Coffee is an important commodity in the world economy. But unfortunately, productivity and quality of those commodities results are still quite low. This is caused by the disease in coffee plants. The research objective is to create an application that can help researchers or observers working in coffee plantation to diagnose diseases of coffee plants. The method used is fuzzy logic-based expert systems, and decision tree using a hierarchical classification. Knowledge about coffee, its symptoms, and its disease is extracted from human expert and then is converted into a decision tree. It will result on the fuzzy logic-based expert systems. From the experiments, accuracy calculation of the system is about 85%. Based on the accuracy, it can be concluded that this application can be a bit much to help researchers or observers of the coffee plants in diagnosing coffee plants diseases earlier.
Saat ini, pertumbuhan sistem operasi Android di perangkat smartphone sedang populer. Bagaimana pun, dibalik popularitas tersebut platform Android juga menjadi target peluang kejahatan dunia maya terhadap ancaman keamanan siber seperti malware. Mengidentifikasi malware ini sangat penting untuk menjaga keamanan dan privasi pengguna. Karena proses identifikasi malware yang semakin rumit, maka perlu digunakan machine learning untuk klasifikasi malware. Penelitian ini mengumpulkan fitur analisis statis dari aplikasi aman dan berbahaya. (malware). Dataset yang digunakan pada penelitian adalah dataset malware DREBIN yang merupakan dataset malware yang tersedia secara publik. Dataset tersebut terdiri dari fitur API CALL, system command, manifest permission, dan Intent. Data tersebut kemudian diproses menggunakan berbagai algoritma supervised machine learning di antaranya Support Vector Machine (SVM), Naive Bayes, Decision Tree dan K-Nearest Neighbors. Kami juga berkonsentrasi pada memaksimalkan pencapaian dengan mengevaluasi berbagai algoritma dan menyesuaikan beberapa konfigurasi untuk mendapatkan kombinasi terbaik dari hyper-parameter. Hasil eksperimen menunjukkan bahwa klasifikasi model SVM mendapatkan hasil terbaik dengan mencapai akurasi 96,94% dan nilai AUC (Area Under Curve) 95%.
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