Judul: Klasifikasi Data Titik Api di Bengkalis Riau Menggunakan Algoritme Pohon Keputusan Berbasis Spatial Entropy
Abstrak: Forest fire can be monitored using satellite by detecting hotspots as fire indicators at certain times and locations. The purpose of this research is to develop a decision tree for predicting hotspot occurences in Bengkalis district, Riau province Indonesia using the spatial entropy-based decision tree algorithm. The data used in this research are forest fire data of Bengkalis area. The data include city centre, river, road, income source, land cover, population, precipitation, school, temperature, and wind speed. This research, using the 5-fold cross validation, yields five decision trees with the average accuracy of 52.05% and 89.04% on the testing set and the training set respectively.The best accuracy of decision tree is 56% on the testing set that has 560 nodes with the land cover layer as the root node. From the decision tree, as 255 rules for classifying hotspot occurences are obtained. There are 20 objects in the testing set that cannot be classified by the decision tree with the highest accuracy on the testing set.
Keyword: spatial entropy, spatial decision tree, hotspot, forest fire, bengkalis

Judul: Klasifikasi Kerapatan Titik Api di Bengkalis Riau dengan Menggunakan Algoritme Pohon Keputusan ID3 Spasial
Abstrak: Forest fire is considered as a yearly problem in Indonesia. About 20,000 hotspots were recorded each year in the period of 2001-2012 in the Sumatera Island. Hotspot occurrence is an indicator for forest fire events. This research aims to determine the classification model based on historical forest fire data in Bengkalis district, Riau Province Indonesia to predict density of hotspots. The data used in this research are hotspots data in Bengkalis in 2008. Characteristics of the study area includes land cover, income source of community, precipitation, temperature, and wind speed. The method applied in this research is the spatial ID3 decision tree algorithm. This research has successfully produced two decision trees to classify the areas into three categories of hotspot density namely low, medium, and high categories. The highest-accuracy of decision tree is 60.47% on the testing set in the area of Rokan Hilir district, Riau province Indonesia.
Keyword: spatial ID3 algorithm, hotspot, forest fire, decision tree, bengkalis

Judul: Hydroxyapatite-Chitosan Composite Coating on Stainless Steel 316 to Improve Corrosion Resistance
Abstrak: Metals that were used in bone implantation should have good corrosion resistance that were not easily corroded when implanted into the body. Metal corrosion resistance of stainless steel 316 can be enhanced through the metal coating by hydroxyapatite-chitosan composite with composition ratio of hydroxyapatite (gram) and chitosan 3% (mL) 0.2:0, 0.2:0.1, 0.2:0.5, 0.2:1.0, and 0.2:1.5. Besides improving the corrosion resistance of metal, the presence of chitosan also maintain the position of hydroxyapatite on metal surfaces. Corrosion resistance increases with increasing amount of chitosan added and the best composition of hydroxyapatite-chitosan composite was 0.2:1.0.
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