Fahrudin, Tresna Maulana
Politeknik Elektronika Negeri Surabaya (PENS)

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Data Mining Approach for Breast Cancer Patient Recovery Fahrudin, Tresna Maulana; Syarif, Iwan; Barakbah, Ali Ridho
EMITTER International Journal of Engineering Technology Vol 5, No 1 (2017)
Publisher : Politeknik Elektronika Negeri Surabaya (PENS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (994.12 KB) | DOI: 10.24003/emitter.v5i1.190

Abstract

Breast cancer is the second highest cancer type which attacked Indonesian women. There are several factors known related to encourage an increased risk of breast cancer, but especially in Indonesia that factors often depends on the treatment routinely. This research examines the determinant factors of breast cancer and measures the breast cancer patient data to build the useful classification model using data mining approach.The dataset was originally taken from one of Oncology Hospital in East Java, Indonesia, which consists of 1097 samples, 21 attributes and 2 classes. We used three different feature selection algorithms which are Information Gain, Fisher’s Discriminant Ratio and Chi-square to select the best attributes that have great contribution to the data. We applied Hierarchical K-means Clustering to remove attributes which have lowest contribution. Our experiment showed that only 14 of 21 original attributes have the highest contribution factor of the breast cancer data. The clustering algorithmdecreased the error ratio from 44.48% (using 21 original attributes) to 18.32% (using 14 most important attributes).We also applied the classification algorithm to build the classification model and measure the precision of breast cancer patient data. The comparison of classification algorithms between Naïve Bayes and Decision Tree were both given precision reach 92.76% and 92.99% respectively by leave-one-out cross validation. The information based on our data research, the breast cancer patient in Indonesia especially in East Java must be improved by the treatment routinely in the hospital to get early recover of breast cancer which it is related with adherence of patient.
Analisis dan Pemetaan Jumlah Penumpang Kereta Api di Indonesia Menggunakan Metode Statistik Deskriptif dan K-means Clustering Wijaya, Benny; Fahrudin, Tresna Maulana; Nugroho, Aryo
Jurnal Mantik Vol 3 No 2, Agustus (2019): Manajemen, Teknologi Informatiak dan Komunikasi (Mantik)
Publisher : Jurnal Mantik

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Abstract

The development of the population in Indonesia continues to increase, which will require more transportation facilities. PT. Kereta Api Indonesia (KAI) is one of the means of transportation in Indonesia. At present the railroad transportation facilities in Indonesia are still not comprehensive, the regions that have railroad transportation facilities are Java (Jabodetabek and outside Jabodetabek), and Sumatra. By taking data on the number of train passengers from the Central Statistics Agency (BPS), the analysis and mapping of the number of train passengers using descriptive statistics and K-means clustering was carried out in this study. This study produced 3 clusters in which each cluster has a measuring value. Cluster 0 is medium, cluster 1 is high, and cluster 2 is low. Calculated using k-means clustering produces a cluster of 0 there are 63, cluster 1 there is 47, and cluster 2 there are 46 with an accuracy of about 97.9%, and calculated using descriptive statistics to produce cluster 0 there are 108, cluster 1 there is 34, and cluster 2 exists 14 with an accuracy of about 93.6%