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Abstrak :
ABSTRAK
Curah hujan adalah satu unsur cuaca yang memiliki pengaruh cukup besar terhadap berbagai sektor kehidupan manusia termasuk dalam sektor kelautan, khususnya terhadap produksi garam. Penambahan curah hujan di masa produksi garam berpotensi menimbulkan penurunan produksi, bahkan pada tingkat ekstrim dapat mengakibatkan kegagalan panen Penelitian ini bertujuan untuk mengetahui pengaruh variabilitas curah hujan terhadap produksi garam sekaligus kesesuaian lahan produksi di Kabupaten Sumenep Jawa Timur dilihat dari jumlah rata-rata curah hujan tahunan, panjang musim kemarau dan jumlah maksimum hari tanpa hujan berturut-turut. Berdasarkan analisis statistik, variabilitas curah hujan, berkorelasi kuat dengan produktivitas garam di Kabupaten Sumenep Jawa Timur, terutama panjang musim kemarau. Sedangkan berdasarkan analisis spasial, desa sentra garam memiliki kesesuaian yang menengah sampai sangat tinggi. Hasil penelitian menyarankan pentingnya informasi panjang musim kemarau dalam informasi iklim kepada pelaku sektor garam.

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ABSTRACT
Rainfall is the weather-climate element that influences various sectors of human activities, such as the marine sector, particularly the salt industry when the production is done in the traditional way. The increase of rainfall will potentially decrease the productivity of salt, moreover at an extreme level, it can lead to total production failure. This study aims to determine the effect of rainfall variability on salt production in Kabupaten Sumenep East Java based on parameters of average amount of annual rainfall, a length of the dry season and the maximum number of consecutive dry days/dry-spell. Based on statistical analysis, the rainfall variability is strongly correlated with the fluctuation of salt productivity, especially a length of the dry season. The spatial analysis shows that the saltworks are located in appropriate areas which have supporting climate conditions. It is recommended that the climate information provides to salt production includes a length of dry season information.

Abstrak :
[ABSTRAK
Pemahaman mengenai interaksi laut dan atmosfer merupakan kunci untuk menjelaskan fenomena iklim dan cuaca di benua maritim Indonesia. Dalam penelitian ini, akan dikaji hubungan antara energi radiasi gelombang panjang yang dipantulkan oleh bumi ke atmosfer, Outgoing Longwave Radiation (OLR), dengan suhu muka laut (SST). Sebagai ilustrasi, uap air (terutama awan), merupakan gas yang cukup efektif menyerap radiasi gelombang panjang. Namun jumlah uap air di atmosfer selalu berubah karena terjadi proses penguapan dan kondensasi secara terus-menerus, sementara sumber uap air utama adalah lautan. Data yang digunakan adalah OLR dan SST tahun 1979 hinggga 2011. Berdasarkan hasil analisis diketahui bahwa nilai koefisien korelasi di wilayah Indonesia menunjukkan ikatan hubungan yang sedang (r = 0,5). Sedangkan hasil pemetaan korelasi dan signifikansi menunjukkan bahwa hubungan OLR dan SST di wilayah Indonesia dipengaruhi oleh fenomena ENSO dan IODM.

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ABSTRACT
Ocean and atmosphere interactions are the key to explain the phenomenon of climate and weather in Indonesia. This study will be assessed the relationship between the energy of longwave radiation reflected by the earth into the atmosphere, Outgoing Longwave Radiation (OLR), and sea surface temperature (SST). As an illustration, water vapor (especially cloud), is an effective gas to absorb longwave radiation. But the amount of water vapor in the atmosphere is always changing due to evaporation and condensation processes continously, while the main source of water vapor is the ocean. The data used is OLR and SST in 1979 until 2011. Based on the analysis it is known that the value of the correlation coefficient in the region of Indonesia shows r = 0,5. While the results of the mapping correlation and significance shows that OLR and SST relationship in Indonesia affected by ENSO and IODM.;Ocean and atmosphere interactions are the key to explain the phenomenon of climate and weather in Indonesia. This study will be assessed the relationship between the energy of longwave radiation reflected by the earth into the atmosphere, Outgoing Longwave Radiation (OLR), and sea surface temperature (SST). As an illustration, water vapor (especially cloud), is an effective gas to absorb longwave radiation. But the amount of water vapor in the atmosphere is always changing due to evaporation and condensation processes continously, while the main source of water vapor is the ocean. The data used is OLR and SST in 1979 until 2011. Based on the analysis it is known that the value of the correlation coefficient in the region of Indonesia shows r = 0,5. While the results of the mapping correlation and significance shows that OLR and SST relationship in Indonesia affected by ENSO and IODM.;Ocean and atmosphere interactions are the key to explain the phenomenon of climate and weather in Indonesia. This study will be assessed the relationship between the energy of longwave radiation reflected by the earth into the atmosphere, Outgoing Longwave Radiation (OLR), and sea surface temperature (SST). As an illustration, water vapor (especially cloud), is an effective gas to absorb longwave radiation. But the amount of water vapor in the atmosphere is always changing due to evaporation and condensation processes continously, while the main source of water vapor is the ocean. The data used is OLR and SST in 1979 until 2011. Based on the analysis it is known that the value of the correlation coefficient in the region of Indonesia shows r = 0,5. While the results of the mapping correlation and significance shows that OLR and SST relationship in Indonesia affected by ENSO and IODM.;Ocean and atmosphere interactions are the key to explain the phenomenon of climate and weather in Indonesia. This study will be assessed the relationship between the energy of longwave radiation reflected by the earth into the atmosphere, Outgoing Longwave Radiation (OLR), and sea surface temperature (SST). As an illustration, water vapor (especially cloud), is an effective gas to absorb longwave radiation. But the amount of water vapor in the atmosphere is always changing due to evaporation and condensation processes continously, while the main source of water vapor is the ocean. The data used is OLR and SST in 1979 until 2011. Based on the analysis it is known that the value of the correlation coefficient in the region of Indonesia shows r = 0,5. While the results of the mapping correlation and significance shows that OLR and SST relationship in Indonesia affected by ENSO and IODM.;Ocean and atmosphere interactions are the key to explain the phenomenon of climate and weather in Indonesia. This study will be assessed the relationship between the energy of longwave radiation reflected by the earth into the atmosphere, Outgoing Longwave Radiation (OLR), and sea surface temperature (SST). As an illustration, water vapor (especially cloud), is an effective gas to absorb longwave radiation. But the amount of water vapor in the atmosphere is always changing due to evaporation and condensation processes continously, while the main source of water vapor is the ocean. The data used is OLR and SST in 1979 until 2011. Based on the analysis it is known that the value of the correlation coefficient in the region of Indonesia shows r = 0,5. While the results of the mapping correlation and significance shows that OLR and SST relationship in Indonesia affected by ENSO and IODM., Ocean and atmosphere interactions are the key to explain the phenomenon of climate and weather in Indonesia. This study will be assessed the relationship between the energy of longwave radiation reflected by the earth into the atmosphere, Outgoing Longwave Radiation (OLR), and sea surface temperature (SST). As an illustration, water vapor (especially cloud), is an effective gas to absorb longwave radiation. But the amount of water vapor in the atmosphere is always changing due to evaporation and condensation processes continously, while the main source of water vapor is the ocean. The data used is OLR and SST in 1979 until 2011. Based on the analysis it is known that the value of the correlation coefficient in the region of Indonesia shows r = 0,5. While the results of the mapping correlation and significance shows that OLR and SST relationship in Indonesia affected by ENSO and IODM.]

Abstrak :
ABSTRAK
Identifikasi keberadaan debu vulkanik dan prediksi sebarannya di udara pada saat terjadi erupsi gunung berapi sangat diperlukan guna keselamatan penerbangan dan publik secara umum. Berbagai metode telah dikembangkan untuk keperluan pemantauan sebaran debu agar dapat memberikan peringatan dini kepada pemangku kepentingan yang terkait. Penelitian ini dilakukan untuk memperoleh informasi tentang perbedaan sebaran debu vulkanik dengan tiga metode deteksi yang berbeda dan membandingkan hasil prediksi model HYPSLITdan observasi sebaran debu vulkanik dengan citra satelit cuaca MTSAT/Himawari.Kasus erupsi gunung yang dikaji berbeda baik tipe erupsi maupun waktu kejadian khususnya pada kasus erupsi Gunung Kelud 13-14 Februari 2015, Gunung Rinjani 16 Juli 2015, dan Gunung Rinjani 3-4 November 2015. Hasil penelitian menunjukkan bahwa terdapat perbedaan pola sebaran debu vulkanik antara tipe erupsi yang berbeda yang disebabkan oleh beberapa faktor antara lain: ketinggian kolom erupsi, volume material vulkanik, arah dan kecepatan angin pada beberapa ketinggian atmosfer. Prediksi sebaran debu vulkanik Gunung Kelud dengan model HYSPLIT memiliki indeks kesamaan yang cukup tinggi dengan hasil observasi satelit, dengan nilai Indeks Similaritas sebesar 59.68 . Sedangkan indeks similaritas untuk G. Raung dan G. Rinjani relatif kecil yaitu sebesar masing-masing 17.96 dan 15.97 .

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ABSTRACT
Identification of the presence of volcanic ash and distribution forecast in the air during a volcanic eruption is very important to flight safety and the general public. Various methods have been developed to monitor the distribution of volcanic ash in order to provide early warnings to the relevant stakeholders. This research was conducted to obtain information about the differences in the distribution of volcanic ash with three different detection methods and comparing the results of HYPSLIT model predictions of volcanic ash dispersion with observation by MTSAT Himawari weather satellite imageries. Different types of eruptions and time of occurrence were examined Mt. Kelud eruption on 13 to 14 February 2015, Mt. Rinjani eruption on 16 July 2015, and Mt. Rinjani eruption on 3 4 November 2015. The results showed that there were differences between the distribution patterns of volcanic ash eruption between different eruption types which were caused by several factors such as height of the eruption column, the volume of volcanic material, wind speed and direction at some altitude atmosphere. Prediction of volcanic ash distribution for Mt. Kelud with HYSPLIT model resulting moderate similarity compared to the results of satellite observations, with the value of Jaccard Similarity Index of 59.68 . Whereas for both Mt. Raung and Mt. Rinjani shown relatively weak similarity index values of 17.96 and 15.97 respectively.

Abstrak :
Debu vulkanik merupakan partikel yang sangat berbahaya bagi aktivitas penerbangan. Objek tersebut dapat diamati secara spasial melalui pengamatan satelit. 8. Namun, satelit ini memiliki kelemahan berupa pergeseran akibat kesalahan sudut baca ketika objek yang diamati jauh dari posisi nadir satelit. Data target output debu vulkanik yang digunakan merupakan hasil interpretasi forecaster berdasarkan pengamatan satelit Terra/Aqua (MODIS) yang memiliki orbit polar sehingga pengamatan dilakukan tepat diatas objek. Algoritma sampel yang dilakukan untuk membuat model adalah dengan variasi sampel berupa data piksel tunggal dan data rata-rata piksel pada citra satelit Himawari Untuk menentukan lokasi debu vulkanik berdasarkan citra satelit, dibutuhkan interpretasi dari forecaster. Pada penelitian ini, dibuat sebuah sistem pemodelan berbasis artificial neural network untuk menghasilkan output sebaran debu vulkanik secara otomatis berdasarkan training data dari citra satelit Himawari 8. Namun, satelit ini memiliki kelemahan berupa pergeseran akibat kesalahan sudut baca ketika objek yang diamati jauh dari posisi nadir satelit. Data target output debu vulkanik yang digunakan merupakan hasil interpretasi forecaster berdasarkan pengamatan satelit Terra Aqua (MODIS) yang memiliki orbit polar sehingga pengamatan dilakukan tepat diatas objek. Algoritma sampel yang dilakukan untuk membuat model adalah dengan variasi sampel berupa data piksel tunggal dan data rata-rata piksel pada citra satelit Himawari Sedangkan variasi data input yang digunakan terdiri dari 3 input, 16 input, dan 4 input kanal satelit. Metode pengujian performa dari model dilakukan dengan melihat citra sebaran debu yang dihasilkan model yang diverifikasi di setiap titik piksel. Berdasarkan hasil penelitian, model dengan menggunakan 3 input kanal satelit dapat mendeteksi sebaran debu vulkanik dengan baik pada data training maupun testing. Untuk koreksi kesalahan paralaks satelit Himawari memiliki dampak yang cukup signifikan terhadap hasil output model. Akurasi dari output model meningkat signifikan setelah dilakukan koreksi spasial akibat kesalahan paralaks yang menghasilkan akurasi model pada saat testing mencapai 95 persen

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ABSTRAK
Hujan menjadi salah satu parameter cuaca yang paling banyak diperhatikan karena fenomena kejadiannya secara signifikan dapat mempengaruhi aktivitas manusia, termasuk dalam bidang pertanian, perkebunan, perikanan, transportasi dan lain-lain. Selain itu informasi curah hujan sangat penting untuk melakukan analisis cuaca, khususnya dalam menganalisis kejadian banjir yang disebabkan oleh hujan lebat sehingga perlu adanya informasi terkait curah hujan yang tepat dan akurat. Penelitian ini bertujuan untuk mendapatkan model estimasi curah hujan yang optimal dengan beberapa metode machine learning. Machine learning merupakan aplikasi artificial intelligence (AI) yang menyediakan sistem pembelajaran bagi mesin untuk belajar secara otomatis tanpa diperintahkan secara eksplisit. Machine learning yang digunakan dalam penelitian ini adalah multi-layer perceptron (MLP), support vector regression (SVR) dan random forest (RF). Data radar dan jarak dari radar digunakan sebagai input model, untuk data target/validasi digunakakan data pengamatan hujan otomatis disekitar pengamatan Radar Polarisasi Tunggal di Yogyakarta. Hasil model akan dievaluasi nilai galat dan tingkat akurasinya, sehingga didapatkan metode machine learning yang optimal dalam mengestimasi curah hujan

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ABSTRACT
Rain is one of the weather parameters that is the most widely considered because the phenomenon of its occurrence can significantly affect human activities, including in agriculture, plantations, fisheries, transportation and others. In addition, rainfall information is very important to do weather analysis, especially in analyzing the occurrence of floods caused by heavy rains so there is a need for accurate and accurate rainfall related information. This study aims to obtain an optimal rainfall estimation model with several machine learning methods. Machine learning is an artificial intelligence (AI) application that provides a learning system for machines to learn automatically without explicit instruction. The machine learning used in this study is multi-layer perceptron (MLP), support vector regression (SVR) and random forest (RF). Radar data and distance from the radar are used as input models, for target/validation data used automatic rain observation data around the Single Polarization Radar observation in Yogyakarta. The results of the model will be evaluated for error values ​​and their level of accuracy, so that an optimal machine learning method is obtained in estimating rainfall.

Abstrak :
Low- Level Wind Shear (LLWS) adalah fenomena yang mana arah dan atau kecepatan angin pada lapisan bawah atmosfer berubah secara drastis dan tiba-tiba. Fenomena ini cukup signifikan dalam dunia penerbangan karena sering menimbulkan kecelakaan pada pesawat terbang yang akan mendarat maupun lepas landas. Untuk mencegah dampak buruk yang dapat terjadi, LLWS perlu diprediksi kejadiannya. LLWS sangat sulit diprediksi karena kejadiannya yang mendadak dan terjadi cenderung dalam waktu singkat. Sampai saat ini, di Indonesia masih belum ada sistem yang berjalan secara operasional yang dapat memprediksi kejadian LLWS. Pada penelitian terdahulu, model prediksi yang digunakan memanfaatkan data dari peralatan pendeteksi LLWS. Kebanyakan dari penelitian tersebut memprediksi LLWS untuk beberapa langkah waktu terbatas periode pengukuran peralatan yang digunakan datanya tersebut. Sementara itu, kejadian LLWS dapat terjadi dalam durasi singkat namun terkadang juga kejadiannya dapat terjadi dalam durasi yang lama. Oleh karena itu, terkadang model yang digunakan tidak dapat mengakomodir beberapa kejadian LLWS yang durasinya lebih panjang daripada panjang kerangka waktu periode prediksi. Pada penelitian ini, diusulkan suatu sistem untuk memprediksi kejadian LLWS menggunakan model Machine Learning (ML). Sistem yang diusulkan pada penelitian ini dapat mengakomodir seluruh kemungkinan panjang durasi kejadian LLWS. Data yang digunakan adalah data jaringan anemometer dari LLWAS berupa arah dan kecepatan angin dalam bentuk data deret waktu. Model prediksi untuk rancangan sistem yang digunakan adalah Temporal Convolutional Network (TCN). Sebagai pembanding dari model yang diusulkan, digunakan juga Multi-Layer Perceptron (MLP) yang merupakan model prediksi yang paling banyak digunakan untuk memprediksi kejadian LLWS pada penelitian sebelumnya, regresi linear yang merupakan model regresi standar dan Long-Short Term Memory (LSTM) serta Gated Recurrect Unit (GRU) yang adalah model khusus data deret waktu yang umum digunakan. Hasil dari pengujian model didapatkan bahwa secara keseluruhan TCN dapat melampaui performa model pembandingnya baik dari segi prediksi kejadian maupun prediksi durasi. Nilai Root Mean Squared Error (RMSE) dari TCN dengan konfigurasi terbaiknya adalah 9.1 detik. Untuk akurasi, TCN dengan konfigurasi terbaiknya mendapatkan skor 0.93. Hasil ini menunjukkan bahwa rancangan sistem dengan model yang diusulkan dapat bekerja dengan baik dimana performanya dapat mengungguli model pembanding yang diujikan ......Low-Level Wind Shear (LLWS) is a phenomenon in which the direction and/or speed of the wind in the lower layers of the atmosphere changes drastically and suddenly. This phenomenon is quite significant in the Aviation sector because it often causes accidents on landing or takeoff aircraft. To prevent the potentially harmful effect, LLWS needs to be predicted. LLWS is challenging to predict because its occurrence is sudden and tends to occur in a short time. Nowadays, in Indonesia, there is still no operating system that can predict LLWS events. In previous studies, the prediction model used utilized data from the LLWS detection equipment. Most of these studies predict LLWS for some time-limited time frame. Meanwhile, LLWS events sometimes occur in a short duration but sometimes also occur in a long duration. Therefore, sometimes the model used cannot accommodate some LLWS events whose duration is longer than the predicted timeframe. In this study, a system is proposed to predict LLWS events using a Machine Learning (ML) model. The system proposed in this study can accommodate all possible durations of LLWS events. The data used is anemometer network data from LLWAS in time-series wind direction and speed form. The prediction model for the system design used is the Temporal Convolutional Network (TCN). As criterion models, Multi-Layer Perceptron (MLP) is also used which is the most widely used predictive model to predict the incidence of LLWS in previous studies, linear regression which is a standard regression model, and Long-Short Term Memory (LSTM) and Gated Recurrent Unit (GRU) which is a model for time-series data which is commonly used. The experiment shows that overall TCN can exceed the performance of the criterion model bothin terms of occurrence prediction and duration prediction. The Root Mean Squared Error (RMSE) value of TCN with its best configuration is 9.1 seconds. For accuracy, the TCN with the best configuration gets a score of 0.93. These results indicate that the system design with the proposed model can work well where its performance can outperform the tested criterion models

Abstrak :
ABSTRAK
Perubahan iklim telah menyebabkan kerugian jiwa dan ekonomi akibat fenomena iklim ekstrem seperti banjir, kekeringan, perubahan karakteristik hujan dan kenaikan suhu di Indonesia. Informasi tentang proyeksi iklim yaitu curah hujan dan tren suhu sangat penting untuk melakukan adaptasi, mitigasi serta perencanaan operasional untuk berbagai sektor yang terkena dampak. Dalam studi ini, peneliti menggunakan data observasi dan data model iklim global. Data observasi harian berasal dari 70 stasiun meteorologi di Indonesia selama 20 tahun dari 1986 hingga 2005. Selanjutnya 29 data model iklim global GCM (Global Circulation Model) dari CMIP5 (Coupled Model Intercomparison Project Phase 5) historical dianalisis berdasarkan kesamaan pola spasial dan pola temporal dengan pola pengamatan stasiun meteorologi di Indonesia menggunakan korelasi. Model proyeksi perubahan iklim masa depan hingga tahun 2100 untuk variabel curah hujan dan suhu udara dikoreksi biasnya untuk skenario RCP 4.5 dan RCP 8.5 dari model terbaik yang didapatkan dari korelasi tertinggi. Proyeksi masa depan dibuat dalam index iklim ekstrem berdasarkan ETCCDI (Expert Team on Climate Change Detection and Indices) menjadi index total curah hujan tahunan (Prcptot), hari kering berturut-turut (CDD), hari hujan berturut-turut (CWD), nilai suhu maksimum bulanan (TXx) dan nilai suhu minimum bulanan (TNn). Index iklim ekstrem berdasarkan ETCCDI proyeksi dibandingkan dengan periode historical (1981-2010) sehingga diperoleh seberapa besar persentase perubahan iklim ekstrim pada periode 2011-2040, 2041-2070 dan 2071-2100. Hasil proyeksi masa depan secara temporal dan spasial indek iklim ekstrim meliputi Prcptot, CWD, TXx dan TNn kecuali indek CDD relatif mengalami kenaikan terhadap periode historicalnya.

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ABSTRACT
Climate change has caused life and economic losses due to extreme climate phenomena such as floods, droughts, changes in the characteristics of rain and rising temperatures in Indonesia. Information about climate projections, namely rainfall and temperature trends is very important for adaptation, mitigation and operational planning for the various sectors affected. In this study, researcher used observational data and global climate model data. Daily observational data obtained from 70 meteorological stations in Indonesia for 20 years from 1986 to 2005. Furthermore, 29 global model GCM (Global Circulation Model) from CMIP5 (Coupled Model Intercomparison Project Phase 5) historical were analyzed based on similarity of spatial patterns and temporal patterns with pattern of observation of meteorological stations in Indonesia using correlation. The projection model of future climate change until 2100 for rainfall variables and air temperature bias corrected for RCP 4.5 and RCP 8.5 scenarios of the best models obtained from the highest correlation. Future projections are made in the extreme climate index based on ETCCDI (Expert Team on Climate Change Detection and Indices) to be an index of total annual rainfall (Prcptot), consecutive dry days (CDD), consecutive wet days (CWD), maximum monthly temperature values (TXx) ​​and minimum monthly temperature values (TNn). Extreme climate index based on projection ETCCDI compared to historical period (1981-2010) so that the percentage of extreme climate change is obtained in the period 2011-2040, 2041-2070 and 2071-2100. The results of temporal and spatial predictions of extreme climate indices include Prcptot, CWD, TXx and TNN except that the CDD index has relatively increased over the historical period.