Judul: Pemutuan belimbing manis dengan citra pelatihan tunggal menggunakan algoritme VF15 berbasis histogen warna
Abstrak: Information on starfruit grading is necessary to professional distributor to give the best service. One of modern technique is digital image processing which using image and manipulating it to get one or more information. RGB, HSV, and YCbCr are standard models used in various color imaging applications. This research uses histogram of RGB, HSV, and YCbCr models and to show that not all of information is effective to classify starfruit. Image histogram is the basic for numerous spatial domain processing technique. Image histogram is used as input to Voting Feature Interval 5 (VFI5) algorithm. Histogram will be divided into several intervals and On training stage, it will be determined vote value from single training image per class. The vote value is used to classify testing data. This research shows that R, H, and Cr give the best accuracy to classify testing data with accuracy reaches 90.20%.
Keyword: Starfruit classification, color models, Voting Feature Interval 5 (VFI5), single training image

Judul: Pengembangan hidden markov model untuk pengenalan tingkat kemanisan buah belimbing
Abstrak: Starfruit (Averrhoa Carambola L) is one of agroproducts which is useful for many people. The fruit looks like a star when it is sliced athwartly. In post-harvest, one of the most important treatment is to classify the fruits based on its sugariness. This treatment aim is to give the best quality information of fruits. In this research, the characteristics of starfruit that can be used to indicate its sugariness is the color intensity. Fruits are grouped by size and ripe phase. In each group, starfruit pictures are analyzed based on their components, (red), (green), and (blue). These components are used to determine the total soluble solids using linear regression. Then the total soluble solids are used in order to determine the sugariness of the fruits. Certainly, an intelligent method is needed to solve this problem. Hidden markov model (HMM) is one the most popular method to recognize some instance in pattern recognition. By using HMM, the sugariness of the fruits can be detected. In order to compare accuracy of HMM, this research uses some different HMMs based on its sum of the hidden state. Finally, conclusion of this research is that the HMM gives good result to recognize the starfruit sugariness and it reaches the maximum accuracy of 75%.
Keyword: Starfruit, Linear regression, Hidden markov model(HMM), Bogor Agricultural University

Judul: Kajian Penggunaan Zeolit dan Kalsium Oksida sebagai Sumber Pemanas Kemasan Kaleng Panas Sendiri (self-heating can).
Abstrak: Penelitian ini mengkaji penggunaan zeolit sebagai alternatif sumber pemanas pada kemasan kaleng panas sendiri yang umumnya menggunakan kalsium oksida. Pada penelitian ini telah dilakukan proses kalsinasi pada suhu 550 oC selama 24 jam terhadap zeolit dan kalsium oksida untuk pembakaran awal dengan tujuan menghilangkan zat-zat yang tidak diinginkan. Kemudian telah dilakukan pengukuran suhu reaksi campuran sejumlah sampel dengan sejumlah air pada masing-masing perbandingan. Hasil penelitian menunjukkan reaksi air dengan kalsium oksida menghasilkan suhu tertinggi pada perbandingan 3:1 dengan ratarata perubahan suhu 46.37 oC. Sedangkan reaksi air dengan zeolit menghasilkan suhu tertinggi pada perbandingan 1:2 dengan rata-rata perubahan suhu 39.27 oC. Massa kalsium oksida yang digunakan 5.00 gram dan zeolit yang digunakan 28.00 gram. Kemudian telah dilakukan pengukuran perubahan volume sampel yang direaksikan dengan air, hasilnya kalsium oksida dan air memiliki kenaikan volume 1.00 ml sedangkan zeolit memiliki kenaikan volume 2.00 ml. Dari hasil penelitian ini dapat disimpulkan bahwa zeolit ternyata belum mampu untuk menggantikan kalsium oksida sebagai sumber pemanas self-heating can dari aspek kapasitas panas yang dihasilkan dan nilai ekonomis penggunaan bahan.
Keyword: kaleng panas sendiri, kalsium oksida, kemasan aktif, sumber pemanas, zeolit