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International Journal of Informatics Engineering and Computing
Published by ASTEEC Publisher
ISSN : -     EISSN : 30909112     DOI : https://doi.org/10.70687/ijimatic
Core Subject : Science,
Computer Science & IT
International Journal of Informatics Engineering and Computing (IJIMATIC) is an international, peer-reviewed, open-access journal that publishes original theoretical and empirical work on the science of informatics and its application in multiple fields. Our concept of informatics encompasses technologies of information and communication, as well as the social, linguistic, and cultural changes that initiate, accompany, and complicate their development. IJIMATIC aims to be an international platform to exchange novel research results in simulation-based science across all computer science disciplines.
Arjuna Subject : Ilmu Komputer (semua kategori) - Kecerdasan Buatan
Articles 26 Documents
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Modeling Automatic Waste Sorting Using Ultrasonic Sensors Akhmad Wakhid; Marselina Junia Sipit
International Journal of Informatics Engineering and Computing Vol. 2 No. 2 (2025): International Journal of Informatics Engineering and Computing
Publisher : ASTEEC

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70687/31bqbr09

Abstract

Waste management remains a critical challenge due to the increasing volume of solid waste and the inefficiency of manual sorting processes. This study develops and implements an Internet of Things (IoT)-based automatic waste sorting system using an ESP32 microcontroller. The proposed system integrates ultrasonic sensors, an inductive proximity sensor, and an MQ135 gas sensor to automatically detect and classify metal and non-metal waste. The system also connects to the Blynk platform to enable real-time monitoring and notification capabilities, allowing users to observe system conditions remotely. Experimental evaluation is conducted using 100 waste samples consisting of 50 metal objects and 50 non-metal objects. The results show that the system correctly classifies 48 metal samples and 42 non-metal samples. Meanwhile, 8 non-metal samples are misclassified as metal, and 2 metal samples are incorrectly detected. Based on these results, the system achieves an overall classification accuracy of 90%, indicating reliable performance in distinguishing between metal and non-metal waste materials. Further evaluation using precision, recall, and F1-score metrics confirms the effectiveness of the proposed system. The metal class achieves a precision of 85.71%, a recall of 96%, and an F1-score of 90.57%. For the non-metal class, the system records a precision of 95.45%, a recall of 84%, and an F1-score of 89.39%. These results demonstrate balanced classification performance for both categories. Therefore, the developed IoT-based automatic waste sorting system provides a practical and reliable approach for improving waste management efficiency and supporting intelligent waste processing based on material characteristics.
Automatic Detection of Cabbage Pest Attacks Based on Leaf Images with Machine Learning Approach Ni Wayan Surya Wardhani; Prayudi Lestantyo; Atiek Iriany; Nur Silviyah Rahmi
International Journal of Informatics Engineering and Computing Vol. 3 No. 1 (2026): International Journal of Informatics Engineering and Computing
Publisher : ASTEEC

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70687/3szcd282

Abstract

Early detection of cabbage pest attacks is essential for reducing crop losses and improving agricultural productivity. This paper proposes an automatic cabbage pest detection system based on leaf images by integrating Gray-Level Co-occurrence Matrix (GLCM) texture feature extraction with a Decision Tree Boosting classification approach. The proposed method consists of image acquisition, preprocessing, GLCM feature extraction, and machine learning-based classification to identify pest damage severity. The extracted texture features include contrast, dissimilarity, correlation, homogeneity, angular second moment (ASM), energy, and entropy, which characterize the texture changes caused by pest infestations. Experimental results demonstrate that the proposed approach effectively distinguishes healthy and pest-attacked cabbage leaves and classifies damage into five severity levels: Normal, Low, Moderate, Severe, and Very Severe. The analysis further shows that contrast and entropy increase with increasing damage severity, whereas homogeneity and energy decrease, indicating that GLCM features provide discriminative texture information for pest identification. The Decision Tree Boosting classifier successfully utilizes these features to produce consistent and objective classifications, reducing the reliance on manual visual inspection. Therefore, the proposed framework provides a practical and computationally efficient solution for automatic cabbage pest detection and damage assessment. The developed system has the potential to support precision agriculture by enabling timely pest management decisions, minimizing crop losses, and improving cabbage production. Future research should evaluate the proposed approach using larger field datasets and investigate the integration of advanced deep learning models and multispectral imaging to further improve detection accuracy and robustness.
Geometric Structured Trend Tunneling: A Hybrid VARIMA-SVR Model for Synthetic Stock Time Series Generation I Wayan Ordiyasa; Ahmad Sahal; Gladies Serren Kutani
International Journal of Informatics Engineering and Computing Vol. 3 No. 1 (2026): International Journal of Informatics Engineering and Computing
Publisher : ASTEEC

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70687/g9r7y321

Abstract

This study presents a novel hybrid framework, Geometric Structured Trend Tunneling (GSTT), for generating synthetic multivariate time series data, specifically applied to stock price data of Medco Energi Internasional (MEDC), a major player in Indonesia’s energy sector. The proposed model integrates the statistical power of Vector Autoregressive Integrated Moving Average (VARIMA) with the nonlinear pattern-capturing capability of Support Vector Regression (SVR), enabling high-fidelity reconstruction of temporal structures and feature dependencies in financial datasets. The dataset used spans over two decades (2003–2024) and includes core trading indicators such as Open, High, Low, and Close prices. Experimental results demonstrate that GSTT achieves excellent performance across multiple evaluation metrics, including MAE, RMSE, R², and KS tests, while preserving inter-feature correlations and distributional fidelity. Visual comparisons and descriptive statistics further confirm the model’s ability to replicate realistic market behavior. Unlike deep generative models such as GANs or VAEs, GSTT offers a more interpretable, stable, and computationally efficient alternative for financial data augmentation, simulation, and robust AI training. This work contributes a scalable solution for addressing data scarcity in financial modeling, with potential applications in backtesting, risk analysis, and algorithmic trading simulations.
Comparing Deep Learning and Machine Learning Approaches for Spam Email Detection Hamzah; Niko Irsyad Maulana; Akhmad Wakhid
International Journal of Informatics Engineering and Computing Vol. 3 No. 1 (2026): International Journal of Informatics Engineering and Computing
Publisher : ASTEEC

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70687/3rzz5124

Abstract

Spam messages remain one of the most prevalent cybersecurity threats because they facilitate phishing, fraud, and malware distribution through digital communication platforms. This paper evaluates the effectiveness of six machine learning algorithms for spam message detection, namely Convolutional Neural Network (CNN), Support Vector Machine (SVM), Decision Tree, K-Nearest Neighbor (KNN), Gaussian Naïve Bayes (GNB), and Gradient Boosting (GBoost). The proposed approach compares the performance of these classifiers using accuracy, precision, recall, and F1-score to identify the most reliable model for distinguishing spam and legitimate messages. Experimental results demonstrate that the CNN achieves the highest overall accuracy of 99%, with a precision, recall, and F1-score of 0.99, 1.00, and 0.99 for normal messages, and 0.99, 0.92, and 0.95 for spam messages, respectively. Gradient Boosting provides the second-best performance with an accuracy of 92%, while SVM, Decision Tree, and KNN achieve accuracies ranging from 88% to 90% but exhibit lower recall for the spam class. Gaussian Naïve Bayes produces the weakest performance with an accuracy of only 16%, indicating that its feature independence assumption is insufficient for modeling the complex relationships within textual spam data. These findings demonstrate that deep learning, particularly CNN, provides more accurate and balanced spam detection than conventional machine learning approaches. The proposed model offers an effective solution for practical spam filtering systems by minimizing both false positives and false negatives while maintaining high classification reliability. Future work will investigate transformer-based language models, hybrid deep learning architectures, explainable artificial intelligence techniques, and larger multilingual datasets to further improve detection accuracy, interpretability, and robustness against evolving spam patterns.
Forest Fire Prediction Analysis Using the Random Forest Classifier Algorithm Ida Bagus Perawita Yasa; Agil Pratama Mandala Saputra; Doni Irawan; Muhammad Zagofari; Jana Pratama; Ahmad Naufal Syafiq
International Journal of Informatics Engineering and Computing Vol. 3 No. 1 (2026): International Journal of Informatics Engineering and Computing
Publisher : ASTEEC

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70687/7f0f9t87

Abstract

Forest fires pose significant threats to ecosystems, biodiversity, human safety, and economic sustainability, making accurate early prediction essential for effective disaster mitigation. This study proposes a forest fire prediction framework based on the Random Forest algorithm using multi-source environmental data. The proposed framework consists of data acquisition, preprocessing, feature normalization, feature selection using Random Forest feature importance, model training, and performance evaluation. Environmental variables, including air temperature, relative humidity, rainfall, wind speed, vegetation index, elevation, slope, and land cover, are utilized to represent conditions associated with forest fire occurrence. The performance of the proposed model is compared with the K-Neighbors (KNN) classifier using standard classification metrics, including accuracy, precision, recall, and F1-score. Experimental results show that the Random Forest classifier achieves an accuracy of 100%, with precision, recall, and F1-score all reaching 1.00, while the KNN classifier records an accuracy of 47% and precision, recall, and F1-score values of 0.26. These findings demonstrate that Random Forest significantly outperforms KNN by effectively capturing complex relationships among environmental variables and minimizing classification errors. The proposed framework provides a reliable and accurate solution for forest fire prediction and has strong potential to support early warning systems and decision-making in forest fire management. Future work will focus on integrating real-time environmental data, satellite observations, and hybrid explainable machine learning techniques to improve model generalization and operational deployment.
Stepping up Phishing Detection using Convolutional Neural Network  Algorithm Beti Fatima Markis; Safaet Hossain
International Journal of Informatics Engineering and Computing Vol. 3 No. 1 (2026): International Journal of Informatics Engineering and Computing
Publisher : ASTEEC

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70687/j6e9yg57

Abstract

Phishing websites continue to pose a significant cybersecurity threat by deceiving users into disclosing sensitive information, making accurate and reliable detection methods increasingly important. This paper evaluates the effectiveness of several machine learning algorithms for phishing website detection, including Convolutional Neural Network (CNN), Support Vector Machine (SVM), Decision Tree, K-Nearest Neighbor (KNN), Gaussian Naïve Bayes (GNB), and Gradient Boosting (GBoost). The proposed approach first analyzes the CNN learning process using training and validation accuracy and loss curves to assess model convergence and generalization capability. Subsequently, the performance of all classifiers is evaluated using confusion matrices, accuracy, precision, recall, and F1-score. Experimental results show that the CNN achieves the highest overall accuracy of 93%, with balanced precision, recall, and F1-score of 0.93 for both legitimate and phishing website classes. SVM, KNN, and GBoost also demonstrate competitive performance with 91% accuracy, whereas the Decision Tree achieves 89% accuracy. In contrast, Gaussian Naïve Bayes produces the weakest performance with an accuracy of only 51%, indicating that its feature independence assumption is inadequate for modeling the complex characteristics of phishing websites. These findings demonstrate that deep learning, particularly CNN, provides more accurate and balanced phishing detection than conventional machine learning methods. The proposed approach offers an effective solution for practical phishing detection and has the potential to strengthen cybersecurity systems by reducing both false positives and false negatives. Future work will investigate advanced deep learning architectures, explainable artificial intelligence techniques, and larger real-world phishing datasets to further improve detection accuracy, robustness, and model interpretability.

Page 3 of 3 | Total Record : 26

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