Judul: Developing Spatial Decision Tree using NBTree Algorithm for Hotspot Distribution in Riau.
Abstrak: One of environment problems that emerged almost every year in Indonesia is forest fire in many regions including Riau. Forest fires cause negative impacts to the environment, such as transboundary haze pollution, health and life problem, and material damages. This phenomenon effects not only on community in Riau but also in neighbor coutries such as Singapore and Malay. Hotspot distribution is important for preventing occurrence of forest and land fire. Hotspot distribution data can be analyzed using spatial data mining techniques. One of techniques in spatial data mining is spatial decision tree. Spatial decision tree will build classifier from spatial data that can be used to create classification rules. This research aimed to develop classifier to predict the amount of hotspot in a territory according to several thematic layers: industrial timber plantation, logging concession, and land use. This research uses spatial join index and complete operator to provide a spatial dataset in which the conventional spatial decision tree algorithm can be applied to the data set. Then, NBTree algorithm will be used to develop a spatial decision tree. The result of this research is a classifier that contains 17 classification rules with accuracy 68,35%. Keywords: spatial decision tree, spatial join index, complete operator, NBTree algorithm
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Judul: Klasifikasi Data Spasial untuk Kemunculan Hotspot di Provinsi Riau Menggunakan Algoritme ID3
Abstrak: Forest fire is one of the environmental issues that occurs almost every year in Indonesia including in Riau Province, which causes negative impacts for human life. The large data of hotspot distribution can be analyzed using one of spatial data mining techniques, namely spatial decision tree. The purpose of this research is to classify the hotspot occurences in Riau province in order to obtain the classification rules. In this research, the spatial decision tree is developed using ID3 algorithm. The result shows that the highest accuracy is 70.80%. The classification model consists 125 classification rules that can be used to predict the hotspot occurrences.
Keyword: spatial decision tree, hotspots, classification, ID3 Algorithm

Judul: Perancangan Proses Produksi Telur Asin Rendah Kolesterol
Abstrak: Telur asin merupakan produk olahan telur yang digemari masyarakat, tetapi mengandung kolesterol yang melebihi batasan konsumsi harian manusia. Tahap pengolahan, telur asin direndam dalam larutan khusus guna menurunkan kandungan kolesterol di dalam telur. Proses ini melibatkan pengaturan waktu pemeraman, konsentrasi larutan, dan bahan-bahan lainnya untuk mencapai hasil yang diinginkan. Formulasi telur asin yang terpilih yaitu pemeraman selama 12 hari dalam larutan garam 32,5% yang dicampur bawang putih, sereh, dan daun jeruk cincang, perendaman dalam larutan soda 20% selama 4 jam, dan dikukus selama satu jam. Kadar kolesterol sebesar 233,39 mg/100 g pada hari pertama dan mengalami penurunan menjadi 131,61 mg/100 g pada hari kesepuluh., Salted eggs are a popular egg product among the community, but they contain cholesterol that exceeds the daily human consumption limit. In the processing stage, the salted eggs are immersed in a special solution to reduce the cholesterol content inside the eggs. This process involves adjusting the soaking time, solution concentration, and other ingredients to achieve the desired results. The selected salted egg formulation is marinating for 12 days in 32.5% salt solution mixed with garlic, lemongrass, and chopped lime leaves, marinating in 20% soda solution for 4 hours, and steaming for one hour. The cholesterol level was measured at 233.39 mg/100g on the first day and decreased to 131.61 mg/100g on the tenth day.
Keyword: Soda solution, Low Cholesterol Salted Eggs, Process Design, Standardized Salted Eggs