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All Journal Jurnal Sintaks Logika (JSilog) Journal of Embedded Systems, Security and Intelligent Systems
M. Yusuf, Andi
Unknown Affiliation
Computer Science & IT

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APLIKASI PANDUAN PESTISIDA PADA TANAMAN PADI BERBASIS ANDROID M. Yusuf, Andi; Syamsuddin, Sumantri
Jurnal Sintaks Logika Vol. 1 No. 3 (2021): Oktober 2021
Publisher : Fakultas Teknik Universitas Muhammadiyah Parepare

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1012.269 KB) | DOI: 10.31850/jsilog.v1i3.1144

Abstract

Rice is one of the food commodity crops that must be available. Various efforts have been made to increase rice productivity in Indonesia, one of which is agricultural intensification. with the condition that the pest population has reached its economic threshold, so this study takes the problem of the application of pesticide guides to android-based rice plants. The research method used is Field Research. The results of testing using Black Box, it is found that the functional application runs well according to the design made. While the results of the White Box test by testing the program algorithm, it is found that the number of flowgraphs with the number of paths in the white box test has the same value, namely V(G) = 6, Predicate (P) = 6 and Region (R) = 6 so it is stated that the programming logic is in accordance with the previous design.
Static Analysis of Android Malware Based on Opcode and Permission Features Using Random Forest Azis, Muhammad Amran; Irianti, Arnita; Firgiawan, Wawan; M. Yusuf, Andi
Journal of Embedded Systems, Security and Intelligent Systems Vol 6, No 3 (2025): September 2025
Publisher : Program Studi Teknik Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59562/jessi.v6i3.9113

Abstract

This study conducts a static analysis of Android applications to detect malware based on opcode and application permission features. A total of 1,000 applications were used, consisting of 500 benign and 500 malware samples. Opcode features were extracted from the classes.dex file and represented as numerical vectors using the Term Frequency–Inverse Document Frequency (TF-IDF) method. A total of 147 unique opcodes were successfully identified. In addition, application permission features were extracted from the AndroidManifest.xml file, resulting in 65 features. These two types of features were then combined to form a dataset used as input for the classification process. The classification algorithms used in this study are Random Forest and Support Vector Machine as a comparison. The model performance was evaluated using accuracy, precision, recall, and F1-score metrics. Based on the test results on the test data, the Random Forest model achieved the best accuracy of 99%, followed by SVM at 98%. These results indicate that the combination of opcode and application permission features using Random Forest is quite effective in distinguishing between benign and malware applications through static analysis. Therefore, the TF-IDF-based classification system utilizing opcode and permission features developed in this study can serve as an initial approach for Android malware detection using static analysis.
Co-Authors Azis, Muhammad Amran Firgiawan, Wawan Irianti, Arnita Syamsuddin, Sumantri