The increasing use of mobile devices has increased the risk of data theft, posing significant security challenges for individuals and organizations. This study proposes an early detection system for data theft on mobile devices using machine learning algorithms. The system is designed to identify suspicious patterns in application usage, network access, and CPU/memory activity, providing early warnings to prevent potential data loss. By employing algorithms such as Random Forest, Support Vector Machine (SVM), and Convolutional Neural Network (CNN), the developed models demonstrated significant performance: CNN achieved the highest accuracy of 95.1%, with a precision of 94.2%, recall of 93.5% , F1-score of 93.8%, and AUC-ROC of 0.96. Random Forest and SVM also showed competitive performance with accuracy rates of 94.7% and 92.5%, respectively. These findings highlight the high potential of machine learning algorithms for real-time detection of data theft threats, providing adaptive protection against evolving cyberattack methods. This approach offers a promising solution to strengthen mobile device security frameworks and safeguard user data against increasingly sophisticated cyber threats.

Android adalah sistem yang kompleks, yang komponen dan aplikasinya dimaksudkan untuk bekerja bersama, menimbulkan hubungan saling ketergantungan yang sangat rumit di antara. Sementara itu, ekosistem Android dikenal sangat beragam dan terdesentralisasi: setiap versi OS dikustomisasi dan dikustomisasi ulang oleh berbagai pihak hampir secara independen dan digunakan oleh siapa saja yang dapat membuat aplikasi untuk versi tersebut. Android melindungi aset informasinya melalui kotak pasir aplikasi dan model izin, di mana setiap aplikasi berjalan dalam kompartemennya sendiri dan hanya dapat mengakses sumber daya global yang sensitif dan komponen aplikasi lainnya (penyedia konten, layanan, aktivitas, penerima broadcast) dengan izin yang tepat. Penelitian ini menggunakan pengurkuran Harehunter dengan tujuan untuk mendeteksi kerentanan Hare dalam aplikasi sistem Android secara otomatis. Eveluasi kinerja Harehunter dan HareGuard dilakukan dalam penelitian ini, yang keduanya terbukti sangat efektif. Pendekatan yang diguanakan disini adalah analisis diferensial, dengan mencari semua kode yang diekstrak, didekompilasi, dan file manifes untuk definisi atribut yang ditargetkan sebagai langkap awal, dan menjalankan parser XML. Hasil penelitian ini menunjukkan bahwa dampak Hares sangat signifikan. Penerapan HareGuard dalam penelitian ini terbukti efektif dalam mendeteksi semua aplikasi serangan yang dibuat. Evaluasi lebih lanjut terhadap kinerja berdampak pada inang sistem minimum. Untuk penelitian dimasa depan, agar Harehunter lebih efektif, disarankan untuk menggunakan pengaalisa yang lebih mumpuni. Sehingga arah ini dapat dieksplorasi lebih dalam lagi.

E-commerce competition in Indonesia makes e-commerce companies compete to develop strategies to win the competition. Customer loyalty is an important factor to lead in the competition. This research focuses on how much influence website service quality has on customer loyalty. This research is also to find out which website quality factors have the most influence on customer loyalty with customer satisfaction as a mediating factor. The factors that influence e-commerce customer loyalty are analyzed using concepts including the dimensions of service quality consisting of (physical evidence, reliability, responsiveness, assurance, empathy) and Corporate Image as the variable that influences and Customer Satisfaction as the variable that is affected. The sample in this study were 96 respondents. With the sampling method using the purposive sampling method. The results obtained that there is a positive and significant influence between the dimensions of service quality and company image on customer satisfaction. 
Keywords: E-commerce website, Service Interaction Quality, Customer Satisfaction