cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
Agus Tedyyana
Contact Email
agustedyyana@polbeng.ac.id
Phone
+6285289866666
Journal Mail Official
jurnaoinformatika@polbeng.ac.id
Editorial Address
Jl. Bathin alam, Sungai Alam Bengkalis-Riau 28711
Location
Kab. bengkalis,
Riau
INDONESIA
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
 22   23   24   25   26 
Classification of Herbal Plant Images Using Transfer Learning EfficientNetV2-B3 Ambarwati, Rizki; Devella, Siska
INOVTEK Polbeng - Seri Informatika Vol. 11 No. 1 (2026): February
Publisher : P3M Politeknik Negeri Bengkalis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35314/fz4jy549

Abstract

Herbal plants are natural resources that have high economic and health value, but the identification process is still done manually, making it prone to errors due to morphological similarities between species. This study aims to develop a leaf image classification model for herbal plants using a Convolutional Neural Network (CNN) with the EfficientNetV2-B3 transfer learning approach and AdamW optimizer. The dataset used is the Indonesian Herb Leaf Dataset 3500, which consists of 3,500 leaf images from 10 types of Indonesian herbal plants, namely Belimbing Wuluh, Jambu Biji, Jeruk Nipis, Kemangi, Lidah Buaya, Nangka, Pandan, Pepaya, Seledri, and Sirih. The research stages included preprocessing, dataset division, and augmentation such as flipping, rotation, zooming, contrast and brightness changes, translation, and the addition of Gaussian noise and salt-and-pepper noise to increase data variation and test model robustness. Evaluation based on accuracy, precision, recall, and f1-score shows that the model without augmentation achieved 98.57% accuracy, 98.63% precision, 98.57% recall, and a 98.58% f1-score, while the model with augmentation and noise addition achieved an accuracy of 97.71%, precision of 97.83%, recall of 97.71%, and an f1-score of 97.72%. These results prove that EfficientNetV2-B3 is capable of effectively classifying herbal plant leaves with good performance.  
Comparative Analysis 0f Random Forest and Xgboost Performance for Network Flow Based Malware Classification Wicaksana, Fajar Adji; Umam, Chaerul
INOVTEK Polbeng - Seri Informatika Vol. 11 No. 1 (2026): February
Publisher : P3M Politeknik Negeri Bengkalis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35314/8f891c76

Abstract

The evolving complexity of cyber threats, particularly malware propagation through network infrastructure, necessitates intrusion detection mechanisms that are both precise and computationally efficient. This study presents an in-depth comparative analysis of two ensemble learning algorithms, Random Forest (RF) and Extreme Gradient Boosting (XGBoost), in classifying network traffic anomalies based on network flow features. Empirical validation was conducted using the CSE-CIC-IDS2018 dataset, which comprehensively represents a spectrum of modern attacks. The research methodology systematically includes data preprocessing, handling class imbalance via weighting techniques, and performance evaluation based on accuracy, F1-score, and inference time metrics. Experimental results indicate that both models achieved high performance convergence with perfect Area Under Curve (AUC) scores. However, XGBoost demonstrated technical superiority with an accuracy of 99.8%, slightly surpassing Random Forest at 99.4%. The most significant finding of this study lies in computational efficiency, where XGBoost proved to be 14% faster (6.36 seconds) in prediction compared to Random Forest (7.42 seconds) on a large-scale test set. This fact confirms that the boosting architecture in XGBoost offers an optimal balance between detection sensitivity and system latency. Based on this evidence, XGBoost is recommended as the best classification model for real-time intrusion detection system implementations that prioritize rapid threat response.
Assessing the Impact of Image Preprocessing on ConvNeXt Performance for Waste Classification Destian Luis, Ivander; Tinaliah
INOVTEK Polbeng - Seri Informatika Vol. 11 No. 1 (2026): February
Publisher : P3M Politeknik Negeri Bengkalis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35314/fsa3at15

Abstract

Waste has become an increasingly urgent environmental issue in everyday life. The waste is constantly increasing due to population growth, urbanization, and consumption. The increasing amount of waste needs more intelligent systems to help with the management of waste, especially with the sorting of waste. Unfortunately, the absence of the public's awareness of the importance of waste management has led to the ineffective collection of waste. Thus, there is a need of classifying the waste into technological systems based on various waste types. This research has computing waste types using ConvNetX. The research methodology is based on the collection and preprocessing of data that includes different image enhancement techniques such as CLAHE and bilateral filtering. This study employed the ‘Garbage Classification Dataset’ found on Kaggle. The dataset is split into 80% of it as training data, 10% of it as testing data, and the last 10% of it as validation data. The ConvNeXt model was trained using one of the training sets after the data was split and was subsequently measured using the validation and test sets for the training of the model. This research analyzed the effects of image preprocessing by using a baseline, which was no preprocessing (Scenario 1), and then using preprocessing (Scenario 2). The results from the experiments showed Scenario 2 had a higher accuracy of 94% compared to the baseline of 90%. The use of CLAHE and bilateral filtering positively impacted the F1 score by increasing it to glass (96%) and plastic (92%) and having a full recall (100%) for metal. Scenario 2 resulted in a total training time of 20.86 minutes, and Scenario 1 was 11.83 minutes, which means that Scenario 2 had a lower computational efficiency. Nevertheless, the additional time was well spent for the considerable consistency improvement in the classification of all categories. This makes it evident that substantial image preprocessing is necessary for the model to be able to generalize and classify images with complex visual details.
Ensemble-Based Machine Learning for Improving Local Weather Prediction Accuracy in Batam, Indonesia Sama, Hendi; Gumolung, Randy; Christian, Yefta
INOVTEK Polbeng - Seri Informatika Vol. 11 No. 1 (2026): February
Publisher : P3M Politeknik Negeri Bengkalis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35314/9p53qe06

Abstract

Accurate short-term rainfall prediction in tropical microclimates such as Batam remains challenging due to strong local atmospheric variability and the limited generalization capability of single-model classifiers. This study proposes an ensemble-based framework that integrates Naïve Bayes, C4.5, and Random Forest through a majority-voting mechanism for multi-class hourly rainfall prediction. The experiments were conducted using multi-year hourly meteorological data collected for Batam City from an open-source weather archive, covering key atmospheric variables and exhibiting an imbalanced rainfall-class distribution. Model performance was evaluated using ten-fold cross-validation with accuracy, precision, recall, and F1-score metrics. The proposed ensemble achieved an accuracy of 84.74%, consistently outperforming the corresponding base classifiers. The model demonstrated strong predictive capability for dominant rainfall classes (TidakHujan and HujanRingan), while reduced performance was observed for HujanSedang and HujanBerat due to class imbalance, a well-documented challenge in tropical rainfall modeling. Overall, the results indicate that combining probabilistic and tree-based learners yields a more stable and reliable prediction framework for localized tropical weather. This work contributes a practical and reproducible ensemble approach tailored to microclimate conditions, offering a foundation for improved data-driven rainfall forecasting in similar high-variability
Application of EfficientNet Deep Learning with Wiener Filter for Freshwater Fish Disease Image Classification Setiawan, Christofer Evan; Tinaliah
INOVTEK Polbeng - Seri Informatika Vol. 11 No. 1 (2026): February
Publisher : P3M Politeknik Negeri Bengkalis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35314/k1xeb958

Abstract

Challenges pertaining to the timely and accurate diagnosis of diseases in freshwater fish have adversely impacted the productivity of the aquaculture industry. Image classification using deep learning techniques has the potential to overcome such challenges. However, this potential has not been realized due to such problems as image noise, motion blur, and small dataset sizes. Most prior studies in this area employ the same Convolutional Neural Network (CNN) architectures and, while using the same or similar techniques, are generic to the studies to preprocess the images. The focus of this study is to compare and benchmark the image classification performance of the EfficientNet architectures (B0 to B7) using the Wiener Filter as a preprocessing technique for the classification of diseases in freshwater fish. The experiments used a publicly available dataset of 1,750 images of seven diseases in fish while maintaining identical training parameters to yield sixteen different experimental configurations. Metrics such as accuracy, precision, recall, and F1-score were exercised while evaluating model performance. The data show that medium-scale architectures surpass both smaller and larger size variants. The optimal performance was achieved by EfficientNet-B4 and the Wiener Filter with an accuracy of 94.89%, a precision of 95.15%, a recall of 94.92%, and an F1-score of 94.89%. The results confirm that preprocessing with the Wiener filter improves performance on classification tasks using medium-sized models and further elucidate the applicable value of the model developed in this study in aquaculture and its related interventions.
Comparison of MobileNetV3-Small and EfficientNetV2-Small for Low-Resolution X-ray Image Classification Andhika Rizky Cahya Putra; Siska Devella
INOVTEK Polbeng - Seri Informatika Vol. 11 No. 1 (2026): February
Publisher : P3M Politeknik Negeri Bengkalis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35314/7j5twc37

Abstract

Lung diseases are a global health concern that require accurate and efficient automated diagnostic systems, particularly in healthcare facilities with limited resources. This study evaluates the performance and computational efficiency of two lightweight convolutional neural network architectures, namely MobileNetV3-Small and EfficientNetV2-Small, on the multi-label classification task of low-resolution ChestMNIST chest X-ray images. Experiments were conducted across eight testing scenarios with and without light spatial data augmentation. The evaluation encompassed predictive performance using accuracy and Area Under the Curve (AUC-ROC) metrics, as well as computational efficiency based on the number of parameters, FLOPs, model size, training time, and inference time. Results indicated that although both models achieved high accuracy (0.93–0.95), MobileNetV3-Small consistently produced higher and more stable AUC-ROC values compared to EfficientNetV2-Small, while being significantly more computationally efficient. Moreover, the application of light spatial data augmentation on low-resolution datasets such as ChestMNIST did not provide consistent performance improvements and instead increased training costs, indicating the limited effectiveness of simple geometric variations when spatial information in the images is highly constrained. These findings provide insight that, in low-resolution medical image multi-label classification, the suitability of an efficient CNN architecture design has a greater impact on overall performance than increasing model complexity or applying light spatial augmentation.
Comparative Analysis of Usability and Efficiency of Interface Design Process in Figma and Adobe XD Muhammad Dyo Haqiqi; Temi Ardiansyah
INOVTEK Polbeng - Seri Informatika Vol. 11 No. 1 (2026): February
Publisher : P3M Politeknik Negeri Bengkalis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35314/e911jr52

Abstract

A variety of platforms are now focused on improving and speeding up the design process by harnessing the growing use of digital technologies in user interface (UI) design. Adobe XD and Figma stand out as two of the most popular tools for user interface and user experience design. User experience can differ due to variations in usability and the effectiveness of the design process, even when both share similar core functions. This study aims to assess the effectiveness and practicality of Adobe XD and Figma. This research utilizes a quantitative descriptive approach, measuring task completion times and leveraging the System Usability Scale (SUS) for evaluation. Twenty students signed up for an informatics program, each bringing with them a solid background in both systems, and successfully completed the survey. Data was gathered using SUS surveys and time observations carried out during the performance of design tasks. According to the data, Figma boasts an impressive average SUS score of 62.75%, while Adobe XD trails behind with a score of 46.75%. Figma outperformed Adobe XD by around 27.98% in executing design tasks. The findings reveal that Figma outshines Adobe XD in terms of efficiency and user-friendliness, especially when it comes to real-time collaboration and a sleek, streamlined interface. These findings aim to guide educational institutions and UI/UX designers in choosing the ideal design platform that aligns with user needs.
Enhancing Generalization of Tomato Leaf Disease Classification via TDR Model and Field-Conditioned Data Augmentation Fernando Feliansyah; Ery Hartati
INOVTEK Polbeng - Seri Informatika Vol. 11 No. 1 (2026): February
Publisher : P3M Politeknik Negeri Bengkalis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35314/tvgfx074

Abstract

Tomato leaf diseases significantly affect agricultural productivity, particularly when detection systems are deployed under real-field conditions characterized by illumination variation, background clutter, and image noise. Although deep learning-based models have achieved high accuracy on laboratory datasets such as PlantVillage, their generalization performance often degrades when applied to real-world environments. This study proposes a lightweight CNN-based tomato leaf disease recognition model, referred to as the TDR-Model, combined with field-conditioned data augmentation strategies. The proposed model integrates MobileNetV3 with Convolutional Block Attention Module (CBAM) and Omni-Dimensional Dynamic Convolution (ODC) to enhance feature representation while maintaining computational efficiency. Field-conditioned augmentation using the Albumentations library to simulate real-world visual variations during training. The model is evaluated on the real-world tomato set consisting of 10 classes and 885 leaf images. Experimental results show that the proposed model achieves an overall test accuracy of 82.94%, with precision, recall, and F1-score of 85.06%, 83.04%, and 83.03%, respectively. Furthermore, the model requires only 3.47 million parameters, 0.23 GFLOPs, and an average inference time of 5.15 ms, making it suitable for real-time and resource-constrained agricultural applications. These results indicate that the proposed approach effectively balances accuracy and efficiency for practical tomato leaf disease detection.
Classification of Tomato Fruit Ripeness Level Using Convolutional Neural Network–Support Vector Machine Based on Digital Image Saputra Edika, Nelson; Hartati, Ery
INOVTEK Polbeng - Seri Informatika Vol. 11 No. 1 (2026): February
Publisher : P3M Politeknik Negeri Bengkalis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35314/1r0wh197

Abstract

Tomato ripeness classification is an important task in post-harvest quality management, as the ripeness level directly influences taste, shelf life, and market value. Conventional ripeness assessment methods based on manual visual inspection are inherently subjective and often yield inconsistent results. To address this limitation, this study proposes an image-based tomato ripeness classification model using a hybrid Convolutional Neural Network–Support Vector Machine (CNN–SVM) approach. In the proposed model, a pretrained ResNet-50 architecture is employed as a fixed feature extractor to derive deep visual representations, while a Support Vector Machine with a Radial Basis Function kernel is utilized for final classification. The model is evaluated using a publicly available tomato image dataset, with the analysis limited to unripe and ripe categories. Image preprocessing procedures include resizing, normalization, and data augmentation, followed by an 80:20 train–test split strategy. Experimental results demonstrate that the proposed CNN–SVM model achieves strong and balanced performance, with an accuracy of 96.56%, a weighted precision of 96.80%, a recall of 96.56%, and an F1-score of 96.57%. These findings indicate that integrating deep feature extraction with an SVM classifier provides an effective and robust solution for tomato ripeness classification, particularly under limited data conditions.
Color and Texture Feature Extraction for Disease Identification in Chili Leaves Using K-Nearest Neighbors Andreyas; Alamsyah, Derry
INOVTEK Polbeng - Seri Informatika Vol. 11 No. 1 (2026): February
Publisher : P3M Politeknik Negeri Bengkalis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35314/s9v7mn76

Abstract

Manual identification of chili leaf diseases has the weakness of subjectivity, which impacts the decline in harvest productivity. This study aims to build an accurate automatic classification system using a machine learning approach. The research methodology integrates the extraction of Hue, Saturation, Value (HSV) color features and Gray Level Co-occurrence Matrix (GLCM) texture on a dataset of 1,856 images divided with a ratio of 80:20. Hyperparameter optimization was performed using Grid Search on the K-Nearest Neighbors (K-NN) algorithm to find the best performance. The test results show that the optimal configuration is achieved at a value of K = 3 with the Manhattan distance metric, which produces a test accuracy of 92%. It is concluded that the integration of color and texture features with appropriate parameter optimization is proven to be effective as a reliable and efficient diagnostic solution.

Page 25 of 26 | Total Record : 256

 22   23   24   25   26 

Filter by Year

2024 2026


Reset
Filter By Issues
All Issue Vol. 11 No. 1 (2026): February Vol. 10 No. 3 (2025): November Vol. 10 No. 2 (2025): July Vol. 10 No. 1 (2025): March Vol. 9 No. 2 (2024): November