Judul: Prediksi Tingkat Keberhasilan Mahasiswa Tingkat I IPB Dengan Metode k-Nearest Neighbor
Abstrak: Analysis of academic data and personal data of first-year students in IPB is necessary to predict the successful of their study in the end of the first year. One of techniques in classification that can be used for completing that task is k-nearest neighbor that will build a classifier. This research aimed to develop classifier to predict the successful of first year students at IPB. The attributes used in this research are selected based on target class-influenced statistic hipotesis test. Chi-square test is implemented for nominal attributes whereas Spearman Rank Correlation Coeficient test is used for selecting the numerical attribute. The result of this research is a classifier with accuracy 52.97%.
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Judul: Penentuan Tingkat Keberhasilan Mahasiswa Tingkat I IPB Menggunakan Induksi Pohon Keputusan dan Bayesian Classifier
Abstrak: Tingkat Persiapan Bersama (TPB) or Collective’s Preparation Level is the term that is used to call the first-year bachelor degree students in IPB (Bogor Agricultural University). IPB decides these first-year students graduation from their academic result in the end of TPB year. The students can continue to the next education year if they complete many graduation requirements that was decided before. The success of the first year IPB’s students can be looked from their academic graduation result. One of techniques can be used to determine student success is data mining. Data mining is used to build classifier that shows the success level of the first year student of IPB. This research aimed to develop classifier to describe the level of IPB’s students success and predict the new student of IPB. This research uses two data mining method, that is decision tree and Bayesian classifier (naïve Bayes). Decision tree is used to describe the level of IPB’s students success and to get crusial factor that determine IPB’s students success in their first year. Naïve Bayes is used to predict the new student of IPB. The results this research are a probabilistic classifier naïve bayes with accuracy 57,160 % and a decision tree classifer that contains 3 classifiaction rules with accuracy 63,542%. According to the rules that we got from decision tree, the factor that influence the first year IPB student 2007/2008 is point of high school final examination.
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Judul: Pengaruh Asam Azelat Pada Bio Gemuk Litium Kompleks Berbasis Minyak Kelapa Sawit (CPO) Terepoksidasi
Abstrak: Gemuk merupakan pelumas semipadat dengan viskositas tinggi, berupa koloid padat-cair yang terbuat dari minyak dasar dan bahan pengental. Gemuk litium kompleks merupakan gemuk yang umum beredar di pasaran. Gemuk litium kompleks adalah jenis gemuk yang menggunakan kompleks sabun litium sebagai pengental utama. Penelitian ini bertujuan agar didapatkan komposisi produk gemuk bio litium kompleks yang mempunyai kualitas lebih baik dan didapatkan hubungan antara penambahan agen pengompleks terhadap penampakan visual dan tekstur. Proses pembuatan gemuk terdiri dari epoksidasi CPO, saponifikasi dan homogenasi. Kinerja gemuk yang dihasilkan dapat dilihat melalui uji dropping point. Hasil penelitian menunjukan bahwa gemuk bio litium kompleks dengan dropping point tertinggi sebesar 207 derajat celcius pada penambahan asam azelat sebanyak 2,5%.
Keyword: Dropping point, Epoksidasi CPO, Gemuk, Saponifikasi