Judul: Identifikasi Jenis Aglaonema Menggunakan Probabilistic Neural Network
Abstrak: Aglaonema is an ornamental plant that is quite popular in Indonesia. It is estimated that there are nearly 8000 species Aglaonema in the world both native and hybrid. The many types of Aglaonema in the world causes difficult in identifying some types of Aglaonema. This research attempts to identify the type of Aglaonema based on the image using Probabilistic Neural Network (PNN). The data used in this study have 900 images of leaves, which consists of 30 types of Aglaonema. Each image of Aglaonema that will be identified by the system will first be subjected to two stages, texture feature extraction and colour feature extraction. The texture feature extraction used Local Binary Pattern Variance (LBPV) and Co-occurrence Matrix while the colour feature extraction used Histogram-162 (HSV-162). We perform two type of experiment, one where we uses each feature seperately and another where the two features are combine. For classifier we used Probabilistic Neural Network (PNN). The results indicates that the combination of Co-occurrence Matrix with HSV-162 yield a better accuracy compact two when the feature are used seperately. On the other hand, the combination of Local Binnary Pattern Variance (LBPV) and HSV-162 does not yield an increase in accuracy, however the accuracy on this case is better than the accuracy of the combination between Co-occurrence Matrix and HSV-162. The highest accuracy is obtained in the case of the Local Binnary Pattern Variance (LBPV) and HSV-162 with the value of 55.56%
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Judul: Identifikasi tanaman hias secara otomatis menggunakan Metode Local Binary Patterns descriptor dan Probabilistic Neural Network
Abstrak: Identification house plant species automatically still be a problem for recognizing various kind of house plants. This research proposes a new method for identifying a house plant using Local Binary Patterns (LBP) descriptor and Probabilistic Neural Network (PNN). There are three kinds of LBP descriptor used in this research, i.e. , , and . Database of 300 house plant images belong to 30 different types of house plant in Indonesia are extracted using , , and based on texture feature and classified using PNN. The experimental result shows that has the best accuracy to indentify house plants with accuracy 73.33%. The proposed system is promising since it is capable to identify house plants species efficienty and accurately
Keyword: Plant extraction, Local binary patterns, Texture feature, Probabilistic neural network, Bogor Agricultural University, Institut Pertanian Bogor, IPB

Judul: The Role of Leaders in Collective Action for the Poor Urban Communities during the Covid-19 Pandemic (Case: An RW in West Bogor District, Bogor City, West Java)
Abstrak: Wabah pandemi Covid-19 sebagai bencana non alam menjadi faktor risiko bagi masyarakat terlebih bagi komunitas miskin. Upaya untuk mencegah serta menanggulangi pandemi Covid-19 ini perlu didukung dengan aksi bersama komunitas serta didorong oleh kepemimpinan yang kuat. Peranan kepemimpinan dalam mengelola bencana selama dan setelah situasi krisis dapat mendorong komunitas berperan aktif dan tangguh di masa pandemi melalui penguatan aksi kolektif. Penelitian ini menganalisis peran pemimpin pada aksi kolektif komunitas miskin perkotaan di masa pandemi Covid-19. Pengumpulan data dilakukan dengan metode survei pada 60 responden yang diperkuat dengan wawancara mendalam. Hasil penelitian menunjukkan terdapat hubungan signifikan antara peran pemimpin dengan tingginya tingkat aksi kolektif pada komunitas miskin perkotaan selama Pandemi Covid-19. Pemimpin yang dinilai mampu menggerakkan warga komunitas untuk berpartisipasi aktif pada setiap aksi kolektif selama masa pandemi Covid-19 adalah tokoh Ketua RW, The Covid-19 pandemic as a non-natural disaster is a risk factor for the community, especially for the poor. Efforts to prevent and overcome the Covid-19 pandemic need to be supported by joint community action and strong leadership. The leadership role in managing disasters during and after crisis situations can encourage active and resilient communities during a pandemic through supportive collective action. This study analyzes the role of leaders in the collective action of urban poor communities during the Covid-19 pandemic. Data was collected using a survey method on 60 respondents who conducted interviews. The results showed that the relationship between the role of leaders and the level of collective action in urban poor communities was significant during the Covid-19 pandemic. The leader who is considered capable of mobilizing the community to actively participate in every joint action during the Covid-19 pandemic is the leader of the RW
Keyword: collective action, poor communities, the role of leaders