Judul: Utilizing Support Vector Regression for Peat Soil Moisture  Estimation based on Sentinel Image Data
Abstrak: Lahan gambut dengan kondisi basah dapat menyerap karbon secara optimal dan dapat mengurangi dampak panas bumi akibat krisis iklim. Sebaliknya, lahan gambut yang kering dapat menyebabkan pelepasan karbon ke atmosfer. Apabila terjadi kebakaran, maka pelepasan karbon akan meningkat secara signifikan. Penelitian ini bertujuan untuk mengestimasi kelembapan tanah pada lahan gambut di Wilayah Kecamatan Sebangau, Kalimantan Tengah dengan menggunakan algoritma Support Vector Regression (SVR) dan kombinasi parameter terbaik yang digunakan. Penelitian ini menggunakan data kelembapan tanah gambut sebagai data prediktan dan data Normalized Difference Water Index, Soil-Adjusted Vegetation Index, Normalized Soil Moisture Index, Normalized Difference Moisture Index, Normalized Difference Vegetation Index dari data Sentinel-2A sebagai data prediktor. Hasil penelitian menunjukkan bahwa kombinasi kernel radial (RBF) dan parameter e = 0.2, C = 10 dan ? = 100 menghasilkan model SVR dengan kemampuan estimasi yang paling baik. Model dengan kombinasi tersebut menghasilkan nilai Mean Absolute Percentage Error sebesar 9,70% untuk data training dan 15,66% untuk data testing. Penerapan model SVR di Kecamatan Sebangau, Kalimantan Tengah menunjukkan bahwa kelembapan tanah di wilayah tersebut berada pada kisaran 33%-55%. Penelitian ini menunjukkan bahwa model SVR dapat diimplementasikan dalam menduga kelembapan tanah gambut., Peatlands with wet conditions can optimally absorb carbon and can reduce the impact of geothermal heat due to the climate crisis. Conversely, dry peatlands can lead to the release of carbon into the atmosphere. If there is a fire, the carbon release will increase significantly. This study aims to estimate soil moisture in peatlands in Sebangau District, Central Kalimantan using the Support Vector Regression (SVR) algorithm and the best combination of parameters used. This research uses peat soil moisture data as predicted data and Normalized Difference Water Index, Soil-Adjusted Vegetation Index, Normalized Soil Moisture Index, Normalized Difference Moisture Index, Normalized Difference Vegetation Index from Sentinel-2A data as predictor data. The results showed that the combination of radial kernel (RBF) and parameters e = 0.2, C = 10 and ? = 100 produced an SVR model with the best estimation capability. The model with this combination produces a Mean Absolute Percentage Error value of 9.70% for training data and 15.66% for testing data. The application of the SVR model in Sebangau District, Central Kalimantan shows that soil moisture in the region is in the range of 33%- 55%. This research shows that the SVR model can be implemented in estimating peat soil moisture.
Keyword: MAPE, kernel, machine learning, sentinel-2A, tuning parameter

Judul: Utilizing Support Vector Regression for Peat Soil Moisture  Estimation based on Sentinel Image Data
Abstrak: Lahan gambut dengan kondisi basah dapat menyerap karbon secara optimal dan dapat mengurangi dampak panas bumi akibat krisis iklim. Sebaliknya, lahan gambut yang kering dapat menyebabkan pelepasan karbon ke atmosfer. Apabila terjadi kebakaran, maka pelepasan karbon akan meningkat secara signifikan. Penelitian ini bertujuan untuk mengestimasi kelembapan tanah pada lahan gambut di Wilayah Kecamatan Sebangau, Kalimantan Tengah dengan menggunakan algoritma Support Vector Regression (SVR) dan kombinasi parameter terbaik yang digunakan. Penelitian ini menggunakan data kelembapan tanah gambut sebagai data prediktan dan data Normalized Difference Water Index, Soil-Adjusted Vegetation Index, Normalized Soil Moisture Index, Normalized Difference Moisture Index, Normalized Difference Vegetation Index dari data Sentinel-2A sebagai data prediktor. Hasil penelitian menunjukkan bahwa kombinasi kernel radial (RBF) dan parameter e = 0.2, C = 10 dan ? = 100 menghasilkan model SVR dengan kemampuan estimasi yang paling baik. Model dengan kombinasi tersebut menghasilkan nilai Mean Absolute Percentage Error sebesar 9,70% untuk data training dan 15,66% untuk data testing. Penerapan model SVR di Kecamatan Sebangau, Kalimantan Tengah menunjukkan bahwa kelembapan tanah di wilayah tersebut berada pada kisaran 33%-55%. Penelitian ini menunjukkan bahwa model SVR dapat diimplementasikan dalam menduga kelembapan tanah gambut., Peatlands with wet conditions can optimally absorb carbon and can reduce the impact of geothermal heat due to the climate crisis. Conversely, dry peatlands can lead to the release of carbon into the atmosphere. If there is a fire, the carbon release will increase significantly. This study aims to estimate soil moisture in peatlands in Sebangau District, Central Kalimantan using the Support Vector Regression (SVR) algorithm and the best combination of parameters used. This research uses peat soil moisture data as predicted data and Normalized Difference Water Index, Soil-Adjusted Vegetation Index, Normalized Soil Moisture Index, Normalized Difference Moisture Index, Normalized Difference Vegetation Index from Sentinel-2A data as predictor data. The results showed that the combination of radial kernel (RBF) and parameters e = 0.2, C = 10 and ? = 100 produced an SVR model with the best estimation capability. The model with this combination produces a Mean Absolute Percentage Error value of 9.70% for training data and 15.66% for testing data. The application of the SVR model in Sebangau District, Central Kalimantan shows that soil moisture in the region is in the range of 33%- 55%. This research shows that the SVR model can be implemented in estimating peat soil moisture.
Keyword: MAPE, kernel, machine learning, sentinel-2A, tuning parameter

Judul: Sintesis Paduan Ti-6Al-4Nb sebagai Matrial Implan Pengganti Komponen Tibial
Abstrak: Synthesis of Ti-6Al-4Nb alloy was done by melting using Tri Arc Melting Furnace. Each sample was remelted five times to ensure the homogenity. Aluminum and niobium addition were expected to stabilize α phase and β phase until the αβ-titanium alloy was resulted. Yet based on the experiment, it was α-titanium alloy resulted. α-titanium alloy was a Hexagonal Close Packed (HCP) crystalline structure which would be cracked if deformation non-uniaxial happens. The presence of oxygen in pure titanium significantly increased the hardness of titanium. The addition of aluminum was also able to increase the hardness of titanium alloy because aluminum more stabilize the α phase in the α-titanium alloy. The corrosion ressistance of titanium could be increased by adding aluminum and niobium. Titanium coating with HA/chitosan composite was also proved in decreasing the corrosion rate because it barried direct interaction between titanium alloy and corrotion solution.
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