Taufik Hidayatulloh
@bsi.ac.id
Faculty of Engineering & Informatics
Universitas Bina Sarana Informatika
RESEARCH INTERESTS
Machine Learning, Data Mining, Expert System, DSS
Scopus Publications
Scholar Citations
Scholar h-index
Scholar i10-index
Scopus Publications
Pirim Setiarso and Taufik Hidayatulloh
Asian Journal of Chemistry
In this work, the composition ratio of graphene oxide-paraffin as the best working electrode for analysis of cadmium solution at optimum conditions using cyclic voltammetry is reported. Graphene oxide from graphite was synthesized and characterized using the improved Hummer method. Testing the composition and condition of graphene oxide:paraffin electrode is best done by manipulating the graphene oxide:paraffin ratio, pH, deposition time and scan rate. The composition with a ratio of 8:2 % of graphene oxide:paraffin electrode produces the best of voltammogram. Recovery analysis is also performed with the results of linear curves in cadmium(II) solution with linearity 0.98992. Cyclic voltammetry analysis using graphene oxide:paraffin electrodes at optimum pH resulted an average recovery of 97.64 %.
Agung Wibowo, Yuri Rahayu, Andi Riyanto, and Taufik Hidayatulloh
IEEE
Indonesia has 13% species of mushroom in the world but there is a very limited study on determining edible or poisonous mushroom. Classification process of poisonous mushroom or not will be easily conducted by learning machine using mining data as one of the ways to extract computer assisted knowledge. Currently, there are three comparisons of the best classification algorithms in data mining, namely: Decision Tree (C4.5), NaïveBayes and Support Vector Machine (SVM). The study method used is experiment with assisted tool of WEKA that has been testing in the comparison of the three algorithms. To conduct the testing, it is used the mushroom data of Agaricus and Lepiota family. The mushroom data were taken from The Audubon Society Field Guide to North American Mushrooms, in UCI machine learning repository. Results of the testing indicate that the C4.5 algorithm has the same accuracy level to the SVM by 100% however, from the speed aspect, process of the C4.5 algorithm is faster than the SVM.