Judul: Penggabungan ciri morfologi, tekstur, dan bentuk untuk identifikasi daun menggunakan Probabilistic Neural Network
Abstrak: This research proposes a new system for leaf identification using classifier combination and probabilistic neural network. The features that used for classification are morphology, texture, and shape of leaves. Classifiers combination is used to combine these features. The method to extract texture and shape are co-occurrence matrix and Fourier descriptors, respectively. After feature extraction, the feature is classified using probabilistic neural network. The experiment data consist of thirty species flora from Bogor Botanical Garden, Indonesia. The experiment results show that classification without classifier combination has the accuracy of 79.05%, and using classifier combination the accuracy increased to 83.33%. Hence, the propose system is promising for leaf identification and supporting plant biodiversity in Indonesia..
Keyword: Leaf identification; Probabilistic Neural Network; Classifier combination, Probabilistic Neural Network, Classifier combination, Bogor Agricultural University, Institut Pertanian Bogor, IPB

Judul: Identifikasi Tumbuhan Obat Menggunakan Fitur Citra Morfologi, Bentuk, dan Tekstur dengan Klasifikasi Probabilistic Neural Network
Abstrak: Indonesia has no less than 2039 species of medicinal plants, but only 20-22% was cultivated, while around 78% obtained through direct collection (exploration) of forest. Generally, identification process of medicinal plants has been done manually by the herbarium taxonomist using guidebook of taxonomy/dendrology. It will be a difficult problem to identify directly it in the forest. Leave is usually used for identification because of its effectiveness and efficiency. This research proposed a new system to identify the medicinal plant leaves use Probabilistic Neural Network classification and to determine the character and specific classes of leaves regarding the best features. The method of identification is basic and derivative feature of leaf, Local Binary Patterns Variance, and Fourier Descriptors. After that, the features are classified by probabilistic neural network classifier which is combined Product Decision Rule (PDR). Then, the result will be analyzed based on overall effectiveness in class and image characteristics. The data of experiments consist of thirty species of flora at Biofarmaka Cikabayan and at Greenhouse Center of Ex-situ Conservation of Medicinal Indonesian Tropical Forest Plants, Faculty of Forestry, Bogor Agriculture University. The results without classifier showed that combination might only have a maximum until 63% accuracy, and if the three classifiers were combined with PDR, the accuracy will increase until 74.67%. Shape is feature with higher probability than morphology and texture feature.
Keyword: 

Judul: Penetapan Umur Simpan Telur Asin Rendah Kolesterol pada Penyimpanan Suhu Ruang
Abstrak: Pemanfaatan telur bebek sebagai bahan pangan salah satunya yaitu telur asin. Namun, muncul kesadaran masyarakat akan tingginya kandungan kolesterol pada telur asin sehingga dikembangkan inovasi telur asin rendah kolesterol. Produk telur asin di pasaran memiliki umur simpan pendek tetapi belum diidentifikasi parameter penentu kerusakan produk. Penelitian ini bertujuan untuk mengetahui perubahan mutu dan menetapkan umur simpan telur asin selama penyimpanan suhu ruang. Penelitian ini dilakukan dengan membuat telur asin kemudian dilakukan pengemasan vakum, lalu disimpan hingga terjadi kerusakan pada suhu ruang. Penentuan umur simpan dilakukan dengan metode extended storage studies berdasarkan parameter kadar air, pH, kadar kolesterol, kecerahan, organoleptik, serta uji total plate count. Hasil karakteristik penyimpanan hari ke-10 telur asin diperoleh nilai tingkat kecerahan putih telur sebesar 25,96, kecerahan kuning telur 25,98, pH 7,19, kadar air putih telur 84,64%, kadar air kuning telur 38,72%, dan total mikroba 4,1 × 105 cfu/mL. Kadar kolesterol sebesar 233,39 mg/100 g pada hari pertama dan mengalami penurunan menjadi 131,61 mg/100 g pada hari kesepuluh. Mutu telur asin mengalami perubahan yang mengarah pada kerusakan selama penyimpanan suhu ruang. Telur asin tanpa kemasan memiliki umur simpan hingga 9 hari dengan total mikroba sebesar 1,0 x 105 cfu/mL., The utilization of duck eggs as a food ingredient includes salted eggs. However, public awareness of the high cholesterol content in salted eggs has led to the development of low-cholesterol salted egg innovations. Salted egg products in the market have a short shelf life, but the determining parameters for product spoilage have not been identified. This research aims to understand the quality changes and establish the shelf life of salted eggs during room temperature storage. The study involves producing salted eggs, vacuum packaging them, and then storing them at room temperature until spoilage occurs. The determination of the shelf life is based on extended storage studies, considering parameters such as moisture content, pH, cholesterol content, brightness, organoleptic, as well as a total plate count test. The characteristic results after 10 days of storage show that the brightness of the egg whites is 25.96, the brightness of the egg yolks is 25.98, pH is 7.19, the moisture content of egg whites is 84.64%, the moisture content of egg yolks is 38.72%, and the total microbial count is 4.1 × 105 cfu/mL. The cholesterol content decreases from 233.39 mg/100 g on the first day to 131.61 mg/100 g on the tenth day. The quality of salted eggs undergoes changes that lead to spoilage during room temperature storage. Unpackaged salted eggs have a shelf life of up to 9 days with a total microbial count of 1.0 x 105 cfu/mL.
Keyword: Duck eggs, Low cholesterol, Salted eggs, Shelf life