Judul: Pengembangan Prototipe Spatial Data Mining Untuk Karakterisasi Desa Miskin di Jawa Barat
Abstrak: Data mining, atau secara populer dirujuk sebagai Knowlegde discovery in databases (KDD) adalah teknik yang secara otomatis. melakukan ekstraksi pola yang merepresentasikan pengetahuan yang secara implisit tersimpan dalam basisdata besar, data warehouse, dan tempat penyimpanan yang masif lainnya. Spatial data mining berurusan dengan pencarian pengetahuan dalam basisdata spasial.Salah satu teknik dalam spatial data mining adalah karakterisasi spasial (spatial characterization). Karakterisasi spasial digunakan untuk mengungkap karakteristik atau deskripsi ringkas spasial dari sifat-sifat yang menarik pada sebuah komunitas (obyek target) tetapi tidak untuk keseluruhan basisdata. Dalam penelitian ini dilakukan pengembangan prototipe sistem yang dapat menerapkan teknik spatial data mining untuk mengungkap karakteristik spasial dari kondisi kemiskinan di Jawa Barat. Prototipe yang dihasilkan terdiri dari empat komponen yaitu modul pengolahan awal, modul spatial data mining, modul basisdata dan modul visualisasi. Modul spatial data mining tersusun atas neighbourhood graph, predicate filter, neighbourhood index dan algoritma karakterisasi spasial.
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Judul: Penerapan Regresi Spasial pada Data Kemiskinan Kabupaten/Kota di Provinsi Jawa Barat Tahun 2012
Abstrak: Poverty is a social problem that normally occurs in the middle of society, especially in developing countries. One important aspect to support poverty reduction strategy is the availability of accurate poverty data. Good data can be used to evaluate government policies on poverty, compare poverty between regions and determine the targeted destitute population with an am to improve their living conditions. One effort to overcome poverty is to identify the variables that affect it. In this research, poverty issue is examined by percentage of poor people districts/cities in West Java Province in 2012, making use of spatial regression analysis. The analysis showed that the spatial lag models (SAR) produced better allegation parameters than classic regression models in case of spatial correlation. This can be seen from the smaller AIC of SAR models (129.62) than classic regression models (133.06). In SAR models, variables that significantly affect the percentage of poor people in district/cities are the percentage of households using clean water (X10) and households with floor area per capita (X21).
Keyword: Bogor Agricultural University (IPB), spatial lag models (SAR), Poverty

Judul: Insecticidal Activity of Essential Oils Leaves of Cinnamomum spp. (Lauraceae) against Crocidolomia pavonana and Their Phytotoxicity on Broccoli Seedlings
Abstrak: Essential oils from leaves of 13 species of Cinnamomum were tested for their insecticidal activity on the cabbage head caterpillar Crocidolomia pavonana with leaf immerse method. The results showed that the treatment with 1% (w/v) essential oils leaves of C. celebicum, C. multiflorum, C. sintoc, and C. verum caused 21.45, 93.65, 60.30, and 76.10% mortality, respectively. Based on the results of phytotoxicity and insecticidal activity tests, C. multiflorum oil was further tested at 6 concentration levels. In all treatments, larval mortality has occurred at 24 hours after treatment (HAT) and increased at 48 HAT. LC50 of C. multiflorum oil at 24 and 48 HAT was 0.504 and 0.396%, respectively. The treatment with C. multiflorum oil at ≤0.31% did not inhibit the development of C. pavonana larvae. Determination of the chemical constituents of Cinnamomum oils were carried out by gas chromatography-mass spectrometry (GCMS). Totally, 345 compounds were identified. Methyleugenol constituted the main component (49.4%) of leaf essential oil of C. multiflorum and was assumed to be responsible for the insecticidal activity of the oil against C. pavonana larvae. LC50 of pure methyleugenol obtained at 0.365% (48 HAT) and 0.363% (72 HAT). The results showed that C. multiflorum was more effective with LC50 value equivalent to methyleugenol content of 0.32% (24 HAT) and 0.24% (48 HAT).
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