Judul: Aplikasi Mobile untuk Rekonstruksi dan Identifikasi Area Kerusakan Daun Menggunakan Convex Hull
Abstrak: This research proposes a method to calculate the area of damaged leaf attacked by pests, implemented as a mobile application on the Android operating system. The mobile application works on mobile device (client) for image acquisition and preprocess, while the calculation are performed on the server. This research uses Otsu method for image segmentation process and Convex Hull to reconstruct the area of damaged leaf. Reconstruction is done because the leaves were damaged so that their original shape was unrecognizable. After the reconstruction, the original shape of the leaves can be identified so the level of leaves damage can be calculated. The average error rate on the accuracy of the reconstruction of damaged salam (Syzygium polyanthum) leaves, kemuning (Murraya paniculata) leaves and sirih (Piper betle L) leaves is 19.19%, 16.81% and 22.31% respectively. Evaluation results show that the level of damage that occurs in the leaves and the reconstruction accuracy rate are inversely related. Convex Hull algorithm can reconstruct damaged leaves that have pinnate bone, but it is not suitable for leaves that have finger bone.
Keyword: Segmentation, Otsu, OpenCV, Convex Hull, Android

Judul: Aplikasi Mobile Identifikasi Penyakit Daun Kubis dengan Fast Fourier Transform dan Probabilistic Neural Network
Abstrak: Disease in cabbage leaves can lead to a decrease in the quality and quantity of crop yields or crop failure. The purpose of this research is to develop a mobile application by applying the Fast Fourier Transform feature extraction technique and Probabilistic Neural Network classification technique to identify the cabbage leaf disease. This mobile application runs on android platform. It is found that the accuracy of FFT feature extraction and PNN classification with bias 0.03 is 58.33%. The obtained accuracy is still not satisfactory since there are some errors in identifying the cabbage leaf disease
Keyword: Probabilistic Neural Network, Fast Fourier Transform, Cabbage leaf disease

Judul: Transisi Fasa Materi Hadronik ke Materi Kuark pada Inti Bintang Neutron.
Abstrak: Bintang neutron terdiri dari beberapa lapisan yang disusun oleh materi nuklir yang berbeda dengan kerapatan yang semakin tinggi ketika mendekati pusat bintang. Inti luar bintang neutron disusun oleh materi hadronik (nukleon, lepton, dan hyperon), sedangkan inti dalam bintang neutron disusun oleh kuark-kuark bebas (up, down, strange) yang saling berinteraksi. Tujuan penelitian ini adalah mempelajari bagaimana transisi dari inti luar ke inti dalam bintang neutron dengan menggunakan pendekatan RMF (Relativistic Mean Field) untuk materi hadronik dan CIDDM (Confined Isospin Density Dependent Mass) untuk materi kuark. Tekanan transisi diperoleh ketika potensial kimia barion materi hadronik dan materi kuark bernilai sama. Nilai tekanan transisi dengan parameter SU6-TM1 sekitar 87 MeV/fm3 dan diperoleh massa maksimum bintang neutron yaitu 1.92M dengan jari-jari sekitar 8.62 km. Nilai tekanan transisi dengan parameter SU6-G2 sekitar 53 MeV/fm3 dan massa maksimum bintang neutron yaitu 1.88M dengan jari-jari sekitar 8.16 km. Jenis transisi fasa adalah orde satu ditandai dengan diskontinuitas dalam kerapatan energi (adanya mixed phase). Parameter-parameter pada model nuklir yang digunakan menyebabkan perbedaan kekakuan persamaan keadaan (EOS) dari materi hadronik dan materi kuark sehingga nilai tekanan transisi dari fasa hadronik ke fasa kuark juga berbeda.
Keyword: bintang neutron, persamaan keadaan, transisi fasa