Judul: Land Cover Classification of Rural Areas By  Coupling Sentinel-2 Imagery and Random Forests
Abstrak: Pembangunan wilayah dan pengelolaan sumberdaya memerlukan data tutupan lahan. Dewasa ini, pembangunan tidak hanya berfokus pada kawasan perkotaan melainkan juga sudah mulai merambah ke kawasan perdesaan dan pinggiran kota. Oleh karena itu, pemantauan dan pemetaan tutupan lahan perdesaan (rural) penting untuk dilakukan. Penelitian ini bertujuan untuk mengkaji karakteristik spektral beberapa tutupan lahan dan melakukan pemodelan tutupan lahan rural dengan metode Random Forests (RF). Penelitian dilaksanakan di perbatasan Kabupaten Pandeglang dan Kabupaten Lebak. Percobaan dilakukan dengan menggunakan data citra Sentinel-2 yang memiliki resolusi tinggi. Metode RF digunakan dalam melakukan klasifikasi berbagai jenis tutupan lahan yang diamati yaitu; lahan terbangun, lahan pertanian semusim, kebun campuran, kebun kelapa sawit, kebun karet monokultur, karet rakyat (jungle rubber), dan tanaman kayu (timber). Berdasarkan percobaan yang telah dilakukan diketahui bahwa kombinasi kanal SWIR (Shortwave Infra Red) cukup mampu memisahkan tutupan lahan yang ada dibandingkan kombinasi kanal lainnya. Selain itu, metode RF cukup efektif untuk klasifikasi tutupan lahan meskipun variasi kepercayaan informasi pada tingkat kelas tutupan lahan masih belum sepenuhnya seragam. Penelitian ini menunjukkan bahwa tidak semua kanal yang digunakan pada citra Sentinel-2 berkontribusi terhadap ekstraksi informasi, khususnya pada tutupan lahan yang dinilai cukup sulit dibedakan seperti karet rakyat. Namun demikian, metode klasifikasi RF yang digunakan cukup efektif dengan akurasi total sebesar 77.7%., Regional development and the management of natural resources require land cover data. The development, nowadays, does not merely set its focus to urban areas, but has been expanded by incorporating rural and suburban areas. Monitoring and mapping of rural land cover is therefore important to accommodate this need. This study aimed to assess spectral characteristics of selected land cover types and to model rural land cover using the Random Forests (RF). The research was conducted over an area bordering Pandeglang and Lebak Regencies. The experiment demonstrated the value of using Sentinel-2 image, which is considerably a high resolution data. The RF method was used in classifying various types of land cover, namely; built-up, seasonal agricultural crops, mixed gardens, oil palm plantations, monoculture rubber plantations, jungle rubber, and timber plants. Based on the experiment, it was discovered that the SWIR (Shortwave Infra Red) channel was quite capable of separating the existing land cover compared to other band combinations. In addition, the RF method is fairly effective for land cover classification although confidence at class level varied. This study showed that not all channels used in Sentinel-2 imagery contributed to the extraction of information, especially on land cover that was considered quite difficult to distinguish such as smallholder rubber. However, the RF classification method was somewhat effective with a total accuracy of 77.7%.
Keyword: klasifikasi, rural, Sentinel-2, tutupan lahan, random forests, karakteristik spektral

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: 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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