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 Model Probabilistic Neural Networks untuk Sortasi Belimbing Manis Berdasarkan Citra RGB
Abstrak: Starfruits (Averrhoa Carambola L.) is one of agroproducts which is liked by most society. Typically it is seen like a star when sliced athwartly. The characteristics of starfruits type which pre-eminent are size big, colour draw, smooth fibre, water containment, and fresh sweet. One of the vital process in post-harvest treatment is sortation. Fruits are grouped according to size measure and ripe phase. Starfruits which marketed have to fulfill the standard which accepted by consumer widely, either in domestic market and also global market. This matter in order to give best quality and also improve product competitiveness by farmer. Determination of starftruits sweetness is generally based on fruit colour visually. But, change of colour gradually complicated determination the taste of fruit. This needs more intelligent sortation methods and tools that overcome the sortcomings of manual process. Probabilistic Neural Network (PNN) is one of Artificial Neural Network (ANN) variants that can be used to develop a computer-based sortation engine for starfruits. This research aim is to develop PNN model for starfruits sortation based on RGB image value. Conclusion at this research indicates that PNN can be used to classify starfruits with high accuration reach 92%.
Keyword: 

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