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: Projections the Distribution of  Wind Energy Potential in Indonesia Using ScenarioMIP and GeoMIP
Abstrak: Informasi potensi energi angin akibat perubahan di Indonesia sangat penting dalam hal pemanfaatannya sebagai energi terbarukan. Penelitian ini mengkaji besar perubahan potensi energi angin di Indonesia dengan periode lampau (1991-2020) dan akan datang (2030-2060) berdasarkan ScenarioMIP dan GeoMIP dengan Multi Model Ensemble (MME), sehingga dapat memberikan informasi potensi wilayah Pembangkit Listrik Tenaga Bayu (PLTB) di Indonesia. Metode koreksi bias distribusi statistik digunakan untuk memperbaiki distribusi kecepatan angin, potensi energi angin yang dilihat dengan kecepatan angin dan besar Wind Power Density (WPD) yang dihitung menggunakan Weibull Parameter dengan skenario SSP 2-4.5, SSP 3-7.0, dan G6Solar untuk gambaran masa depan. Rata-rata kecepatan angin Indonesia digambarkan mengalami penurunan dari 6.65 m/s menjadi 4.79 m/s, 4.65 m/s, dan 5.03 m/s pada SSP2-4.5, SSP 3-7.0, dan G6Solar. Sebanding dengan kecepatan angin, nilai rata-rata WPD Indonesia juga mengalami penurunan dari 312.20 W/m2 menjadi 96.76 W/m2, 96.56 W/m2, dan 194.85 W/m2. Pada ketiga skenario terjadi penurunan persentase potensi energi angin sebesar 28.57%, 30.51%, dan 24.77%. Provinsi yang masih memiliki potensi pengembangan PLTB yaitu NTT, NTB, Sulawesi Tenggara, Sulawesi Selatan, Maluku, Sulawesi Utara, Kepulauan Riau, Jawa Timur, Aceh, Papua Selatan, Papua, NTB, Sulawesi Tenggara., Information on wind energy potential in the future due to climate change in Indonesia is very important in terms of utilizing wind energy as renewable energy. This study examines the magnitude of changes in wind energy potential in Indonesia with the past (1991-2020) and future (2030-2060) periods based on Multi Model Ensemble (MME) of ScenarioMIP and GeoMIP to provide information on the potential area of Wind Power Plant (PLTB) in Indonesia. The statistical distribution bias correction method is used to correct the distribution of wind speed, wind energy potential seen by wind speed and Wind Power Density (WPD) calculated using Weibull Parameters with SSP 2-4.5, SSP 3-7.0, and G6Solar scenarios for the future picture. The average wind speed of Indonesia is depicted to decrease from 6.65 m/s to 4.79 m/s, 4.65 m/s, and 5.03 m/s under SSP2-4.5, SSP 3-7.0, and G6Solar, respectively. Comparable to the wind speed, Indonesia's average WPD also decreased from 312.20 W/m2 to 96.76 W/m2, 96.56 W/m2, and 194.85 W/m2. In the three scenarios there was a decrease in the percentage of wind energy potential by 28.57%, 30.51%, and 24.77%. Provinces that still have the potential to develop wind power plants are NTT, NTB, Southeast Sulawesi, South Sulawesi, Maluku, North Sulawesi, Riau Islands, East Java, Aceh, South Papua, Papua, NTB, Southeast Sulawesi.
Keyword: Wind Speed, SSP2-45, G6Solar, Wind Power Density

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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