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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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Evaluation of the Effect Of Regularization on Neural Networks for Regression Prediction: A Case Study of MLLP, CNN, and FNN Models Susandri; Zamsuri, Ahmad; Nasution, Nurliana; Ramadhani, Maya
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/m2rcsf96

Abstract

Regularization is an important technique for developing deep learning models to improve generalization and reduce overfitting. This study evaluated the effect of regularization on the performance of neural network models in regression prediction tasks using earthquake data. We compare Multilayer Perceptron (MLP), Convolutional Neural Network (CNN), and Feedforward Neural Network (FNN) architectures with L2 and Dropout regularization. The experimental results show that MLP without regularization achieved the best performance (RMSE: 0.500, MAE: 0.380, R²: 0.625), although prone to overfitting. CNN performed poorly on tabular data, while FNN showed marginal improvement with deeper layers. The novelty of this study lies in a comparative evaluation of regularization strategies across multiple architectures for earthquake regression prediction, highlighting practical implications for early warning systems.
Classification of Skin Diseases Using YOLOv11 Tappi, Liputra Pronimus; Dewi, Christine
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: 10.35314/9zv65764

Abstract

The skin, as the largest organ in the human body, is susceptible to various diseases that can be transmitted through direct contact or environmental exposure. Early detection of conditions such as cancer is crucial for effective treatment. This study implements the YOLOv11 algorithm to classify four types of skin diseases: Actinic Keratosis, Basal Cell Carcinoma, Melanocytic Nevus, and Melanoma. Using a Kaggle dataset of 2,000 images (500 per class), the images were processed by resizing them to 640×640 pixels and applying augmentation techniques (flipping, rotation, lighting adjustments) to enhance model robustness. The data was split into training (85%), validation (10%), and testing (5%). Model training on Google Colab (T4 GPU, 100 epochs) achieved an overall accuracy of 79%. Evaluation metrics showed strong results for Actinic Keratosis (precision=0.92, recall=0.92, F1=0.92) but lower performance for Melanoma (recall=0.59), likely due to class imbalance. Aggregate metrics indicated precision=0.80, recall=0.73, and F1=0.76, demonstrating reliable detection despite uneven performance across disease types. The main limitations include: a limited dataset size affecting model generalization; variability in image quality and lighting; and bias toward certain classes.
Optimization of LBP Texture Feature Extraction using Correlation And Mi For SVM-Based Diabetic Retinopathy Classification costaner, loneli; lisnawita, lisnawita; Guntoro, Guntoro
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: 10.35314/4vrj4930

Abstract

Diabetic retinopathy (DR) is a leading cause of blindness, making early detection based on retinal fundus images crucial. This study proposes a DR classification method with a primary contribution in feature optimization: integrating the LBP Contrast feature with a Local Binary Pattern (LBP) histogram and performing hybrid feature selection based on Mutual Information (MI) to assess relevance and correlation analysis to reduce redundancy. This method was tested using 168 images from the public Messidor dataset, with 100 images for training and 68 for testing to evaluate performance. Classification was performed using a Support Vector Machine (SVM) with a linear kernel, where model performance was evaluated before and after optimization to measure the significance of the improvement. The results showed a significant improvement after optimization, with accuracy increasing from 88% to 94%, recall increasing from 88% to 100%, and F1-score increasing from 0.92 to 0.96. Although precision decreased slightly from 96% to 93%, increasing recall to 100% is considered more crucial in a medical context as it minimizes the risk of missed positive cases. These findings confirm that the proposed feature optimization approach can significantly improve the accuracy and reliability of the DR detection system, offering potential clinical relevance for supporting early intervention.
Development Information Website Based on ReactJS to Improve Image Smait Granada with the Agile Method Ginting, Wahyu Pinanda; Muhammad Akhyar , Ramaulvi
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: 10.35314/9xd1kg44

Abstract

SMAIT Granada Samarinda website continues to face issues, including an unresponsive display on both mobile and desktop devices and an inconsistent UI layout. In addition, the website's users experience discomfort due to the excessively contrasting color scheme. The objective of this development is to reimagine the website to enhance the user experience, thereby facilitating its use in terms of informative website features, updated websites, and the school's digital appearance. Through a series of stages, including planning, needs analysis, design, development, testing, and deployment, the Agile method and ReactJS technology are employed in the development process. The SMAIT Granada Samarinda website as a whole effectively passed the ISO 9126 standard test, and the PRTG tool demonstrated a 100% functional success rate with a teacher usability level of 85.8%, according to the researchers' findings. The findings indicated that the web interface is more responsive and organized on both mobile and desktop devices. It is designed to be user-friendly and includes various supplementary features, including the ability to display the names and photos of school staff, attendance features based on the school's location, a more detailed school profile, updated school news, the school's vision and mission, and a new student admissions page that is clear and user-friendly.
Data Visualization using Tableau With K-Mean Clustering Method uor Identification Of Poor Areas in North Sumatera Syahputra, Andrian; Ikhsan, Muhammad
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/pqkbwx68

Abstract

This study aims to identify and visualize poverty-prone areas in North Sumatra Province using a data mining approach with the K-Means clustering method. Unlike previous studies that often relied on a single indicator or lacked spatial visualization, this study integrates three key indicators: Poverty Rate (P0), Poverty Depth Index (P1), and Poverty Severity Index (P2), and utilizes Tableau software to present the results through interactive geo-mapping. Data were obtained from the Central Statistics Agency (BPS) of North Sumatra for the period 2018–2022. The clustering results indicate that North Nias and West Nias Regencies consistently fall into Cluster 3 with the highest poverty indicators, while Medan City, Deli Serdang, and Binjai are in Cluster 1 with the lowest indicators. Cluster quality evaluation using the Silhouette Score method shows that the P0 indicator yields the best cluster separation with a score of 0.71, followed by P1 at 0.50 and P2 at 0.40. These findings confirm that P0 is the most effective indicator for representing interregional poverty levels, while P1 and P2 serve as supporting variables. The resulting visualizations provide a comprehensive, data-driven overview that can serve as a strategic reference for policymakers in designing more targeted and effective poverty reduction programs.
Detecting Smoking Activity Behavior using YOLOv8 and YOLOv11 Salsabilla Azahra Putri; Murinto; Sunardi
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 Rizqa Ramadhian, Putri; Putra Aryono, Gagah Dwiki; Masyhuri, Maman
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 Hidayatullah Nuriadi, Siti; Sabri, Erlin; Hajjah, Alyauma; Noratama Putri, Ramalia
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 Djema, Gayus Gregorius Ferdinand; 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/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 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.

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