Judul: Klasifikasi Titik Api di Bengkalis Riau Menggunakan Algoritme ID3 Spasial yang Diperluas
Abstrak: Forest fire in Riau Province including Bengkalis district, are frequently occurred every year. Hotspot is an indicator for forest fire events. Hotspots monitoring by the NOAA satellite is one of the efforts to prevent forest fires. Hotspot data are spatial data. In order to analyze the data, spatial algorithms are required. The extended spatial ID3 algorithm is a spatial classification algorithm for creating a spatial decision tree from spatial datasets. This research applied the extended spatial ID3 algorithm to the forest fires data in Bengkalis district, Riau. The data include hotspots and non-hotspots, weather data, socio-economic data, and geographical characteristics of the study area. The result of this research is a decision tree with income source layer as the root node. As many as 137 rules were generated to the tree. The accuracy of the tree is 75.66% and 41.38% respectively on the forest fires dataset in Bengkalis district and Rokan Hilir district Riau.
Keyword: spatial decision tree, ID3, hotspots, forest fires

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: Enteric Collibacillosis Pada Anak Babi Baru Lahir: Patogenesa dan Perubahan-Perubahan Patologisnya
Abstrak: Diare disebabkan oleh infeksi bakteri Escherichia coli pada anak-anak babi baru lahir yang peka terhadap en terotoksin produk bakteri tersebut. Dikenal sebagai colibacillosis. Patogenesa dari colibacillosis anak babi baru lahir adalah (1) infeksi oleh E. coli patogen, (2) proliferasi E. coli di lumen usus halus, (3) produksi dan pelepasan enterotoksin, dan (4) aktifitas toksin pada usus halus. Dalam proses proliferasi, adanya antigen pili K88 sangat penting karena memungkinkan bakteri perlekatan dengan epitel mucosa usus halus. mengadakan Setelah ko lonisasi dan proliferasi segera toksin dilepaskan. Akti- fitas toksin menginduksi sekresi yang berlebihan dari u- sus sehingga banyak cairan yang dikeluarkan ke lumen usus. Hal ini mengakibatkan banyak cairan dan elektrolit hilang sehingga timbul diare, dehidrasi dan asidosis yang sering menyebabkan kematian. Perubahan-perubahan patologis yang umum adalah dis tensi usus halus, dilatasi lambung dan infark pada kurva- tura mayor. Perubahan-perubahan histologis yang terjadi adalah enteritis hemorrhagica acuta, oedem dan kongesti pembuluh darah di lamina propria, akumulassi leukosit inti polimorf, sel monosit, dan inti sel mengalami piknosis Thrombus pada vena dan degenerasi serta desquamasi sel epitel usus….
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