Judul: Pemilihan Data Training untuk Meningkatkan Kinerja Voting Feature Interval 5 (VFI 5)
Abstrak: Voting Feature Interval 5 (VFI 5) is a supervised algorithm and an inductive learning algorithm for inducing knowledge classification from training information. VFI 5 algorithm is capable of classifying sample very well and can provide an explanation why and how the class groups of new samples from the classification can be predicted in the individual vote that each feature has been assigned to the class. VFI 5 algorithm determines the point interval for the classification process. Point interval is obtained by taking the lowest and the highest value of the sample in each class. In the testing process, if the test data are outside the sample interval they will have zero voting value and will reduce the accuracy of the classification results. The selection of training data with non-random sampling method uses the purposive sampling technique. The selection process is done by taking a few of the lowest and the highest feature values from each feature data to be training data. The remaining data which are not used as training data will be used as testing data. The propotion of training and testing data is 2:1. Among the three data used in the VFI 5 algorithm with the selection training data using the lowest and highest feature values, the iris data produced an accuracy of 98.04%, the accuracy of wines data is 96.56% and the acuracy of gender koi data is very high reaching 100%. The result of this study shows that the algorithm VFI 5 data selection method using the lowest and the highest feature values can improve the performance of the algorithm VFI 5.
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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: Perencanan Lanskap Sunda Kalapa, Jakarta Utara Sebagai Kawasan W isata Sejarah
Abstrak: Perkembangan kota Jakarta dari waktu ke waktu sangatlah cepat, sehingga menyebabkan kota-kota tua banyak yang tergusur untuk dijadikan daerah baru yang lebih modern. Apabila hal tersebut tidak ditanggulangi secara dini maka keberadaan Kota Lama Jakarta akan hilang. Hal inilah yang melatar belakang untuk dilakukan tindakan perlindungan dan revitalisasi kawasan bersejarah Sunda Kalapa.
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