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All Journal Prosiding Seminar Nasional Sisfotek (Sistem Informasi dan Teknologi Informasi) Journal of Artificial Intelligence and Engineering Applications (JAIEA)
Indra Wiguna Marthanu
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Automotive Engineering Computer Science & IT Control & Systems Engineering

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Analisis Kinerja Algoritma Machine Learning untuk Klasifikasi Prestasi Mahasiswa pada Mata Kuliah Bahasa Inggris Riri Narasati; Dadang Sudrajat; Ahmad Faqih; Indra Wiguna Marthanu; Agus Bahtiar
Prosiding SISFOTEK Vol 9 No 1 (2025): SISFOTEK IX 2025
Publisher : Ikatan Ahli Informatika Indonesia

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Abstract

This study analyzes the performance of several machine learning algorithms in classifying student achievement in English language courses. The research focuses on comparing the performance of K-Nearest Neighbors (KNN), Naïve Bayes, Random Forest, and Support Vector Machine (SVM) using the K-Fold Cross Validation approach to evaluate accuracy, F1-score, and fairness. The dataset, consisting of students’ final grades, was processed through data pre-processing and feature scaling. Results show that the KNN model with K=5 achieved the highest accuracy of 100%, followed by Naïve Bayes with 95.59%. Statistical tests indicated a significant performance difference between Random Forest and SVM, while fairness evaluation revealed that Random Forest provided the most balanced error distribution. These findings confirm that KNN and Random Forest algorithms are highly effective for academic performance classification based on numerical data. The study highlights the potential of machine learning to enhance adaptive, objective, and equitable educational evaluation systems.
Optimasi Akurasi dan Efisiensi Deteksi Intrusi pada Lingkungan Komputasi Awan dengan Analisis Deret Waktu CNN-LSTM Martanto; Khaerul Anam; Indra Wiguna Marthanu; Puji Pramudya Marta
Prosiding SISFOTEK Vol 9 No 1 (2025): SISFOTEK IX 2025
Publisher : Ikatan Ahli Informatika Indonesia

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Abstract

This study proposes a CNN-LSTM time series analysis-based intrusion detection system (IDS) model to improve accuracy and efficiency in cloud computing environments. With more organizations moving to the cloud, security threats are becoming more sophisticated, rendering traditional detection methods inadequate. The objective of this study is to develop and evaluate a hybrid model that can address these challenges. The methodology used involves an experimental quantitative approach on a representative CSE-CIC-IDS2018 dataset. This dataset underwent rigorous data preprocessing, including data cleaning, conversion to time series format, and feature selection using stationarity and Granger causality tests. The CNN-LSTM model was then trained and evaluated using accuracy and computational efficiency metrics. The results showed superior model performance with an accuracy of 0.910, precision of 0.874, and F1-Score of 0.882. The model also demonstrated good computational efficiency, with a training time of 3.9887 seconds and a prediction time of 0.3607 seconds, making it suitable for real-time detection. This study concludes that the CNN-LSTM model is a viable solution for improving cloud computing security, offering a balance between high accuracy and good computational efficiency. Future research could explore multi-dataset validation and the integration of interpretation methods to improve its application.
Mitigating Imbalanced Citrus Disease Image Datasets with Oversampling Arya Gunawan; Nana Suarna; Agus Bahtiar; Indra Wiguna Marthanu; Kaslani
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 5 No. 2 (2026): February 2026
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59934/jaiea.v5i2.1862

Abstract

Dataset imbalance is a critical challenge in plant disease image classification because it causes bias towards the majority class. This study evaluates the effectiveness of augmentation-based oversampling techniques on the classification performance of citrus leaf images using the MobileNetV2 architecture. The four leaf disease classes classified include Greening, Fresh, Canker, and Blackspot. The dataset was obtained from a public repository and processed through preprocessing (resize, normalization) and augmentation (rotation, flipping, zoom) stages. The model was trained and tested in two scenarios: baseline (unbalanced data) and mitigation (data balanced through augmentation). The experimental results show that the mitigation approach was able to increase accuracy from 91.92% to 93.94%. The F1-score, precision, and recall values also increased significantly, especially in the minority class. Evaluation using a confusion matrix reinforced the finding that augmentation-based oversampling is effective in reducing classification errors. This study shows that the integration of augmentation techniques and MobileNetV2-based transfer learning can significantly improve classification performance and contribute to the development of early detection systems for plant diseases in precision agriculture.
Co-Authors agus bahtiar Ahmad Faqih Arya Gunawan Dadang Sudrajat Kaslani Khaerul Anam Martanto Nana Suarna Puji Pramudya Marta Riri Narasati