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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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Shap and Lime Analysis on CNN-GRU Deep Learning Models for IoT Network Intrusion Detection Satria Purfie Purnama Putra; 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: 10.35314/5r93tw26

Abstract

As digital devices evolve quickly and cyberattacks grow more diverse, the Internet of Things (IoT) ecosystem faces a much higher security risk than before. An AI-based security system is commonly used for post-attack mitigation. However, this approach has several issues, such as a high computational load, data imbalance, and a black box that does not explain the attack patterns. This study addresses a research gap by implementing a hybrid CNN-GRU architecture. With XAI, this architecture remains lightweight and robust while mapping out malicious attack patterns quite clearly. The technique breaks down these security incidents by providing both global and local explanations. This study uses two IoT datasets, BoT-IoT with 72 million records and IoTID20 with 625,783 records. The SMOTE post-split technique was performed on 80% of the total data to address data imbalance and avoid data leakage. To validate the results, stratified holdout is used to evaluate the training results. This research has very satisfying results with 99% accuracy and a 99% F1-score so that it can minimize errors in both datasets. The contributions made by this research are (1) adapting a stable model for anomaly classification, (2) handling data imbalance and avoiding data leakage, and (3) integrating SHAP and LIME to overcome black boxes.
Needs Analysis of a 360° Virtual Tour as Campus Information Medium at STTNF Using PIECES Annisa Nurul Nabilah; Jemiro Kasih
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/wgcvc241

Abstract

This study aims to analyze the system requirements for developing a 360° virtual tour as a campus information medium for Nurul Fikri Integrated Technology College (STTNF) using the PIECES model, comprising Performance, Information, Economy, Control, Efficiency, and Service dimensions. A descriptive qualitative approach was employed, with data collected through observation, semi-structured interviews, and documentation involving 15 informants consisting of prospective students, active students, and New Student Admission Team representatives. Data were analyzed through transcription, coding, categorization, technique triangulation, mapping into the PIECES dimensions, and requirements formulation. The credibility of the findings was strengthened through member checking. The findings reveal that existing campus information media at STTNF remain static and non-interactive, limiting users from obtaining a comprehensive overview of the campus environment. Based on the PIECES analysis, 23 functional requirements and 22 non-functional requirements were formulated, with information completeness, navigation support, and system performance emerging as the primary user priorities. An initial use case diagram was developed to translate the validated requirements into a preliminary system model. This study contributes a validated and user-centered requirements specification for campus-based 360° virtual tour development using the PIECES model, distinguishing itself from prior studies that focused predominantly on implementation and usability evaluation by emphasizing requirements elicitation prior to system development in higher education institutions.
Automated Fish Feeding System and Real-Time Water Quality Monitoring Based on Internet of Things for Milkfish Aquaculture Muhammad Hibrian Wiwi; La Ode M. Junaidin Sirza; Virna Astari Wally; Siti Halija Ulla
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/efdrvx61

Abstract

Milkfish (Chanos chanos) aquaculture in Indonesia still relies heavily on manual feeding and periodic water quality monitoring, which increases the risk of operational inefficiency and human error. This study designs and implements an integrated Internet of Things (IoT)-based prototype to automate feeding and monitor real-time water quality parameters, including temperature, turbidity, and water level, in milkfish ponds. The system uses an ESP32 microcontroller connected to a DS18B20 temperature sensor, HC-SR04 ultrasonic sensors for water level and feed volume detection, an FC-51 infrared sensor for feed presence detection, and a turbidity sensor. Data transmission is carried out using the MQTT protocol over WiFi and displayed through web and smartphone-based interfaces. The novelty of this study lies in the integration of automated feeding with multi-parameter real-time water quality monitoring specifically designed for milkfish pond conditions in Indonesia, incorporating MQTT-based communication architecture and a dual-platform monitoring interface. Prototype validation showed that scheduled feeding was executed successfully across three daily feeding sessions. Sensor testing produced small measurement deviations, with a temperature deviation of 0.1°C, a water level deviation of 0.2 cm, and a turbidity deviation of 0.2 NTU. The MQTT communication latency ranged from 120 to 180 ms under normal conditions. These results confirm that the proposed system effectively supports automated feeding, real-time pond monitoring, and remote management with low-cost IoT infrastructure.
Web-Based Job Recommendation Based on LinkedIn Profiles Using Domain-Aware SBERT Retrieval and TF-IDF Reranking Maharaya Bintangku Aridhana; Prio Handoko
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: 10.35314/12r31r31

Abstract

Online job search often relies on keyword matching, while the semantic relationship between candidate profiles and job descriptions may not be captured adequately. This study develops a web-based job recommendation system based on LinkedIn-style candidate profiles using SBERT retrieval and domain-aware TF-IDF reranking. Candidate profiles are constructed from target role, headline, skills, experience, education, preferred location, and work preference, with non-English input translated into English when needed. The job corpus consists of approximately 1.3 million job postings represented by precomputed 384-dimensional SBERT embeddings. The system retrieves initial candidates using cosine similarity and reranks them using TF-IDF similarity with domain, experience, and location constraints. Manual evaluation on 1,012 judged profile-job pairs shows that the proposed method achieves Precision@5 of 0.428, Precision@10 of 0.368, NDCG@10 of 0.531, and MRR of 0.605. An additional validated pseudo-label evaluation achieves Precision@5 of 0.840, with 83.33% agreement and a Cohen’s Kappa of 0.75 against human-checked samples. These results indicate that semantic retrieval combined with explainable domain-aware reranking can improve the relevance of web-based job recommendations.
Weather Prediction Using CNN-LSTM-Based AI for Weather Pattern Analysis in Banten Province Advani Rayandra Kahfi; Prio Handoko
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/ejaw3909

Abstract

Weather variability in Banten Province poses challenges across various sectors, including community activities, agriculture, and disaster preparedness, necessitating accurate weather prediction methods. This study proposes a hybrid Convolutional Neural Network–Long Short-Term Memory (CNN-LSTM) model to predict air temperature and rainfall based on historical weather time-series data. The dataset was obtained from the Open-Meteo API and BMKG for the observation period from January 2019 to May 2024. Input variables include air temperature, rainfall, wind speed, sea surface temperature anomaly, and the El Niño–Southern Oscillation (ENSO) index. The data preprocessing stages involve data cleaning, normalization using Robust Scaler, and the construction of data sequences using the sliding window method prior to the model training process. Model performance was evaluated using Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) and compared against baseline models. The experimental results demonstrate that the CNN-LSTM model achieves an MAE of 0.60°C and an RMSE of 0.73°C for air temperature prediction and an MAE of 6.15 mm and an RMSE of 8.31 mm for rainfall prediction. The prediction outcomes were subsequently integrated into a web-based dashboard to facilitate information visualization. Initial validation against BMKG observation data in South Tangerang showed a relatively low temperature deviation during the testing period. These findings confirm that the proposed approach has adequate potential to support short-term weather prediction systems in Banten Province.
Implementation of a Convolutional Neural Network Using VGG19 for Ogan Malay Script Recognition Steven Liem; Hafiz Irsyad
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/annm4j49

Abstract

Regional languages and scripts, including the Melayu Ogan script, face the threat of extinction due to declining usage and limited digital documentation in the modern era. While current Indonesian script research primarily focuses on popular scripts, research addressing the Ogan Malay script remains severely limited. To address this gap, this study provides one of the earliest implementations of the Convolutional Neural Network (CNN) VGG-19 architecture specifically designed for Ogan Malay script classification. This research utilizes a primary dataset provided by the Language Center of South Sumatra Province, consisting of 185 distinct character classes, with each class initially containing one original image. The VGG-19 architecture is applied and supported by data augmentation techniques to enrich spatial variability, followed by evaluation using k-fold cross-validation. Evaluation results demonstrate excellent classification performance. The model achieved maximum convergence without any indications of overfitting at an optimal configuration of 30 epochs with a learning rate of 0.0001. This configuration successfully resulted in an accuracy of 99.14%, a precision of 0.9870, a recall of 0.9914, and an F1-score of 0.9885. The success of this classification model provides a strong foundation for future real-time regional script recognition applications to support cultural preservation.
Low-Sugar Diet Recommendations for Bangrajanmuaythai Boxing Athletes Using Collaborative Filtering Ananda Gilang Ariyanto; Prio Handoko
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/wxvz1f16

Abstract

Adjusting diet patterns according to nutritional requirements, training intensity, and an athlete's physical condition is often a challenge in implementing a healthy diet, particularly a low-sugar food diet. This study aims to develop an artificial intelligence (AI)-based recommendation system that can help boxing and Muay Thai athletes in implementing a more targeted diet program through food recommendations tailored to their individual behaviors and nutritional needs. The methods used are collaborative filtering with a nutrition scoring approach, athlete preference analysis, and dynamic nutrition planning. The results show that the developed system, namely the Smart Nutrition System, is able to provide recommendations based on similarities among athletes’ preferences and nutritional requirements, thus supporting more effective decision-making in managing athlete diet patterns. Furthermore, the Smart Nutrition System also has the potential to evolve into an “athlete intelligence nutrition platform" that supports the implementation of personalized nutrition for combat sports athletes to support athlete performance.
Web-Based Customer Loyalty Point System Using QR Code with Whatsapp Notification and Reward Management at Bismole Elektrik Store Qatrhunnada Abiyu Akhdan; Aditya Akbar Riadi; Ahmad Abdul Chamid
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/apv6sm19

Abstract

The development of information technology encourages retail business operators to implement digital systems to improve service quality and operational efficiency. At Bismole Elektrik Store, the processes of recording customers, purchase transactions, and calculating loyalty points were previously done manually, leading to data-recording errors and slowing down the service process. Based on the observation of 120 customer transaction data and interviews with 2 store owners and cashiers, several issues were found, such as difficulties in searching for customer data and discrepancies in point calculations. This research aims to develop a web-based customer loyalty point system using QR codes as a digital customer identity integrated with reward management, sales reports, and real-time WhatsApp notifications. The system development uses the Waterfall method, which consists of the stages of requirements analysis, design, implementation, testing, and maintenance. The system is developed using the programming languages PHP, HTML, CSS, JavaScript, and the MySQL database. The system evaluation was conducted using the Black Box Testing method with 9 testing scenarios and User Acceptance Testing involving 5 users consisting of the store owner, cashier, and customers. The results of the Black Box Testing showed that all system features operated with a success rate of 100%, while the User Acceptance Testing results indicated a user satisfaction level of 92%, demonstrating that the system is easy to use and capable of supporting store operational activities. The research results show that the implementation of QR codes can accelerate the customer identification process, automate point calculations, manage the reward redemption process, and provide transaction information through WhatsApp notifications. Thus, the developed system can enhance the efficiency and accuracy of managing the customer loyalty program at Toko Bismole Elektrik.
Implementation of YOLO26 for Mold Detection on White Bread Based on Digital Imagery Malvin Hendrawan; Yoannita
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/ybbmhr95

Abstract

White bread is highly susceptible to visible mold contamination, which causes physical deterioration and potential health risks. Conventional manual visual inspection is slow, subjective, and inconsistent, necessitating an automated detection system. This study implemented the YOLO26n algorithm for mold contamination detection on white bread based on digital imagery. A primary dataset of 300 images (150 fresh bread and 150 moldy breads) was collected independently, annotated via Roboflow, and split into 70% training, 20% validation, and 10% testing. The model was trained on Google Colab using the MuSGD optimizer with 200 epochs. The YOLO26n model achieved an overall precision of 0.827, recall of 0.734, and mAP50 of 0.711, with an inference speed of 8.1 ms per image, demonstrating its potential as a fast and lightweight solution for automated mold inspection, though further improvement in moldy bread detection performance is required before reliable deployment in bakery production lines.
Optimization of MobileNet Architecture with Ghost Module for Dental Enamel Caries Classification M. Zaky Naufal Farisky; Yohannes
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/ar0ts195

Abstract

Dental and oral diseases, particularly dental caries, represent a global health issue that requires early identification at the enamel stage to prevent further demineralization and damage. The utilization of artificial intelligence technology through Convolutional Neural Networks (CNNs) has been widely applied for medical image analysis. However, complex conventional models often incur high computational loads. Therefore, this study aims to implement and evaluate MobileNet variants (V1, V2, V3, and V4) optimized using the Ghost Module to classify dental caries images. The integration of the Ghost Module aims to mitigate feature redundancy and enhance feature representation without compromising image extraction quality. A dataset of 2,000 clinical dental images from the public "Caries-Spectra," categorized as advanced enamel caries, early-stage enamel caries, and no enamel caries, was curated and expanded to 12,000 images using preprocessing and augmentation. The dataset splitting was executed with a final learning proportion of 80% training data, 10% validation data, and 10% testing data. The image preprocessing utilized histogram equalization, CLAHE, and adaptive thresholding methods. The overall hybrid architecture was then evaluated based on performance metrics (such as accuracy, precision, recall, and F1 score). The experimental results demonstrate that the integration of the Ghost Module consistently enhances the classification accuracy across all MobileNet variants. The proposed Hybrid MobileNetV1 with Ghost Module achieved the highest performance, securing an overall accuracy of 96.83%, with well-balanced precision and recall across all enamel caries stages

Page 35 of 55 | Total Record : 543

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