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All Journal CESS (Journal of Computer Engineering, System and Science) EXPLORE Technologia: Jurnal Ilmiah International Journal of Informatics and Computation Jurnal Teknimedia: Teknologi Informasi dan Multimedia Jurnal Pengembangan Sistem Informasi dan Informatika Jurnal Teknologi Kharisma Tech EXPLORE Computing and Education Technology Journal Coreid Journal Jurnal Inovasi Teknologi Terapan JURNAL FLOBAMORATA MENGABDI International Journal of Informatics Engineering and Computing
M. Hizbul Wathan
Politeknik Manufaktur Negeri Bangka Belitung, Indonesia
Agriculture, Biological Sciences & Forestry Humanities Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering Environmental Science Health Professions Languange, Linguistic, Communication & Media Mechanical Engineering Social Sciences Other

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Journal : international journal of informatics engineering and computing

 1 
Tomato Ripeness Identification Using Recurrent Neural Network Algorithm Dede Hamdani; M.Hizbul Wathan
International Journal of Informatics Engineering and Computing Vol. 1 No. 2 (2024): International Journal of Informatics Engineering and Computing
Publisher : ASTEEC

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70687/ijimatic.v1.i2.43

Abstract

Tomatoes undergo distinct ripeness stages, typically categorized into ripe, semi-ripe, and unripe phases. Traditional methods for assessing ripeness often face challenges in accuracy due to difficulties in comparing variables and subjective interpretations. This study proposes an innovative approach to classify tomato ripeness using a dataset of 200 tomato images and employs a Recurrent Neural Network (RNN) for precise classification. The experimental results demonstrate that the RNN-based model achieves a 95.0% accuracy rate in identifying ripeness stages, significantly outperforming conventional methods. This high level of accuracy highlights the model's potential to minimize errors and provide reliable assessments of tomato maturity. The proposed method offers a robust and efficient solution for agricultural applications, enabling improved quality control and harvest timing. Future research could explore the integration of additional data sources or advanced machine learning techniques to further enhance the model's performance and applicability across diverse agricultural contexts.
Detection of DDoS Attacks Using Hybrid LSTM and SVM Algorithm Ivansius Nahak; M. Hizbul Wathan
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/vd7kk061

Abstract

Distributed Denial of Service (DDoS) attacks pose serious threats to network infrastructures by disrupting services through massive malicious traffic. This study proposes a hybrid detection model that integrates Long Short-Term Memory (LSTM) with a Support Vector Machine (SVM) classifier to improve the accuracy of DDoS detection in network traffic. The LSTM model captures temporal patterns within sequential traffic data, while the SVM performs the final classification to distinguish between normal and anomalous traffic. The experiment uses a dataset containing 104,345 records with 23 features that undergo preprocessing, encoding, scaling, and class balancing before model training. Experimental results demonstrate that the proposed hybrid model achieves stable learning performance with training accuracy reaching approximately 93% and validation accuracy around 94%. The loss curves show consistent decreases across 50 training epochs, indicating effective convergence and minimal overfitting. Confusion matrix analysis shows that the model correctly classifies the majority of normal and anomalous traffic samples, with relatively low false positive and false negative rates. Overall evaluation results show that the hybrid LSTM–SVM model achieves 95% accuracy with balanced classification performance. The model records strong precision, recall, and F1-score values for both normal and anomalous traffic classes.
Optimizing Flood Prediction Using Machine Learning Algorithms M. Hizbul Wathan; Md Safaet Hossain
International Journal of Informatics Engineering and Computing Vol. 3 No. 2 (2026): International Journal of Informatics Engineering and Computing
Publisher : ASTEEC

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70687/869y5616

Abstract

Flooding is a major environmental hazard that requires accurate and reliable prediction to support disaster mitigation and early warning systems. This study applied a combination of K-Means clustering and Random Forest classification to predict flood conditions based on water-level data from DKI Jakarta. The dataset was obtained from Open Data Jakarta and contained observations recorded from January to December 2020. We utilized K-Means to group water-height observations and evaluated cluster configurations from k = 3 to k = 20. The resulting cluster information was then incorporated as an additional feature into the Random Forest model. We evaluated the model using Accuracy, Precision, Recall, F1 Score, and a confusion matrix. The results showed that the K-Means configuration affected the classification performance. The highest F1 Score reached 0.90 at k = 14, while the highest Accuracy reached 0.96 at k = 15 and k = 20. The Random Forest model achieved an Accuracy of 0.95, with weighted Precision, Recall, and F1 Score of 0.96, 0.95, and 0.95, respectively. Class 0 achieved the highest F1 Score of 0.98, while Class 2 obtained a Recall of 0.99. These findings demonstrate that the K-Means and Random Forest approach can effectively support flood prediction using water-level data and provide a promising basis for developing data-driven flood monitoring and prediction systems.
Co-Authors Ahmat Josi Ali, Hendrik Aulia, Rosaura Better Swengky Better Swengky Bradika Almandin Wisesa Cahyadi, Irsan Dede Hamdani Deswita Deswita Deswita Syahrani Efendi, Muhamad Masjun Evvin Faristasari Fadhilah, Indirokan Fadli, Sofiansyah Fadli Fatir Atthariq Zami Indra Irawan Indra Irawan Indra Irawan Irwan Irwan Irwan Ivansius Nahak Khemal Fasyah Ishaq Lesta, Lesta LINDA FUJIYANTI Md Safaet Hossain Moh. Aziz Muhammad Kalam Sabili Oktaviani, Ajeng Prayogi, M. Bagus Ramadani, Ardi Rani Laple Satria Rani Laple Satria Putra Riki Afriyansyah Riski Meliya Ningsih Satria Agus Darma Silvia Agustin Sirlus Andreanto Jasman Duli Sofiansyah Fadli Tegar Priyadi Tri Agusti Farma Wardianto, Wardianto Yang Agita Rindri Zain, M Syafrizal