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Andri Triyono
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Computer Science & IT

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GENETIC ALGORITHM FOR FEATURE SELECTION IN NAÏVE BAYES IN LIFE RESISTANCE CLASSIFICATION ON BREAST CANCER PATIENT Dhika Malita; Andri Triyono
Julia: Jurnal Ilmu Komputer An Nuur Vol. 1 No. 01 (2021): Julia Jurnal
Publisher : LPPM Universitas An Nuur

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Abstract

Breast cancer is the most common cancer in women's suffering and is the second leading cause of death for women (after lung cancer). More than one million cases and nearly 600,000 breast cancer deaths occur worldwide each year. Survival is generally defined as surviving patients over a period of time after the diagnosis of the disease. Accurate predictions about the likelihood of survival of breast cancer patients can allow doctors and healthcare providers to make more informed decisions about patient care. To classify the survival of breast cancer patients can do the utilization of data mining techniques with Naive Bayes algorithm. Naive Bayes is very simple and efficient but very sensitive to the features so from it the selection of the appropriate features is in need because irrelevant features can reduce the level of accuracy. Naive Bayes will work more effectively when combined with some attribute selection procedures such as Genetic Algorithm. In this study the researchers proposed the Genetic Algorithm for Feature Selection on Naive Bayes so as to improve the accuracy of breast cancer survival classification results. In this study using a private dataset breast cancer patients. The results show that Naive Bayes Genetic Algorithm has a higher accuracy of 90% compared to Naive Bayes with 86% accuracy .   Keywords; Breast Cancer, Survival, Classification, Feature Selection, Naive Bayes, Genetic Algorithm
COMPARISON OF SVM, KNN, AND NAIVE BAYES METHOD WITH N-GRAM IN TRAFFIC ACCIDENT CLASSIFICATION Dhika Malita; Andri Triyono
Julia: Jurnal Ilmu Komputer An Nuur Vol. 1 No. 01 (2021): Julia Jurnal
Publisher : LPPM Universitas An Nuur

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Traffic accidents that occur in Indonesia are still relatively high, the information can be easily obtained through social media, one of which is Twitter. The amount of traffic accident information can be processed and classified according to certain categories. Traffic accident data classification is done using SVM, KNN and Naïve Bayes methods using n-gram feature extraction. The results of this study indicate the best accuracy is 87.63 using the KNN method.   Keywords; Traffic Accident, Classification, SVM, KNN, Naïve Bayes, N-Gram
OPTIMIZATION OF PARTICLE SWARM OPTIMIZATION IN NAÏVE BAYES FOR CAESAREAN BIRTH PREDICTION Dhika Malita; Andri Triyono; Eko Supriyadi; Rahmawan Bagus Trianto
Julia: Jurnal Ilmu Komputer An Nuur Vol. 2 No. 02 (2022): Julia Jurnal
Publisher : LPPM Universitas An Nuur

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Abstract

The Maternal Mortality Rate (MMR) in 2017 according to the World Health Organization (WHO) is estimated to reach 296,000 women who die during and after pregnancy or childbirth. Caesarean birth is the last alternative in labor if the mother cannot give birth normally due to certain indications with a high risk, both for the mother and the baby. factors of a mother giving birth by caesarean section, such as placenta previa, hypertension, breech baby, fetal distress, narrow hips, and can also experience bleeding in the mother before the delivery stage. It is hoped that delivery by caesarean method can minimize problems for the baby and mother. Accurate prediction of the condition of the mother's pregnancy can enable d octors, health care providers and mothers to make more informed decisions regarding the management of childbirth. To predict caesarean births, data mining techniques using the Naive Bayes algorithm can be used. Naive Bayes is very simple and efficient but very sensitive to features, therefore the selection of appropriate features is very necessary because irrelevant features can reduce the level of accuracy. Naive Bayes will work more effectively when combined with several attribute selection procedures such as Particle Swarm Optimization. In this study, the researcher proposes a Particle Swarm Optimization algorithm for attribute weighting in Naive Bayes so as to increase the accuracy of Caesarean birth prediction results
Co-Authors Dhika Malita Dhika Malita Eko Supriyadi Rahmawan Bagus Trianto