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 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: 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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