Judul: Utilizing of preprocessing PCA on VFI5 algorithm
Abstrak: Nowadays, classification methods are growing rapidly. One of them is a Voting Feature Interval algorithm (VFI5). VFI5 is a supervised learning algorithm by generates a function that maps inputs to desired outputs. Phases of the training VFI5 algorithm produce the intervals. Large number of features can be effected many interval are produced, the other way little features can be effected less of interval. If data have large number of features, then the classification process is not efficiently. So, preprocessing data are needed by applying PCA for reducing feature dimension to make more effisien process. Data is used in this research, has three times repetition process using preprocessing PCA and without preprocessing PCA. Average best accuracy on Iris using preprocessing PCA is 92.67% and without PCA is 96%. Next, average best accuracy for data Wine using preprocessing PCA is 72.09% and without PCA is 93.22%, different with two previously data, New Thyroid have average best accuracy using preprocessing PCA is 91.72% and without PCA is 88.33%. Comparasion accuracy between data using preprocessing PCA and data without preprocessing PCA, only New Thyroid that using preprocessing PCA has higher accuracy than New Thyroid without preprocessing PCA. This condition caused contribution value of data New Thyroid is spread out, not gather in first principal component. Keywords : machine learning, classification, PCA, voting feature interval.
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Judul: Pengenalan citra wajah menggunakan algoritme VFI5 dengan praproses principal components analysis
Abstrak: Pattern recognition is a scientific discipline for the classification of objects into a number of categories or classes. Today, one of the intensive researches in the field of pattern recognition is face recognition. In this research, we tried to perform face recognition by using Voting Feature Interval 5 (VFI5) classification algorithm. VFI5 algorithm is an algorithm that represents a description of a concept by a set of feature intervals and performs classification based on the voting feature. Features used in this research were generated from gray level face images. The face image dimension is 112×92 pixels which means there will be 10304 features which caused difficulties to achieve better accuracy with less storage and computational complexity. For that, Principal Components Analysis is used to reduce the dimensions. This is done by projecting an image on eigenface. Eigenface is obtained by projecting training data on eigen vector. Experiments were performed by using cross validation. Ten fold cross validation was used as cross validation technique with 10 repetition of single observation from the original sample as the validation data and the remaining as training data. The result of 10 observations subset are respectively 80%, 100%, 90%, 100%, 90%, 100%, 100%, 90%, 100% and 90%. Resulting in an average accuracy of 94%. We conclude the VFI5 algorithm good at recognizing face images.
Keyword: Image processing, Face recognition, Principal components analysis, VFI5 algorithm, Bogor Agricultural University, Institut Pertanian Bogor, IPB

Judul: Perilaku Reproduksi dan Karakteristik Mikrohabitat tempat Bertelur Burung Gosong (Megapodius reinwardtii Dummont 1823) Di Taman Buru Pulau Moyo
Abstrak: Burung gasang (Megapodius reinwardtii) merupakan jenis burung terestrial yang selalu berpasangan sepanjang hidupnya. Burung ini dilindungi berdasarkan SK Menteri Kehutanan No. 882/Kpts-II/1992 tanggal 28 September 1992. Penyebaran satwa ini hanya terbatas di wilayah Indonesia bagian timur.
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