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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 67 Documents
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Search results for , issue "Vol. 10 No. 2 (2025): July" : 67 Documents clear
Instagram-Based Sentiment Analysis on the Oil Refinery Project in Batam Using SVM and XGBoost Rumapea, Doni Immanuel; Ozzi Suria
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/am1zxb64

Abstract

This sentiment analysis of Instagram comments regarding the planned construction of an oil refinery in Batam classifies public opinion into three categories: positive, neutral, and negative. The initial dataset of 1,576 comments was reduced to 1,441 after text preprocessing (tokenization, stop‑word removal, and stemming), and then split into 1,152 training instances and 289 testing instances. Two machine learning algorithms, Support Vector Machine (SVM) with class_weight='balanced' and Extreme Gradient Boosting (XGBoost) with oversampling, were applied to address class imbalance. In addition to accuracy (SVM: 81.25%; XGBoost: 96%), precision, recall, and F1‑score metrics were evaluated to assess the balance between true‑positive and true‑negative classifications. The results indicate that XGBoost not only outperformed SVM in terms of accuracy but also achieved the highest F1‑score on the minority class, demonstrating its ability to detect negative opinions that have often been overlooked. This study offers a novel contribution to Instagram-based sentiment analysis a platform that is visually distinct from Twitter by focusing on public opinions surrounding the strategic issue of energy infrastructure development. The findings can be utilized for real-time sentiment mapping, supporting policy formulation, urban planning, and monitoring industry responses to critical projects in the digital era.
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.

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