Judul: Identifikasi Tumbuhan Obat Berbasis Fraktal Menggunakan Clustering Fuzzy C-Means.
Abstrak: Identification medicinal plant species automatically still be a problem for recognizing various kind of medicinal plants in Indonesia. The research purpose is to develop medicinal plant identification system by using fractal and Fuzzy C-Means (FCM) clustering. Fractal method is used to extract image feture of the medicinal plant leaf. Beides, FCM clustering is needed to classify image feature medicinal plant into some lass or cluster. Two fractal approach used in this research are fractal dimension and fractal code. Fractal dimension is based on similiarity of the medicinal plant leaf image pattern and counted with box counting method, however fractal code is based on similiarity of the medicinal plant leaf image texture. This research used the data from the collection in Biofarmaka IPB plantation and glasshouse of Ex-Situ conservation of Indoensia Tropicana. 600 data is used contain of 20 variety of medicinal plants for each 30 sampel. This experimental result shows that 85.04% of FCM clustering based on dimension and 79.94% FCM clustering based on the fractal code. According to this result, medicinal plant identification system which are easy and with high accuracy will motivate public to empower and use medicinal plant in Indonesia which will increase Indonesian public health and wealth.
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Judul: Penggabungan Dimensi Fraktal dan Kode Fraktal untuk Identifikasi Tumbuhan Obat Indonesia Menggunakan Probabilistic Neural Network
Abstrak: This research proposed a new method to identify Indonesia medicinal plants using fractal dimension (FD) and fractal code (FC) using Probabilistic Neural Network (PNN). This research investigates the effectiveness of fusion of FD and FC in order to identify Indonesia medicinal plants. FD is measured by Box Counting method. FD has non-integer value that represents the self-similarity of fractal. FC is based on the self-similarity in a picture. It means that small pieces of the picture can be approximated by transformed versions of some other (larger) pieces of the picture. The fusion of FD and FC is done by using vector fusion and Product Decision Rule (PDR). The total medicinal plants used in this research are 20 species taken from Biofarmaka Farm, Cikabayan and Green House Center Ex-Situ Conservation of Medicinal Plants Indonesia Tropical Forest. Each species consists of 30 images, thus the total images used in this research are 600 images. The PNN is trained using 450 images to classify 20 kinds of plants. The experimental results shows that fractal dimension has an accuracy rate of 57%, fractal code 21%, vector fusion 58%, and PDR 58%. The experimental result also shows that fractal fusion is not necessary because the fractal dimension has significant information compared to the fractal code.
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Judul: Sintesis, Iuraicterisasi Struictur Dan Sifat Termal Pada Bahan Iconduktor Superioniic Berbasis Gelas (Ag2S)x(AgP03)l-x
Abstrak: Bahan Konduktor superionik telah banyak diteliti terutama sebagai zat padat elektrolit untuk perangkat elektronik maupun elektrokimia dan salah satunya adalah bahan dengan Ag+ ditemukan memiliki konduktivitas tinggi pada temperatur ruang yaitu 0.01 A/cm. Pada penelitian ini gelas konduktor (Ag+S)x(AgPO3)l-xmelalui metoda pendinginan cepat (rapid quenching)di Laboratorium sediaan Cuplikan, Balai Spektometri Batan.
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