scholarly journals Information Systems of Forecasting Incidence Rates of Dengue Fever Disease Using Multivariate Fuzzy Time Series

2020 ◽  
Vol 202 ◽  
pp. 14005
Author(s):  
Ardian Fakhru Rosyad ◽  
Farikhin ◽  
Jatmiko Endro Suseno
Keyword(s):  
Time Series ◽  
Dengue Fever ◽  
Time Series Data ◽  
Incidence Rates ◽  
Fuzzy Time Series ◽  
Series Data ◽  
Central Java ◽  
Time Series Method ◽  
Low Incidence ◽  
Central Java Province

Demak Regency is one of the regions in Central Java Province with a low incidence of Dengue Fever compared to other cities and districts. Even so, DHF control needs to be done to minimize the occurrence of dengue fever, because DHF is a fairly dangerous disease. One form of controlling the number of DHF events that is widely used is using forecasting models, one of them is using Fuzzy Time Series. The Multivariate Fuzzy Time Series (MFTS) model is a development of the Fuzzy Time Series model that can be used to forecast using time series data by using more than one variable for forecasting, compared to the Fuzzy Time Series method that usually using only one variable. Based on the research results obtained, the MFTS model has a fairly accurate MAPE value, wherein the best MAPE was at 3 years scenario with MAPE 10,728%.

PERBANDINGAN FUZZY TIME SERIES DENGAN METODE CHEN DAN METODE S. R. SINGH (Studi Kasus : Nilai Impor di Jawa Tengah Periode Januari 2014 – Desember 2019)

2020 ◽  
Vol 9 (3) ◽  
pp. 306-315
Author(s):  
Febyani Rachim ◽  
Tarno Tarno ◽  
Sugito Sugito
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Fuzzy Time Series ◽  
Series Data ◽  
Percentage Error ◽  
Series Method ◽  
Fuzzy Variables ◽  
Central Java ◽  
Time Series Method ◽  
Fuzzy Logical

Import is one of the efforts of an area to meet the needs of its population in order to stabilize prices and maintain stock availability. The value of imports in Central Java throughout 2016 amounted to 8811.05 Million US Dollars. The value of imports in Central Java is the top 10 in all provinces in Indonesia with a percentage of 6.50%. Import data in Central Java is included in the time series data category. To maintain the stability of imports in Central Java, it is deemed necessary to make a plan based on a statistical model. One of the time series models that can be applied is the fuzzy time series model with the Chen method approach and the S. R. Singh method because the method is suitable for cyclical patterned data with monthly time periods such as Import data in Central Java. Important concepts in the preparation of the model are fuzzy sets, membership functions, set basic operators, fuzzy variables, universe sets and domains. The fuzzy time series modeling procedure is carried out through several stages, namely the determination of universe discourse which is divided into several intervals, then defines the fuzzy set so that it can be performed fuzzification. After that the fuzzy logical relations and fuzzy logical group relations are determined. The accuracy calculation in both methods uses symmetric Mean Absolute Percentage Error (sMAPE). In this study the sMAPE value obtained in the Fuzzy Time Series Chen method of 10.95% means that it shows good forecasting ability. While the sMAPE value on the Fuzzy Time Series method of S. R. Singh method by 5.50% shows very good forecasting ability. It can be concluded that the sMAPE value in the S. R. Singh fuzzy time series method is better than the Chen method.Keywords: Import value, fuzzy time series , Chen, S. R. Singh, sMAPE

scholarly journals Prediksi Penjualan Produk Promo PT. Unilever, Tbk Menggunakan Metode Fuzzy Time Series

2020 ◽  
Vol 6 (2) ◽  
pp. 51-57
Author(s):  
Yehoshua Yehoshua ◽  
Kustanto Kustanto ◽  
Retno Tri Vulandari
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Fuzzy Time Series ◽  
Series Data ◽  
Literature Study ◽  
Price Discounts ◽  
Input Design ◽  
Time Series Method ◽  
Item Data ◽  
Body Care

PT. Unilever is a multinational company headquartered in Rotterdam, the Netherlands (under the name Unilever N.V.), London, England (under the name Unilever pic.) And in Indonesia has a subsidiary, PT. Unilever, Tbk was established on December 5, 1933. Unilever produces food, drinks, cleaners, and also body care. Unilever is the third largest producer of household goods in the world, if based on the amount of revenue in 2012, behind P & G and Nestle. In forecasting products, it is often influenced by the sale of these products because there are also changes in sales for each period. Usually there is an increase in sales of these products which, among other things, is caused by price discounts, new products, one free one to buy promo, or a saving package from Unilever or from a rival company. Data collection method used by the author is a method of observation or directly observing the process of transmission, interview methods and literature study methods. While the method for processing data uses fuzzy time series algorithms, context diagrams, data flow diagrams, HIPO, relational diagram entities, data dictionary design, input design, output design, relation diagrams between tables, system implementation and testing. The method for implementation uses vb.net and Mysql. The results of this thesis are a system for calculating the forecasting amount of sales or sales of promo products for the following year. From this system, information on store data, item data, sales year history data, and forecasting data from fuzzy time series data will be displayed.. From rinso goods promotion data which have been calculated using fuzzy time series method which get MAPE value equal to 3,2%, so sales data for category of goods will experience increase based on calculation equal to 3,2%.

scholarly journals Kluster Tanaman Pangan Unggulan di Provinsi Jawa Tengah

2021 ◽  
Vol 21 (1) ◽  
pp. 55-68
Author(s):  
Choiroel Woestho ◽  
Milda Handayani ◽  
Adi Wibowo Noor Fikri
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Food Plants ◽  
Series Data ◽  
Food Crops ◽  
Food Crop ◽  
Land Area ◽  
Location Quotient ◽  
Central Java ◽  
Central Java Province

The food crop sector has an important role for regions in Indonesia. Food plants can be a determinant for an area in meeting the needs of the people in that area. In addition, the food crop sector, if developed, can become revenue for the region. This study aims to analyze the leading food plants in 35 districts / cities in Central Java Province. By using the location quotient (LQ) method and the Regional Specialization Index. The data used is time series data from 2014 to 2019 in 35 districts / cities in Central Java Province for food crops based on land area and production. The results obtained for the average LQ value of food crops based on land area, there are only 12 districts / cities which are the basis for superior food crops with Wonogiri Regency at the top. Meanwhile, based on the average LQ value based on production, only 11 districts / cities are the basis for superior food crops with Semarang Regency being the top. For the specialization index based on both land area and production, there is no Regency / City that specializes in Central Java Province.   Keywords: Foodcrop Sector, Location Quotient, Specialization Index, Central Java   Abstrak   Sektor tanaman pangan mempunyai peranan penting bagi daerah di Indonesia. Tanaman pangan dapat menjadi penentu bagi suatu daerah dalam memenuhi kebutuhan masyarakat yang ada di daerah tersebut. Selain itu, sektor tanaman pangan jika dikembangkan dapat menjadi pendapatan bagi daerah. Penelitian ini bertujuan untuk menganalisis tanaman pangan unggulan yang ada di 35 Kabupaten/Kota pada Provinsi Jawa Tengah. Dengan menggunakan metode location quotient (LQ) dan Indeks Spesialisasi Regional. Data yang digunakan adalah data time series selama tahun 2014 hingga tahun 2019 pada 35 Kabupaten/Kota di Provinsi Jawa Tengah untuk tanaman pangan berdasarkan luas lahan dan produksi. Hasil yang diperoleh untuk nilai rata – rata LQ tanaman pangan berdasarkan luas lahan, hanya terdapat 12 Kabupaten/Kota yang menjadi basis bagi tanaman pangan unggulan dengan Kabupaten Wonogiri berada di urutan teratas. Sementara berdasarkan nilai rata – rata LQ berdasarkan produksi, hanya 11 Kabupaten/Kota yang menjadi basis tanaman pangan unggulan dengan Kabupaten Semarang menjadi urutan teratas. Untuk indeks spesialisasi baik berdasarkan luas lahan dan produksi, tidak ada Kabupaten/Kota yang mempunyai spesialisasi terhadap Provinsi Jawa Tengah.   Kata kunci: Tanaman Pangan, Indeks Lokalisasi, Indeks Spesialisasi, Jawa Tengah

scholarly journals The Effect of PAD, DAU and DAK on Poverty Rates in Java

2021 ◽  
Vol 2 (5) ◽  
pp. 1668-1683
Author(s):  
Suryani Yuli Astuti ◽  
Muhammad Ali Basyah ◽  
Farokhah Muzayinatun Niswah
Keyword(s):  
Time Series ◽  
Regression Analysis ◽  
Time Series Data ◽  
Series Data ◽  
Analysis Method ◽  
Simultaneous Effect ◽  
Central Java ◽  
Negative Effect ◽  
The Relationship ◽  
Central Java Province

This study was made to determine the extent of the influence of Regional Original Income (PAD), General Allocation Funds (DAU) and Special Allocation Funds (DAK) on poverty through Regional Expenditures in Bitung City. Based on the time series data for 2016-2018 and processed based on the multiple regression analysis method for testing the path analysis used, it can be seen that the relationship between PAD, DAU, DAK and poverty rates on the island of Java. The results showed that the province of West Java on PAD, DAU and DAK had a partial effect on poverty rates and PAD, DAU and DAK had a simultaneous effect on poverty rates. Central Java Province on PAD has a negative effect on poverty rates, DAU and DAK have a partial effect on poverty rates and PAD, DAU and DAK have a simultaneous effect on poverty rates. DIY Province, although PAD has a negative effect on poverty rates, DAU has an effect on poverty and DAK has no partial effect on poverty rates and PAD, DAU and DAK have a simultaneous effect on poverty rates. East Java Province, Partially PAD has no effect on poverty rates, DAU has no effect on poverty rates and DAK has no effect on poverty rates and the influence of PAD, DAU and DAK simultaneously affects poverty rates.

scholarly journals FAKTOR - FAKTOR YANG MEMPENGARUHI PDRB KABUPATEN / KOTA JAWA TENGAH TAHUN 2008-2012

JEJAK ◽  
2015 ◽  
Vol 8 (1) ◽  
Author(s):  
Yozi Aulia Rahman ◽  
Ayunda Lintang Chamelia
Keyword(s):  
Time Series ◽  
Regression Analysis ◽  
Time Series Data ◽  
Linear Regression Analysis ◽  
Multiple Linear Regression Analysis ◽  
Series Data ◽  
Main Condition ◽  
Independent Variables ◽  
Central Java ◽  
Central Java Province

<p>Pertumbuhan ekonomi yang tinggi merupakan kondisi utama bagi kelangsungan pembangunan ekonomi daerah. Untuk mengukur kemajuan perekonomian daerah dengan mengamati seberapa besar laju pertumbuhan ekonomi yang dicapai daerah tersebut yang tercermin dari kenaikan Produk Domestik Regional Bruto (PDRB). PDRBKabupaten/Kota di Jawa Tengah selama tahun 2008- 2012 mengalami pertumbuhan karena banyak yang mempengaruhinya, seperti: Tabungan, Kredit, PAD dan Belanja Daerah. Penelitian ini bertujuan untuk menganalisis seberapa besar faktor-faktor tersebut mempengaruhi tingkat PDRB kabupaten/Kota di Jawa Tengah selama tahun 2008-2012. Variabel dependen yang digunakan dalam penelitian ini adalah PDRB, sedangkan variabel-variabel independen yaitu Tabungan, Kredit, Pendapatan Asli Daerah (PAD) dan Belanja Daerah. Penelitian ini menggunakan analisis regresi linear berganda melalui metode OLS dengan menggunakan data    time series 2008  –2012 dan data crosssection 35 kabupaten/kota di Provinsi Jawa Tengah atau yang dimaksud dengan data panel. Pengujian model dalam penelitian ini menggunakan metode FixedEffect. Hasil estimasi menunjukkan bahwa hasil analisis regresi pada α=5%menunjukkan bahwa secara parsial  variabel tabungan   dan kredit berpengaruh signifikan, sedangkan variabel PAD, dan Belanja Daerah tidak signifikan terhadap PDRB kabupaten/kota di Provinsi Jawa Tengah tahun 2008–2012. </p><p>High economic growth is the main condition for the continuation of regional economic development. To measure the progress of the regional economy, observation on the economyc growth rate in each area can be conducted. It is reflected in the increase of Gross Regional Domestic Product (GDP). The increase of GDP of regency/city in Central Java during the year of 2008- 2012 was influenced by several factors such as savings, credit, local generated revenue (PAD), and Expenditure. This study intends to analyze the affect of these factors to the level of GDP on districts / cities in Central Java during the years 2008-2012. The dependent variable used in this study is GDP. Meanwhile, the independent variables are savings, credit, revenue (PAD) and expenditure. This study uses multiple linear regression analysis by the OLS method using time series data in 2008 -2012 and data crosssection of 35 districts / cities in Central Java province which are often called as the data panel. The model is tested by using Fixed Effect. The result indicates that the results of the regression analysis on the α = 5% shows that in partial,  saving and loan have significant effect on GDP.  Meanwhile,  PAD variable and expenditurehave no significant effect on GDP districts / cities in Central Java province in 2008-2012.</p>

A Score Function-Based Method of Forecasting Using Intuitionistic Fuzzy Time Series

2018 ◽  
Vol 14 (01) ◽  
pp. 91-111 ◽  
Author(s):  
Abhishekh ◽  
Surendra Singh Gautam ◽  
S. R. Singh
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Stock Exchange ◽  
Market Price ◽  
Intuitionistic Fuzzy Set ◽  
Score Function ◽  
Fuzzy Time Series ◽  
Series Data ◽  
Intuitionistic Fuzzy Numbers ◽  
Intuitionistic Fuzzy

Intuitionistic fuzzy set plays a vital role in data analysis and decision-making problems. In this paper, we propose an enhanced and versatile method of forecasting using the concept of intuitionistic fuzzy time series (FTS) based on their score function. The developed method has been presented in the form of simple computational steps of forecasting instead of complicated max–min compositions operator of intuitionistic fuzzy sets to compute the relational matrix [Formula: see text]. Also, the proposed method is based on the maximum score and minimum accuracy function of intuitionistic fuzzy numbers (IFNs) to fuzzify the historical time series data. Further intuitionistic fuzzy logical relationship groups are defined and also provide a forecasted value and lies in an interval and is more appropriate rather than a crisp value. Furthermore, the proposed method has been implemented on the historical student enrollments data of University of Alabama and obtains the forecasted values which have been compared with the existing methods to show its superiority. The suitability of the proposed model has also been examined to forecast the movement of share market price of State Bank of India (SBI) at Bombay Stock Exchange (BSE). The results of the comparison of MSE and MAPE indicate that the proposed method produces more accurate forecasting results.

An Efficient Method for Forecasting Using Fuzzy Time Series

2017 ◽  
pp. 287-304 ◽  
Author(s):  
Pritpal Singh
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Weather Forecasting ◽  
Small Error ◽  
Fuzzy Time Series ◽  
Series Data ◽  
Data Sets ◽  
Proposed Model ◽  
Temperature Forecasting ◽  
The University

Forecasting using fuzzy time series has been applied in several areas including forecasting university enrollments, sales, road accidents, financial forecasting, weather forecasting, etc. Recently, many researchers have paid attention to apply fuzzy time series in time series forecasting problems. In this paper, we present a new model to forecast the enrollments in the University of Alabama and the daily average temperature in Taipei, based on one-factor fuzzy time series. In this model, a new frequency based clustering technique is employed for partitioning the time series data sets into different intervals. For defuzzification function, two new principles are also incorporated in this model. In case of enrollments as well daily temperature forecasting, proposed model exhibits very small error rate.

scholarly journals The Effect of Inequality of Educational Facilities, Health, and Road Conditions toward Income Distribution in Central Java Province, Indonesia

2018 ◽  
Vol 73 ◽  
pp. 10014
Author(s):  
Antono Herry ◽  
Purnomo Adhi ◽  
Firmansyah
Keyword(s):  
Income Distribution ◽  
Gini Index ◽  
Time Series Data ◽  
Series Data ◽  
Educational Facilities ◽  
Public Facilities ◽  
Central Java ◽  
Road Condition ◽  
The Relationship ◽  
Central Java Province

This study examines the effect of inequality of public facilities, namely education, health, and road condition, on the income inequality in Central Java Province, Indonesia. By employing the time-series data of 15 years, this study analyzes the Gini index and the relationship between the Gini index and Index of public facilities by the regression model. The study finds that the inequality of the provision of public facilities affects the income distribution in Central Java, Indonesia

scholarly journals Analisis Potensi Gempa di Selatan Pulau Jawa Berdasarkan Pengamatan GPS

2020 ◽  
Vol 11 (3) ◽  
pp. 151
Author(s):  
Irwan Meilano ◽  
Agidia L. Tiaratama ◽  
Dudy D. Wijaya ◽  
Putra Maulida ◽  
S. Susilo ◽  
...  
Keyword(s):  
Time Series ◽  
Subduction Zones ◽  
Velocity Vector ◽  
Time Series Data ◽  
Principal Component ◽  
Reference Station ◽  
Tectonic Activity ◽  
Series Data ◽  
Local Data ◽  
Central Java

ABSTRAKPulau Jawa merupakan salah satu pulau yang memiliki kepadatan penduduk tinggi dengan aktivitas tektonik yang sangat aktif. Hal ini dikarenakan Pulau Jawa terletak di zona konvergensi Lempeng Indo-Australia dan Lempeng Eurasia. Aktivitas tektonik ini menghasilkan kegempaan di zona subduksi dan sesar di daratan Penelitian ini menganalisis pola vektor kecepatan yang dihasilkan melalui pengolahan data stasiun pengamatan GPS (Global Positioning System) CORS (Continuously Operating Reference Station) BIG (Badan Informasi Geospasial) di wilayah Pulau Jawa bagian selatan. Data koordinat harian dianalisis dengan metode PCA (Principal Component Analysis) untuk memisahkan sinyal tektonik berupa data deret waktu global dan non-tektonik berupa data deret waktu lokal dengan penerapan aturan pemilihan varian dominan nilai eigen dalam pembetukan PC (Principal Component) dan orthogonal vektor eigen sebagai bobot dalam meminimalkan korelasi. Hasil dari data deret waktu global dan lokal digunakan untuk menghitung besar kecepatan pergeseran dari tahun 2011 sampai 2018. Hasil pengolahan menunjukkan besar resultan vektor kecepatan pada data awal berselang 0,06 sampai 10,46 mm/tahun, pada data global antara 0,06 mm/ tahun sampai 10,39 mm/tahun, dan data lokal sebesar 0,0037 sampai 1,99 mm/tahun. Variasi spasial vektor kecepatan pengamatan GPS data domain PCA menunjukkan variasi pergeseran horizontal di wilayah Banten bergerak ke arah timur laut; Jawa Barat, Daerah Istimewa Yogyakarta, dan Jawa Tengah bergerak ke arah tenggara; dan Jawa Timur bergerak ke arah timur laut. Hasil dari inversi data pergeseran terhadap slip pada zona subduksi, menunjukkan terjadinya kekurangan slip atau terjadi coupling pada zona subduksi Jawa bagian timur dan barat, sementara terjadi kelebihan slip pada bagian tengah yang merupakan efek postseismic dari gempa Pangandaran 2006.Kata kunci: GPS, PCA, potensi gempa, vektor kecepatanABSTRACTJava is one of the island that has a high population density with very active tectonic activity. This is because Java Island is located in the convergence zone of the Indo-Australian Plate and the Eurasian Plate. This tectonic activity produces seismicity in subduction zones and inland faults. This study analyzes the velocity vector patterns generated through data processing of the GPS (Global Positioning System) CORS (Continuously Operating Reference Station) BIG (Geospatial Information Agency) observation station in the southern part of Java. Daily coordinate data were analyzed using PCA (Principal Component Analysis) method to separate time series of tectonic signals as global data and non-tectonic time series data as local data by applying the rules for selecting dominant variants of eigen values for PC formation and orthogonal eigen vectors as weights in minimizing correlations. The results from global and local time series data were used to calculate the magnitude of the displacement velocity from 2011 until 2018. The processing results show the resultant velocity vector in the initial data intermittent 0.06 to 10.46 mm/year, global data from 0.06 to 10.39 mm/year, and local data of 0.0037 to 1.99 mm/year. The spatial variation of the velocity vector in PCA domain data shows the horizontal displacement in the Banten region to the northeast; West Java, Yogyakarta Special Region, Central Java to southeast; and East Java moving to northeast. The results of the inversion of the surface displacement to slip data in the subduction zone show that there is a slip deficiency or coupling occurs in the subduction zones of Eastern and Western Java, while there is excess slip in the Central Java which is a post-seismic effect of the 2006 Pangandaran earthquake.Keywords: earthquake potential, GPS, PCA, velocity vector

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