![](bg5.png)
Figure 2: Flowchart Fuzzy Time Series Average-Based.
Table 9: Forecasting Accuracy Rate.
County Actual Forecasting
|
X
t
−F
t
|
X
t
Town HDI 2022 HDI 2022
KS 71.09 69.45 0.0230693
IHU 70.46 69.45 0.0143344
IHI 67.37 77.70 0.1533323
PEL 72.93 71.70 0.0168655
SI 74.50 71.70 0.0375839
KPR 73.84 71.70 0.0289816
RHU 70.31 69.45 0.0122315
BKS 74.38 71.70 0.0360312
RHI 70.10 69.45 0.0092725
MRT 66.52 77.70 0.1680698
PKU 82.06 76.20 0.0714112
DMI 75.26 76.20 0.01249
4.86% and the forecast accuracy is 95.14%. It is
known that the MAPE value is ¡10%, based on the
MAPE criteria in Table 3, this shows that the ac-
curacy of the forecasting level of the Human De-
velopment Index (IPM) in Riau Province in 2022
using the Average-based Fuzzy Time Series Algo-
rithm is very good.
4 CONCLUSIONS
Based on the discussion, it can be concluded that there
has been an increase in HDI values in three cities in
Riau Province for 2023, this means that stakeholders
must make a planning strategy so that HDI values can
also increase in eight other counties that have experi-
enced a decline. The accuracy of forecasting accuracy
is 95.14%, with MAPE 4.86%.
ACKNOWLEDGEMENTS
The author would like to thank all those who partici-
pated in this research, especially the respondents who
filled out the questionnaire in this research.
REFERENCES
Akbar, I., Rahmat, B., and Anggraeny, F. (2021). Imple-
mentation of the average-based fuzzy time series al-
gorithm to predict the intensity of waste in final pro-
cessing sites.
Alfian, D., Sasmito, A., and Vendyansyah, N. (2021). Im-
plementation of fuzzy logic in the design of arduino-
based irrigation systems.
Ekananta, Y., Muflikhah, L., and Dewi, C. (2018). Method
of application of average-based fuzzy time series for
predicting indonesian electrical energy consumption.
Farida, Y., Sulistiani, D., and Ulinnuha, N. (2021).
Forecasting the human development index (ipm) in
bojonegoro regency using the double exponential
smoothing brown method. Theorem: Theory and
Mathematical Research, 6:10 25157 6 2 5521.
Haryati, M., Rosdiana, K., Arsani, O., and Wahyudi, M.
(2019). Educational analysis of the accuracy level of
the backpropagation model in predicting the human
development index (ipm) in ntb.
Irawan, R., Laksito, W., and Setiyowati (2019). The dou-
ble exponential smoothing method for forecasting the
level of human development index based on geo-
graphic information systems in central java province.
Journal of Information and Communication Technol-
ogy (Tikomsin, 7:10 30646 7 2 437.
Kirana, I., Nasution, Z., and Wanto, A. (2019). Projection of
the human development index in indonesia using sta-
tistical parabolic methods in welcoming the industrial
revolution 4.0. Journal of Technology and Vocational
Education, 16.
Moh.Firdaus, A. and R, U. (2022). Prediction of forex clos-
ing value using the fuzzy time series cheng method.
Muhajirah, A., Safitri, E., Mardiana, T., Hartina, and Seti-
awan, A. (2019). Analysis of the level of accuracy of
the neuro fuzzy method in predicting ipm data in ntb.
Jtam — Journal of Mathematical Theory and Appli-
cation, 3:58.
Muhammad, M., Wahyuningsih, S., and Siringoringo, M.
(2021). Forecasting the exchange rate of farmers in the
livestock subsector using fuzzy time series lee. Jam-
bura Journal Of Mathematics, 3:1–15.
Rahmawati, A. and Septia, W. (2021). Prediction of rupiah
exchange rate against australian dollarusing the chen
fuzzy time series method.
Rahmawati, A., Suryani, I., and Sari, Y. (2020a). Applica-
tion of fuzzy logic in determining the number of health
bpjs participants using sugeno’s fuzzy inference sys-
tem.
Rahmawati, E. and Susilowati, K. Fuzzy time series method
cheng’s fuzzy time series method in predicting.
Rahmawati, R., Sari, D., Rahma, A., and Soleh, M. (2021a).
Prediction of rainfall in bukit sentang ppks using fuzzy
time series ruey chyn tsaur. Integrative Mathematics
Journal, 17:51.
Rahmawati, S., Yuliana, and Hanafiah, A. (2021b). Pre-
diction of number of bpjs participants recipient of
apbn prize assistance (pbi) using the fuzzy time series
Application of Fuzzy Time Series Average-Based Algorithm in Forecasting the Human Development Index (HDI)
273