Judul: Sistem Pakar Prediksi Produktivitas Padi Pandan Wangi Menggunakan Logika Fuzzy
Abstrak: The increase of superior commodity production value is Indonesian government's efforts to survive in competitive trade competition. Superior commodity that has become the hallmark of Indonesia is beras pandan wangi. This commodity has a distinctive taste and fragrance, well-known in regional, national and international markets. Expert system can help the effort in order to run effectively and efficiently by predict pandan wangi paddy productivity. This system has three inference systems, the first is to determine the condition of the land with the input level of nitrogen, phosphorus, potassium and acidity level of the soil, the second is to determine the environmental conditions with rainfall input, land elevation and irrigation (water discharge). The second output of the inference will become an input for the third inference system which has pandan wangi paddy productivity as the final output. This expert system using mamdani fuzzy logic model and mean of maximum (MOM) for defuzzification. This expert system has an accuracy of 61.72%, 62.96% and 88.88% for the first, second, third inference system respectively.
Keyword: Bogor Agricultural University (IPB), productivity of pandan wangi, MOM, mamdani, fuzzy logic

Judul: Pemodelan Sistem Pakar Berbasis Komputasi Lunak untuk Evaluasi Kesesuaian Lahan Komoditas Serealia: Studi Kasus Kabupaten Bogor
Abstrak: Nowadays, the threat of food shortages are happen in Indonesia. Most of crops are cereals commodities. Cereals cultivation often experience some problems in defining if the land is good or not for the plant. Expert system can help researcher and practitioners to choose the suitable kind of cereals for land. In this research, a model of fuzzy rule within fuzzy inference system using artificial neural network and genetic algorithm was built. The model was used to develop an expert system of land suitability for cereals commodity. This model implement soft computing which combine fuzzy system, artificial neural network and genetic algorithm. In this research, an expert system model of land suitability for cereals plant were built. The model implement soft computing methods for inference engine which combine fuzzy system and genetic algorithm. There are 16 parameters to define the land suitability which consists of 12 numeric parameters and 4 categorical parameters. The kind of cereals that will be used are wetland paddy and corn. Trapezoid membership functions was used here. Genetic algorithm was used for tuning the membership function of fuzzy for land suitability which is consist of very suitable (S1), quite suitable (S2), marginal suitable (S3) and not suitable (N). This research has produced a model of soft-computing-based expert system. The experimental result by entering some of test cases in the system and compared with the results of the expert show that the system was built in accordance with the results of the expert with accuracy 85% for rice and 90% for corn. Errors of land suitability class grouping occurs because the input value was on the edge of the membership function. We suggest the usage of actual data for future research, so that the output of the model appropriate to real condition. Each parameter should be weighted based on its importance.
Keyword: cereals, expert system, fuzzy, genetic algorithm, land suitability evaluation, soft computing

Judul: Infeksi Cacing Jantung Dirofilaria Immitis, Railliet & Henry, 1911 Pada Anjing
Abstrak: Cacing Dirofilaria immitis dewasa yang tinggal dalm ventrikel kanan jantung anjing, sering kali menimbulkan gejala klinis yang cukup berat. Cacing Tersebut menghasilkan mikrofilaria yang selanjutnya berkembang menjadi larva infektif dalam saluran malphigi induk semang entara, yaitu nyamuk dari genus Culex, Aedes dan Anopheles.
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