Judul: Prediksi Indeks Prestasi Mahasiswa Menggunakan Algoritma VFI5 (Studi Kasus Mahasiswa Program Mayor Minor Departemen Ilmu Komputer IPB)
Abstrak: Feature Voting Intervals 5 describes a concept by performing classification on each feature separately. VFI5 is a non-incremental algorithm in which all training instances are processed simultaneously. Two steps of VFI5 algotirhms are training and predicting steps. Training is used to explain the character and relationships between features, and predicting is used to test the pattern resulted from training step to get accuration value. This study applied VFI5 algorithm to predict GPA value of the following academic year based on feature which is value of current academic course. First step training was conducted to explain the distribution class of student’s GPA and the ability of college students, while the second step predicting can reveal opportunities to learn and know the GPA value that can be obtained by students in the following academic year. The set of data had been used for this research, the first is first year student data that predicted to second year student GPA from generation 2005/2006, second is second year student data that predicted to third year student GPA from generation 2005/2006, and the last is first year student data that predicted to second year data GPA from generation 2006/2007. In the training step, features on the first data which value equals to a given class are Ekonomi Umum and Pengantar Matematika, features on the second data which value equals to a given class are comonly all features, and the last data which value equals to a given class are Ekonomi Umum, Agama, Bahasa Indonesia, PIP, Pengantar Matematika and Pengantar Kewirausahaan. In predicting step, process had been done with and without GPA feature. As the result, we got accuration value in the first data with GPA feature is 45.68 %, and without GPA feature is 46.91 %. In the second data with GPA feature got value 49.56 % and without GPA feature got value 50.80 %. In the last data with GPA feature got value 60.28 % and without GPA feature got value 51.80 %. The result showed that there was a declining accuration as compared to the previous studies due to differences in the testing data and type of prediction.
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Judul: Prediksi Indeks Prestasi Kumulatif Mahasiswa Ilmu Komputer IPB Menggunakan Algoritma VFI5
Abstrak: First year evaluation point cannot show end year evaluation point that generate value, namely Grade Point Average (GPA), because GPA is a cumulative grade that take from first year evaluation point up to end year evaluation value. In this research, we want to know GPA without seeing GPA in every year, just look grade from the first year evaluation point and some courses feature that we can say GPA Prediction. In this research, we use VFI5 algorithm for GPA prediction. VFI5 algorithm have two processes, training process and prediction process. The training process output can describe feature character and relation between features that are courses feature and GPA relation.This research aims to analyze and looks for TPB courses that influence to GPA of Computer Science students, analyze with describing distributing point for each GPA classes, and predict GPA classes based on TPB courses. Training description is very important thing in this research. Training description can explain distributing points of courses grade. Distributing points is important thing for everyone, students and teachers. Students can see courses that influence to their GPA, otherwise teacher can look for the most succesful courses for their students. On General Sociology course, this research show that it is very difficult to get A grade. On File System and Mathematic Discret courses, with 2.00 GPA <2.50 class always have vote for A grade, and little possibility to get D grade. The highest accuration rate of 2001/2002 generation is found from combination between Fisika II, Calculus, and GPA of TPB features. Accuration rate that is resulted by VFI5 Algorithm on prediction process with the features is 70.61%. In otherwise that case is not same with 2002/2003 generation, because accuration rate that resulted is 60.03%. The similiarity between 2001/2002 and 2002/2003 generations is highest accuration rate for each features test showed from Fisika II feature. Accuration rate of fisika II feature on 2001/2002 generation is 65.35%, otherwise on 2002/2003 generation is 65.39%.
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Judul: Koksidiosis pada Sapi Disebabkan Eimeria Zuernii (Rivolta,1887)
Abstrak: Koksidia merupakan parasit intraseluler yang menyerang traktus digestiyus terutama bagian USUS. Penyebab penyakit ini adalah dari genus Eimeria. Koksidia yang paling patogen terhadap ternak sapi adalah Eimeria Ziuernii (Rivolta, 1887 dalam Todd dan Ernst, 1977). Siklus hidup Eimeria zuernii terdiri dari fase eksogen yang disebut juga dengan tahap sporogoni dan fase endogen yang terdiri dari tahap skizogoni dan tahap gametogoni (Soulsby, 1982).
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