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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 256 Documents
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Analysis of Mikrotik Network Bandwidth Management Using the Hierarchical Token Bucket Method at the Sriwijaya State Polytechnic Kiara Sofia Syahrani; Suroso; Eka Susanti
INOVTEK Polbeng - Seri Informatika Vol. 10 No. 2 (2025): July
Publisher : P3M Politeknik Negeri Bengkalis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35314/a301e829

Abstract

This study seeks to evaluate the efficacy of the Hierarchical Token Bucket (HTB) approach in regulating Mikrotik network capacity at the Sriwijaya State Polytechnic Telecommunications Engineering Laboratory.  The study encompasses the execution of HTB configuration using the WinBox program and the assessment of Quality of Service (QoS) in accordance with the TIPHON standard, utilising the Wireshark application.  Testing was performed at three local network sites: the Student Laboratory, the Faculty Room, and the Inventory Room, utilising the Mikrotik RB-2011 device as the primary router.  Assessments of four Quality of Service parameters—throughput, latency, packet loss, and jitter—were performed prior to and after the implementation of the Hierarchical Token Bucket technique.  The QoS testing findings indicated that the use of HTB markedly enhanced the average throughput from 525 kbps to 1,321 kbps, concurrently diminishing the average delay and jitter from 16.90 ms to 3.68 ms.  Despite a little escalation in packet loss from 0.07% to 0.6%, the outcomes remained under the Quality of Service categorisation criterion.  This study offers novel insights due to an extended observation time and a greater volume of analysed packets relative to prior research.  This research substantiates the findings that the HTB approach may efficiently regulate bandwidth and enhance network performance, especially in academic settings.  
Design of Facial Skin Type Detection Application Using CNN with Inceptionv3 Model and Google Cloud Platform Nur; Ade; Ahmad
INOVTEK Polbeng - Seri Informatika Vol. 10 No. 2 (2025): July
Publisher : P3M Politeknik Negeri Bengkalis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35314/v171yr28

Abstract

The advancement of Artificial Intelligence (AI) and Computer Vision technologies has significantly impacted the beauty industry, particularly in facial skin type detection. This study developed a mobile application that utilizes CNN with the InceptionV3 architecture deployed on the Google Cloud Platform (GCP). The system uses a dataset of 1,735 facial images categorized into normal, dry, oily, and acne-prone skin types. The photos were preprocessed and augmented before being processed by the CNN model. Firestore and Cloud Storage were used to maintain the data, while Cloud Run was used to publish the trained model into a Flask-based API. The accuracy, precision, recall, and F1-score reached 91.7%, 91%, 91%, and 91% respectively. Compared to previous studies, this system offers real-time classification through a lightweight mobile application integrated with cloud computing, aiming to improve accessibility and efficiency in dermatological analysis and personalized skincare services.
Modelling, Simulation, and Analysis of Sequence-Based Models for Smart Lighting Voice Command Classifiers with MFCC-Based Data Augmentation Yohanes Batara Setya; Feddy Setio Pribadi
INOVTEK Polbeng - Seri Informatika Vol. 10 No. 2 (2025): July
Publisher : P3M Politeknik Negeri Bengkalis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35314/r4p60871

Abstract

Voice command classification is essential for smart lighting systems in IoT environments. However, existing approaches often struggle in real-world scenarios with background noise and speaker variability due to limited and imbalanced training data. This indicates a need for models that maintain high accuracy under such conditions. To address this, the study evaluates three deep learning architectures: a Deep Neural Network (DNN), a Gated Recurrent Unit (GRU), and a bidirectional Long Short-Term Memory (LSTM) network, run on the Google Speech Commands dataset. The classification targets six voice commands (“right”, “off”, “left”, “on”, “down”, “up”) using Mel-Frequency Cepstral Coefficients (MFCCs) as features. Data augmentation techniques, including pitch shifting, time stretching, mix-up, and noise injection, are used to expand the dataset, balance class distributions, and simulate acoustic conditions such as background noise and speaker differences. Model performance is assessed through confusion matrices and receiver operating characteristic curves (ROC-AUC) across training, validation, and test sets. The bidirectional LSTM achieves the highest test accuracy (94%), followed by GRU (92%) and DNN (79%). The LSTM model also generalizes well, showing no signs of overfitting and maintaining stable performance in the presence of acoustic variation. These results suggest that combining bidirectional LSTM with MFCC-based augmentation provides a more robust approach to voice command recognition, particularly in IoT-based smart lighting contexts, where environmental variability is common.
Audit of the Tejamari Village Service Website Information System Using the COBIT 5 Framework Aldiyanti, Fitri; Auliana, Sigit; Dwiki Putra Aryono, Gagah
INOVTEK Polbeng - Seri Informatika Vol. 10 No. 2 (2025): July
Publisher : P3M Politeknik Negeri Bengkalis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35314/9ynwyb87

Abstract

Digital transformation in public services at the village level requires systematic evaluation to ensure the effectiveness of information technology implementation. The research gap is identified from the lack of studies auditing village service information systems using a combination of DSS01 and BAI01 domains of the COBIT 5 framework. This study contributes to filling the gap in the literature by auditing the website-based service information system of Tejamari Village, Serang Regency, using the COBIT 5 framework, focusing on the DSS01 and BAI01 domains. The research methodology adopts a qualitative approach with a case study design using the COBIT 5 Process Assessment Model across seven systematic stages. Data triangulation was conducted through structured observations, in-depth interviews with key stakeholders, and comprehensive document analysis. The scope of the study is limited to the two specified COBIT 5 domains, with an evaluation period restricted to one month at a single location. The evaluation results show that both domains are at capability level 1, with DSS01 scoring 46.66% and BAI01 achieving 72.60%. The findings identify critical deficiencies in procedure documentation, operational standardisation, and IT resource management. The system reached level 0 with "Fully Achieved" status but did not meet the 85% threshold required to progress to the next level. The theoretical contribution of this research enriches the literature on information system audits in public services through a domain-specific COBIT 5 approach, while the practical contribution provides a roadmap for improving digital village service quality through recommendations for procedural standardization and resource optimization.
K-Means Algorithm Implementation for IoT-Based Early Fire Detection in Oil Palm Plantations Utomo, Tri Binarko; Suroso; Fadhli, Mohammad
INOVTEK Polbeng - Seri Informatika Vol. 10 No. 2 (2025): July
Publisher : P3M Politeknik Negeri Bengkalis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35314/9xjpmv81

Abstract

Oil palm plantation fires continue to be a significant problem, significantly impacting the environment, public health, and economic activity. By combining the K-Means algorithm, processed directly on an ESP32 microcontroller, with an Internet of Things (IoT)-based early detection system, this research has produced an innovation that does not require an external server. To monitor hazardous gases, smoke, and temperature, the system uses thermocouples and MQ-2 and MQ-135 sensors. Conditions are then categorized into Safe, Alert, and Fire. Using 15 test data samples, the evaluation was conducted in the field, specifically in the oil palm plantation area in Banyuasin, South Sumatra. The test results showed that the classification had 100% accuracy. However, the limited amount of data was one of the obstacles to this study, so additional testing is needed to ensure the accuracy of the large-scale study. This system is suitable for remote and limited infrastructure, helping to develop effective and responsive early fire detection technology.
Information System Audit on the Simampu BPBD Web Application of Serang District using the COBIT 5 Framework Irfansyah, Robby; Dwi Purnama, Eris; Auliana, Sigit
INOVTEK Polbeng - Seri Informatika Vol. 10 No. 2 (2025): July
Publisher : P3M Politeknik Negeri Bengkalis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35314/f3f9ky94

Abstract

This research was conducted to evaluate the effectiveness and efficiency of the Simampu information system implementation at the Regional Disaster Management Agency (BPBD) of Serang Regency using the COBIT 5 framework. The information system was designed to assist BPBD in disaster data management, reporting, monitoring, and facilitating quick and accurate decision-making. However, the current implementation of Simampu was found to be suboptimal as it is still under development, and several issues were identified, such as system errors, network disruptions, and inadequate data security, hindering effective disaster data processing and reporting. The research utilized a qualitative approach involving direct observations and in-depth interviews with the system management team at BPBD Serang Regency. This study focused on four COBIT 5 domains: BAI01 (Manage Programmes and Projects), DSS02 (Manage Service Requests and Incidents), DSS03 (Manage Problems), and DSS05 (Manage Security Services). These domains were selected due to their relevance to BPBD’s operational needs and strategic objectives in disaster management. Findings revealed several weaknesses in the management of the Simampu information system, particularly concerning IT service management processes, incident handling, problem management, and information security services. To address these weaknesses, the researcher provided technical recommendations, including improvements in data security, network infrastructure enhancements, and optimization of data processing procedures.
Application of Machine Learning in Analyzing Bandwidth Usage Patterns for Internet Service Providers Nurmakhlufi, Alfin Hilmy; Zuliarso, Eri
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/h2p5s858

Abstract

This study aims to address bandwidth management challenges faced by Internet Service Providers (ISP) through the application of machine learning techniques for analyzing usage patterns and forecasting future demand. A key novelty of this research lies in the combined use of K-Means clustering for dynamic customer segmentation based on real-time utilization patterns, followed by accurate short-term forecasting using Random Forest regression, specifically tailored for corporate client bandwidth planning. Data was collected from 12 corporate customers over a three-month period (January–March 2025) at five-minute intervals using the PRTG Network Monitor. The analytical workflow included data preprocessing, customer segmentation using K-Means clustering, and short-term bandwidth prediction using Random Forest regression. The clustering results classified customers into three main categories: underutilized, optimal, and overutilized, with a silhouette score of 0.663 indicating good cluster separation. The regression model achieved a coefficient of determination (R²) of 0.931, a Mean Absolute Error (MAE) of 0.036 Mbps, and a Root Mean Square Error (RMSE) of 0.062 Mbps, demonstrating high predictive accuracy for operational planning. This study is limited by the relatively short observation period and the exclusion of external variables in the modeling process. For future work, the use of deep learning methods such as Long Short-Term Memory (LSTM) or Temporal Convolutional Networks (TCN) is recommended, along with the integration of external features such as time-based traffic anomalies and customer profiles, to enhance model robustness, accuracy, and generalization.
Decision Support System for Inventory Prediction using Fuzzy Tsukamoto Method (Case Study: UMKM Bayou Indonesia) Galih Agil Febri Hidayatullah; Sri Mujiyono
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/sfyymk96

Abstract

Bayou Indonesia, an MSME engaged in acrylic product manufacturing, faces overproduction issues due to manual production planning, leading to stockpiling and wasted resources. This study aims to develop a decision support system using the Fuzzy Tsukamoto method to predict production quantities more accurately by analyzing historical data such as orders, shipments, and final stock. Data processing is performed with fuzzy logic to generate reliable production forecasts for the upcoming periods. The novelty of this research lies in the real-world integration of the Fuzzy Tsukamoto method within a CodeIgniter-based web application, which is directly implemented in the MSME environment, moving beyond the purely theoretical simulations of prior studies. The system significantly improves production planning accuracy, reducing manual errors (MAPE) from 21.5% to 8.7%, with an RMSE of 11.2 units. Furthermore, it helps decrease excess production discrepancies by up to 30% per month, raises prediction precision to 85%, and accelerates the decision-making process from two to three days to real-time. The resulting operational efficiency gains are estimated at 60–70%. These findings indicate that the system provides a practical solution for MSMEs to minimize overproduction risks, optimize resource usage, and enhance production planning through data-driven methods.
Underwater Single and Multiple Objects Detection Based on the Combination of YOLOv7-tiny and Visual Feature Enhancement Sari, Dewi Mutiara; Marta, Bayu Sandi; Dwito Armono, R. Haryo; Rizaldy Pratama, Alfan; Putra Pratama, Firmansyah
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: 10.35314/91b9qn06

Abstract

Breakwater construction in Indonesia frequently employs tetrapods to dissipate wave energy. However, the placement process remains manual, relying on divers to guide crane operators. This approach not only poses safety risks but also limits visibility due to underwater turbidity. While prior research has focused on underwater image enhancement, the integration of tetrapod object detection remains unexplored. This study proposes a combined method of underwater image enhancement and tetrapod object detection to support land-based operator visualization. Auto-level filtering and histogram equalization techniques were applied to enhance image clarity, followed by object detection using the YOLOv7-tiny model. Tetrapod models at a 1:20 scale were used for training and testing. The proposed system achieved a mean average precision (mAP) of 0.95. Evaluation was conducted across 12 scenarios, involving four lighting levels and two water conditions: clear and 45.8% turbidity. The object detection confidence scores were 0.80 without enhancement, 0.85 with histogram equalization, and 0.84 with auto-level filtering. Multiple object detection achieved an accuracy of 88.75%, outperforming previous approaches using YOLOv4-tiny. The results demonstrate the potential of integrating image enhancement and deep learning-based object detection for improving underwater operational safety and placement precision in breakwater construction.
Expert System in Analyzing Stress Levels in Factory Employees Using the Certainty Factor Method Dinafa, Aya Sofia; Rohman, Abdul
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/qc6ag389

Abstract

Factory employees are jobs that have high enough pressure, therefore they are prone to stress. Employees who experience stress have an impact on reducing productivity. This study aims to design and build to diagnose stress levels in factory employees with the Certainty Factor (CF) method. Data collection is done by means of a mental specialist and the distribution of questionnaires to factory employees. In this technological development, expert systems can be used to prevent employees from experiencing high work stress by identifying it early on so that advice can be given. This system is designed with the PHP programming language and MySQL database. The expert system with the Certainty Factor (CF) method has a fairly high level of accuracy, with a certainty level of 85% and can be a management tool in making decisions related to employee mental health.

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