Judul: Sistem Penunjang Keputusan Intelijen Pengendalian Produktivitas Pada Industri Susu
Abstrak: The government campaigned “Milk Drink Campaign”, so as to make it succesful, the milk industry must increase their production. But the national milk production fulfiled 25% from the national milk needed so the other 75% is by import. It can happen by inefficiency to use resources that can make minimum production and this reason are decreases productivity. Because of that, the milk industry needs a tool to facilitate to count and control productivity. The objective of this research is to design, expand, and apply intelligent decission support system for controlling the productivity in the milk industry, to integrate genetic algoritms and expert system in this Intelligent Decission Support System for solve the productivity problem, and to the integration approach productivity that are labor, material, energy, machine and other expense productivity in the intelligent decision support system. The name of the intelligent decision support system of controlling productivity in milk industry is IDSS_MP. This model consists of 9 models that are base model of total output, labor productivity, material productivity, energy productivity, machine productivity, of other expense productivity, total productivity, decision quantity from packaging labor, and model of consultation from expert. The verification of this base model used data from January until June 2008. The quantity selling in June 2008 was chilled milk 3.488.669 kg, prepack milk 188.739 pack and cup milk 387.662 cup. The objective from the base model of total output was to know the total income in one month. The result in June 2008 is Rp 12.096.903.190. The objective from the base model of labor productivity was to know the value of labor productivity and this index. The labor in MT KPBS consists of 12 levels and 8 head levels that consist of 3 parts those are candidate labor, contract labor and permanent labor with a different salary. The result of that the value of labor productivity in June 2008 was 175,175 with index 1,062. The objective from the base model of material productivity was to know the value of material productivity and this index. This material used 3 products. In the end of Mei 2008, the price of fuel had been increased so that it could influence the price of material that increased 20%. The result of that the value of material productivity in June 2008 was 1,191 with index 1,077. The objective from the base model of energy productivity was to know the value of energy productivity and this index. This energy was fuel (diesel fuel and diesel industry), electricity and water. In the end of Mei 2008, the price of fuel was increased so that it influenced the price and quantity in use. The result of that the value of energy productivity in June 2008 was 37,052 with index 0,965. The objective from the base model of machine productivity was to know the value of productivity and index productivity of prepack machine and cup machine whether operation machine was good or not. The result of that the value of prepack machine productivity was 0,970 with index 0,997 and the value of cup machine productivity was 0,982 with
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Judul: Sistem Intelijen Penilaian dan Prediksi Kualitas Susu Pasteurisasi (Studi Kasus pada PT . Industri Susu Alam Murni Bandung)
Abstrak: The aim of this research is developing an intelligent system for assessment and prediction of pasteurized milk quality. The system is implemented by integrated expert systems and Artificial Neural Network (ANN). This system is designed and developed by Matlab 6.5 and called SINKUAL-SP. The logical strategy model used is “Forward Chaining” and the tracing method used is “Best First Search” method. The method of Certainty Factor (CF) is used for handling uncertainty. Model Based Management system consist of five model : quality of fresh milk model, quality of process model, quality packaging and storage model, statistical process control model and prediction model by neural network. Multilayer system is used for the neural network architecture with one input layer, one hidden layer dan one output layer. The activation function is Sigmoid Bipolar activation function which gives the best performance network with learning rate 0.005 and momentum 0.9 together with RMSE, MSE and SSE as criterion error. The validation for neural network indicates the conformity between output of neural network and output of goal with value of RMSE 0.009959, MSE 0.00999 and SSE 0.009950. Decision for acceptance fresh milk and pasteurized of milk is implemented by acceptance grade which gives control quality process is easier than previously system. Based on the grade acceptance, the management of industry can take decision and action allow with a significant output of grade. In the research implementation stage, the data, gained in the company of “PT. ISAM” in April 2005, brings a significant output of research regarding quality of fresh milk such as the grade B, grade A for quality of process, and grade B for quality packaging and storage. This system gives a suggestion for user to improve the quality of pasteurized milk. Based on the outputs from the systems used, highly priority strategy quality system reconstruction, with 0.236 as weighting value. The quality improvement system upon the results of research is seemed to be a way of improving the quality of milk in process of pasteurization.
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Judul: Posisi Filogeni Orlitia borneensis dan Hieremys annandalei (Testudines) Berdasarkan Gen 16s rRNA
Abstrak: Urutan DNA mitokondria (mtDNA) banyak digunakan sebagai penanda molekuler dalam menentukan hubungan filogeni suatu organisme karena struktur organisasinya yang stabil. Seiring dengan kemajuan bioteknologi, pengelompokkan kura-kura Bataguridal masih menjadi perdebatan. Hal ini disebabkan oleh perbedaan karakter yang diamati. Beberapa hasil penelitian mengelompokkan kura-kura ini ke dalam famili Emydidae tetapi ada yang menempatkan kurakura ini ke dalam famili tersendiri yaitu famili Bataguridae. Pada penelitian ini digunakan urutan nukleotida gen 16S rRNA O. borneensis dan H. annandalei (Testudines) yang disejajarkan dengan urutan nukleotida beberapa spesies Testudines lainnya yang terdapat di Genbank. Hasil rekonstruksi pohon filogeni berdasarkan urutan nukleotida 16S rRNA menunjukkan bahwa O. borneensis dan H. annandalei berkerabat dengan Chinemys reevesi (famili Bataguridae). Hasil amplifikasi gen 16S rRNA O. borneensis dan H. annandalei menunjukkan komposisi nukleotida didominasi oleh nukleotida adenin dan timin dengan nilai perbandingan antara A/T dan G/C = 60.1%:39.9% untuk O. borneensis dan 63%:37% untuk H. annandalei. Rasio transisi terhadap transversi (ts/tv) gen 16S rRNA berkisar antara 0.67 sampai dengan 6.75. Nilai jarak genetik terkecil terjadi antara Trachemys scripta dan Chrysemys picta dan antara Dogania subplana dan Tracemys scripta yaitu sekitar 0.005 sedangkan nilai jarak genetik terbesar terjadi antara Pelomedusa subrufa dan Emydura subglobusa yaitu sekitar 0.869.
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