Judul: Eksplorasi Metode Penentuan Nilai End Point pada Algoritma Voting Feature Intervals 5
Abstrak: Voting Feature Intervals 5 adalah salah satu algoritma klasifikasi yang merepresentasikan deskripsi sebuah konsep oleh sekumpulan interval nilai-nilai feature (Güvenir 1998). Klasifikasi menggunakan algoritma ini didasarkan pada vote dari nilai-nilai pada feature. Cara kerja algoritma ini yaitu membuat interval dari setiap feature menggunakan instance yang terdapat pada feature tersebut. Interval yang dibuat dapat berupa range interval atau point interval. Point interval terdiri dari seluruh end point semua feature secara berurut sedangkan range interval terdiri dari nilai-nilai antara dua end point yang berdekatan namun tidak termasuk kedua end point tersebut. Nilai vote setiap kelas akan disimpan pada setiap interval. Tujuan dari penelitian ini adalah untuk mengeksplorasi metode penentuan nilai end point pada algortima VFI5. Pengeksplorasian nilai end point pada algoritma VFI5 dilakukan dengan cara menempatkan kembali end point yang lebih merepresentasikan batas setiap kelas. Penempatan end point itu dilakukan dengan berbagai cara, antara lain : metode desil dan metode min(n+1) max(n-1).
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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: Hydroxyapatite-Chitosan Composite Coating on Stainless Steel 316 to Improve Corrosion Resistance
Abstrak: Metals that were used in bone implantation should have good corrosion resistance that were not easily corroded when implanted into the body. Metal corrosion resistance of stainless steel 316 can be enhanced through the metal coating by hydroxyapatite-chitosan composite with composition ratio of hydroxyapatite (gram) and chitosan 3% (mL) 0.2:0, 0.2:0.1, 0.2:0.5, 0.2:1.0, and 0.2:1.5. Besides improving the corrosion resistance of metal, the presence of chitosan also maintain the position of hydroxyapatite on metal surfaces. Corrosion resistance increases with increasing amount of chitosan added and the best composition of hydroxyapatite-chitosan composite was 0.2:1.0.
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