Judul: Prediksi Panjang Musim Hujan Menggunakan Cascade Neural Network
Abstrak: In Indonesia, agriculture becomes an important sector for national development and national economy. The length of rainy season is one of the rainfall variables that affect agricultural production. The changing of the rainy season length can impact on crop failures. This research aims to develop a model for predicting the length of rainy season using Cascade Neural Network (CNN). Predictor data used in this research is sea surface temperature from region of NINO 1+2, NINO 3, NINO 4, dan NINO 3.4 from 1982 to 2011. Observational data used is the length of rainy season of Pacitan region in Arjosari, Kebon Agung, and Pringkuku weather stations from 1982/1983 to 2011/2012. This research obtained the best model from Pringkuku weather station with R2 of 0.72 and RMSE of 1.87 using a learning rate of 0.3, 10 hidden neurons and NINO 4 region.
Keyword: sea surface temperature, the length of rainy season, neural network, cascade neural network

Judul: Development of a Rainy Season Length Prediction Model in Java Island Using Convolutional Neural Network
Abstrak: Pertanian di Pulau Jawa sangat dipengaruhi oleh variabilitas iklim musiman. Prediksi panjang musim hujan sangat penting untuk perencanaan pertanian dan pengelolaan sumber daya air. Studi ini bertujuan mengembangkan model prediksi panjang musim hujan di Pulau Jawa menggunakan Convolutional Neural Network dan mengevaluasi model tersebut. Prediktor yang digunakan adalah empat data curah hujan luaran North American Multi-Model Ensemble dengan issued time Bulan Juni, Juli, Agustus, dan September serta lead time Bulan September hingga Mei. Penelitian ini berhasil mengembangkan prediksi panjang musim hujan di Pulau Jawa menggunakan CNN. Korelasi Pearson bernilai 0,38 hingga 0,51 dengan nilai korelasi tinggi terjadi pada issued time Bulan Juni di Jawa Timur. RMSE bernilai 3,29 hingga 3,80 dengan nilai berada di Jawa Barat bagian selatan. Standar deviasi ternormalisasi bernilai 0,68 hingga 0,94 dengan nilai terbaik berada di Jawa Barat dan Jawa Tengah. Model memiliki kemampuan moderat dalam menangkap hubungan linear antara data aktual dan prediksi tetapi kurang dalam menangkap nilai ekstrem dari data aktual. Hasil skill score cenderung merata di seluruh pulau dengan model CanSIPS-IC3 memiliki skill yang terbaik. Perbandingan panjang musim hujan menunjukkan bahwa prediksi model cenderung mengikuti pola aktualnya tetapi terdapat beberapa perbedaan nilai prediksi yang overestimate ataupun underestimate daripada nilai aktualnya., Agriculture on Java Island is highly influenced by seasonal climate variability. Predicting the length of the rainy season is crucial for agricultural planning and water resource management. This study aims to develop a rainy season length prediction model in Java Island using a convolutional neural network and evaluate the model. The predictors used are four rainfall data outputs from the North American Multi-Model Ensemble with issued times in June, July, August, and September and lead times from September to May. This research successfully developed a rainy season length prediction model in Java Island using a CNN. Pearson correlation values ranged from 0.38 to 0.51, with high correlation values occurring at the issued time of June in East Java. RMSE values ranged from 3.29 to 3.80, with the highest values located in the southern part of West Java. Normalized standard deviation values ranged from 0.68 to 0.94, with the best values found in West Java and Central Java. The model has a moderate ability to capture the linear relationship between actual and predicted data but is less effective in capturing extreme values from the actual data. The skill score results tend to be evenly distributed across the island, with the CanSIPS-IC3 model having the best skill. A comparison of the rainy season length shows that the model's predictions tend to follow the actual pattern, but there are some differences in predicted values that are overestimated or underestimated compared to the actual values.
Keyword: deep learning, evaluasi performa, liebmann, model output statistic, north american multi-model ensemble

Judul: Karakteritsik kimia dan organoleptik tablet effervescent putih telur bersitarasa lemon dengan konsentrasi effervescent mix yang berbeda
Abstrak: Egg is an animal origin food product which has high nutritional value especially protein. Cholesterol content of eggs is only found in egg yolk, on the other hand albumen does not countain cholesterol at all but high content of protein, so it can be used as a free cholesterol-protein sources. The application of albumen as egg instant drink effervescent tablet can be an alternative of protein source for people who need free-cholesterol diet. It can also become an egg product diversification.
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