scholarly journals PREDIKSI JUMLAH PESERTA BPJS PENERIMA BANTUAN IURAN (PBI) APBN MENGGUNAKAN METODE FUZZY TIME SERIES CHENG

2021 ◽  
Vol 15 (2) ◽  
pp. 373-384
Author(s):  
Rahmawati Bakri ◽  
Syarifah Inayati ◽  
Yuliana Yuliana ◽  
Anggi Hanafiah
Keyword(s):  
Time Series ◽  
Fuzzy Time Series ◽  
Logical Relationship ◽  
Fuzzy Logical

BPJS merupakan salah satu badan Penjaminan Kesehatan yang ada di Indonesia. Jenis BPJS terdiri dari BPJS mandiri, BPJS PPU khusus untuk pekerja diperusahaan, dan BPJS PBI khusus masyarakat yang tidak mampu yang iurannya dibayarkan oleh pemerintah yang diditetapkan dalam APBN. Dari ketiga kategori tersebut jumlah kepesertaan BPJS PBI meningkat dari tahun ke tahunnya. Penelitian ini bertujuan untuk memprediksi jumlah peserta BPJS PBI pada tahun 2019 hingga tahun 2021 dengan menggunakan metode Fuzzy Time series Cheng. Fuzzy Time Series Cheng mempunyai cara yang sedikit berbeda dalam penentuan interval, menggunakan Fuzzy Logical Relationship (FLR) dengan memasukkan semua hubungan dan memberikan bobot berdasarkan pada urutan dan perulangan FLR yang sama. Perhitungan akurasi prediksi pada model ini menggunakan MAPE. Hasil dari penelitian ini diperoleh kenaikan peserta BPJS PBI APBN pada tahun 2019 sampai dengan 2021 sebesar 52.071 peserta dengan hasil MAPE 0,97% dan ketepatan hasil prediksi diperoleh sebesar 99,03%.

scholarly journals High-Order Fuzzy Time Series Model Based on Generalized Fuzzy Logical Relationship

2013 ◽  
Vol 2013 ◽  
pp. 1-11 ◽  
Author(s):  
Wangren Qiu ◽  
Xiaodong Liu ◽  
Hailin Li
Keyword(s):  
Time Series ◽  
Stock Exchange ◽  
Composite Index ◽  
High Order ◽  
Fuzzy Time Series ◽  
Time Series Models ◽  
Logical Relationship ◽  
Proposed Model ◽  
The University ◽  
Fuzzy Logical

In view of techniques for constructing high-order fuzzy time series models, there are three methods which are based on advanced algorithms, computational methods, and grouping the fuzzy logical relationships, respectively. The last kind model has been widely applied and researched for the reason that it is easy to be understood by the decision makers. To improve the fuzzy time series forecasting model, this paper presents a novel high-order fuzzy time series models denoted asGTS(M,N)on the basis of generalized fuzzy logical relationships. Firstly, the paper introduces some concepts of the generalized fuzzy logical relationship and an operation for combining the generalized relationships. Then, the proposed model is implemented in forecasting enrollments of the University of Alabama. As an example of in-depth research, the proposed approach is also applied to forecast the close price of Shanghai Stock Exchange Composite Index. Finally, the effects of the number of orders and hierarchies of fuzzy logical relationships on the forecasting results are discussed.

scholarly journals A TIME SERIES FORECASTING MODEL BASED ON LINGUISTIC FORECASTING RULES

2021 ◽  
Vol 37 (1) ◽  
pp. 23-42
Author(s):  
Pham Đinh Phong
Keyword(s):  
Time Series ◽  
Historical Data ◽  
Time Series Forecasting ◽  
Fuzzy Time Series ◽  
Forecasting Model ◽  
Logical Relationship ◽  
Forecasting Models ◽  
Model Based ◽  
Time Invariant ◽  
Fuzzy Logical

The fuzzy time series (FTS) forecasting models have been being studied intensively over the past few years. Most of the researches focus on improving the effectiveness of the FTS forecasting models using time-invariant fuzzy logical relationship groups proposed by Chen et al. In contrast to Chen’s model, a fuzzy set can be repeated in the right-hand side of the fuzzy logical relationship groups of Yu’s model. N. C. Dieu enhanced Yu’s forecasting model by using the time-variant fuzzy logical relationship groups instead of the time-invariant ones. The forecasting models mentioned above partition the historical data into subintervals and assign the fuzzy sets to them by the human expert’s experience. N. D. Hieu et al. proposed a linguistic time series by utilizing the hedge algebras quantification to converse the numerical time series data to the linguistic time series. Similar to the FTS forecasting model, the obtained linguistic time series can define the linguistic, logical relationships which are used to establish the linguistic, logical relationship groups and form a linguistic forecasting model. In this paper, we propose a linguistic time series forecasting model based on the linguistic forecasting rules induced from the linguistic, logical relationships instead of the linguistic, logical relationship groups proposed by N. D. Hieu. The experimental studies using the historical data of the enrollments of University of Alabama observed from 1971 to 1992 and the daily average temperature data observed from June 1996 to September 1996 in Taipei show the outperformance of the proposed forecasting models over the counterpart ones.

scholarly journals Peramalan Nilai Tukar Petani Subsektor Peternakan Menggunakan Fuzzy Time Series Lee

2021 ◽  
Vol 3 (1) ◽  
pp. 1-15
Author(s):  
Mahadi Muhammad ◽  
Sri Wahyuningsih ◽  
Meiliyani Siringoringo
Keyword(s):  
Time Series ◽  
Fuzzy Sets ◽  
Historical Data ◽  
Mean Absolute Percentage Error ◽  
Fuzzy Time Series ◽  
Percentage Error ◽  
Logical Relationship ◽  
Absolute Percentage Error ◽  
The Mean ◽  
Fuzzy Logical

ABSTRAKFuzzy time series (FTS) Lee adalah suatu metode peramalan yang digunakan ketika jumlah data historis yang tersedia sedikit, serta tidak mensyaratkan asumsi-asumsi tertentu yang harus terpenuhi. Metode ini menggunakan data historis berupa himpunan fuzzy yang berasal dari bilangan real atas himpunan semesta pada data aktual. FTS Lee adalah perkembangan dari FTS Song dan Chissom, FTS Cheng, serta FTS Chen. Pada penelitian ini dibahas penerapan FTS Lee pada data Nilai Tukar Petani Subsektor Peternakan (NTPT) di Kalimantan Timur. Tujuan penelitian ini adalah memperoleh hasil peramalan NTPT di Kalimantan Timur pada bulan Januari 2020 dengan menggunakan FTS Lee. Langkah awal dalam penelitian ini yaitu menentukan himpunan semesta pembicaraan, langkah kedua menentukan banyaknya himpunan fuzzy, langkah ketiga mendefinisikan derajat keanggotaan himpunan fuzzy terhadap  dan melakukan fuzzyfikasi pada data aktual, langkah keempat membuat fuzzy logical relationship, langkah kelima membuat fuzzy logical relationship group, langkah keenam melakukan defuzzyfikasi sehingga diperoleh hasil peramalan, serta dilanjutkan dengan menghitung nilai mean absolute percentage error. Hasil penelitian menunjukkan bahwa peramalan menggunakan FTS Lee pada bulan Januari 2020 adalah 110,25. Nilai mean absolute percentage error pada  hasil peramalan dengan menggunakan FTS Lee adalah sangat baik.  ABSTRACTLee’s Fuzzy time series (FTS) is a forecasting method that is used when the number of historical data that available was small and does not require certain assumptions to be fulfilled. This method uses historical data in the form of fuzzy sets derived from real numbers over the set of universes in the actual data. FTS Lee is a development of FTS Song and Chissom, FTS Cheng, and FTS Chen. This research discusses the application of FTS Lee to the Exchange Rate of Farmers Subsectors Farm (ERFSF) in Kalimantan Timur. The purpose of this study was to obtain the results of ERFSF forecasting in Kalimantan Timur in January 2020 using FTS Lee. The first step during research is to determine the set of speech universes, the second step is to determine the number of fuzzy sets, the third step is to define the degree of fuzzy association membership and fuzzification on the actual data, the fourth step is to create a fuzzy logical relationship, the fifth step is to create a fuzzy logical relationship group, the sixth step is to perform defuzzification in order to obtain forecasting results, and continue by calculating the mean absolute percentage error value. The results showed that forecasting using FTS Lee in January 2020 was 110,25. The mean absolute percentage error value in forecasting results using FTS Lee is very good.

A New Type 2 Fuzzy Time Series Forecasting Model Based on Three-Factors Fuzzy Logical Relationships

2019 ◽  
Vol 27 (02) ◽  
pp. 251-276 ◽  
Author(s):  
Abhishekh ◽  
Surendra Singh Gautam ◽  
Shiva Raj Singh
Keyword(s):  
Time Series ◽  
Historical Data ◽  
Time Series Forecasting ◽  
Fuzzy Time Series ◽  
Forecasting Model ◽  
Logical Relationship ◽  
New Type ◽  
Accuracy Rates ◽  
Fuzzy Logical

The study of fuzzy time series models have been extensively used to improve the accuracy rates in forecasting problems. In this paper, we present a new type 2 fuzzy time series forecasting model based on three-factors fuzzy logical relationship groups. The proposed method uses a new technique to define partitions the universe of discourse into different length of intervals for different factors. Also, the proposed method fuzzifies the historical data sets of the main factors, second factors and third factors to their maximum membership grades obtained by their corresponding triangular fuzzy sets and construct the fuzzy logical relationship groups which is based on the three-factors to enhance in the forecasting accuracy rates. This paper introduces a new defuzzification technique based on their frequency occurrences of fuzzy logical relationships in fuzzy logical relationship groups. The fitness of the propose method is verified in the forecasting of Bombay Stock Exchange (BSE) Sensex historical data and compare in terms of root mean square and average forecasting errors which indicates that the proposed method produce more accurate forecasted output over the existing models in fuzzy time series.

scholarly journals Implementasi Metode Fuzzy Time Series untuk Peramalan Jumlah Pengunjung di Benteng Fort Rotterdam

2020 ◽  
Vol 2 (1) ◽  
pp. 1
Author(s):  
V. Vivianti ◽  
Muhammad Kasim Aidid ◽  
Muhammad Nusrang
Keyword(s):  
Time Series ◽  
Fuzzy Logic ◽  
Fuzzy Time Series ◽  
Logical Relationship ◽  
Logic Relationship ◽  
Fuzzy Logical

Abstract, Peramalan merupakan kegiatan yang dilakukan untuk memprediksi nilai suatu variable di waktu yang akan datang. Tujuan dari penelitian ini adalah mengimplementasikan Metode Fuzzy Time Series untuk memprediksi jumlah Pengunjung Benteng Fort Rotterdam. Metode Fuzzy Time Series adalah sebuah metode peramalan yang menggunakan himpunan Fuzzy sebagai dasar dalam Proses prediksi. Tahapan Peramalan dalam penelitian ini adalah mendefinisikan semesta pembicaraan U, menentukan jumlah dan Panjang kelas interval, defuzzifikasi dan mendefenisikan himpunan Fuzzy pada U, melakukan Fuzzifikasi pada data jumlah pengunjung, menentukan Fuzzy logic relationship (FLR), membentuk Fuzzy Logical Relationship Group (FLRG), melakukan defuzzifikasi, dan melakukan perhitungan peramalan. Dalam meramalkan jumlah Pengunjung di Benteng Fort Rotterdam dengan menggunakan Metode Fuzzy Time Series diperoleh hasil peramalan sebanyak 16240,35 atau dibulatkan menjadi 16240 Pengunjung pada bulan selanjutnya, dengan nilai MAPE sebesar 119,93 dan RMSE sebesar 4739,08.Keywords: Fuzzy, Time Series, Peramalan, Fort Rotterdam

scholarly journals IMPLEMENTASI METODE FUZZY TIME SERIES UNTUK PREDIKSI PENJUALAN BOX MOTOR BERBASIS WEB (STUDI KASUS : TOKO SIPIT BOX MALANG)

2020 ◽  
Vol 4 (1) ◽  
pp. 256-263
Author(s):  
Dwi Adi Saputra ◽  
Yosep Agus Pranoto ◽  
Febriana Santi Wahyuni
Keyword(s):  
Time Series ◽  
Fuzzy Time Series ◽  
Logical Relationship ◽  
Fuzzy Logical

Toko Sipit Box Malang merupakan toko yang menjual bermacam-macam asesoris dan variasi motor. Toko Sipit Box malang menyediakan box motor mulai dari box atas dan box samping, selain itu juga menyediakan braket box untuk pemasangan di berbagai jenis motor. Pada proses pendataan data penjualan toko sipit box malang masih menggunakan cara lama yaitu dengan memasukkannya ke dalam buku nota. Karena pada toko belum ada sistem yang membantu untuk memprediksi penjualan maka pemilik toko kesulitan untuk menentukan stok barang yang akan disediakan. Pada penelitian yang dikembangkan ini peneliti menggunakan metode fuzzy time series untuk melakukan.prediksi penjualan box motor menggunakan data histori penjualan toko Sipit Box Malang. Proses yang dilakukan dalam penelitian ini adalah yang pertama mengambil data historis penjualan box motor mulai dari januari 2016 – oktober 2019, kemudian mencari jumlah interval dan Panjang interval didapat hasil 16 jumlah interval dan 1 panjang interval. Kemudian melakukan proses fuzzifikasi pada data histori. Lalu menentukan fuzzy logical relationship dan menentukan fuzzy logical relationship grup. Langkah terakhir adalah melakukan proses prediksi atau defuzzifikasi menggunakan fuzzy logical relationship grup sebagai acuan. Hasil pengujian yang dilakukan oleh peneliti dengan menerapkan metode fuzzy time series pada sistem yang dibuat menghasilkan prediksi untuk bulan September 2019 sebanyak 13 pcs. Pada pengujian manual didapatkan hasil prediksi untuk bulan September 2019 sebanyak 12 pcs. Kemudian nilai error Dari hasil perhitungan sistem dengan hasil manual didapatkan tingkat kesalahan  sebesar 7,88% untuk prediksi penjualan box motor tipe GIVI E20.

scholarly journals Peramalan KB Baru IUD dengan Metode Automatic Clustering and Fuzzy Logical Relationship

2018 ◽  
Vol 6 (2) ◽  
pp. 144
Author(s):  
Yunidar Ayu Pratama ◽  
Diah Indriani
Keyword(s):  
Time Series ◽  
Secondary Data ◽  
Fuzzy Time Series ◽  
Percentage Increase ◽  
Logical Relationship ◽  
Automatic Clustering ◽  
Forecasting Method ◽  
Time Series Method ◽  
The Government ◽  
Fuzzy Logical

This research aims for forecasting the number of participants Family Planning (FP) new IUD in East Java in 2017 method using Automatic Clustering And Fuzzy Logic Relationship (ACFLR). Make forecasting for the number of participants FP new IUD in the future important done. Forecasting will support the increase of the number of participants program FP new IUD as emphasized by the Government so that it can be used to take better decisions. Forecasting method of Automatic Clustering And Fuzzy Logical Relationship was chosen because the method has a higher degree of accuracy compared to the classical time series method and fuzzy time series. This study used secondary data recorded in Perwakilan BKKBN East Java in the form of the number of participants KB new IUD in East Java in 2011 to 2016. Based on the research results obtained forecasting the number of participants KB new IUD in 2017 is 65.616 participants with error rate prediction of 0.97% and the percentage increase in the number of participants from the previous year is 0.28%.

Implement multi-step-ahead forecasting with multi-point association fuzzy logical relationship for time series

2021 ◽  
pp. 1-17
Author(s):  
Fang Li ◽  
Lihua Zhang ◽  
Xiao Wang ◽  
Shihu Liu
Keyword(s):  
Time Series ◽  
High Order ◽  
Fuzzy Time Series ◽  
Superior Performance ◽  
Forecasting Model ◽  
Logical Relationship ◽  
Multiple Observations ◽  
One Step ◽  
The One ◽  
Fuzzy Logical

In the existing high-order fuzzy logical relationship (FLR) based forecasting model, each FLR is used to describe the association between multiple premise observations and a consequent observation. Therefore, these FLRs concentrate on the one-step-ahead forecasting. In real applications, there exist another kind of association: the association between multiple premise observations and multiple consequent observations. For such association, the existing FLRs can’t express and ignored. To depict it, the high-order multi-point association FLR is raised in this study. The antecedent and consequent of a high-order multi-point association FLR are consisted of multiple observations. Thus, the proposed FLR reflects the influence of multiple premise observations on the multiple consequent observations, and can be applied for multi-step-ahead forecasting with no cumulative errors. On the basis of high-order multi-point association FLR, the high-order multi-point trend association FLR is constructed, it describes the trend association in time series. By using these two new kinds of FLRs, a fuzzy time series based multi-step-ahead forecasting model is established. In this model, the multi-point (trend) association FLRs effective in capturing the associations of time series and improving forecasting accuracy. The benefits of the proposed FLRs and the superior performance of the established forecasting model are demonstrated through the experimental analysis.

IMPROVING THE FORECASTED ACCURACY OF MODEL BASED ON FUZZY TIME SERIES AND K-MEANS CLUSTERING

2017 ◽  
Vol 3 (2) ◽  
pp. 46
Author(s):  
Nghiem Van Tinh ◽  
Nguyen Cong Dieu
Keyword(s):  
Time Series ◽  
Clustering Algorithm ◽  
Historical Data ◽  
Fuzzy Time Series ◽  
Interval Length ◽  
Logical Relationship ◽  
Forecasting Accuracy ◽  
Model Based ◽  
Proposed Model ◽  
Fuzzy Logical

There are many approaches to improve the forecasted accuracy of model based on fuzzy time series such as: determining the optimal interval length, establishing fuzzy logic relationship groups, similarity measures, …wherein, the length of intervals is a factor that greatly affects forecasting results in fuzzy time series model. In this paper, a new forecasting model based on combining the fuzzy time series (FTS) and K-mean clustering algorithm with three computational methods, K-means clustering technique, the time - variant fuzzy logical relationship groups and defuzzification forecasting rules, is presented. Firstly, we apply the K-mean clustering algorithm to divide the historical data into clusters and tune them into intervals with proper lengths. Then, based on the new intervals obtained, the proposed method is used to fuzzify all the historical data and create the time -variant fuzzy logical relationship groups based on the new concept of time – variant fuzzy logical relationship group. Finally, Calculate the forecasted output value by the improved defuzzification technique in the stage of defuzzification. To evaluate performance of the proposed model, two numerical data sets are utilized to illustrate the proposed method and compare the forecasting accuracy with existing methods. The results show that the proposed model gets a higher average forecasting accuracy rate to forecast the Taiwan futures exchange (TAIFEX) and enrollments of the University of Alabama than the existing methods based on the first – order and high-order fuzzy time series.

scholarly journals Penerapan Metode Fuzzy Time Series Untuk Prediksi Penjualan Berbasis Web Pada Cv.AGVA Kota Pasuruan

2020 ◽  
Vol 4 (1) ◽  
pp. 35-41
Author(s):  
Andrian Irfie Hamdani ◽  
Yosep Agus Pranoto ◽  
Nurlaily Vendyansyah
Keyword(s):  
Time Series ◽  
Fuzzy Time Series ◽  
Logical Relationship ◽  
Fuzzy Logical

Cv.AGVA kota pasuruan  merupakan toko yang menjual bermacam-macam alat drumband. Cv.AGVA menyediakan alat drumband mulai dari senar drum HTS, senar drum, marching bell, bass, terompet, dan lain-lain Pada proses pendataan data penjualan Cv.AGVA kota pasuruan masih menggunakan cara lama yaitu dengan memasukkannya ke dalam buku nota. Karena pada toko belum ada sistem yang membantu untuk memprediksi penjualan maka pemilik toko kesulitan untuk menentukan stok barang yang akan disediakan. Pada penelitian yang dikembangkan ini peneliti menggunakan metode fuzzy time series untuk melakukan.prediksi penjualan alat drumband menggunakan data histori penjualan Cv.AGVA kota pasuruan. Proses yang dilakukan dalam penelitian ini adalah yang pertama mengambil data historis penjualan marching bell mulai dari januari 2016 – desember 2018, kemudian mencari jumlah interval dan Panjang interval didapat hasil 33 jumlah interval dan 1 panjang interval. Kemudian melakukan proses fuzzifikasi pada data histori. Lalu menentukan fuzzy logical relationship dan menentukan fuzzy logical relationship grup. Langkah terakhir adalah melakukan proses prediksi atau defuzzifikasi menggunakan fuzzy logical relationship grup sebagai acuan. Hasil perhitungan akurasi keakuratan metode Fuzzy Time Series berdasarkan data penjualan dari bulan Januari 2016 sampai Desember 2018. Baik melalui simulasi program dan manual. Nilai akurasi keakuratan sebesar berapa 2,28%. Serta aplikasi menyediakan informasi tentang hasil penjualan setiap barang. Dengan adanya aplikasi diharapkan pengguna dapat meminimalisir penumpukan stok barang setiap bulannya.

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