Andi Riyanto
Verified email at bsi.ac.id
Information of Technology
Universitas Bina Sarana Informatika
RESEARCH INTERESTS
Accounting, Auditing, Financial, Human Resources Management, Information Systems
Scopus Publications
Scholar Citations
Scholar h-index
Scholar i10-index
Scopus Publications
Agung Wibowo, Dhia Fauziah, Yuri Yuliani, Yuri Rahayu, Andi Riyanto, and Renny Oktapiani
Journal of Physics: Conference Series, ISSN: 17426588, eISSN: 17426596, Volume: 1179, Published: 30 August 2019
IOP Publishing
Autistic Spectrum Disorder (ASD) can faze brain development, growth, and social behaviour. Teens with ASD tend to become bullying object. Autism can be recognized and diagnosis using a test with a questionnaire. The answers from the questionnaire usually converted into an integer value using the Linkers scale, whereas this answer still contains biased and this bias cannot be captured using this scale. Through our initial test results shows that if a user uses an answer choice slightly agree or slightly disagree, the results value is low enough it's about 6% to 20%. Autistic according to the CDC, it has eight symptoms, and we apply it to form fuzzy memberships. Previous Researchers had been shared autism screening dataset to the UCI machine learning repository, this dataset we filtered for "Who is completing the test" attribute with "self "value. We used the classification algorithms in WEKA to find a model, then this model we applied in fuzzification. This system is still a simulation study and has not been clinically tested. This system is assigned to diagnose autism based on the recognized symptoms, where the number of asked questions can vary according to needs but still refers to the symptoms. This paper proposed an autism screening test diagnosis using the fuzzy logic with a better interpellation and precision.
Agung Wibowo, Yuri Rahayu, Andi Riyanto, and Taufik Hidayatulloh
2018 International Conference on Information and Communications Technology, ICOIACT 2018, Volume: 2018-January, Pages: 250-253, Published: 26 April 2018
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.