Judul: Rainfall occurence modeling with dynamic bayesian networks (Case study: rainfall occurence in Indramayu)
Abstrak: The objective of this research was to build Dynamic Bayesian Networks (DBN) model for modeling time series data and to predict occurence of dry, normal and wet month in Indramayu, West Java. DBN is the extention of Bayesian Networks (BN) considering a sequence of time slices corresponding to different snapshots in time of the same static BN, connected by temporal links. BN itself is a direct acyclic graph in which nodes represent random variables and edges indicate conditional dependencies among them. Thus, DBN can represent spatial and temporal dependences among variables. In this research, a discrete DBN model was constructed by learning network structures and parameters from the real data. Criteria for learning are introduced by a Bayesian networks viewpoint. In this case, K2/K3 algorithm and Adaptive Importance Sampling - Bayesian Network (AIS-BN) were used as learning algorithm. The research use rainfall data in 1997 to 2008 from 14 stations at Indramayu, West Java. It was categorized into 3 states: dry (0-100 mm), normal (100-200 mm), and wet (above 200 mm). The result shows DBN could model rainfall data from 14 stations which considered spatial and temporal dependences. The state probabilities of stations could be read from conditional probability of networks and it could be changed by instanted an evidence from other station. The state occurrences prediction of 12 months in 2008 by DBN model shows 12 of 14 stations have a value greater than 75% of accuracy. The accuracy measured by way of transformed Hellinger Distance.
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Judul: Development of Probabilistic Neural Networks Models for Prediction of Rainfall Categories on Subseasonal to Seasonal Scales
Abstrak: Pengembangan model untuk memprediksi kategori curah hujan menjawab kebutuhan manusia dalam mengetahui kondisi cuaca dan iklim di masa yang akan datang. Model prediksi yang berfokus pada skala cuaca atau skala iklim dinilai tidak dapat sepenuhnya menjelaskan skala prediksi submusiman hingga musiman. Prediksi pada skala submusiman hingga musiman menjadi pengisi celah pada periode antara prediksi cuaca dan prediksi iklim. Pada penelitian ini, proses prediksi curah hujan pada periode submusiman hingga musiman dirancang untuk wilayah Kabupaten Indramayu. Perancangan model memanfaatkan beberapa parameter udara atas pada ketinggian 200 hPA dan 850 hPA yang diperoleh dari luaran General Circulation Model (GCM) S2S yang dikembangkan oleh ECMWF. Metode prediksi kategori yang dipilih untuk membangun model prediksi pada penelitian ini adalah Probabilistic Neural Networks (PNN) dengan memanfaatkan Principal Component Analysis (PCA) sebagai alat untuk melakukan reduksi dimensi pada prediktor. Data curah hujan yang digunakan untuk peubah terikat berupa data presipitasi yang dikeluarkan oleh CHIRPS. Model dievaluasi dengan menghitung akurasi model. Hasil percobaan membangun model dengan menggunakan n-component sama dengan 5 memiliki performa keseluruhan yang baik dengan rata-rata akurasi sebesar 82,177% dan waktu pelatihan model sebesar 76 menit., The development of models to predict rainfall categories answers human needs in knowing future weather and climate conditions. Prediction models that focus on the weather scale or climate scale are considered unable to fully explain the sub-seasonal to seasonal prediction scale. Predictions on a sub-seasonal to seasonal scale fill the gap in the period between weather prediction and climate prediction. In this study, the rainfall prediction process in the sub-seasonal to seasonal period was designed for the Indramayu Regency area. The model design utilizes several upper air parameters at 200 hPA and 850 hPA altitudes obtained from the General Circulation Model (GCM) S2S output developed by ECMWF. The category prediction method chosen to build the prediction model in this study is Probabilistic Neural Networks (PNN) by utilizing Principal Component Analysis (PCA) as a tool to reduce the dimensions of the predictors. The rainfall data used for the dependent variable is precipitation data issued by CHIRPS. The model was evaluated by calculating model accuracy. The experimental results of building the model using n-components equal to 5 had good overall performance with an average accuracy of 82.177% and a model training time of 76 minutes.
Keyword: Prediction, Rainfall, GCM, PNN, S2S

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