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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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UNO Studio's Digital Reservation and Payment Process System Uses a Prototype Approach and Midtrans Payment Integration Rayangga Fariansyah Arkanda; Wildan Suharso
Journal of Innovation and Technology Polbeng Series on Informatics (INOVTEK Polbeng - Seri Informatika) Vol. 11 No. 1 (2026): February
Publisher : P3M Politeknik Negeri Bengkalis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35314/reknzy77

Abstract

Digital transformation is crucial for small and medium-sized enterprises (SMEs) to remain competitive and more efficient in the modern era. This research focuses on the digitalization process of UNO Studio, a company that provides photography and studio rental services. Currently, UNO Studio faces operational challenges due to its manual reservation system. This manual method can lead to delays, scheduling errors, and inefficiencies. Therefore, this research aims to upgrade UNO Studio's reservation system to a web-based system fully integrated with a digital payment system. The method used is a prototype approach applied in the software reengineering process. The system was built using HTML, CSS, and JavaScript, with Supabase as the data manager and Midtrans to integrate payments automatically and in real time. Black-box testing results show that all key system features run smoothly without errors. Furthermore, User Acceptance Testing (UAT) results indicate a user satisfaction rate of 88.70%, considered excellent. Overall, this system successfully automates the entire reservation and payment process, improving operational efficiency and service accuracy and enhancing the quality of customer service through secure and easy-to-use digital integration.
Application of Genetic Algorithm and Or-Tools for Cloud-Based Course Scheduling Optimization Salamul Jabbar; Safwandi; Kurniawati; Eva Darnila; Wahyu Fuadi
Journal of Innovation and Technology Polbeng Series on Informatics (INOVTEK Polbeng - Seri Informatika) Vol. 11 No. 1 (2026): February
Publisher : P3M Politeknik Negeri Bengkalis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35314/qymmt569

Abstract

Course scheduling in higher education institutions is a complex combinatorial optimization problem involving numerous constraints such as lecturer availability, room capacity, time slots, and course distribution across semesters. Manual scheduling practices often result in conflicts, inefficient resource utilization, and prolonged preparation time. This study proposes a hybrid course scheduling system that integrates a genetic algorithm (GA) and constraint programming using the CP-SAT solver from OR-Tools. The GA is employed in the first phase to generate optimal course sections based on student enrollment, lecturer workload, and capacity constraints. The best solution produced by the GA is then refined using CP-SAT to generate a conflict-free timetable that satisfies all hard constraints, including lecturer, room, and time conflicts, while also optimizing selected soft constraints. The proposed system is implemented as a web-based application deployed on Microsoft Azure, enabling scalability and accessibility. Experimental results using real academic data demonstrate that the hybrid approach successfully produces feasible schedules with zero conflicts and significantly reduces scheduling time compared to manual methods. The results confirm that the integration of GA and CP-SAT provides an effective and flexible solution for university course scheduling problems.
Implementation of AES-128 Encryption for Fingerprint Template Protection in ESP32-Based Biometric Ticketing System Muhammad Asep Subandri; Agus Tedyyana; I Gusti Agung Putu Mahendra
Journal of Innovation and Technology Polbeng Series on Informatics (INOVTEK Polbeng - Seri Informatika) Vol. 11 No. 1 (2026): February
Publisher : P3M Politeknik Negeri Bengkalis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35314/mkks1830

Abstract

Biometric ticketing systems utilizing fingerprint recognition provide enhanced security and convenience for passenger identification in public transportation. However, the transmission of fingerprint templates over wireless networks without adequate cryptographic protection exposes the system to interception attacks and privacy breaches. This research implements AES-128 encryption in Cipher Block Chaining (CBC) mode to protect fingerprint templates transmitted within an ESP32-based biometric ticketing system. The implementation leverages the ESP32’s integrated mbedTLS library with hardware acceleration to achieve efficient cryptographic operations. Experimental evaluation using 10 fingerprint template samples demonstrates a 100% success rate for encryption-decryption operations. Performance measurements indicate an average encryption latency of 2.30 ms and decryption latency of 2.10 ms, with a data size overhead of 32 bytes (6.25%) due to Initialization Vector (IV) and PKCS7 padding. The results confirm that the proposed encryption scheme effectively secures biometric data transmission while maintaining system responsiveness suitable for real-time applications.
Design of an Intelligent Vehicle Manifest Recording System at the Bengkalis-Sungai Pakning Ro-Ro Ferry Crossing Based on Deep Learning and Optical Character Recognition Jaroji; Danuri; Agus Tedyyana
Journal of Innovation and Technology Polbeng Series on Informatics (INOVTEK Polbeng - Seri Informatika) Vol. 11 No. 1 (2026): February
Publisher : P3M Politeknik Negeri Bengkalis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35314/894v9b70

Abstract

Vehicle manifest recording in Ro-Ro ferry services is still predominantly conducted manually, which may lead to operational inefficiencies and data inconsistencies. This study presents an automated vehicle manifest recording system for the Bengkalis–Sungai Pakning Ro-Ro ferry crossing by leveraging deep learning and Optical Character Recognition (OCR) technologies. The proposed system utilizes CCTV or IP cameras to capture vehicle images, performs frame extraction from video streams, and applies YOLOv11 for real-time vehicle and license plate detection. The detected license plate regions are subsequently processed using an OCR module to extract textual vehicle identification information. The detection model was trained using a publicly available vehicle and license plate dataset. Experimental evaluation on the vehicle and license plate dataset shows that the YOLOv11 model achieves a precision of 85.9%, recall of 84.0%, and mAP@0.5 of 87.8% for vehicle and plate detection. OCR evaluation conducted on real operational test images indicates a recognition success rate of 57.5%, with an average confidence score of 0.63 for successfully recognized plates. Further analysis reveals that illumination level and plate scale (distance proxy) are the dominant factors affecting OCR performance, while tilt angle exhibits moderate influence. These results indicate that the proposed framework provides reliable visual detection performance and identifies critical environmental constraints that must be addressed for robust automated manifest deployment in Ro-Ro ferry environments.
Sentiment Analysis of the Free Nutritious Meal Program Using IndoBERT and RCNN Methods Ancilla Nebrisca Valonika; Kristoko Dwi Hartomo
Journal of Innovation and Technology Polbeng Series on Informatics (INOVTEK Polbeng - Seri Informatika) Vol. 11 No. 1 (2026): February
Publisher : P3M Politeknik Negeri Bengkalis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35314/qg9bfb89

Abstract

This study examines public sentiment regarding the Free Nutritious Meal Program through a deep learning-based sentiment classification methodology applied to X and TikTok. The suggested method uses a hybrid IndoBERT RCNN architecture, with IndoBERT being used to extract features and RCNN being used to classify sentiment. There are 10,000 comments from each platform in the dataset. These comments went through preprocessing and sentiment labeling steps. Model evaluation was conducted using stratified K-fold cross-validation with different combinations of learning rate, batch size, and epochs. The best configuration achieved an accuracy and F1-score of 78% on X and 83% on TikTok. The model performs well in identifying overall sentiment patterns, although neutral sentiment remains challenging to classify, particularly in X data containing sarcastic or indirect language. These findings provide empirical insights into cross-platform sentiment characteristics and highlight the potential of this approach for testing sentiment monitoring strategies across platforms.
Comprehensive Analysis: A Review of Loan Origination Systems from Information Systems and Regulatory Perspectives R. Ali Fajar Saleh; Patah Herwanto; Harmansyah Nasution
Journal of Innovation and Technology Polbeng Series on Informatics (INOVTEK Polbeng - Seri Informatika) Vol. 11 No. 1 (2026): February
Publisher : P3M Politeknik Negeri Bengkalis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35314/s2302h34

Abstract

The Loan Origination System (LOS) has become a key infrastructure in Indonesia’s digital mortgage lending ecosystem, where technological innovation increasingly intersects with regulatory governance. This study examines LOS through an integrated perspective that bridges information systems architecture and legal-regulatory frameworks. Using a qualitative normative-analytical approach grounded in systematic document analysis (2021–2026) and thematic synthesis, the research identifies a triple-layer compliance gap: a regulatory gap in technical specification, an implementation gap between regulatory intent and system design, and a legal defensibility gap concerning evidentiary robustness. The study proposes a conceptual legal-by-design framework integrating technical security, regulatory alignment, and evidentiary considerations within a unified architectural model. Rather than offering a validated industry standard, the framework serves as an analytical proposal to inform future empirical research and institutional system development.
Strategic Information Systems Planning in the Sheet Glass Distribution Industry Angelique Stefany Rusli; Hanna Prillysca Chernovita
Journal of Innovation and Technology Polbeng Series on Informatics (INOVTEK Polbeng - Seri Informatika) Vol. 11 No. 1 (2026): February
Publisher : P3M Politeknik Negeri Bengkalis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35314/8k05fx86

Abstract

This study aims to design strategic planning for Information Systems (IS) and Information Technology (IT) at PT. Sentral Kaca Cirebon, a flat glass distributor facing operational efficiency challenges. Amidst the increasingly fierce competition in the building materials industry, the current manual processes are the main obstacle to company agility. This study uses a qualitative method with the Ward and Peppard framework of the Digital Strategy Model (DSM) methodology to align business strategy with IS/IT strategy. Environmental analysis was conducted using SWOT analysis, Porter's value chain, Porter's five forces, PESTLE, and McFarlan's strategic grid. The results of the study formulate a roadmap and portfolio of future applications called "SISKA" (Central Glass Information System), which includes an inventory management module, a B2B customer portal, and a glass cutting optimization system. The implementation of this strategy is projected to improve operational efficiency, stock data accuracy, and the company's competitiveness in the West Java regional market.
Implementation U-Net for Semantic Segmentation and Perspective Transformation in Floor Tile Virtual Try-On Systems Fahrul Rozi; Ayu Ratna Juwita; Yusuf Eka Wicaksana; Deden Wahiddin; Sutan Faisal; Dwi Sulistya Kusumaningrum
Journal of Innovation and Technology Polbeng Series on Informatics (INOVTEK Polbeng - Seri Informatika) Vol. 11 No. 3 (2026): August (Inpress)
Publisher : P3M Politeknik Negeri Bengkalis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35314/68r7wj29

Abstract

Conventional floor tile VTO systems rely on printed catalogs, physical samples, or marker-based AR, making it difficult for consumers to visualize ceramic products in real rooms. This study proposes a markerless VTO framework integrating U-Net floor segmentation and homography-based perspective transformation. The model was pre-trained on 5,285 SUN RGB-D images and fine-tuned on 1,338 customer-room images from PT Concord Industry, divided into training, validation, and independent test sets using a 70:20:10 ratio. Segmentation was evaluated using IoU, Dice Coefficient, Pixel Accuracy, Precision, and Recall. Perspective alignment was evaluated using four-corner Mean Angular Error (MAE) on two room images representing conditions with and without excessive furniture, while system functions were validated through black-box testing. The fine-tuned U-Net with a ConvNeXt-V2-Huge encoder achieved an IoU of 0.9483, a Dice Coefficient of 0.9671, a Pixel Accuracy of 0.9891, a Precision of 0.9675, and a Recall of 0.9702. The perspective module achieved an average MAE of 5.92° and produced visually coherent tile alignment. These results demonstrate the framework's potential to support realistic floor tile visualization and reduce uncertainty during product selection.  
Design and Evaluation of a REST API Backend with Payment Integration for AR Self-Service Retail Aji Sapta Pramulen; Jauari Akhmad Nur Hasim; Irma Wulandari; Fardani Annisa Damastuti; Ibrohim Yofid Fananda
Journal of Innovation and Technology Polbeng Series on Informatics (INOVTEK Polbeng - Seri Informatika) Vol. 11 No. 3 (2026): August (Inpress)
Publisher : P3M Politeknik Negeri Bengkalis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35314/tczw1281

Abstract

Augmented reality (AR) virtual try-on has become common in self-service retail, yet most research still concentrates on the frontend, and the server side and its measured performance receive little attention. This study designed, implemented, and evaluated a REST API backend for AR retail transactions, including end-to-end payment, in a smart-mirror deployment. Built on Laravel, the backend exposes a single JSON contract to both the web-based AR try-on client and an administrative dashboard, protects every privileged endpoint with JWT, and integrates a Midtrans Snap gateway through an asynchronous, SHA-512-verified webhook that reconciles each transaction server-to-server, independently of client connectivity. The backend was evaluated using four methods: black-box testing of twelve CRUD modules, five formal integration scenarios, a load test from one to one hundred concurrent users (about four thousand requests), and a structured expert review. Under the intended single-station workload, login averaged about 0.4 s at a 0.83% error rate and catalog reads about 0.15 s with no errors; the read endpoints remained error-free up to fifty concurrent users, whereas the password-hashing path saturated first. Three experts rated the system 120 of 135 (“very good”). These results apply to the single-station scenario tested, not to multi-store or high-concurrency deployment.
Comparative Analysis of Performance and Interpretability of XGBoost and TabNet Models in IDS Using XAI Ahmad Jauharul Ilmi; Denar Regata Akbi
Journal of Innovation and Technology Polbeng Series on Informatics (INOVTEK Polbeng - Seri Informatika) Vol. 11 No. 3 (2026): August (Inpress)
Publisher : P3M Politeknik Negeri Bengkalis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35314/js504z34

Abstract

Cyberattacks are becoming a more apparent danger to modern network traffic. Robust and transparent Intrusion Detection Systems (IDS) are increasingly needed to counter this massive wave. The emergence of machine learning (ML) and deep learning (DL) offers a huge advantage in detecting this wave with high precision; nevertheless, they also have a downside due to their secretive nature regarding decision-making algorithms, often referred to as the “black-box” problem. Digital forensics is affected by this nature as well. As a result, XGBoost (ML) and TabNet (DL) were selected and compared based on their performance and interpretability using Explainable AI (XAI) to understand the reasoning behind their decisions with the CIC-IDS-2017 dataset in this study. Both models naturally achieved extremely high classification capabilities, with XGBoost slightly outperforming TabNet in overall detection reliability and in minimizing false negatives. The XAI evaluation additionally uncovered equally valid decision-making logic: TabNet prioritizes connection states and temporal variances, while XGBoost primarily relies on rigid volumetric payload statistics. LIME analysis also affirms that both models maintain high consistency with SHAP global explanations. In summary, XGBoost is recommended as the primary real-time detector due to its superior precision and transparent logic, while TabNet serves as a secondary validator.

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