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All Journal Techno Nusa Mandiri : Journal of Computing and Information Technology
Agus Subekti
Pusat Penelitian Elektronika dan Telekomunikasi Lembaga Ilmu Pengetahuan Indonesia (LIPI)
Computer Science & IT

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PERMODELAN PREDIKTIF AUTISTIC SPECTRUM DISORDER DENGAN ALGORITMA C.45 Oktafian Farhan; Agus Subekti
Jurnal Techno Nusa Mandiri Vol 15 No 2 (2018): Techno Nusa Mandiri : Journal of Computing and Information Technology Periode Se
Publisher : Lembaga Penelitian dan Pengabdian Pada Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1070.408 KB) | DOI: 10.33480/techno.v15i2.18

Abstract

Autism is a developmental disability experienced throughout the life of a patient with Autistic Spectrum Disorder (ASD). The sooner it ishandled, the more likely the child will return to normal. For this reason, a new method is needed that can help parents to quickly recognize thesymptoms of autism in their children. In a previous study conducted by Fadi Fayez Tabhtah a data set was produced to detect whether a child has autism or not. But the research only produces data sets, he does not examine more in which algorithm is suitable for the data sets that have been produced. The data set attributes have some mising value, which invite a question about the accuracy of data. In this study researchers used the CRISP-DM method and test the accuracy of data sets of previous studies using the C.45 algorithm. Furthermore, the WEKA applicationusing feature selection and influence of the missing value for each attribute and find the most significant. These attributes are then tested withthe C.45 algorithm so that the predictive model of the data set is obtained. The A6 attribute of the decision tree calculation does not appear at all as a branch. A new model is obtained where the A6 attribute is omitted, so that when measured by the C.45 algorithm, a better accuracy value isobtained. The results of the new model were then tested on the new questionnaire data, which produced precise predictions.
PREDIKSI KEKAMBUHAN KANKER PAYUDARA DENGAN ALGORITMA C4.5 Ai Rita Rizqiah; Agus Subekti
Jurnal Techno Nusa Mandiri Vol 15 No 2 (2018): Techno Nusa Mandiri : Journal of Computing and Information Technology Periode Se
Publisher : Lembaga Penelitian dan Pengabdian Pada Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (959.194 KB) | DOI: 10.33480/techno.v15i2.19

Abstract

Breast cancer is known as the fifth cause of death based on WHO data in 2015. The risk of developing breast cancer will increase with age, family medical history, personal medical history, caucasian descent, early menstruation, late menopause and others. This study aims to predict the use of Naïve Bayes and C4.5 data mining algorithms to classify the recurrence of cancer patients based on certain attributes in the breast cancer dataset. The data mining process will help identify the range or value of various attributes of what causes breast cancer. The results of this study indicate that the C4.5 algorithm has an accuracy value of 75.5% better than Naïve Bayes which only has an accuracy value of 72.7%.
Co-Authors Ai Rita Rizqiah Oktafian Farhan