Judul: Estimation and Identification of Wind Speed as a Potential  Renewable Energy Source in Indonesia
Abstrak: Energi angin sebagai energi terbarukan dapat dikembangkan untuk mengurangi penggunaan bahan bakar fosil dalam produksi listrik dan untuk meningkatkan rasio elektrifikasi di Indonesia. Data pengamatan kecepatan angin diperlukan untuk mengetahui potensi angin suatu daerah tetapi pengamatan kecepatan angin dalam skala lokal belum mencakup seluruh wilayah di Indonesia. Penelitian ini bertujuan memodelkan data reanalisis ERA5 untuk menduga data kecepatan angin secara spasial di seluruh Indonesia dan untuk mengidentifikasi potensi energi angin sebagai sumber energi terbarukan di Indonesia. Model jaringan saraf tiruan (JST) digunakan untuk menduga nilai kecepatan angin observasi dari data reanalisis. Model yang dihasilkan menunjukkan nilai Huber loss sebesar 0,19 dan R2 sebesar 0,57. Nilai Mean Squared Error (MSE) model lebih kecil dari nilai MSE data ERA5. Nilai MSE model adalah 0,42 sedangkan nilai MSE data ERA5 adalah 1,7. Hasil potensi energi angin menunjukkan bahwa sebanyak 22 kota di Indonesia memiliki rata-rata rapat daya angin di atas 100 W/m2di ketinggian 100 m dari permukaan. Penambahan titik data observasi kecepatan angin dapat dilakukan untuk meningkatkan performa model., Wind energy as renewable energy can be developed to reduce the use of fossil fuel in electricity production and to increase the electrification ratio in Indonesia. Wind speed observation data is needed to determine the wind potential over particular area. However, the availability of the data is spatially uneven throughout Indonesia. This study aims to model ERA5 reanalysis data to determine observational wind speed data throughout Indonesia and to determine the potential of wind energy as a renewable energy source in Indonesia. The artificial neural network (ANN) model is used to estimate the observed wind speed values from reanalysis data. The resulting model shows the Huber loss value of 0,19 and R2 of 0,57. The value of Mean Squared Error (MSE) of the model is smaller than MSE of ERA5 data. The MSE values of the model is 0,42, while the MSE value of the ERA5 data is 1,7. The results of the potential for wind energy show that as many as 22 cities in Indonesia have an average wind power density above 100 W/m2 at an altitude of 100 m from the surface. The addition of wind speed observation data points can be done to improve the performance of the model.
Keyword: Artificial neural network, electrification ratio, loss function, reanalysis

Judul: Algortithm Design for Wind Prediction in Berakit Bay,  Bintan Island Using The Long Short Term Memory (LSTM) Method
Abstrak: Parameter angin merupakan parameter fisik yang memiliki berbagai macam peran aspek dalam kehidupan manusia. Teluk Berakit merupakan Teluk kecil yang terletak pada timur laut Pulau Bintan yang di nilai rentan terhadap fenomena-fenomena alam dan bencana alam. Penelitian ini di awali dengan konversi komponen angin, preprocessing, pengaplikasian algoritma, proses training, pembuatan grafik prediksi dan melakukan dua uji yaitu RMSE dan korelasi. Preprocessing dataset dilakukan dengan memberi perlakuan normalisasi pada dataset, Persiapan algoritma dilakukan dengan perancangan arsitektur dan penentuan nilai pada tiap hyperparameter. Waktu proses pembelajaran atau training algoritma ditempuh dengan waktu 5 hingga 24 menit. Nilai prediksi LSTM dibandingkan dengan data ECMWF 2020, dan didapatkan bahwa hasil prediksi mampu menyamai pola kecepatan angin pada lokasi penelitian. Pola yang terbentuk dinilai cukup sesuai dengan fenomena musim yang terjadi di Indonesia. Hasil menunjukkan bahwa arsitektur LSTM mampu mendapatkan nilai RMSE yang paling optimal yaitu 1,87, dengan penggunaan dua layer LSTM 256 units pada layer pertama dan 128 pada layer kedua. Akurasi korelasi yang didapatkan yaitu 39%. Algoritma LSTM dalam memprediksi angin dapat diaplikasikan juga pada daerah lain di Indonesia., Wind is a physical parameters that has various roles in human life. Berakit Bay is a small bay located in the northeast of Bintan Island which is considered vulnerable to natural phenomena and natural disasters. This research begins with the conversion of wind components, preprocessing, application of algorithms, training process, making prediction graphs and conducting two tests, namely RMSE and correlation. Dataset preprocessing is done by giving normalization treatment to the dataset, Algorithm preparation is done by architectural design and determining the value for each hyperparameter. The learning or training process or algorithm training takes 5 to 24 minutes. The LSTM prediction value was compared with ECMWF 2020 data, and it was found that the prediction results were able to match the wind speed pattern at the study site. The pattern formed is considered quite in accordance with the seasonal phenomenon that occurs in Indonesia. The results show that the LSTM architecture is able to obtain the most optimal RMSE value of 1,87, with the use of two LSTM layers of 256 units in the first layer and 128 in the second layer. The correlation accuracy obtained is 39%. The LSTM algorithm in predicting wind can also be applied to other areas in Indonesia.
Keyword: wind, predict, LSTM

Judul: Hydroxyapatite-Chitosan Composite Coating on Stainless Steel 316 to Improve Corrosion Resistance
Abstrak: Metals that were used in bone implantation should have good corrosion resistance that were not easily corroded when implanted into the body. Metal corrosion resistance of stainless steel 316 can be enhanced through the metal coating by hydroxyapatite-chitosan composite with composition ratio of hydroxyapatite (gram) and chitosan 3% (mL) 0.2:0, 0.2:0.1, 0.2:0.5, 0.2:1.0, and 0.2:1.5. Besides improving the corrosion resistance of metal, the presence of chitosan also maintain the position of hydroxyapatite on metal surfaces. Corrosion resistance increases with increasing amount of chitosan added and the best composition of hydroxyapatite-chitosan composite was 0.2:1.0.
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