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All Journal Jurnal SOLMA Jurnal Anak Usia Dini Holistik Integratif (AUDHI) SCIENCE : Jurnal Inovasi Pendidikan Matematika dan IPA JUARA: JURNAL WAHANA ABDIMAS SEJAHTERA Journal of Engineering and Science Application
Ningsih, Yunia
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Aerospace Engineering Humanities Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Education Engineering Environmental Science Social Sciences Other

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Journal : Journal of Engineering and Science Application

 1 
Evaluating Imputation Approaches and Support Vector Regression Parameters in Weather Forecasting Priyatno, Arif Mudi; Ningsih, Yunia
Journal of Engineering and Science Application Vol. 2 No. 2 (2025): October
Publisher : Institute Of Advanced Knowledge and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.69693/jesa.v2i2.34

Abstract

Rainfall plays a vital role in various sectors such as transportation, agriculture, and industry. Having accurate rainfall information enables stakeholders in these fields to take proper measures and minimize potential losses caused by inaccurate data. This study focuses on identifying an effective method for rainfall forecasting by examining imputation techniques in data preprocessing and parameter settings within Support Vector Regression (SVR). The experimental findings indicate that the most effective imputation method for SVR is determined using the Mean Squared Error (MSE) and Mean Absolute Error (MAE) evaluation metrics. Based on MSE, the k-nearest neighbor method proves to be the most reliable approach for data imputation preprocessing. The preprocessing results were then applied to Polynomial SVR with parameters C = 1000, tolerance = 0.001, epsilon = 0.01, and unlimited iterations. Conversely, MAE results highlight Artificial Neural Network (ANN) as the optimal imputation method. ANN, when combined with a radial basis function kernel, gamma = 0.001, C = 1000, tolerance = 0.001, and unlimited iterations, was further tested using RBF SVR under the same parameter settings.
A Robust Hybrid Approach for Malware Detection: Leveraging CNN and LSTM for Encrypted Traffic Analysis Priyatno, Arif Mudi; Ningsih, Yunia; Vandika, Arnes Yuli; Muhammadong, Muhammadong
Journal of Engineering and Science Application Vol. 1 No. 2 (2024): October
Publisher : Institute Of Advanced Knowledge and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.69693/jesa.v1i2.10

Abstract

The rapid growth in Internet usage and advancements in network technologies have escalated the risk of network attacks. As the adoption of encryption protocols increases, so does the difficulty in identifying malware within encrypted traffic. Malware represents a significant danger in cyberspace, as it compromises personal data and harms computer systems. Network attacks involve unauthorized access to networks, often aiming to disrupt or damage them, with potentially severe consequences. To counter these threats, researchers, developers, and security experts are constantly innovating new malware detection techniques. Recently, deep learning has gained traction in network security and intrusion detection systems (IDSs), with models such as Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) showing promise in detecting malicious traffic. Despite these advancements, extracting relevant features from diverse malware types remains a challenge. Current solutions demand substantial computational resources and are often inefficient for large datasets. Additionally, existing image-based feature extraction methods consume significant resources. This study tackles these issues by employing a 1D CNN alongside LSTM for the detection and classification of encrypted malicious traffic. Using the Malware Analysis benchmark dataset, which consists of 42,797 malware and 1,079 goodware API call sequences, the proposed model achieved an accuracy of 99.2%, surpassing other state-of-the-art models
Co-Authors Agus Salim Andayan, Iwan Andayana, Iwan Arif Mudi Priyatno Ariwibowo, Anung B Arnes Yuli Vandika Giffary, Ridho Julika, Wahyu Indah Lissa’adah, Tri Rif’atun Munawwarotu Muhammadong Ratna Shofiati Rohita Safitri, Yesi Andri Sari, Syandra Sati, Sati Wulan Dari, Wulan Zaki, Arviandri