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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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Application of KNN Voting Classification and Naive Bayes for Classification of Type II Diabetes Mellitus I Gusti Agung Made Suparta Yasa Agung; Eddy Muntina Dharma; Nengah Widya Utami
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/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.
An Indonesian Chatbot for Disease Diagnosis Using Retrieval-Augmented Generation Muhammad Adrinta Abdurrazzaq; Edwin Lesmana Tjiong; Aulia Fasya; Michelle Hiu; Joses Tanuwidjaya
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: 10.35314/9nnkn955

Abstract

The rapid advancement of Large Language Models (LLMs) has enabled their use in medical information systems, although challenges such as hallucinations, domain mismatches, and the lack of a verified knowledge base remain significant, particularly in low-source languages ​​like Indonesian. This study introduces an Indonesian-language medical chatbot based on the open-source GPT-OSS-20B model enhanced through a Retrieval-Augmented Generation (RAG) pipeline. The system combines semantic retrieval using jina-embeddings-v3, lexical re-ranking with the BM25 algorithm, and a lightweight Logistic Regression-based domain filter as an initial filter to prevent out-of-domain LLM usage. Evaluation using Indonesian medical articles and annotated patient-doctor conversations shows that the domain filter works well on synthetic data but results in misclassification of natural queries. A hybrid weighted reranker (FAISS L2 + BM25) performed the best with a Top-30 accuracy of 0.699. Black-box testing indicates that the system flow functions as designed, although the response quality has not been validated by clinical experts. These findings suggest that RAG-based open-source LLMs can improve access to Indonesian-language medical information, but still have important limitations such as the lack of clinical validation, potential errors in scraped data, and suboptimal robustness of domain filters.
Early Warning and Real-Time Ship Tracking using AIS Data and Smartphone GPS Supria Supria; Wahyat Wahyat; Ryci Rahmatil Fiska Ryci Rahmatil Fiska
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/t62fpe35

Abstract

The high risk of maritime accidents in congested waters such as the Malacca Strait requires an affordable safety system specifically for small fishing vessels. This research proposes and evaluates a mobile-based early warning framework that integrates shore-based AIS data with fishermen’s smartphone GPS. The system was tested under 3 operational scenarios using 4G cellular networks over a coastal area of Bengkalis, involving 60 collision simulation events and 180 API requests. Performance evaluation shows an average system latency of 2.3 seconds with a maximum latency of 4.8 seconds. The early warning mechanism successfully detected dangerous proximity (≤50 meters) with an accuracy of 93.3% and an error rate of 6.7%. Position logging via JSON POST achieved a success rate of 96.1% during continuous operation for 2 hours. Although this study demonstrates improved situational awareness and reliable last-known position recording, the system currently uses distance-based detection and does not yet implement CPA/TCPA prediction, which remains future work. The framework contributes as a low-cost monitoring and early warning solution with potential support for SAR operations through reliable historical position data.
Residual-Gated Attention U-Net with Channel Recalibration for Polyp Segmentation in Colonoscopy Images William Tanuwijaya; Yohannes
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: 10.35314/4qmfa987

Abstract

This study proposed a modification to the Attention U-Net architecture by integrating a Residual-Gated mechanism and Squeeze-and-Excitation (SE) Block-based channel recalibration within the Attention Gate to enhance feature selectivity in polyp segmentation. This integration reinforces both spatial and channel attention, enabling the model to better highlight polyp regions while suppressing irrelevant background features. Experiments were conducted on three colonoscopy datasets, CVC-ClinicDB, CVC-ColonDB, and CVC-300, using IoU and DSC metrics. Compared to the Attention U-Net baseline, the proposed model achieves noticeable improvements, with performance gains of mIoU 0.0043 and mDSC 0.0094 on CVC-ClinicDB, mIoU 0.0012 on CVC-ColonDB, and a larger margin of mIoU 0.0224 and mDSC 0.0127 on CVC-300. The best results were obtained on CVC-ClinicDB (mIoU 0.8889, mDSC 0.9412). Although the absolute scores on CVC-ClinicDB and CVC-ColonDB are lower than those reported in several recent studies, these datasets contain higher variability in polyp size, boundary ambiguity, and illumination, contributing to more challenging segmentation conditions. Visual evaluation further shows smoother and more coherent boundaries, especially on small or low-contrast polyps. Overall, the integration of the residual-gated mechanism and SE block within the attention gate effectively improves model accuracy and generalization, particularly in challenging scenarios.
Implementation of Concurrency Control to Prevent Race Condition in a Web-Based Billiard Table Reservation System Mohammad Luthfi Hermawan; Wildan Suharso
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/vrnggg84

Abstract

The development of information technology has driven the digitalization of various services, including web-based billiard table reservation systems. However, web systems that operate in real time are prone to race conditions when multiple users attempt to book the same table simultaneously, potentially leading to double booking. This study aims to implement a Concurrency Control mechanism using the Firebase Transaction feature to prevent such booking conflicts. The research method adopts a Research and Development (R&D) approach with the ADDIE model, which consists of the stages of Analysis, Design, Development, Implementation, and Evaluation. Furthermore, testing was conducted through pre-test and post-test simulations across 10 trials with concurrent users ranging from 2 to 11 individuals. In the pre-test stage, all users were able to successfully book the same resource simultaneously, resulting in 100% double booking across all trials. In the post-test stage, after implementing the Concurrency Control mechanism using Firebase Transaction, only one request was accepted out of the same 10 trials, while all other requests were automatically rejected, resulting in 0% double booking. These findings demonstrate that the applied concurrency control mechanism is effective in maintaining data consistency and preventing race conditions in the web-based billiard table reservation system.
Optimization of Household Energy use Prediction Using Random Forest with Genetic Algorithm Feature Selection Nyimas Nisrinaa Kamilah; Abdul Rahman
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/zedpkg51

Abstract

Electrical energy consumption continues to increase every year, so accurate prediction models are needed to support household electrical energy efficiency. This study analyzed high-resolution household electricity consumption data using the Random Forest (RF) algorithm and evaluated the influence of feature selection based on Genetic Algorithms (GA) in improving the performance of RF predictions. The base RF model achieves an RMSE of 0.6148, a MAE of 0.3478, and an R² of 0.5047. After implementing GA-based feature selection, the RF model with GA yields an RMSE of 0.6125 and an R² of 0.5084, indicating a marginal performance improvement. However, the MAE value increased slightly to 0.3503, which suggests that the increase was not uniform across the evaluation metrics. Overall, the RF approach with GA offers a modest improvement in prediction stability but with very limited accuracy gains, which highlights its potential and limitations in optimizing household energy consumption prediction.
Arduino UNO Application for Temperature Control Chicken Eggs Incubator Machine Rufinus Nainggolan; Idham Kamil; Al Qadry; Marlon Tua Pangihutan Sibarani; Dohar Sinabutar; Berta Br. Ginting
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/h94a3q23

Abstract

Poultry animals such as chickens incubate their eggs in three weeks to hatch their eggs, which sometimes the egg yields are not optimal so that a poultry egg incubator and its system are needed to optimize the results of hatching in which this built-in incubator with a capacity of 300 eggs with a rack system that rotates automatically based on Arduino Uno, using a digital temperature and hygrometer sensor so that the temperature can be set at 38-39 0C and the humidity can be set at 55-70% according to the egg hatching temperature and humidity, and from the test results obtained, the number of eggs that do not hatch and hatch both that hatch normally, defective, and die, the research conducted, for chicken eggs, which originally amounted to 300 eggs to be hatched, 291 eggs that hatched well, 5 defective eggs, and 3 eggs that died and 1 egg that does not have any embryos, and the result that this chicken egg incubator machine is capable of hatching eggs 97%.
Comparative Evaluation of Hand-Engineered Features for CRF-Based Named Entity Recognition in Indonesian Clinical Research Articles Nurmaya Maya; Rofi Tulus; Nova Eka Diana; Herika Hayurani; Sri Puji Utami Atmoko; Yurika Sandra
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/y1v81j78

Abstract

Extracting clinical entities such as signs, symptoms, diagnoses, and treatments from Indonesian clinical research makes clinical information more accessible to readers with varying levels of medical knowledge. Conditional Random Fields (CRFs) combined with hand-engineered features have proved effective for health domain named entity recognition (NER). However, studies comparing hand-engineered feature combinations for clinical NER in Indonesian clinical research articles remain limited. This study compares five hand-engineered feature combinations using a CRF model to identify the most effective combination for recognizing seven clinical entity types, using a newly annotated corpus of 29 Indonesian internal medicine research articles. The dataset was split into 80% training and 20% testing data. Model performance was evaluated using weighted-average and macro-average precision, recall, and F1-score, reported as mean ± SD across five random states (5, 10, 15, 20, 25) and training epochs (50, 100, 150). The combination of contextual, prefix and suffix, and orthographic features achieved the best weighted F1-score of 78.90% at Epoch 50, though macro-F1 was considerably lower at 64.32%, reflecting weaker performance on minority entity types. This gap indicates a need for further corpus expansion and data balancing.
Sentiment Analysis of User Reviews for the Locket Widget Application on Google Play Store Using the Naive Bayes Classifier Athia Aisy Bakhita; Arif Setiawan; Eko Darmanto
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/s90w5308

Abstract

User reviews on the Google Play Store contain valuable opinions that can be utilized to evaluate application quality, including the Locket Widget application. This study aims to classify user review sentiment into positive, neutral, and negative categories using the Naïve Bayes Classifier algorithm with Term Frequency–Inverse Document Frequency (TF-IDF) weighting. The data were collected through web scraping, resulting in 589 user reviews. The dataset then underwent preprocessing, sentiment labeling, TF-IDF weighting, and sentiment classification. The model was evaluated using an 80:20 stratified train–test split. The results showed that the proposed model achieved an accuracy of 76.27%, with a weighted precision of 0.69, weighted recall of 0.76, and weighted F1-score of 0.72. The findings indicate that the combination of TF-IDF and the Naïve Bayes classifier is effective in classifying positive and negative user review sentiments. However, the model was unable to effectively classify the neutral class, resulting in an F1-score of 0.00 for this category. This finding indicates that further improvements are needed to enhance the model's ability to distinguish neutral sentiment from positive and negative classes.
Decision Support System for Furniture Product Recommendation Using Item-Based Collaborative Filtering and Min-Max Normalization KharisatunNisa; Eko Darmanto; Arif Setiawan
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/j8dchq29

Abstract

The increasing variety of furniture products available in the market makes it difficult for consumers to identify products that best match their preferences. Conventional product selection is often carried out manually, requiring consumers to compare product specifications one by one, which is time-consuming and may lead to less appropriate purchasing decisions. This study proposes a web-based decision support system for furniture product recommendation by integrating the item-based collaborative filtering (IBCF) method with min–max normalization. The recommendation process utilizes historical user ratings to identify similarities among products, while min–max normalization is applied to standardize product attributes, including price, category, material, color, and size, into a comparable scale. To address the cold-start problem for newly added products with insufficient rating data, a content-based similarity mechanism is incorporated into the recommendation process. The system was developed using a dataset consisting of 322 furniture products, 50 consumers, and 1,099 rating transactions. System functionality was evaluated using black-box testing, while user acceptance was assessed through user acceptance testing (UAT) involving ten respondents. The evaluation results show that all primary system functions operated as expected and achieved an overall UAT score of 88.0%, indicating that the proposed system is acceptable and capable of assisting consumers in selecting furniture products based on their preferences. This study contributes by integrating collaborative filtering, multi-attribute normalization, and a content-based cold-start mechanism into a single recommendation framework for furniture product recommendation

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