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All Journal Jurnal Teknologi Informasi dan Multimedia Jurnal Bumigora Information Technology (BITe)
Tanwir Tanwir
Program Studi Ilmu Komputer, Universitas Bumigora
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering

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Deteksi Malware pada Perangkat Android Menggunakan Ensemble Learning Muhamad Azwar; Lilik Widyawati; Raisul Azhar; Kartarina Kartarina; Tanwir Tanwir; Andi Sofyan Anas
Jurnal Teknologi Informasi dan Multimedia Vol. 7 No. 3 (2025): August
Publisher : Sekawan Institut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35746/jtim.v7i3.573

Abstract

The increasing use of permission-based applications on mobile platforms has raised concerns regarding privacy and security. Android, being one of the most widely used operating systems for interacting with mobile applications, is particularly susceptible to various security risks that must be promptly addressed. Low digital literacy and a lack of user awareness about security risks—especially when installing applications from unofficial sources or without paying attention to access permissions—make users vulnerable to malware attacks. Uninformed users can easily become victims of malware insertion by irresponsible parties, turning them into targets for data manipulation and even data theft, which may then be sold on illegal forums. Attackers exploit the permission system, allowing them to freely access the target smartphone. This lack of awareness among users increases their vulnerability to malware injection and subsequent threats such as data manipulation and the theft of personal information, which can be traded on underground markets. One approach to detecting malicious behavior in mobile applications is the use of machine learning techniques. These techniques can analyze application patterns and behaviors based on features such as requested permissions. Popular algorithms for malware detection include Support Vector Machine (SVM) and Random Forest (RF), both of which have demonstrated strong performance in various studies. However, to further improve accuracy and reduce classification errors, ensemble learning approaches such as Adaptive Boosting (AdaBoost) are increasingly being adopted. Ensemble learning combines multiple predictive models to produce more reliable classification results compared to single models. This study evaluates the performance of several classification algorithms in detecting malicious Android applications. The results show that AdaBoost achieved a high accuracy rate of 91.65% and an AUC value of 95%, effectively distinguishing between safe applications and malware. Therefore, the use of machine learning algorithms—particularly ensemble methods like AdaBoost—can serve as a promising solution to enhance the security and privacy of Android-based mobile application users.
Peningkatan Kinerja Klasifikasi Scabies Sapi MenggunakanEdited Nearest Neighbours (ENN) pada Model Random Forestdan XGBoost Ihsan, M. Khaerul; Maulana, Muhammad; Tanwir, Tanwir; Mas’ud, Abi; Hanif, Naufal; Resmiranta, Dading Oktaviadi
Jurnal Bumigora Information Technology (BITe) Vol. 7 No. 2 (2025)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/bite.v7i2.6055

Abstract

Background: Scabies disease in cattle causes significant economic losses for farmers due to declines in the animals’physical condition and productivity.Objective: This study aims to evaluate the effectiveness of the Edited Nearest Neighbours (ENN) method in improvingclassification performance for scabies in cattle.Methods: This research employs machine learning methods, including Random Forest and XGBoost. A dataset of 600clinical symptom samples was converted to numerical data and cleaned of noise using the ENN technique.Result: Applying ENN significantly improved the accuracy of both the Random Forest and XGBoost models, increasing itfrom around 0.60 to 0.91. In addition, both models achieved a perfect recall of 1.00, indicating maximum capability todetect positive cases.Conclusion: This study concludes that noise reduction using ENN can produce a more accurate and reliable diagnosticsystem. This method is highly recommended to optimize the performance of classification algorithms on animal clinicaldata with high levels of inconsistency. 
Pengenalan Bahasa Isyarat Hijaiyah: Augmentasi Data dengan EfficientnetB7 Tanwir, Tanwir; Husain, Husain; Hammad, Rifqi; Anas, Andi Sofyan; Azwar, Muhammad
Jurnal Teknologi Informasi dan Multimedia Vol. 7 No. 4 (2025): November
Publisher : Sekawan Institut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35746/jtim.v7i4.728

Abstract

Sign language plays an important role as the primary means of communication for individuals with hearing impairments. This study aims to improve the accuracy of hijaiyah sign language detection through the application of the EfficientNetB7 architecture and data augmentation tech-niques. The method used, namely the EfficientNetB7 algorithm, was chosen as the base model be-cause of its ability to balance high accuracy with optimal resource utilization by performing data augmentation with rescale, shear, zoom, rotation, and flip horizontal techniques applied to enrich the variation of the original dataset of 6,811 images to 105,615 images. The experimental results show that the combination of EfficientNetB7 and data augmentation produces 99% accuracy on the test data, with consistent performance seen from the confusion matrix and accuracy loss graph for 50 epochs. This study proves that this approach not only improves model generalization but also reduces the risk of overfitting, thus potentially supporting social inclusion through efficient and reliable technology.
Co-Authors Andi Sofyan Anas Andi Sofyan Anas Azhar, Raisul Hammad, Rifqi Husain Husain Kartarina Kartarina Lilik Widyawati Mas’ud, Abi Muhamad Azwar Muhammad Maulana Naufal Hanif Resmiranta, Dading Oktaviadi Tanwir, Tanwir