Judul: Kemampuan Transfer Model Kelembapan Tanah yang Dikembangkan dari Data SAR Sentinel-1
Abstrak: Soil moisture serves as one of the important variables in hydrological processes or climate change. The availability of water in agricultural fields is also a critical information for agriculture, where drought events can affect plant production. Tackling with the large areas covering agricultural fields, spatial information, usually derived from remote sensing, needs to be timely developed. Techniques for retrieving soil moisture have been established using multispectral imageries. Nevertheless, weather condition often constraints information retrievals. In that sense, developing the estimation based on Synthetic Aperture Radar (SAR) data, including Sentinel-1, is a necessity for agricultural planning. The main goals of this research were to model soil moisture using only dual-polarized SAR data and to investigate its potential in transfer modeling using subsequent data acquisition with the presence of vegetation. Soil moisture was modeled using random forest in a set of data and was validated using ground data utilizing soil moisture meter. The result showed that random forest yielded overall accuracy about 0.64 with RMSE 1.54. However, the model was inadequate to perform well when exposed to new data, with averaged accuracy about 0.0045 during transfer modelling. This suggested that the model could be overfit into single date data and was insufficient for a more diverse data range. The research concluded that random forest modelling can be used to estimate soil moisture using remote sensing data but may not easily be transferable. The study, however, suggests a further examination into the characteristics of crop canopy above the soil.
Keyword: random forest, sentinel-1, soil moisture, transfer modelling

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: Pemerian Cacing Amcylostomu bradfiertsis (Comes de Faria, 1910) dan Ancylostoma ceylanicum (Loess, 1911) Pada Kucing di Wilayah Kota Bogor.
Abstrak: Kucing dikenal sebagai hewan kesayangan yang mempunyai nilai khusus bagi manusia. Manusia telah memelihara kucing sejak ribuan tahun yang lalu melalui proses domestikasi sehingga kucing menjadi hewan peliharaan. Kucing tanpa pemilik yang berkeliaran di sekitar hewan peliharaan sangat potensial sebagai sumber infeksi Wagai penyakit antara lain kecacingan. Oleh karena itu setiap pemilik kucing sebaiknya melakukan pencegahan dengan tidak membiarkan kucing peliharaannya berkeliharaan dengan bebas di lingkungan yang terdapat populasi kucing liar.
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