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: Model Prediksi Capaian Akademik Mahasiswa Program Sarjana Ilmu Komputer IPB dengan Algoritme C5.0
Abstrak: Penelitian ini mengembangkan prediksi capaian akademik mahasiswa Program Sarjana Ilmu Komputer IPB di tingkat 2 berdasarkan capaian di tingkat 1 dengan pohon keputusan menggunakan algoritme C5.0. Data yang digunakan ialah nilai mahasiswa yang masuk pada tahun 2012 hingga 2014 pada mata kuliah mayor dan interdepartemen. Penelitian ini menggunakan 2 kelas (low risk dan high risk) dan 3 kelas (low risk, medium risk, dan high risk). Jumlah data tidak imbang sehingga penelitian ini menggunakan metode undersampling dan oversampling. Penelitian ini juga melihat keterhubungan antara mata kuliah dan prasyaratnya dengan menggunakan korelasi Spearman dan pohon keputusan algoritme C5.0. Hasil uji model prediksi menghasilkan akurasi terbaik sebesar 94.92% untuk percobaan 3 kelas (2.76 ≤ IPK ≤ 4.00 untuk kelas low risk, 2.00 ≤ IPK< 2.76 untuk kelas medium risk, dan 0 ≤ IPK< 2.00 untuk kelas high risk). Percobaan tersebut menggunakan metode oversampling. Korelasi Spearman menunjukkan adanya keterhubungan antara mata kuliah dan prasyaratnya dengan nilai korelasi 0.308 - 0.839.
Keyword: Algoritme C5.0, keberhasilan studi, klasifikasi, korelasi Spearman, pohon keputusan

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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