Judul: Klasifikasi Mangrove Berbasis Objek dan Piksel Menggunakan Citra Satelit Multisensor di Sungai Liong, Bengkalis, Provinsi Riau
Abstrak: Mangrove merupakan salah satu sumberdaya pesisir yang memiliki fungsi sebagai sistem ekologis dan sumber ekonomi. Upaya perlindungan dan pengelolaan ekosistem mangrove perlu menjadi perhatian serius dari berbagai pihak. Data pengindraan jauh telah terbukti secara luas dalam memantau dan memetakan ekosistem mangrove yang terancam. Penelitian ini dilaksanakan di kawasan mangrove Sungai Liong, Pulau Bengkalis, Provinsi Riau. Pengamatan lapang dilaksakan pada bulan Januari- Februari 2018 menggunakan pesawat tanpa awak (unmanned aerial vehicle/UAV). Bahan yang digunakan pada penelitian ini yaitu: citra Sentinel-2B, seri Landsat dan data transek tahun 2013. Adapun langkah-langkah dalam penelitian ini, yaitu: (i) persiapan data, (ii) pra pengolahan data pengindraan jauh, (iii) rancangan pengambilan data lapangan, (iv) survei lapangan, (v) skema klasifikasi untuk pemetaan mangrove, (vi) pemetaan mangrove (vii) uji akurasi (viii) deteksi perubahan tutupan mangrove. Skema klasifikasi penutup lahan pada penelitian dibangun oleh 9 kelas, yaitu : badan air (BA), daerah pertanian (DN), kebun karet (KT), kebun kelapa (KA), lahan terbangun (LN), mangrove (ME), semak belukar (SB), kebun kelapa sawit (KS) dan vegetasi transisi (VI). Penggunaan UAV sebagai alat untuk pengamatan tutupan lahan sangat efektif digunakan karena mampu menempuh wilayah yang sulit untuk di jangkau dan memepercepat kerja di lapangan. Sruktur komunitas mangrove sungai Liong dibangun oleh 27 spesies tumbuhan mangrove yang terdiri dari 13 spesies mangrove sejati dan 14 spesies ikutan. Rhizophora apiculata, Lumnitzera racemosa, dan Xylocarpus granatum merupakan komunitas dominan dan selalu dijumpai di lokasi studi dibandingkan dengan jenis-jenis lainnya. Berdasarkan analisis AHC, komunitas mangrove hanya mampu membentuk 2 kelas, yaitu kelas komunitas 1 dan kelas komunitas 2. Hasil akurasi keseluruhan (OA) yang diperoleh untuk klasifikasi penutup lahan berbasis objek dan piksel berturut-turut, yaitu 78,7% dan 66,9% Sedangkan akurasi keseluruhan (OA) untuk komunitas mangrove dengan teknik klasifikasi berbasis objek dan piksel berturut-turut yaitu 76,6% dan 75,0%. Sekitar 12% peningkatan akurasi keseluruhan (OA) untuk penutup lahan dengan tingkat signifikan 2,84 dan sekitar 1,6% untuk komunitas mangrove dengan metode klasifikasi berbasis objek. Hasil penelitian ini menunjukkan bahwa metode OBIA dapat menjadi pilihan yang menjanjikan untuk pemetaan mangrove. Berdasarkan deteksi perubahan penutup lahan mangrove, kawasan mangrove Sungai Liong mengalami penurunan dari tahun ke tahun. Penurunan tersebut disebabkan oleh faktor antropogenik dan faktor alam. Sekitar 4,2% penurunan mangrove dari tahun 1990 ke tahun 2017. Ekploitasi mangrove terbesar tejadi pada tahun 2007 dengan laju penurunan sebesar 31,5%.
Keyword: klasifikasi berbasis objek dan piksel, mangrove, Sentinel-2B, Sungai Liong, UAV

Judul: Klasifikasi Mangrove Berbasis Objek dan Piksel Menggunakan Citra Satelit Multispektral di Sungai Kembung, Bengkalis, Provinsi Riau
Abstrak: Mangrove that generally found in coastal regions plays important roles such as (1) coastal protection from hurricane, tsunami, wind and wave, (2) spawning ground for many fishes and other faunas, (3) a place for recreational, (4) source of nutrients for organisms, and (5) source of wood. Recently, mangrove ecosystems in Kembung River, Bengkalis Island Riau Province tend to get pressure from antrophogenic. Protection and management of mangrove ecosystems needs to be a serious concern of various parties. Such efforts require data acquisition techniques and information mangrove spatially and accurately. Field data were collected in June and December 2012. Satellite imageries were used in this study consisted of Landsat 5 TM, Landsat 7 ETM +, Landsat 8 OLI, SPOT 6 multispectral, SPOT 6 panchromatic, ALOS PALSAR and SENTINEL-1. Pre-processing in the satellite imageries were applied including atmospheric correction, radiometric calibration, geometric correction, and spectral transformation. Field data observations and measurements were conducted on mangrove vegetation and land cover. Scheme development of mangrove community classification was conducted by using clusters and similarity percentage (SIMPER) analyses. Furthermore, the scheme was used to charactirized satellite images spectral reflectance by using spectral analysis. Satellite data were classified by using object-based approach which applied random forest algorithm. Object-based classification results was then compared to pixel-based classification technique which used maximum likelihood algorithm. The succesfull object-based classification then applied on magrove change detection in the study area. Kembung River mangrove community was assembled by 69 mangroves species which consists of 22 true mangrove species and 47 species of associate mangrove. Xylocarpus granamun, Rhizophora. apiculata, Lumnitzera. racemosa and Scyphiphora hidrophyllaceae were dominant species and always found in the study area compared with other species, for all strata. Based on standard criterias and guidelines for the determination of mangrove destruction which issued by the Minister of the Environment No. 201 of 2004, Mangrove at Kembung River was categorized as good and very dense mangrove ecosystem. Based on the composition of mangrove species, one level classification scheme was succesfully developed. The scheme consisted of 12 mangrove community classes, however most of the mangrove species belong to the two main classes of Rhizophora apiculata (Ra) composed of 33 samples and Xylocarpus granatum (Xg) composed of 93 samples Other 10 classes had limited sample number, therefore the 10 other classes were groupped into one class (La) that composed of 28 samples. Both classification techniques were able to identify all land cover classes and mangrove communities. Some misclassifications were still found to produce salt and pepper effects on both classifications, however, the random forest algorithm v could reduced such errors than the maximum likelihood algorithm. The best result of land cover classification using object-based and pixel-based classifications was obtained through input image layer M6. The object-based classification approach was better than pixel-based and it can improved the classification results by 1-24.5%. Change detection analyses showed that the mangrove area in Kembung River was relatively stable. For nearly two decades, we found mangrove loss about 197.2 ha, gain of 251.1 ha, and unchanged of 2904.9 ha. Changes in mangrove covers were generally caused by anthropogenic factors
Keyword: classification, Kembung River, mangrove, object-based, pixel-based

Judul: Pengaruh Pairing pada Isotop Sn dengan Menggunakan Pemodelan Sembilan Tingkat Energi
Abstrak: Formation of the neutron-rich tin isotopes (Sn isotopes) is the one of the interesting topics in the field of nuclear reaction, especially regarding the stability of the heavy nuclei. Calculations of total energy using the BCS approach using five energy levels can only calculate the total energy up to Sn50132. On the other hand, the experiment continues and has exceeded the formation of isotopes. Therefore, there is need for a new model to accomadate more neutrons. In this research, Sn isotopes is modelled using nine energy levels and constant interaction potential to explain the pairing phenomenon up to Sn50154. However, to obtain more accurate results, other methods to minimize the differences with the experimental results are clearly needed here.
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