Judul: Perbandingan Metode Koreksi Bias Deep Learning dan Empirical Quantile Mapping pada Prediksi Sub-seasonal to Seasonal di Pulau Jawa
Abstrak: Climate information and predictions on the sub-seasonal to seasonal (S2S) scale are important and useful for planning and decision-making in various fields such as disaster management, health, energy, hydrology and agriculture. However, until now, the prediction accuracy on the S2S scale is still relatively low compared to weather and seasonal predictions. The existence of bias and uncertainty from the global climate model (GCM) output is a factor causing the low accuracy of S2S predictions. Problems related to the bias of the GCM output can be overcome through bias correction. This study compares the performance of conventional bias correction methods, empirical quantile mapping (EQM) and DL based on convolutional neural networks (CNN) on S2S rainfall prediction. Daily rainfall observation data from 477 stations and rain posts spread across the island of Java were selected based on the level of data availability of more than or equal to 80% for a period of 20 years (1999-2019). The rain post data is then used to build grid-based daily rainfall data with a resolution of 0.05x0.05° through multiple linear regression methods and residual kriging with CHIRPS satellite data predictors, latitude, longitude and elevation. The resulting grid data is then used to perform bias corrections on the output of the Sub-seasonal Experiments (SubX) model. The output of the SubX model consists of 7 (seven) individual models, including CCSM4, CFSv2, FIMr1p1, GEFS, GEPS6, GEOS_v2p1, and NESM. Each model has a member number that varies from 1 to 21. The empirical quantile mapping (EQM), convolutional autoencoder (CAE) and UNET method is then applied to bias correct the SubX model output. The evaluation was carried out on aggregate weekly rainfall during the training period, namely 1999 – 2016, through kfold cross-validation with k=3, and during the testing or unseen data period, namely 2018 – 2019. The performance indicators used to evaluate the bias correction method are the correlation coefficient, root mean square error (RMSE) and continuous ranked probability skill scores (CRPSS). The DL-based bias correction method for the individual SubX model outputs generally performs better than the EQM method. It is indicated by the average value of the correlation coefficient and CRPSS in the output of the individual SubX- corrected DL model, which is generally higher than the results of the EQM correction. The performance advantage of this DL-based bias correction method consistently occurs, both in the training period and the testing period. In the individual SubX models, the DL-based bias correction method increased the correlation coefficient by up to 16%, decreased the average RMSE by 26.5%, and increased the average CRPSS by up to 95.1% in the training period. However, the EQM-based bias correction decreases the average correlation coefficient value in most models, reduces the average RMSE value to 18.5% and increases the CRPSS value to 68.3%. During the testing period, the DL-based bias correction method succeeded in increasing the average correlation coefficient value by up to 15%, reducing the average RMSE value by up to 6.9% and increasing the CRPSS value by up to 59%. However, in the EQM bias correction, the average correlation coefficient only increases to
Keyword: bias  correction, deep learning, multi-model ensemble, precipitation, sub-seasonal to seasonal

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: Trichomonas Gallinae Penyebab Trichomonosis Pada Unggas
Abstrak: Trichomonas gallinae adalah protozoa yang bergerak aktof dengan keempat flagella yang mempunyai layar dan pada salah satu sisinya terdapat membran seperti sirip ikan yang berombak. Spesies dari genus Trichomonas ini mempunyai arti penting pada unggas, karena dapat menyebabkan infeksi pada alat pencernaan bagian atas.
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