DISTRIBUTION BASED FUZZY TIME SERIES MARKOV CHAIN PADA PERAMALAN INFLASI

Penelitian ini membahas tentang penerapan metode Fuzzy Time Series Markov Chain (FTSMC) yang dikembangkan dengan penentuan panjang interval menggunakan metode distribusi. Pada peramalan dengan menggunakan metode fuzzy penentuan panjang interval merupakan hal penting yang akan berpengaruh pada pembentukan himpunan fuzzy yang akhirnya akan berpengaruh pada hasil peramalan. Pengembangan model peramalan ini bertujuan untuk mendapatkan akurasi hasil peramalan yang lebih baik. Data yang digunakan dalam penelitian ini adalah data inflasi umum Kota Bandung dari Januari 2016 sampai Juni 2021. Data dibagi ke dalam dua kelompok yaitu data training dan data testing dengan rasio 90 : 10. Dalam penelitian ini digunakan program Python untuk proses pengolahan data. Berdasarkan uji akurasi menggunakan MAPE dapat disimpulkan bahwa metode Distribution Based Fuzzy Time Series Markov Chain memberikan hasil peramalan yang lebih baik dengan nilai MAPE sebesar 1,16%. This study discusses the application of the Fuzzy Time Series Markov Chain (FTSMC) method which was developed by determining the length of the interval using the distribution method. In forecasting using the fuzzy method, determining the length of the interval is an important thing that will affect the formation of fuzzy sets which will ultimately affect the forecasting results. The development of this forecasting model aims to get better accuracy of forecasting results. The data used in this study is general inflation data for the city of Bandung from January 2016 to June 2021. The data is divided into two phases, namely training data and testing data with the ratio of 90: 10. Python program is used for data processing. Based on the accuracy test using MAPE, it can be concluded that the Distribution Based Fuzzy Time Series Markov Chain method provides better forecasting results with a MAPE value of 1.16%

Distribution Based Fuzzy Time Series Markov Chain Models for forecasting Inflation in Bandung

This study discusses the application of the Fuzzy Time Series Markov Chain method which was developed by determining the length of the interval using the distribution method. In the fuzzy forecasting method, the determination of the length of the interval is an important thing that will affect the accuracy of the forecasting results. The development of this forecasting model aims to get better forecasting accuracy results. In this study, general inflation data for the city of Bandung is used for the period January 2016 – June 2021. The data is divided into two groups, namely in sample data and out sample data with a ratio of 90: 10. In the data processing process, the Python programming language is used. Based on the accuracy test using the MAPE method, it can be concluded that this method provides better forecasting results with a MAPE value of 1.16%

Exploring the role of social determinants in the risk reduction of landslide-prone settlements: a case study of Giripurno Village in Central Java, Indonesia

Abstract Background The world population is still growing. The growing population caused a changes in the trend of selecting settlements location. Due to the limited flat land, people were starting to form settlements in a hilly or mountainous area which is prone to landslide. The community used to move from place to place to avoid landslides, however, it is no longer possible to implement those actions. While a lot of research has been conducted to assess the vulnerability and risk of settlements to disasters, there needs to be more research on developing settlements in landslide-prone area and their impact on disaster management. Results High social influences can be found in the development of landslide-prone settlements of Giripurno Village. The community shows a high consideration on relatives in deciding their settlement location. Moreover, high consideration of kinship and social activity affects the arrangement of spaces in the house and directly affects the amount of space occupancy. Layout of houses in Giripurno Village were found to have large living room to accommodate family and community gatherings. Although high social dependences of one community can be beneficial in the disaster emergency response and recovery, it can also hinder the disaster mitigation effors by allowing development in an unsafe area, thus increase the risk of disasters. Conclusion This paper discuss about how the social factors can relate to the disaster management with an emphasis on the development of settlements. This paper also highlight the aspects of space occupancy which is rarely being discussed in the disaster management related research. The result obtained by this study could provide important insight into the future disaster management in the landslide-prone settlements area