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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 42 Documents
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Search results for , issue "Vol. 11 No. 1 (2026): February" : 42 Documents clear
A Comparative Analysis of Deep Learning Models for Stock Price Prediction Ayu Nandia Lestari, I Gusti; Deviana; Tubagus Mahendra Kusuma
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/aweyzn77

Abstract

Indonesian equities exhibit high volatility and non-stationary dynamics, making consistent price forecasting difficult under realistic deployment settings. This study presents a comparative benchmark of Long Short-Term Memory (LSTM) and Bidirectional LSTM (Bi-LSTM) for one-step-ahead (t+1) stock price prediction using Walk-Forward Validation (WFV) to preserve temporal causality and avoid optimistic single-split estimates. Historical data are retrieved from Yahoo Finance and modeled in a multivariate OHLCV setting (Open, High, Low, Close, Volume). After missing-value removal, feature standardization, and Min–Max scaling, the series is converted to supervised samples via a sliding window with lookback = 30 trading days; evaluation is focused on the Close variable. Model performance is assessed using MAE, RMSE, and R², including inter-fold variability to quantify stability across market regimes. Across five Indonesian tickers (AGRO, ADES, ADMF, AALI, ADHI), LSTM consistently outperforms Bi-LSTM (5/5 tickers) in both MAE and RMSE, indicating that the added bidirectional complexity does not translate into improved out-of-sample forecasting under WFV. The best error performance is achieved by LSTM on AGRO (MAE = 26.99, RMSE = 32.72), while the least-negative goodness-of-fit is observed on LSTM AALI (R² = -0.63), suggesting that both deep models may still underperform naïve baselines in several folds. Overall, the results support LSTM as a more stable and implementation-ready benchmark for Indonesian stock forecasting under time-aware evaluation, while highlighting the need for explicit baseline comparisons and stronger feature/target designs to improve out-of-sample generalization.
Alzheimer's Disease Classification Using the Tabnet Model Enhanced by Hyperparameter Optimization Triyadi, M Bagus; Sri Kusuma Aditya, Christian
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/yb05hg15

Abstract

Alzheimer's disease is a progressive neurodegenerative disorder that leads to a gradual decline in cognitive function and remains challenging to diagnose at an early stage, as clinical symptoms often emerge after substantial brain damage has occurred. Therefore, accurate and efficient predictive models based on clinical data are essential to support early detection. Recent advances in deep learning for tabular data, particularly the TabNet model, enable adaptive feature selection through attention mechanisms while preserving interpretability. This study applies TabNet for Alzheimer’s disease classification using clinical tabular data and enhances its performance through hyperparameter optimization employing grid search, random search, and Bayesian optimization. Model evaluation was conducted using accuracy, area under the curve (AUC), confusion matrix analysis, and execution time. Experimental results show that random search achieved the highest classification accuracy of 90.53%, whereas Bayesian optimization obtained the highest AUC of 94.82%, indicating superior discriminative capability. These results demonstrate that integrating TabNet with appropriate hyperparameter optimization strategies provides a competitive, efficient, and interpretable approach for Alzheimer’s disease classification, supporting its potential application in data-driven clinical decision support systems.
A PSO-Optimized Stacking Ensemble with Hybrid SMOTE-NC and Tomek Links for Bid-Based Winning Prediction in Procurement Projects Handoko, Kokoh; Purnomo, Agus; Mulyati, Erna
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/dft7w786

Abstract

This research aims to establish a classification model for the prediction of procurement winning outcomes based on bid value and owner cost estimation data. The main challenge in procurement analysis lies in severe class imbalance and complex non-linear relationships among pricing and procurement attributes. The research object utilizes procurement tender data from PT Pos Indonesia, including project owner, owner cost estimation, bid value, and procurement method. The proposed approach integrates hybrid SMOTE and Tomek links for class balancing, regulation-driven feature engineering, and a stacking ensemble model optimized using particle swarm optimization. The stacking framework combines Random Forest, Extra Trees, and Gradient Boosting as base learners. The experimental evaluation demonstrates that the proposed approach delivers the strongest performance, achieving an AUC of 0.92, an accuracy of 0.89, and an F1-Macro of 0.81, thereby surpassing all individual classifiers and homogeneous ensemble methods considered in this study. This study concludes that the hybrid optimization-based ensemble approach is effective for improving procurement winning prediction accuracy and provides a reliable decision-support tool for data-driven and regulation-compliant procurement processes.
Applying Clustering Techniques for Customer Segmentation Based on Shipping Behavior, Cost, and Satisfaction in Logistics Services Sunara, Jaka; Purnomo, Agus; Maniah
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/h62gy548

Abstract

In modern logistics operations, behavioral data-based customer segmentation plays a crucial role in optimizing service delivery and achieving competitive differentiation. This study proposes a clustering-based approach using K-Means, Agglomerative, and Gaussian Mixture to segment sender-level customer profiles in a logistics network based on shipping cost and delivery duration, while customer satisfaction is used for post-cluster profiling and interpretive analysis. A comprehensive preprocessing pipeline is implemented, including temporal feature engineering and sender-based statistical aggregation. Grid search is used for hyperparameter tuning, and clustering performance is evaluated using the Silhouette Score, Calinski-Harabasz Index, and Davies-Bouldin Index. The results indicate that K-Means with two clusters achieves the highest silhouette score (0.843), outperforming the aggregative and Gaussian mixture models. Principal Component Analysis (PCA) reveals clear separability between clusters labeled as Efficient Senders and Costly & Slow Senders. These findings provide actionable information for logistics service providers to improve pricing strategies, delivery efficiency, and customer satisfaction through intelligent segmentation.
UNO Studio's Digital Reservation and Payment Process System Uses a Prototype Approach and Midtrans Payment Integration Fariansyah Arkanda, Rayangga; Suharso, Wildan
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 Jabbar, Salamul; Safwandi; Kurniawati; Eva Darnila; Fuadi, Wahyu
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.
Optimising Financial Transparency in the Oregon Cluster through the Development of a Web-Based System and Intelligent AI Chatbot Tyoffadhil Haidar; Permata Sari, Dian
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/r3k83309

Abstract

Managing finances within neighborhood associations frequently encounters persistent hurdles regarding transparency and the speed of information delivery. This study develops a web-based financial platform for the Oregon Cluster, uniquely integrated with a Gemini-powered virtual assistant to enable real-time data inquiries. The novelty of this research lies in the integrated service model that pairs an automated payment gateway with conversational AI, effectively removing traditional bottlenecks in community reporting. Performance evaluation showed significant improvements compared to manual processes payment verification latency dropped by over 99.9% (from 24–48 hours to less than 10 seconds), and the time required for residents to access specific balance information was reduced by approximately 95% (from 5–10 minutes to near-instantaneous retrieval). Functional validation via Black Box Testing achieved a 100% success rate across 15 core modules. Furthermore, a System Usability Scale (SUS) evaluation with 21 respondents yielded an average score of 85.36, placing the system in the "Good" to "Excellent" category. While highly effective, feedback indicates a need to further simplify the administrative interface to reduce the treasurer's cognitive load. Overall, this integrated system markedly strengthens local financial accountability and empowers residents to monitor cash reports independently and efficiently
Implementation of AES-128 Encryption for Fingerprint Template Protection in ESP32-Based Biometric Ticketing System Subandri, Muhammad Asep; Tedyyana, Agus; Putu Mahendra, I Gusti Agung
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; Tedyyana, Agus
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
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.

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