR, TOPSIS, and GIS methods to optimize the prioritization of the realization of Work Request (SPK) documents for the house holds gas network project in Tangerang City. FAHP is used to determine the weights of five main criteria, namely: workforce readiness (28%), material availability (22%), location accecibility (20%), customer urgency (18%), and permit status (12%). VIKOR and TOPSIS are used for ranking the alternative SPK, while GIS is used for spatial analysis and visualization. Testing (UAT) involving 10 end-users using a Likert Scale questionnaire and the System Usability Scale (SUS). The evaluation result show an SUS score of 82 (categorized as “excellent”) and a reduction in decision-making time from an average of 2 weeks to 2 days. The FAHP weighting results also demonstrated valid consistency (CR = 0.09). This system is proven to provide a comprehensive solution in supporting energy infrastructure project priority decisions by simultaneously considering technical, administrative, and geographical aspects.
Predicting Technical Intern Training Program Trainee Success: A Comparative Machine Learning Analysis For Risk Mitigation Syaban Maulana; Nenden Siti Fatonah; Gerry Firmansyah; Agung Mulyo Widodo
Journal of Innovation and Technology Polbeng Series on Informatics (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: 10.35314/1r93bf26

Abstract

Japan's demographic crisis has increased demand for the Technical Intern Training Program (TITP). However, for Sending Organizations (SOs) in Indonesia, this process carries high financial risk due to an upfront talent funding scheme, where significant costs (up to IDR 35,000,000) are paid in advance. Trainee failure (dropouts or runaways) leads to substantial bad debt. This research aims to develop and validate a robust machine learning model for risk mitigation. We compare XGBoost and Random Forest on a dataset of 784 historical trainee records, characterized by extreme class imbalance (75.5% majority class). To address prior methodological weaknesses and prevent data leakage, we implement a 10-fold stratified cross-validation pipeline incorporating StandardScaler and SMOTE. The results show XGBoost (mean macro F1-Score: 0.5470 ± 0.15) significantly outperforms Random Forest (mean macro F1: 0.5098 ± 0.15), which is confirmed as statistically significant (p=0.0384) by a paired t-test. Furthermore, SMOTE is validated as a superior imbalance strategy compared to class_weight (p=0.0076). SHAP analysis identified 'contract duration' and lifestyle factors (e.g., 'alcohol consumption') as key predictors. The final model effectively predicts 'Runaway' cases (F1=0.533) but struggles with 'Training Dropouts' (F1=0.170), indicating a key limitation and a need for temporal features in future work.
Implementation of Convolutional Neural Network and Support Vector Machine Classification for Disease Detection in Rice Plants Gema Umara Muhammad; Erna Zuni Astuti
Journal of Innovation and Technology Polbeng Series on Informatics (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/r2wzfn43

Abstract

Rice is a major staple crop that is highly susceptible to various leaf diseases, necessitating an accurate early detection method to prevent yield losses. This study proposes a hybrid approach combining Convolutional Neural Network (CNN) and Support Vector Machine (SVM) for rice leaf disease classification based on digital images. The CNN is employed as a deep feature extractor, while the SVM serves as the main classifier. The dataset consists of rice leaf images categorized into four disease types: bacterial blight, blast, brown spot, and tungro. The data were divided into training and validation sets, and the CNN model was trained for 10 epochs, achieving a validation accuracy of 98.14% at the 10th epoch. The extracted CNN features were then evaluated using different SVM kernels, namely Linear, Polynomial, RBF, and Sigmoid. The experimental results show that the Sigmoid kernel achieved the best performance with an accuracy of 49%, followed by Polynomial, RBF, and Linear kernels.

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