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All Journal Jurnal Gaussian Perfecting a Video Game with Game Metrics Bulletin of Electrical Engineering and Informatics International Journal of Advances in Intelligent Informatics EKSAKTA : Jurnal Penelitian dan Pembelajaran MIPA Unisda Journal of Mathematics and Computer Science (UJMC) Mathematics and Applications (MAp) Journal Journal of Fundamental Mathematics and Applications
Danardono Danardono
Departemen Matematika, FMIPA Universitas Gadjah Mada
Agriculture, Biological Sciences & Forestry Computer Science & IT Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Mathematics Other

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K-means and bayesian networks to determine building damage levels Devni Prima Sari; Dedi Rosadi; Adhitya Ronnie Effendie; Danardono Danardono
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 17, No 2: April 2019
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v17i2.11756

Abstract

Many troubles in life require decision-making with convoluted processes because they are caused by uncertainty about the process of relationships that appear in the system. This problem leads to the creation of a model called the Bayesian Network. Bayesian Network is a Bayesian supported development supported by computing advancements. The Bayesian network has also been developed in various fields. At this time, information can implement Bayesian Networks in determining the extent of damage to buildings using individual building data. In practice, there is mixed data which is a combination of continuous and discrete variables. Therefore, to simplify the study it is assumed that all variables are discrete in order to solve practical problems in the implementation of theory. Discretization method used is the K-Means clustering because the percentage of validity obtained by this method is greater than the binning method.
Discretization methods for Bayesian networks in the case of the earthquake Devni Prima Sari; Dedi Rosadi; Adhitya Ronnie Effendie; Danardono Danardono
Bulletin of Electrical Engineering and Informatics Vol 10, No 1: February 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v10i1.2007

Abstract

The Bayesian networks are a graphical probability model that represents interactions between variables. This model has been widely applied in various fields, including in the case of disaster. In applying field data, we often find a mixture of variable types, which is a combination of continuous variables and discrete variables. For data processing using hybrid and continuous Bayesian networks, all continuous variables must be normally distributed. If normal conditions unsatisfied, we offer a solution, is to discretize continuous variables. Next, we can continue the process with the discrete Bayesian networks. The discretization of a variable can be done in various ways, including equal-width, equal-frequency, and K-means. The combination of BN and k-means is a new contribution in this study called the k-means Bayesian networks (KMBN) model. In this study, we compared the three methods of discretization used a confusion matrix. Based on the earthquake damage data, the K-means clustering method produced the highest level of accuracy. This result indicates that K-means is the best method for discretizing the data that we use in this study.
Variable precision rough set model for attribute selection on environment impact dataset Ani Apriani; Iwan Tri Riyadi Yanto; Septiana Fathurrohmah; Sri Haryatmi; Danardono Danardono
International Journal of Advances in Intelligent Informatics Vol 4, No 1 (2018): March 2018
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/ijain.v4i1.109

Abstract

The investigation of environment impact have important role to development of a city. The application of the artificial intelligence in form of computational models can be used to analyze the data. One of them is rough set theory. The utilization of data clustering method, which is a part of rough set theory, could provide a meaningful contribution on the decision making process. The application of this method could come in term of selecting the attribute of environment impact. This paper examine the application of variable precision rough set model for selecting attribute of environment impact. This mean of minimum error classification based approach is applied to a survey dataset by utilizing variable precision of attributes. This paper demonstrates the utilization of variable precision rough set model to select the most important impact of regional development. Based on the experiment, The availability of public open space, social organization and culture, migration and rate of employment are selected as a dominant attributes. It can be contributed on the policy design process, in term of formulating a proper intervention for enhancing the quality of social environment.
ANALISIS SURVIVAL UNTUK DURASI PROSES KELAHIRAN MENGGUNAKAN MODEL REGRESI HAZARD ADDITIF Triastuti Wuryandari; Sri Haryatmi Kartiko; Danardono Danardono
Jurnal Gaussian Vol 9, No 4 (2020): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/j.gauss.v9i4.29259

Abstract

Survival data is the length of time until an event occurs. If  the survival  time is affected by other factor, it can be modeled with a regression model. The regression model for survival data is commonly based  on the Cox proportional hazard model. In the Cox proportional hazard model, the covariate effect act  multiplicatively on unknown baseline hazard. Alternative to the multiplicative hazard model is the additive hazard model. One of  the additive hazard models is the semiparametric additive  hazard model  that introduced by Lin Ying in 1994.  The regression coefficient estimates in this model mimic the scoring equation in the Cox model. Score equation of Cox model is the derivative of the Partial Likelihood and methods to maximize partial likelihood with Newton Raphson iterasi. Subject from this paper is describe the multiplicative and additive hazard model that applied to the duration of the birth process. The data is obtained from two different clinics,there are clinic that applies gentlebirth method while the other one no gentlebirth. From the data processing obtained the factors that affect on the duration of the birth process are baby’s weight, baby’s height and  method of birth. Keywords: survival, additive hazard model, cox proportional hazard, partial likelihood, gentlebirth, duration
PENERAPAN MODEL PENYEMBUHAN DENGAN REGRESI COX HAZARD PROPORSIONAL PADA PENYAKIT KANKER KOLOREKTAL Wirna Arifitriana; Danardono Danardono
Eksakta : Jurnal Penelitian dan Pembelajaran MIPA Vol 4, No 1 (2019): Eksakta : Jurnal Penelitian dan Pembelajaran MIPA
Publisher : Fakultas Keguruan Dan Ilmu Pendidikan, UM-Tapsel

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (169.281 KB) | DOI: 10.31604/eksakta.v4i1.66-72

Abstract

Survival  analysis  is  a  statistical  technique  used  to  analyze  the  data,  aims  to determine the variables that affect the outcome of a beginning to end the incident. One model of survival is a cure model is useful for estimating the proportion of patients who recover and the probability of survival of patients who did not recover until   the   deadline   given.   Analysis   on   Cox   regression   cure   model   Hazard Proportional with Maximum Likelihood Estimates and Algorithm Expectation Maximization (EM). Keywords: Cox Proportional Hazard Cure Model, MLE, EM algorithm, likelihood ratio test, Wald test. 
PEMODELAN REGRESI COX DAN REGRESI WEIBULL WAKTU SEMBUH DIARE PADA BALITA Siti Alfiatur Rohmaniah; Danardono Danardono
Unisda Journal of Mathematics and Computer Science (UJMC) Vol 2 No 1 (2016): Unisda Journal of Mathematics and Computer Science
Publisher : Mathematics Department of Mathematics and Natural Sciences Unisda Lamongan

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (961.098 KB) | DOI: 10.52166/ujmc.v2i1.449

Abstract

There are some consequences that occur because of diarrhea such as low levels of hemoglobin, weight loss, and dehydration. Severe dehydration can lead to death. This study aims to model the time cured of diarrhea on infants used the Cox regression method and Weibull regression to determine the factors that signicantly influence the long diarrhea. The method used to analyze data on infants under diarrhea includes sex, duration of diarrhea from beginning to heal, age of the children, the value of nutrition in infants and toddlers hemoglobin levels. Further, it continued by modeling these factors using cox regression and regression weibull. The results obtained the nutritional value on infants signicantly eect on diarrhea in infants.
METODE MULTIPLE IMPUTATION UNTUK MENGATASI KOVARIAT TAK LENGKAP PADA DATA KEJADIAN BERULANG Rianti Siswi Utami; Danardono Danardono
Journal of Fundamental Mathematics and Applications (JFMA) Vol 2, No 2 (2019)
Publisher : Diponegoro University

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (391.475 KB) | DOI: 10.14710/jfma.v2i2.36

Abstract

Multiple imputation is one of estimation method used to impute missing observations. This method imputes missing observation several times then it is more possible to get the right estimate than just one time imputation. In this research, the method will be applied to estimate missing observations in covariates of recurrent event data. Some multiple imputation methods will be considered including combination of the event indicator, the event  times,   the logarithm of event times, and the cumulative baseline hazard. To compare these methods, Monte Carlo simulation will be used based on relative bias and Mean Squared Error (MSE). The recurrent events will be modelled using Cox proportional hazard model. Furthermore, real data application will be presented.
METODE SCREENING KOLMOGOROV-SMIRNOV UNTUK DATA SURVIVAL BERDIMENSI TINGGI Syarto Musthofa; Danardono Danardono
MAp (Mathematics and Applications) Journal Vol 3, No 1 (2021)
Publisher : Universitas Islam Negeri Imam Bonjol Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (549.82 KB) | DOI: 10.15548/map.v3i1.2779

Abstract

Ada banyak metode screening variabel yang bisa menangani data berdimensi tinggi. Beberapa dari metode tersebut bisa mengurangi dimensi data secara efektif dan menjamin semua variabel aktif tetap muncul dengan probabilitas tinggi. Namun, kebanyakan prosedur screening yang ada saat ini dikembangkan hanya untuk data lengkap berdimensi tinggi dan tidak layak diterapkan pada data survival dengan informasi tersensor. Metode Screening Kolmogorov-Smirnov dapat dimodifikasi untuk mengatasi masalah ini dengan mengganti fungsi distribusi kumulatif dengan fungsi survival yang diestimasi dengan estimator Kaplan-Meier. Metode ini dapat bekerja dengan berbagai tipe kovariat baik itu kontinu, diskrit, maupun kategorikal. Performa dari metode ini diukur berdasarkan studi simulasi. Suatu contoh data riil mengenai ekspresi gen juga digunakan sebagai aplikasi dari metode ini.
Co-Authors Adhitya Ronnie Effendie Adhitya Ronnie Effendie Ani Apriani Dedi Rosadi Devni Prima Sari Iwan Tri Riyadi Yanto Rianti Siswi Utami Septiana Fathurrohmah Siti Alfiatur Rohmaniah Sri Haryatmi Sri Haryatmi Kartiko Syarto Musthofa Triastuti Wuryandari Wirna Arifitriana