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: Penggabungan Fitur Morfologi, Local Binary Pattern Variance, dan Color Moments untuk Aplikasi Mobile Identifikasi Citra Tumbuhan Obat
Abstrak: This research proposed a new Android mobile application for medicinal plants identification using some features of leaf, i.e. texture, shape, and color. This research used 51 species of medicinal plants and each species consists of 48 images, so the total images used in this research are 2448 images. Local Binary Pattern Variance (LBPV) is used to extract the texture, morphological feature is used to extract the shape, and color moments is used to extract color feature based on color distribution. Further research was conducted on the combination of features to get a better result in medicinal plants identification. The combination technique used is the Product Decision Rule (PDR). This research uses the Probabilistic Neural Network (PNN) technique to classify the morphological, LBPV, and color moments features vector. The experimental results show that the combination of the morphological, LBPV, and color moments features can improve the accuracy of medicinal plants identification. The accuracy of the combination of the morphological, LBPV, and color moments features is 72.16%.
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Judul: Pengaruh Pairing pada Isotop Sn dengan Menggunakan Pemodelan Sembilan Tingkat Energi
Abstrak: Formation of the neutron-rich tin isotopes (Sn isotopes) is the one of the interesting topics in the field of nuclear reaction, especially regarding the stability of the heavy nuclei. Calculations of total energy using the BCS approach using five energy levels can only calculate the total energy up to Sn50132. On the other hand, the experiment continues and has exceeded the formation of isotopes. Therefore, there is need for a new model to accomadate more neutrons. In this research, Sn isotopes is modelled using nine energy levels and constant interaction potential to explain the pairing phenomenon up to Sn50154. However, to obtain more accurate results, other methods to minimize the differences with the experimental results are clearly needed here.
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