Judul: Identifikasi shorea menggunakan k-nearest neighbour berdasarkan karakteristik morfologi daun
Abstrak: Dipterocarpaceae is a group of tropical plants that is used in a timber industry. One of Dipterocarpaceae clans is Shorea, that is the best timber-producing plant in the industrial world. Shorea is difficult to be identified because it has a lot of diversity. The inability to recognize Shorea in forest will enlarge the exploitation of Shorea that has a good timber quality, and silviculture work becomes less of the target because it is not known which Shorea species that will extinct. Shorea tree is usually identified by using the stems, leaves, fruits, and flowers. However, leave is used for the identification in this research because it tends to be available as a source of observation at anytime. The leaves in this reasearch are the collection from Bogor Botanical Gardens. Data are obtained by manual calculating to get the characteristics of the leaves. The obtained data will be processed using k-Nearest Neighbor to get the closeness of new data and training data. The leaves that are included in this research are Shorea multiflora, palembanica, balangeran, lepida and assamica, each one has 10 data. Each set of data has 10 attributes that support the leaf characteristics. From the whole data, Shorea are divided into five subsets including data training and data testing for each subset. This research has two experiments the first experiment without normalization produces 84% accuracy and the second experiment with a normalization produces 100% accuracy.
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Judul: Identifikasi Daun Shorea Menggunakan K-Nearest Neighbor dengan Ciri Statistical Textures
Abstrak: Various species of Shorea from Dipterocarpaceae family are dominating the lowland rain forests in Indonesia. Each species of Shorea has a unique function and can produce a high qualified timber in the construction industry. This research built an identification system of Shorea leaves using the statistical textures feature extraction with k-nearest neighbor as classifier. Statistical methods were utilized to analyze the spatial distribution of gray level values, by computing local features at each point in the image and deriving a set of statistical values from the distribution of the local features. The calculation of gray level values resulted in six parameters, namely mean, standard deviation, smoothness, third moment, uniformity, and entropy. A highest accuracy of 90% was obtained for the identification of 10 species of Shorea
Keyword: statistical textures, Shorea, k-nearest neighbor

Judul: Pemanasan Sekunder Reaksi Fusi Plasma dengan Menggunakan Radio Frequency
Abstrak: Plasma merupakan bentuk ke-empat dari materi setelah padat, cair dan gas yang terbentuk dari proses ionisasi gas. Plasma dapat digunakan dalam reaksi fusi, yaitu reaksi penggabungan dua inti atom ringan menjadi inti atom yang lebih berat sambil melepaskan energi. Untuk mendapatkan energi dari reaksi fusi, inti yang akan direaksikan harus dipanaskan pada temperatur yang sangat ekstrem tingginya. Salah satu cara agar dapat mencapai kondisi ini adalah memberikan pemanasan tambahan atau pemanasan sekunder kepada plasma dalam reaksi fusi tersebut. Nilai daya serap rata-rata ion Deuterium yang sesuai dengan selang nilai daya dari data ITER adalah daya yang menggunakan frekuensi 50 MHz sampai dengan 60 MHz. Dengan menggunakan frekuensi 60 Hz diperoleh nilai daya rata-rata yang diserap sebesar 1,235.10-12 W.
Keyword: gelombang radio, pemanasan sekunder, plasma, reaksi fusi