Judul: Development of Mangosteen Maturity Classification Model on Color Based Using Fuzzy Neural Network.
Abstrak: Fuzzy Neural Network (FNN) has a capability to classify a pattern located within two different classes where a classical Neural Network (NN) is failed to do so. The fuzzy pattern classification is using membership degree on output of neuron as learning target. Objective of this research is to develop an artificial intelligence system model for non-destructive classification of fresh mangosteen using Fuzzy Neural Network. Component of color result in from image processing that influential against level of mangosteen’s maturity is used as input parameter. Percentage accuracy ratio of FNN model compare to NN for five, three, and two classification classes is 70:40, 86:65 and 90:90 respectively. The best result of FNN modeling is achieved on three class target classification (unripe, export and local) with green color index, value, a* u*, v*, entropy, contrast, energy and homogeinity as predictor parameters and 15 neurons hidden layer. Comparison of percentage capability of FNN against NN to identify the class is 100:0, 100:87 and 63:75.
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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%.
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Judul: Identifikasi Kejadian Banjir Rob (Pasang) di DAS Sunter pada 9-13 Januari 2008.
Abstrak: Banjir Rob merupakan fenomena meluapnya air laut ke daratan, istilah tersebut pertama kali dikenal di daerah Semarang. Fenomena banjir Rob dipengaruhi oleh pasang surut air laut. Pasang surut sendiri dipengaruhi gravitasi bulan dan matahari, serta dipengaruhi pula posisi bulan dan matahari terhadap bumi. Banjir ini biasa terjadi pada saat pasang air laut yaitu bertepatan dengan kejadian bulan baru dan bulan purnama. Pada bulan Januari 2008, di wilayah utara Jakarta yang berbatasan dengan laut, fenomena banjir Rob (pasang) melanda kawasan tersebut. Fenomena ini mengakibatkan sejumlah kawasan di wilayah utara Jakarta terendam air laut, hal ini diperburuk dengan penurunan muka tanah Jakarta. Selain dilanda banjir pasang air laut, kawasan utara Jakarta sering dilanda banjir akibat luapan sungai-sungai yang bermuara di Jakarta, salah satu sungai yang menyumbang banjir Jakarta adalah sungai Sunter. Tutupan lahan di DAS Sunter yang sebagian besar berupa pemukiman makin meningkatkan limpasan permukaan ketika terjadi hujan. Tanggal 9-13 Januari 2008 di perairan Teluk Jakarta sedang terjadi pasang air laut karena bertepatan dengan bulan baru yang mengakibatkan terjadinya banjir Rob di beberapa wilayah di Jakarta Utara, termasuk DAS Sunter. Berdasarkan pemetaan banjir Rob di DAS Sunter pada tanggal 9-13 Januari 2008 diketahui bahwa daerah yang tergenang air laut yaitu Kalibaru dan Marunda dengan tinggi genangan 48-72 cm. Luas genangan maksimum terjadi pada tanggal 11 Januari 2008 yaitu sebesar 66.000 m2. Pada tanggal 9-13 Januari, DAS Sunter tidak ikut menyumbangkan limpasan permukaan yang dapat menyebabkan banjir karena nilai limpasan permukaan berkisar 0-3,03 mm. Sehingga kejadian banjir pada tanggal 9-13 Januari 2008 di DAS Sunter hanya berasal dari pasang air laut. Kata Kunci : Banjir Rob, Pasang-Surut, Limpasan Permukaan, DAS Sunter.
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