ANALYSIS OF KUMARASWAMY LOG-LOGISTIC GOMPERTZ (CASE STUDY: CYBER CRIME DATA IN BANDUNG CITY)

• Main Author: Amalia Maresti, Fatia
• Format: Theses
• Language: Indonesia
• Online Access: https://digilib.itb.ac.id/gdl/view/50097
• Institution: Institut Teknologi Bandung

The Kumarawamy distribution was introduced by Poondi Kumaraswamy in 1980. Firstly, this distribution was formulated to model random variables in the hydrological field, such as daily rainfall and waterflow. The Kumaraswamy Log-Logistic Gompertz, K-LLGo, distribution are new distribution that develop by combining the generelized Kumaraswamy, Kw-G, and Log-Logistic Gompertz, LLGo, distribution. The Kw-G distribution has various forms of probability density functions. Log-Logistic is a heavy-tailed distribution and the Gompertz distribution is often used to model lifetime data. So that the new distribution has various forms of probability density functions and can be used to model time data with outliers. Some structural properties of the new distribution are series expansions equation, identify of heavy-tailed distribution, hazard, and moment function. The numerical method of genetic algorithm is adopted for estimating the distribution parameters. The usefulness of the new distribution is illustrated in real dataset. Cyber crime data in Bandung City such as the Inter-Reporting, IR, and the Time Between Events and Reporting, TBER, data. of cyber crime. Data reported in West Java Regions Police from 2017 to 2019. Fitting IR data was also applied to others distribution namely, LLGo, 3 parameters Weibull, and Pareto. This new distribution becomes the best distribution with the highest fitness. The measure of goodness models is the smallest number of sum residual square.