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All Journal International Journal of Informatics and Communication Technology (IJ-ICT) Jurnal Masyarakat Informatika
Sirait, Dheo Ronaldo
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Computer Science & IT

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Improved inception-V3 model for apple leaf disease classification Sirait, Dheo Ronaldo; Sutikno, Sutikno; Sasongko, Priyo Sidik
International Journal of Informatics and Communication Technology (IJ-ICT) Vol 13, No 2: August 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijict.v13i2.pp161-167

Abstract

Apple, a nutrient-rich fruit belonging to the genus Malus, is recognized for its fiber, vitamins, and antioxidants, giving health benefits such as improved digestion and reduced cardiovascular disease risk. In Indonesia, the soil and climate create favorable conditions for apple cultivation. However, it is essential to prioritize the health of the plant. Biotic factors, such as fungal infections like apple scabs and pests, alongside abiotic factors like temperature and soil moisture, impact the health of apple plants. Computer vision, specifically convolution neural network (CNN) inception-V3, proves effective in aiding farmers in identifying these diseases. The output layer in inception-V3 is essential, generating predictions based on input data. For this reason, in this paper, we add an output layer in inception-V3 architecture to increase the accuracy of apple leaf disease classification. The added output layers are dense, dropout, and batch normalization. Adding a dense layer after flattening typically consolidates the extracted features into a more compact representation. Dropout can help prevent overfitting by randomly deactivating some units during training. Batch normalization helps normalize activations across batches, speeding up training and providing stability to the model. Test results show that the proposed method produced an accuracy of 99.27% and can increase accuracy by 1.85% compared to inception-V3. These enhancements showcase the potential of leveraging computer vision for precise disease diagnosis in apple crops.
Intrusion Detection Systems pada Bot-IoT Dataset Menggunakan Algoritma Machine Learning Sibarani, Jonathan Nicholas; Sirait, Dheo Ronaldo; Ramadhanti, Salma Safira
Jurnal Masyarakat Informatika Vol 14, No 1 (2023): JURNAL MASYARAKAT INFORMATIKA
Publisher : Department of Informatics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/jmasif.14.1.49721

Abstract

Semakin berkembangnya dunia teknologi, semakin banyak juga penggunaan internet dalam kehidupan sehari hari. Pertumbuhan dalam penggunaan internet tersebut menimbulkan kekhawatiran tentang keamanan saat menggunakan layanan internet. Untuk menjamin keamanan pengguna, dapat menggunakan Intrusion Detection System (IDS). Intrusion Detection System merupakan sebuah sistem yang akan mengawasi aktivitas dalam jaringan komputer dengan menggunakan berbagai macam metode seperti machine learning. Dalam jurnal penelitian ini, digunakan tiga macam algoritma machine learning untuk membantu IDS dalam mengenali serangan. Algoritma machine learning yang digunakan adalah K-Nearest Neighbor, Random Forest, dan Gaussian Naïve Bayes. Untuk membantu penelitian juga digunakan BoT-IoT Dataset yang dibuat oleh UNSW Canberra dengan lebih dari 72.000.000 baris data. Penelitian ini dilakukan dengan tujuan untuk menentukan algoritma yang paling sesuai dalam melakukan deteksi intrusi dengan dataset BoT-IoT.
Co-Authors Priyo Sidik Sasongko Ramadhanti, Salma Safira Sibarani, Jonathan Nicholas Sutikno Sutikno