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<http://digilib.unila.ac.id/id/eprint/59249> <http://purl.org/dc/terms/title> "ALGORITMA EXPECTATION-MAXIMIZATION UNTUK PENDUGAAN\r\nPARAMETER DISTRIBUSI GENERALIZED GAMMA PADA\r\nDATA TERSENSOR KANAN TIPE 1"^^<http://www.w3.org/2001/XMLSchema#string> .
<http://digilib.unila.ac.id/id/eprint/59249> <http://purl.org/ontology/bibo/abstract> "Generalized Gamma distribution is the most popular model for analyzing skewed\r\ndata and various forms of hazard functions, this makes Generalized Gamma\r\ndistribution often used in survival analysis. Survival analysis aims to predict the\r\nchances of survival, recurrence of disease, death, and other events up to a certain\r\nperiod of time. One characteristic of survival data is the possibility of censorship,\r\nthis study uses right-type censored data type 1. Estimation of Generalized Gamma\r\ndistribution parameters in survival analysis uses maximum likelihood estimation\r\nwhich is solved by the expectation-maximization algorithm. In this study an\r\nanalysis of the survival time of patients with gastric cancer will be conducted.\r\nBased on the results of the iteration, the estimated value of the parameter is\r\nobtained, namely α = 3.406516, β = 1.072, and θ = 59.766. Based on survival\r\nfunction, the chances of survival of patients will continue to decrease until death\r\noccurs, while hazard functions have an increasing rate, namely the increasing\r\nsurvival time of patients, the chances of death will increase.\r\nKeywords: Generalized Gamma, Type 1 Right Censored Data, Maximum\r\nLikelihood Estimation, Eexpectation-Maximization Algorithm\r\nDistribusi Generalized Gamma adalah model paling popular untuk menganalisis\r\ndata yang skewed dan berbagai bentuk fungsi hazard, hal ini membuat distribusi\r\nGeneralized Gamma sering digunakan dalam analisis survival. Analisis survival\r\nbertujuan untuk menduga peluang bertahan hidup, kekambuhan penyakit,\r\nkematian, dan peristiwa-peristiwa lainnya sampai pada periode waktu tertentu.\r\nSalah satu karakteristik data survival adalah kemungkinan adanya sensor,\r\npenelitian ini menggunakan data tersensor kanan tipe 1. Pendugaan parameter\r\ndistribusi Generalized Gamma pada analisis survival menggunakan maximum\r\nlikelihood estimation yang diselesaikan dengan algoritma expectationmaximization. Dalam penelitian ini akan dilakukan analisis waktu bertahan hidup\r\npasien penderita penyakit kanker lambung. Berdasarkan hasil iterasi diperoleh\r\nnilai dugaan parameter, yaitu α = 3.406516, β = 1.072, dan θ = 59.766.\r\nBerdasarkan fungsi survival, peluang bertahan hidup dari pasien akan terus\r\nmengalami penurunan sampai terjadinya kematian, sedangkan fungsi hazard\r\nmemiliki laju increasing yaitu semakin meningkatnya waktu bertahan hidup dari\r\npasien maka peluang kematiannya akan semakin meningkat.\r\nKata Kunci: Generalized Gamma, Data tersensor kanan tipe 1, Maximum\r\nLikelihood Estimation, Algoritma Expectation-Maximization"^^<http://www.w3.org/2001/XMLSchema#string> .
<http://digilib.unila.ac.id/id/eprint/59249> <http://purl.org/dc/terms/date> "2019" .
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