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: Klasifikasi Tutupan Lahan Menggunakan Pendekatan Pemelajaran Mesin pada Data Sentinel-1
Abstrak: Sektor pertanian, khususnya subsektor perkebunan, berkontribusi besar sebagai penghasil devisa negara. Komoditas perkebunan yang menjadi andalan bagi pendapatan nasional dan devisa negara adalah karet dan kelapa sawit. Perencanaan, pengembangan, serta pemantauan lahan perkebunan dan pertanian seringkali memanfaatkan perkembangan teknologi penginderaan jauh untuk mempermudah proses pengambilan keputusan. Pemetaan menggunakan citra optik sering terkendala kondisi iklim Indonesia dengan cuaca yang sering tertutup awan dan kabut yang dapat menghalangi objek saat perekaman. Pada wilayah berbukit dan bergunung, frekuensi liputan awan umumnya lebih tinggi dibandingkan wilayah dataran. Oleh karena itu, penelitian ini mengkaji citra radar Sentinel-1 yang dapat beroperasi baik pada segala kondisi cuaca sehingga meminimalkan efek dari atmosfer. Namun demikian, citra Sentinel-1 belum banyak ditelaah pada wilayah berbukit/bergunung, sehingga dampak terain terhadap iluminasi dan kemampuannya dalam pemisahan kelas tutupan lahan belum banyak diketahui. Klasifikasi tutupan lahan dilakukan dengan pendekatan pemelajaran mesin untuk membandingkan kinerja terbaik dari model klasifikasi Support Vector Machines (SVM) dan Random Forests (RF). Pada model RF, akurasi tertinggi didapatkan dengan persentase sebesar 64.8% pada citra akuisisi musim hujan, dan 63.7% pada citra akuisisi musim kemarau. Sedangkan akurasi terendah didapatkan dengan model SVM kernel Linier dengan persentase 53.2% pada citra akuisisi saat musim hujan, dan 52.7% pada citra akuisisi saat musim kemarau. Penelitian ini menunjukkan bahwa akurasi sangat dipengaruhi oleh pengaturan parameter algoritma pemelajaran mesin. Pemilihan nilai parameter yang tepat dapat menghasilkan akurasi yang lebih baik. Hasil penelitian mengindikasikan bahwa terdapat perbedaan yang cukup signifikan antara hasil klasifikasi pada musim yang berbeda, yang memerlukan pendalaman lebih jauh di masa mendatang., Agricultural sector, especially plantation sub-sector, greatly contributes to Indonesian economy. Plantation commodities commonly cultivated in Indonesia include rubber and oil palm. Planning, development, and monitoring over plantations and agricultural fields often take the advantage of remote sensing technology to facilitate appropriate decision making. Mapping using optical imageries is often hampered by Indonesia's climatic conditions. Clouds and fogs can obstruct objects during data acquisition. In hilly and mountainous areas, the frequency of cloud coverage is generally higher than in plain areas. Therefore, this study examined Sentinel-1 radar which can operate in all weather conditions so as to minimize atmospheric effects. However, Sentinel-1 has yet been widely studied in hilly/mountainous areas, so that terrain impact on illumination and its ability to separate land cover classes are yet to be fully understood. Land cover classification was carried out using machine learning approach to compare the performance of Support Vector Machines (SVM) and Random Forests (RF) classification. Highest accuracy of RF was obtained in rainy season with 64.8% overall accuracy and 63.7% overall accuracy in dry season, while the lowest accuracy of SVM was achieved during dry season with 52.7% overall accuracy, and 53.2% overall accuracy in rainy season. This study showed that the accuracy is strongly influenced by parameter setting of machine learning algorithms. Selection of suited parameter values can yield a better accuracy. The results indicate that accuracy difference is expected when season is taken into account. This warrants further studies in the future.
Keyword: Land cover, random forest, Sentinel-1, support vector machine

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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