Judul: Seleksi Fitur Menggunakan Fast Correlation Based Filter Pada Algoritma Voting Feature Intervals 5
Abstrak: Seleksi fitur adalah salah satu tahapan praproses klasifikasi. Seleksi fitur dilakukan dengan cara memilih fitur-fitur yang relevan yang mempengaruhi hasil klasifikasi. Seleksi fitur digunakan untuk mengurangi dimensi data dan fitur-fitur yang tidak relevan. Seleksi fitur digunakan untuk meningkatkan efektifitas dan efisiensi kinerja dari algoritma klasifikasi. Pada penelitian ini dilakukan seleksi fitur menggunakan Fast Correlation Based Filter pada klasifikasi data menggunakan algoritma Voting Feature Intervals 5. Penelitian ini bertujuan menganalisis kinerja seleksi fitur pada klasifikasi data menggunakan algoritma Voting Feature Intervals 5. Penelitian ini akan membandingkan tingkat akurasi data pada algoritma klasifikasi Voting Feature Intervals 5 jika sebelumnya dilakukan seleksi fitur dan tanpa dilakukan seleksi fitur. Data yang digunakan pada penelitian ini memiliki dimensi yang beragam.
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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: Hydrothermal Synthesis of Biphasic Calcium Posphate
Abstrak: Biphasio calsium phosphate (BCP), which was a mixture of hydroxypatite (HA) and tricalcium phosphate (TCP), was synthesized by hydrothermal method followed by sintering process. Hydroxyapatite was produced by using natural calcium source CaO from hen's eggshell and (NH4)2HPO4 as phosphate souce, other hydroxyapatite sample was produce by using synthetic calcium source Ca(OH) and H3PO4 as phosphate source.
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