E jurnal Matematika

90-97

Authors: I Wayan Radika Apriana, Ni Ketut Tari Tastrawati, Kartika Sari

Cat Swarm Optimization (CSO) algorithm is a metaheuristic algorithm which is based on two behaviors of cat, seeking and tracing. CSO algorithm is used in solving optimization problems. One of the optimization problems which can be seen in daily life is Job Shop Scheduling Problem (JSSP). This study aimed to observe the performance of CSO algorithm in solving JSSP. This study focused on 5 job-12 machine cases. According to this study, CSO algorithm was effective in solving real case of JSSP in 5 jobs – 12 machines scheduling at CV Mitra Niaga Indonesia agriculture tools industry. In implementing CSO algorithm in JSSP, a correct parameter choosing could lead to an optimal result. On other hand, the greater the number of jobs or machines the more complex and difficult the JSSP that needed to be solved.

103-110

Authors: Palupi Purnama Sari, Made Susilawati, I Gusti Ayu Made Srinadi

This research was conducted to determine the variables that significantly influence nutritional status of children based on indicators that defined as height for age (H/A) and to classify children nutritional status into normal, short or very short categories. Height for age (H/A) is indicator used to describe the circumstances of malnutrition short. Short children (stunting) is children who fail to reach optimal growth. The secondary data was list of 116 data of children aged 24-59 months at UPT. Puskesmas Klungkung I in 2015. The method was used was ordinal logistic regression and bagging ordinal logistic regression. Based on the research results, it was obtained variables children body length at birth, birth weight, and length of mid-upper arm circumference (MUAC) in pregnant woman were significantly affects the nutritional status of children by the classification accuracy level of ordinal logistic regression 62,86% and misclassification 37,14%. Classification accuracy of ordinal logistic regression can be improved by bagging ordinal logistic regression method. Bagging works well on classification method which has unstable procedures. One of classification method which has unstable procedures is ordinal logistic regression. Bagging ordinal logistic regression method by 501 times replication capable to improve classification accuracy of ordinal logistic regression model from 62,86% to 68,57%, increased 5,71%.

117-125

Authors: I Putu Gede Dian Gerry Suwedayana, I Wayan Sumarjaya, Ni Luh Putu Suciptawati

The purpose of this research is to forecast the number of Australian tourists arrival to Bali (y_t) using Time Varying Parameter (TVP) model based on inflation of Indonesia (x_1t) and exchange rate AUD to IDR (x_2t) from January 2010 – December 2015 as explanatory variables. TVP model is specified in a state space model and estimated by Kalman filter algorithm. The result shows that the TVP model can be used to forecast the number of Australian tourists arrival to Bali because it satisfied the assumption that the residuals are distributed normally and the residuals in the measurement and transition equations are not correlated. The estimated TVP model is (y_t ) ̂=201.8253x_1t+8.903399x_2t. This model has a value of mean absolute percentage error (MAPE) is equal to 1.77% dan root mean square percentage error (RMSPE) is equal to 2.3418%. The number of Australian tourists arrival to Bali for the next five periods is predicted: 87,534; 86,708; 88,864; 90,651; and 88,077 (January - May 2016).