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: Evaluasi Capaian Akademik Mahasiswa  Sekolah Vokasi IPB Menggunakan Regresi Logistik Biner Multilevel
Abstrak: Sekolah Vokasi IPB didirikan pada tahun 1979. Program diploma ini memiliki visi menjadi Sekolah Vokasi yang terdepan dan berkelas Internasional. Tercapainya tujuan tersebut dapat dilihat dari indeks prestasi kumulatif setiap mahasiswa. Oleh karena itu, penelitian ini mencatat data nilai IPK setiap mahasiswa setiap tahunnya. Data ini disebut data longitudinal dan cara melakukan pemodelannya adalah menggunakan model regresi linier multilevel. Eksplorasi data peubah respons pada penelitian ini menunjukkan tidak berdistribusi normal sehingga salah satu solusinya adalah dengan membuat model regresi logistik biner multilevel. Oleh karena itu, penelitian ini bertujuan membangun model regresi logistik biner multilevel pada data siswa Sekolah Vokasi IPB dan menduga komponen ragam IPK mahasiswa Sekolah Vokasi IPB. Hasil model yang terbentuk menunjukkan bahwa faktor-faktor yang memengaruhi IPK pada level satu adalah semester dan SKS per semester, kemudian pada level dua adalah jenis kelamin, jalur masuk, dan status beasiswa. Model terbaik mencakup ragam intersep acak pada tingkat mahasiswa dan tingkat program studi. Selain itu, model terbaik juga menunjukkan ragam kemiringan acak semester pada tingkat mahasiswa serta semester dan jalur masuk pada tingkat program studi., IPB Vocational School was established in 1979. This diploma program aims to become a leading and international-class Vocational School. The achievement of this goal can be seen in each student's cumulative grade point average. Therefore, this study records the GPA value data for each student every year. This data is called longitudinal data, and the way to do the modeling is using the multilevel linear regression model. The data exploration of the response variable in this study shows that it is not normally distributed, so one of the solutions is to make a multilevel binary logistic regression model. Hence, this research aims to build multilevel binary logistic regression model on IPB Vocational School student data and estimate the variance components of IPB Vocational School students' GPA. The results of the model formed show that the factors that influence GPA on level one are semester and credits per semester, and then on level two, they are gender, entry route, and scholarship status. The best model included the variance of random intercept on the student level and study program level. It also shows the variance of random slopes of semesters at the student level as well as semester and entry route at the study program level.
Keyword: binary logistic regression, GPA, longitudinal data, multilevel regression, study achievement

Judul: Ornithonyssus Bursa (Berlese, 1888) Famili Macronyssidae yang Menyerang Unggas Dan Penaggulangannnya
Abstrak: Ornithonyssus bursa adalah tungau penghisap darah pada unggas di daerah tropis dan subtropis. Tungau ini relatif kecil, dengan panjang tubuh antara 700-1000 mikron dan lebarnya 250-490 mikron. Tubuhnya bulat berwarna coklat kekuningan sebelum menghisap darh dan berwarna merah setelah menghisap darah dari induk semangnya.
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