Judul: Application of Spatial Decision Tree in Identifiying Mangrove Area Using C4.5 Algorithm.
Abstrak: Mangrove forests have a lot of benefits for life, such as beach abration protector, building material and fuel, as well as meal supplier for plankton. Therefore, mangrove forest should be protected and developed. Mangrove forests are located along tropical and subtropical beach that are influenced by tide water. East Kalimantan is one of provinces in Kalimantan that has potential coast teritory for mangrove’s growth. In one region there are some districts which have larger mangrove potency than neighbor districts. For that, it is required a spatial analysis for mangrove area identification in order to be able to know description of potential region for mangrove’s growth. One of techniques in extracting knowledge in spatial database is spatial data mining.This research uses a spatial data mining method, especially spatial decision tree using C4.5 algorithm to develop a classifier to predict new data of mangrove area. This research applies the Spatial Join Index (SJI) and the complete operator to apply conventional classification technique in spatial database. The SJI is created using topological relation to find relation between two spatial objects, then the result is simplified using complete operator. The result of this research shows that classes of mangrove area are described by some attributes : slope, topography, substrat, and landuse. The classifier contains 23 rules with 60,66% accuracy. Keywords: spatial decision tree, C4.5 algorithm, spatial join index, complete operator
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Judul: Algoritma Pohon Keputusan Pembelajar Mesin (Decision Tree of Machine Learning) dalam Mendeteksi Hutan Mangrove Menggunakan Citra SPOT 7
Abstrak: Tulisan ini menerangkan tentang pembangunan algoritma klasifikasi non-parametrik berbasis pohon keputusan dari pembelajar mesin dalam mengidentifikasi tutupan pada ekosistem mangrove menggunakan data penginderaan jauh dan data geospasial. Tujuan dari penelitian ini adalah untuk menemukan peubah (atribut) serta parameter algoritma pohon keputusan dalam mendeteksi dan mengidentifikasi tutupan hutan mangrove berbasis citra resolusi tinggi SPOT 7. Kajian ini menguji peubah-peubah geo-sosio-biofisik dan peubah spektral yang diturunkan dari citra SPOT 7. Penelitian ini menemukan bahwa peubah paling berpengaruh adalah elevasi dan NDVI. Penelitian ini juga menemukan parameter dari algoritma pohon keputusan terbaik adalah gain ratio tanpa kombinasi pemangkasan (pruning) dan pra-pangkas (prepruning), kedalaman pohon 31, alternatif prepruning 30, sampling data split dengan otomatis dan ukuran daun 61. Kombinasi tersebut menghasilkan overall accuracy (OA) sebesar 93.7% dan kappa accuracy (KA) 93.2%. Penelitian ini menyimpulkan bahwa peubah geo-sosio-biofisik lebih handal perannya dalam mengidentifikasi kelas-kelas tutupan lahan yang dibangun dibandingkan peubah indeks vegetasi., This paper describes the development of a decision tree machine learning, a non-parametric classification algorithm for identifying mangrove forest using remote sensing and geospatial data. The study objective is to find the decision tree algorithm's variables (attributes) and parameters in detecting mangrove forest cover based on SPOT 7 high-resolution imagery. This study examined geo-socio-biophysicalvariables and spectral variables derived from SPOT 7 imagery. This study also found that the best decision tree algorithm parameters were the gain ratio without a combination of pruning and pre-pruning, tree depth of 31, alternative prepruning 30, split data sampling with automatic, and leaf size of 41. The algorithm provided an overall accuracy (OA) of 93.7% and a Kappa accuracy (KA) of 93.2%. This study concludes that the geo-socio-biophysicalvariable is more reliable than vegetation index variables in identifying built-up land cover classes.
Keyword: indeks vegetasi, geo-sosio-biofisik, mesin pembelajar, pohon keputusan, decision tree, machine learning, geo-socio-biophysics, vegetation index

Judul: The effect on niacin supplementation on production and egg cholesterol of hysex brown laying hens
Abstrak: Telur merupakan salah satu bahan makanan asal ternak unggas yang kaya akan gizi, seperti karbohidrat, protein, lemak, vitamin dan mineral, disamping harganya murah, sehingga disebut wonderful food. Oleh karena itu, tidak mengherankan konsumsi telur di Indonesia cukup tinggi dibandingkan dengan bahan makanan sumber protein hewani lain. Disisi lain, adanya persepsi, bahwa sebutir telur mengandung kolesterol relatif cukup tinggi, yaitu 200 - 250 mg atau 1280 mg per 100 g kuning telur (yolk).
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