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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 298 Documents
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Implementation and Analysis of Security Information and Event Management at Bina Darma University Suryayusra; Derri Anjuju; Aan Restu Mukti; Akhmad Khudri
INOVTEK Polbeng - Seri Informatika Vol. 11 No. 2 (2026): May
Publisher : P3M Politeknik Negeri Bengkalis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35314/kdgv7x74

Abstract

This research aims to implement Security Information and Event Management (SIEM) using Wazuh on the Bina Darma University server for real-time network security monitoring. The research uses the action research method with planning, action, observation, and reflection stages. Testing was carried out using three attack scenarios, namely Brute Force, SYN Flood, and SQL Injection, on Windows- and Ubuntu-based virtual machine environments. The research results show that Wazuh succeeded in detecting four attempted brute force attacks, a real-time SQL injection attack, and a SYN flood attack with the help of Suricata. Telegram bot integration successfully sends automatic notifications on brute force attacks. Performance testing showed CPU usage increased from 15% to 60% during the attack, while memory usage remained stable. This research is still limited to a simulation environment with a limited number of endpoints
Implementation of a Real-Time Dashboard for Monitoring Backflow Detection in IoT-Based IV Lines Albert Wijaya; Geraldy Asad Arslan Pinem; Novi Enjeli
INOVTEK Polbeng - Seri Informatika Vol. 11 No. 2 (2026): May
Publisher : P3M Politeknik Negeri Bengkalis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35314/d0k49b45

Abstract

Continuous monitoring of patients receiving intravenous therapy is crucial to prevent blood from flowing back into the IV line (reflux), which can endanger patient safety. This study aims to design and develop an Internet of Things (IoT)-based monitoring device capable of detecting blood backflow in the IV line and presenting data in real-time via a web-based dashboard. The system utilizes a TCS3200 color sensor to detect specific color changes in the infusion fluid when blood enters the line. Data obtained from the sensor is processed by an ESP8266 microcontroller and transmitted to a Flask-based server via the HTTP protocol, then visualized on a Dash-based web dashboard. Experimental testing was conducted through 50 trials under various ambient light conditions. The test results show that the system achieved a 100% accuracy rate in differentiating clear infusion fluid from blood backflow. Furthermore, the system demonstrated a rapid data transmission response with an average latency of 1.5 to 2.2 seconds to trigger notifications on the medical staff's dashboard. This device successfully minimizes the risk of blood coagulation and is expected to improve healthcare efficiency and patient safety in medical facilities.  
An Offstage WebView Architecture for API-Less Digital Public Service Integration in Local Government Super Applications Fina Febrianti; Alim Hardiansyah; Yulian Ansori
INOVTEK Polbeng - Seri Informatika Vol. 11 No. 2 (2026): May
Publisher : P3M Politeknik Negeri Bengkalis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35314/e6yxff70

Abstract

The fragmentation of digital public services in Indonesian local governments, compounded by the near-universal absence of publicly accessible APIs on government service websites, prevents direct integration into unified super applications through conventional mechanisms. This study proposes a Flutter-based offstage WebView scraping architecture that resolves this barrier without requiring backend modification or server-side infrastructure. The architecture introduces a hidden WebView processing layer that fully renders the target government website and executes a dual-strategy JavaScript extraction pipeline, combining DataTables API retrieval with DOM fallback scraping, before transmitting structured data to a native Flutter presentation layer. This separation of the retrieval and rendering layers eliminates the interface inconsistency of conventional WebView integration while removing the deployment overhead of server-side approaches. Evaluated through implementation in the Lebak Super Application, the technique achieved a 97.0% extraction success rate across one hundred trials, a mean end-to-end response time of 4.31s under Wi-Fi conditions, and a peak memory consumption of 6.2 MB during the extraction phase. All thirty functional black box testing scenarios passed. These results demonstrate that the proposed architecture is a viable transitional integration strategy for local governments operating under limited API infrastructure readiness.
Multimodal ECG-PPG Clinical Fusion for Myocardial Infarction Classification Using Ensemble Learning I Gede Angga Candrawibawa; I Gede Aris Gunadi; Luh Joni Erawati Dewi
INOVTEK Polbeng - Seri Informatika Vol. 11 No. 2 (2026): May
Publisher : P3M Politeknik Negeri Bengkalis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35314/s7j9qt66

Abstract

This study presents a comparative analysis of multimodal ECG, PPG, and clinical feature fusion for myocardial infarction (MI) classification using four ensemble learning algorithms: Random Forest, XGBoost, LightGBM, and CatBoost. The experiments were conducted in two classification scenarios: binary classification for normal vs. MI and multi-class classification for normal, STEMI, NSTEMI, and old MI. Five feature scenarios were evaluated, including clinical-only, ECG-only, PPG-only, ECG + PPG, and ECG + PPG + clinical. The results show that ECG features were the most dominant modality for MI classification. In binary classification, XGBoost with ECG-only features achieved perfect performance with accuracy, macro F1-score, macro recall, and MCC of 1.0000. For multi-class classification, the best result was obtained by CatBoost using ECG + PPG + clinical features, achieving an accuracy of 0.9000, a macro F1-score of 0.5394, and an MCC of 0.6912. These findings indicate that multimodal fusion is more beneficial for MI subtype classification, while ECG-only features are highly effective for binary MI detection
Smart Pill Dispenser with Naive Bayes Algorithm for Predicting Medication Compliance Based on Patient Behavior Patterns Achmad Ridwan; Samuel Jackonia Sembing; Sere Lonian Sihombing; Diventranus Lei; Rio Utama Putra Pasaribu
INOVTEK Polbeng - Seri Informatika Vol. 11 No. 2 (2026): May
Publisher : P3M Politeknik Negeri Bengkalis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35314/ercsa238

Abstract

Medication adherence is a critical challenge in chronic disease management, with global non-adherence rates estimated at 50% or higher among patients with long-term conditions. This study presents the design and implementation of an ESP32-based smart pill dispenser prototype integrating a Naive Bayes classification algorithm for real-time, on-device prediction of patient medication compliance behavior. The system collects multimodal interaction data through an RTC DS3231, a load cell weight sensor, and a push button to extract four behavioral features: time delay (ΔT), weight change (ΔW), compliance frequency, and signal quality. These features are locally classified on the ESP32 into three categories: Compliant, Late, and Non-Compliant. A dataset of 100 interaction records was generated through controlled laboratory experiments, with a stratified 80:20 train-test split applied for evaluation. The trained Naive Bayes model achieved an overall accuracy of 80.0% on the test set, with per-class precision, recall, and F1-score reported. Class imbalance effects are analyzed, and results are compared against decision tree, k-NN, logistic regression, and random forest. The prototype demonstrates feasibility as a low-cost, portable medication management device, though clinical validation with real patients remains required.
Evaluating Academic Information Systems at Bali Tourism Polytechnic Using the DeLone and McLean Model Ni Putu Kasih Sumariani; I Made Candiasa; Luh Joni Erawati Dewi
INOVTEK Polbeng - Seri Informatika Vol. 11 No. 2 (2026): May
Publisher : P3M Politeknik Negeri Bengkalis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35314/m2wz3846

Abstract

Academic information systems are essential for supporting integrated academic services in higher education. However, SIAMIK at Bali Tourism Polytechnic still faces issues related to system integration, interface interactivity, manual grade processing, and limited digital service integration. This study evaluates SIAMIK success using the DeLone and McLean Information System Success Model and formulates system development recommendations based on empirical findings. A quantitative survey was conducted involving 102 users consisting of management, lecturers, administrative staff, and students. Data were collected through questionnaires and analyzed using descriptive statistics and Partial Least Squares Structural Equation Modeling. The results show that system quality significantly affected user satisfaction (β = 0.324, p < 0.001), information quality had the strongest effect (β = 0.433, p < 0.001), and service quality also had a significant effect (β = 0.170, p = 0.039). User satisfaction significantly influenced net benefits (β = 0.724, p < 0.001). The model explained 67.2% of user satisfaction and 52.3% of net benefits, with strong predictive relevance (Q² = 0.843). Findings suggest that SIAMIK development should prioritize information completeness, responsive access, technical support assurance, and cross-system integration.
Convolutional Block Attention Module Integration into YOLO11 Architecture for MRI Image-based Brain Tumor Detection Jendraja Husin Kotan; Yohannes; Hafiz Irsyad
INOVTEK Polbeng - Seri Informatika Vol. 11 No. 2 (2026): May
Publisher : P3M Politeknik Negeri Bengkalis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35314/n4nrvj87

Abstract

Brain tumor is one of the deadly diseases in the world that can affect anyone, this disease is characterized by the growth of abnormal cells or tissues in the brain, medically it can be life-threatening if not treated properly. Most tumor detection tasks are done by manual assessment by radiologists or pathologists where this work is time-consuming, so accurate and reliable detection is needed in the medical field in diagnosing brain tumors. The purpose of this study is to integrate CBAM on the YOLO11 architecture in detecting brain tumors and determine the performance of the brain tumor detection model using the YOLO11 architecture with CBAM integration. The method used to detect brain tumors is the YOLO11 architecture with CBAM integration. The dataset used is an image in the form of brain MRI. The results of this study indicate that the precision is 86.9%, recall is 86.2%, mAP50 is 91%, and mAP50-95 is 64% in the validation data and precision is 89.1%, recall is 92%, mAP50 is 79%, mAP50-95 is 51.6%, and F1 score is 90.5% in the test data which can be used to help medical personnel in detecting and treating brain tumors considering that this model has outstanding results, especially in the recall metric section which reaches 92% in the test data.
Knowledge Management System Development Using Tiwana Roadmap and Agile Scrum at HP Service Center Denpasar Ni Wayan Astuti; I Gede Aris Gunadi; Luh Joni Erawati Dewi
INOVTEK Polbeng - Seri Informatika Vol. 11 No. 2 (2026): May
Publisher : P3M Politeknik Negeri Bengkalis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35314/9z4bmv04

Abstract

This study aims to design and develop a Knowledge Management System (KMS) at HP Service Center Denpasar by integrating the Tiwana Roadmap framework and the Agile Scrum development method. HP Service Center Denpasar faces challenges in knowledge retention and dissemination, where valuable knowledge assets, both tacit and explicit knowledge, are still stored individually. Therefore, this study focuses on documenting all knowledge possessed by various operational units, ranging from Senior PC/Laptop Technicians and Printer Technicians to logistics staff, in order to prevent knowledge loss and accelerate the knowledge transfer process. In the development process, the Tiwana Roadmap was selected as the main framework because of its ability to provide structured, systematic, and gradual guidance for identifying, managing, auditing, and optimally utilizing organizational knowledge. Meanwhile, to execute the blueprint into software form, the Agile Scrum method was implemented to support a system development process that is flexible, adaptive, and responsive to changing requirements and dynamic field conditions through iterative sprint cycles. After the system has been developed, the testing phase will be conducted using two comparative approaches: the User Experience Questionnaire (UEQ) to quantitatively measure user experience aspects, and Cognitive Walkthrough / Cognitive Task Analysis (CTA) to evaluate cognitive efficiency and system usability when used by staff. The expected final result of this study is a KMS platform that is not only technically valid but also interactive and capable of improving operational efficiency at HP Service Center Denpasar.

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