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SELEKSI FITUR ALGORITMA NEURAL NETWORK MENGGUNAKAN PARTICLE SWARM OPTIMIZATION UNTUK MEMPREDIKSI KELAHIRAN PREMATUR Redaksi Tim Jurnal
KILAT Vol 6 No 2 (2017): KILAT
Publisher : Sekolah Tinggi Teknik - PLN

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (724.004 KB) | DOI: 10.33322/kilat.v6i2.134

Abstract

Premature birth, defined as delivery in pregnant women with gestation age 20 - 36 weeks. Research related to preterm birth has been done by the researchers by using the neural network method. However such research only showcase about the results of the sensitivity and specificity. The results of research using the method of neural network in predicting preterm birth has a value of the resulting accuracy is still less accurate and only limited to presenting the results of the sensitivity and specificity. In this study produced a model of the neural network algorithm and model of neural network algorithm based on particle swarm optimization to get the architecture in predicting preterm birth and gives a more accurate value for accuracy on a data set of RSUPN Cipto Mangunkusumo , RS Sumber Waras and in its entirety. After you are done testing with two models of neural network algorithms and neural network algorithm based on particle swarm optimization and the results obtained are the neural network algorithm generates value accuracy of 94,60%, 96,40%, 91,33%, and AUC values of 0,973, 0,982, 0,953, however, after the addition of the neural network algorithm based on particle swarm optimization value accuracy of 95,20%, 96,80%, 92,40% and AUC values of 0,979 , 0,987, 0,965. So both of these methods has the distinction of accuracy which amounted to 0.60%, 0.40%, 1.07% and AUC value difference of 0.006, 0.005, 0.012.
PEMBENTUKAN MODEL KLASIFIKASI DATA LAMA STUDI MAHASISWA STMIK INDONESIA MENGGUNAKAN DECISION TREE DENGAN ALGORITMA NBTREE Redaksi Tim Jurnal
KILAT Vol 6 No 2 (2017): KILAT
Publisher : Sekolah Tinggi Teknik - PLN

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (647.459 KB) | DOI: 10.33322/kilat.v6i2.135

Abstract

One of the assessment criteria for the accreditation of the study program is the assessment of the duration of the study of students who graduated on time. not a few students who pursue the study period exceeds the established standard of graduation. So it is important for the study program to know which students have the possibility of passing is not timely. For that it is necessary to predict the length of student study. One way to predict the length of a student's study is to build a classification model. This study aims to build a long prediction model of student study using Decision Tree with NBTree algorithm. The data used are academic value data and student academic leave data. The result obtained is a classification model of Naïve Bayes Decision Tree with 73.45% accuracy.
Co-Authors Abdul Haris Ahmad Yani Beni Saputra