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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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Detecting Smoking Activity Behavior using YOLOv8 and YOLOv11 Salsabilla Azahra Putri; Murinto; Sunardi
Journal of Innovation and Technology Polbeng Series on Informatics (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: 10.35314/0q6grf14

Abstract

Smoking behavior in public spaces remains a major challenge in the implementation of public health policies, particularly within designated smoke-free zones. This study aims to examine whether architectural improvements and spatio-temporal modeling in object detection models can enhance the accuracy of real-time smoking behavior detection. Specifically, the performance of YOLOv8 and an experimental version, YOLOv11, is compared using a vision-based approach. A dataset of 3,000 annotated images is used, consisting of smoking and non-smoking activities such as drinking or phone use, with variations in lighting, body posture, and camera angles. The dataset was divided into 80% for training, 20% for validation, and 20% for testing, with data augmentation applied to improve generalization. YOLOv11 incorporates spatio-temporal modules and attention mechanisms not present in YOLOv8. Evaluation results show that YOLOv11 outperforms YOLOv8, achieving a Precision of 0.95, Recall of 0.91, and F1-Score of 0.93, while YOLOv8 reached 0.89, 0.87, and 0.88 respectively. These findings indicate that YOLOv11 offers a more robust and adaptive solution for automatically recognizing smoking behavior in real-world environments and supports the development of intelligent surveillance systems for enforcing smoke-free policies.
Audit of the SRIKANDI Information System at the Banten Regional Library and Archives Department using the COBIT 5 Framework Putri Rizqa Ramadhian; Gagah Dwiki Putra Aryono; Maman Masyhuri
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/yp9vec46

Abstract

Digital transformation in the Indonesian government requires reliable and standardized information systems to support quality public services. The Integrated Dynamic Archives Information System (SRIKANDI) is a mandatory archival application for government agencies but faces various technical challenges in implementation. This research aims to evaluate SRIKANDI implementation at the Banten Regional Library and Archives Office using the COBIT 5 framework to identify system maturity levels and areas requiring improvement. The research method uses a quantitative approach with a case study, implementing the COBIT 5 Process Assessment Model (PAM) on EDM01, DSS01, and DSS03 domains through triangulation of observation, interviews, and documentation studies. Research results show the system is at capability level 1 with scores of DSS03 (80%), EDM01 (61.11%), and DSS01 (16.66%), indicating a gap of 2 to achieve target level 3. The study concludes that SRIKANDI has been operational but requires improved documentation, process standardization, and supporting feature implementation to achieve optimal maturity level in supporting digital archival transformation.
Soursop Leaf Disease Detection With CNNs:   From Training to Deployment Siti Hidayatullah Nuriadi; Erlin Sabri; Alyauma Hajjah; Ramalia Noratama Putri
Journal of Innovation and Technology Polbeng Series on Informatics (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/sn8avr92

Abstract

Soursop (Annona muricata) is a valuable tropical fruit crop that is highly susceptible to leaf diseases caused by fungal, bacterial, and viral infections. These diseases can significantly impact crop yield and quality, posing challenges for farmers, especially when early detection is delayed. This study proposes an automated solution using Convolutional Neural Networks (CNNs) to detect soursop leaf diseases through image classification. A dataset of 400 labelled leaf images, including healthy and diseased leaves (Leaf Rust, Leaf Spot, and Sooty Mold), was collected and preprocessed for the dataset. Three CNN architectures—MobileNetV2, VGG19, and ResNet50—were evaluated based on accuracy, precision, recall, and F1-score. Among them, MobileNetV2 outperformed the others, achieving 73% accuracy, 72% precision, 65% recall, and 66% F1-score and demonstrated strong consistency across classes. The best-performing model was deployed using the Flask web framework, enabling users to upload soursop leaf images and receive instant disease classification along with suggested treatments and preventive measures. This study’s novelty lies in the end-to-end pipeline, from model training to deployment via Flask, providing a ready-to-use solution for farmers.
Public Sentiment Analysis of Danantara Policy through Social Media X Using SVM and Random Forest Gayus Gregorius Ferdinand Djema; Ozzi Suria
Journal of Innovation and Technology Polbeng Series on Informatics (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/rcr21h75

Abstract

Abstract - This study aims to analyze public sentiment toward the establishment of the Danantara Investment Management Agency (Danantara) through the X social media platform (formerly Twitter) using a machine learning-based text classification approach. While sentiment analysis has been widely applied across various domains, there remains a research gap in examining public responses to new national policies particularly Danantara on platform X. A total of 1,713 tweets were collected using Python-based web scraping via Google Colab during the period from February to June 2025. The research involved data preprocessing, manual sentiment labeling, model training using Support Vector Machine (SVM) and Random Forest algorithms, and performance evaluation using metrics such as accuracy, precision, recall, and F1-score. The classification results show that positive sentiment dominates at 55.6%, while negative sentiment accounts for 44.4%. Random Forest outperformed SVM with an accuracy of 92.36% and an F1-score of 92.19%, compared to SVM's accuracy of 85.45% and F1-score of 87.54%. These findings indicate that Random Forest is more effective in handling short-text public opinion data that is often unstructured. Practically, this study recommends the integration of real-time sentiment monitoring through social media as a strategic tool for policymakers and state-owned enterprises (SOEs) in formulating more responsive and data-driven public policies
Instagram-Based Sentiment Analysis on the Oil Refinery Project in Batam Using SVM and XGBoost Doni Immanuel Rumapea; Ozzi Suria
Journal of Innovation and Technology Polbeng Series on Informatics (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
Journal of Innovation and Technology Polbeng Series on Informatics (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
Journal of Innovation and Technology Polbeng Series on Informatics (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
Journal of Innovation and Technology Polbeng Series on Informatics (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 Fitri Aldiyanti; Sigit Auliana; Gagah Dwiki Putra Aryono
Journal of Innovation and Technology Polbeng Series on Informatics (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 Tri Binarko Utomo; Suroso; Mohammad Fadhli
Journal of Innovation and Technology Polbeng Series on Informatics (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.

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