Judul: Klasifikasi Tutupan Lahan Berbasis Rona dan Tekstur dengan Menggunakan Citra ALOS PRISM
Abstrak: Land cover classification is important issue in remote sensing, so its accuracy needs to be increased. The aim of this research was to analyze ability of tone and texture in differ land cover classes, to measure classification accuracy of each methods, and to analyze pattern of accuracies. This research was using ALOS PRISM imagery. Texture processing was done by using co-occurence matrix starting at kernel size 3x3 up to 41x41, with some extensions at 53x53, 73x73, and 141x141. Classification was performed by using Minimum Distance to Mean, Decision Tree QUEST, and SVM. Results showed that tone was unable to differ land cover properly so there has to be texture in addition to classify. At the beginning, accuracy increased and then reached a peak and fairly stable at that level. It decreased when kernel size was too large. It indicated the law of deminishing return phenomenon, because of excessive complexity within statistical analysis. Texture or the combination of tone and texture based classification's accuracy were higher than tone based classification, except to small or too large kernels, i.e. 3x3 and 141x141. Minimum Distance to Mean showed the lowest accuracy. Accuracy of multi-scale based classification was higher than single scale based classification. The choice of textures also affects the classification accuracy. Accuracy of Decision Tree QUEST were the most difficult to be modeled by rational equation.
Keyword: Bogor Agricultural University (IPB), tone, texture, co-occurence, classification

Judul: Evaluasi Akurasi Klasifikasi Penutupan Lahan Menggunakan Citra Alos Palsar Resolusi Rendah Studi Kasus Di Pulau Kalimantan
Abstrak: A wide variety of data and information on forest cover in Indonesia may be due to the variety of source of data, date of acquisition, and methods applied. For a wide area, terrestrial inventory methods are usually costly and time consuming. One alternative that may be used to minimize the cost and time is satellite based remote sensing technology. In the tropical country such Indonesia, cloud, fog, and smoke mainly limit the use of optical remote sensing during identification process and object monitoring on earth surface. Objects under the cloud, fog, and smoke could be identified using using Radar images. ALOS is remote sensing satellite which launched by Japan in 2006. One of its censor is PALSAR (Phased-Array type L-band Synthetic Aperture Radar). PALSAR is an advanced development from SAR which carried by the former satellite JERS-1. This is the microactive wave censor which can observe day and night without weather influence. Through one observation mode that is Scan SAR, this censor can observe earth surface in wide area than the conventional SAR. The objective of this study is to evaluate the ability of low resolution ALOS PALSAR image to classify regional scale land cover in Kalimantan Island. ALOS PALSAR image have 200 x 200 m resolution acquired in 2007. Other supporting data used are Land Cover Map year 2003, Administration Border Map, Forest Area Map, and the Base Thematic Forestry Map. The data were analyzed using GIS 3.2 and Erdas Imagine 9.1. The method are consisted of image preprocessing, image processing, separability accuracy evaluation and spatial analysis The study shows that low resolution ALOS PALSAR image could classify land cover into six classes. There are water body, rice field, shrub/bush, estate crop, and forest. Separability analysis for these classes show that there are 2 unseparable class pairs. These classes were then reclassified into four classes. The new classes are water body, sparse vegetation, medium density vegetation, and high density vegetation. The result of separability analysis shows that the these class separabilities are good (well separated). The accuracy of the classification are 88,21% for Overall Accuracy and 85,26% for Kappa Accuracy. Based on ALOS low resolution images (200 m x 200 m spatial resolution, the acreages of each land cover are 11.459.400 hectares (21,33%) for sparse vegetation, 5.070.008 hectares (9,44%) for medium density vegetation, and 36.806.058 hectares (68,52%) for high density vegetation. While the acreages of each land cover based on Land Cover Map year 2003 are 802.233 hectares (1,51%) for sparse vegetation, 20.841.843 hectares (39,32%) for medium density vegetation, 27.583.553 hectares (52,04%) for high density vegetation and 2.457.825 hectares (4,64%) for smoke. Key words : ALOS PALSAR Image, Land cover, Separability, Radar images
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Judul: Induksi Perkembangan Gonad Betina Ikan Gabus (Channa Striata, Bloch) Dengan Penyuntikan Hormon Hcg Dalam Wadah Budidaya.
Abstrak: Penelitian ini bertujuan untuk mengetahui pengaruh penyuntikan hormon HCG terhadap perkembangan gonad ikan gabus (Channa striata, Bloch) yang dipelihara dalam wadah terkontrol. Dua kelompok induk betina; I) sebagai ikan kontrol dan II) sebagai ikan yang akan diberi induksi hormon, masing-masing sebanyak tiga ekor dipelihara dalam enam buah akuarium. Induk diberi pakan benih ikan nila secara ad atiation dua kali sehari. Penyuntikan hormon HCG dengan dosis 500 IU/kg dilakukan secara intramuskular seminggu sekali selama tiga minggu pemeliharaan sedangkan penyuntikan larutan fisiologis dilakukan pada ikan kontrol. Mulai minggu kedua, ikan yang disuntik hormon HCG dengan dosis 500 IU memiliki jumlah oosit berukuran besar (0,8 -1,2mm) yang lebih banyak dibandingikan kontrol. Nilai IKG, IHS, fekunditas, dan LPH antara ikan kontrol dan perlakuan HCG menunjukkan nilai yang tidak berbeda nyata (P>0,05). Dari hasil yang didapat, dapat dibuktikan bahwa penyuntikan hormon HCG dengan dosis 500 IU/kg dengan frekuensi penyuntikan tiga kali dapat mempercepat perkembangan oosit ikan gabus betina.
Keyword: Channa striata, HCG, Ikan gabus, Perkembangan gonad.