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All Journal PROSIDING SEMINAR NASIONAL
Ivandari Ivandari
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SISTEM PENDUKUNG KEPUTUSAN DETEKSI PENYAKIT KANKER PAYUDARA MENGGUNAKAN ALGORITMA NAIVE BAYES Ivandari Ivandari; Erni Rahmawatie; M. Adib Al Karomi
PROSIDING SEMINAR NASIONAL & INTERNASIONAL 2018: SEMINAR NASIONAL PENDIDIKAN SAINS DAN TEKNOLOGI
Publisher : Universitas Muhammadiyah Semarang

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Abstract

Cancer is one of the biggest causes of death in the world. Data from the International Agency for Research of Cancer (IARC) states that in 2012 more than 8.2 million people died from cancer. From these data it is known that breast cancer is the most common type of cancer suffered by 19.2% of all cancer cases. The amount of data and records related to cancer patient cases can  be  useful  if  from  this  data  an  information  or  new  knowledge  can  be retrieved. Data mining is a field of knowledge that processes past data to be used as new information and knowledge. From the comparative research of data mining algorithms for detection of breast cancer in 2017 naive bayes is the best algorithm. Naive Bayes is proven to have a higher level of accuracy than other algorithms. In this study a decision support system for the detection of breast cancer was made. The system created using this Excel application can be one of the recommendations. The method used for calculation is the probability of naive bayes..Keywords: Naive bayes, Microsoft excell, decission support system
PENINGKATAN PERFORMA ALGORITMA NAIVE BAYES DENGAN GAIN RATIO UNTUK KLASIFIKASI KANKER PAYUDARA Muhammad Faizal Kurniawan; Jusak Nugraha Irawan; Ivandari Ivandari
PROSIDING SEMINAR NASIONAL & INTERNASIONAL 2018: SEMINAR NASIONAL PENDIDIKAN SAINS DAN TEKNOLOGI
Publisher : Universitas Muhammadiyah Semarang

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Abstract

Cancer  is  one  of  the  diseases  that  has  so  far  claimed  many  lives. Recorded in 5 years from 2012 data the International Agency for Research of Cancer (IARC) released more than 14 million people with cancer and 8.2 million of them died of cancer suffered. From these data the most common type of cancer is breast cancer, which is 19.2% of all 14 million more cases. Records related to patients and many types of cancer are carried out in the medical world. The data is increasing and will only become garbage if it cannot be used as new knowledge. Data mining is a field of science that answers the challenges of many data. Classification is part of data mining that allows the creation of new information and knowledge from past data. One of the best and proven classification techniques used is naive bayes. From the 2016 study, naive bayes had the best performance for the classification of breast cancer. Large datasets with many attributes do not guarantee the performance of the algorithm will be better. One process of improving algorithm performance is by selecting features. Gain ratio is the development of an information gain algorithm that is proven to be reliable and can handle high-dimensional  data.  This  study  proves  that  the  use  of  gain  ratio feature selection algorithm can improve the performance of Naive Bayes in the classification of Wisconsin Cancer Cancer dataset. Naive Bayes performance without  feature selection  was  92.7% while after  feature selection using the accuracy gain ratio rose 4.01% to 96.71%. Keywords: Data Mining, gain ratio, breast cancer wisconsin, naive bayes
KOMPARASI ALGORITMA UNTUK KLASIFIKASI HEREGISTRASI CALON MAHASISWA Dadang Aribowo; Aris Ekyanto Heru Setiadi; Ivandari Ivandari
PROSIDING SEMINAR NASIONAL & INTERNASIONAL 2018: SEMINAR NASIONAL PENDIDIKAN SAINS DAN TEKNOLOGI
Publisher : Universitas Muhammadiyah Semarang

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Abstract

Students are the most valuable asset in a private university (PTS). Because most of PTS's revenues and operating costs are obtained from students. The number of students who do registration clearly will be a breath of fresh air for the institution. In the last 5 years, around 20% of STMIK Widya Pratama students did not register. Early knowledge of prospective students who might not register will be a reference for the institution to take action to maintain students. The recording of student data that is neatly arranged can be used by management to analyze the characteristics and causes of students not registering. Data mining can process past data into new information or knowledge. In data mining, there is one major function, namely the classification that processes training data to calculate new data / data testing. Methods or algorithms that can be used in the classification process are numerous with various characteristics of each. Some of the best classification algorithms include naive bayes, knn, and C4.5. The results showed that the three algorithms, namely, naive bayes and the C45 decission tree can be used to classify prospective student registrations. The accuracy of the C45 decission tree algorithm is the best, 80.72% followed by the algorithm with an accuracy rate of 80.46%. While the accuracy of naive bayes is the lowest with 74.49%. Keywords: KNN, Naive Bayes, Decission Tree C45
Co-Authors Aris Ekyanto Heru Setiadi Dadang Aribowo Erni Rahmawatie Faizal Kurniawan Jusak Nugraha Irawan M. Adib Al Karomi