PREDICTION OF ECONOMIC GROWTH RATE OF TUBAN REGENCY WITH ADAPTIVE NEURO-FUZZY INFERENCE SYSTEM ALGORITHM

  • Maya Muaziza Mathematics Study Program, Faculty of Mathematics and Natural Sciences, Universitas PGRI Ronggolawe, Indonesia https://orcid.org/0009-0002-6318-5260
  • Ahmad Zaenal Arifin Mathematics Study Program, Faculty of Mathematics and Natural Sciences, Universitas PGRI Ronggolawe, Indonesia https://orcid.org/0000-0002-1073-1646
  • Suzatmo Putro Central Buerau Statistics of Tuban, Indonesia
DOI: https://doi.org/10.30598/barekengvol19iss3pp1699-1710
Keywords: Adaptive Neuro Fuzzy Inference System, Economic Growth, Fuzzy, Prediction

Abstract

This research aims to implement and evaluate the accuracy of the Adaptive Neuro Fuzzy Inference System (ANFIS) forward stage method to predict the economic growth rate of the Tuban Regency. In the application of ANFIS, two types of variables are required, namely, input variables which include road length, the number of electricity customers, the number of health workers, the number of high schools, and the number of cases of ordinary theft. Meanwhile, the predicted output variable is the economic growth rate. The fuzzification process uses a triangular membership function to map the input values. The data used in this study were obtained from the Central Bureau of Statistics (BPS) of Tuban Regency for 2014-2024. The prediction results show a very low Mean Absolute Percentage Error (MAPE) value of 0.14%, which reflects a very high level of accuracy. With MAPE < 10%, the accuracy of this model reaches 99.86% based on calculations made through the Matlab GUI. This research shows that the Adaptive Neuro Fuzzy Inference System (ANFIS) method can be used effectively and accurately to predict the economic growth rate of the Tuban Regency.

Downloads

Download data is not yet available.

References

B. K. Tuban, “PERTUMBUHAN EKONOMI KABUPATEN TUBAN,” Badan Pus. Stat. Kabupaten Tuban, no. 04, pp. 1–12, 2023, [Online]. Available: https://tubankab.bps.go.id/pressrelease/2024/02/28/170/pertumbuhan-ekonomi-kabupaten-tuban-tahun-2023.html

E. Wahyuanto, MENAKAR KINERJA DAN PROFESI DOSEN. Arta Media Nusantara, 2024.

N. G. Mankiw, “GOVERNMENT DEBT AND CAPITAL ACCUMULATION IN AN ERA OF LOW INTEREST RATES,” Brookings Pap. Econ. Act., vol. 2022, no. 1, pp. 219–231, 2022.

W. Bank, POVERTY AND SHARED PROSPERITY 2020: REVERSALS OF FORTUNE. The World Bank, 2020.

A. Susano, W. Anggraeni, and N. Kustian, “PREDIKSI PERTUMBUHAN EKONOMI DI PROVINSI BANTEN MENGGUNAKAN FUZZY INFERENCES SYSTEM (FIS) MAMDANI,” Pros. Semin. Nas. Sains, vol. 1, no. 1, pp. 681–695, 2020, [Online]. Available: http://proceeding.unindra.ac.id/index.php/sinasis/article/view/4084%0Ahttp://proceeding.unindra.ac.id/index.php/sinasis/article/download/4084/705

A. Riski, W. N. Haqqi, and A. Kamsyakawuni, “RAINFALL PREDICTION IN JEMBER REGENCY WITH ADAPTIVE NEURO FUZZY INFERENCE SYSTEM BASED ON GSMAP SATELLITE DATA,” BAREKENG J. Ilmu Mat. dan Terap., vol. 17, no. 3, pp. 1713–1724, 2023, doi: 10.30598/barekengvol17iss3pp1713-1724.

I. P. S. Wijayaa, M. A. Raharjaa, L. A. A. R. Putria, I. P. G. Hendra, I. B. M. M. Suputraa, and I. G. S. Astawaa, “PENERAPAN METODE ADAPTIVE NEURO FUZZY INFERENCE SYSTEM (ANFIS) DENGAN MEMBERSHIP FUNCTION TIPE GAUSSIAN DAN GENERALIZED BELL DALAM PREDIKSI HARGA TERTINGGI SAHAM,” J. Elektron. Ilmu Komput. Udayana p-ISSN, vol. 2301, p. 5373, 2022.

Y. E. A. Seputra and M. Meirinaldi, “PREDIKSI INDEKS GABUNGAN HARGA SAHAM (ISHG) MENGGUNAKAN ADAPTIVE NEURAL FUZZY INFERENCE SYSTEM (ANFIS),” J. Ekon., vol. 22, no. 2, pp. 131–146, 2020.

A. Damayanti and D. Agustina, “IMPLEMENTASI METODE ADAPTIVE NEURO FUZZY INFERENCE SYSTEM (ANFIS) DALAM PREDIKSI HARGA SAHAM X,” Euler J. Ilm. Mat. Sains dan Teknol., vol. 12, no. 1, pp. 71–76, 2024.

R. Dinur, “IMPLEMENTASI METODE FUZZY LOGIC MAMDANI DALAM MEMPREDIKSI KEBUTUHAN DAYA LISTRIK JANGKA PENDEK DI PT. PLN (PERSERO) PEMATANG SIANTAR,” Tek. Inform. dan Tek. Elektro, vol. 5, no. 1, pp. 21–26, 2019.

M. Susanti, S. Handoko, and B. Winardi, “PERAMALAN BEBAN PUNCAK HARIAN PADA PT. PLN (PERSERO) APB JATENG DAN DIY MENGGUNAKAN ANFIS (ADAPTIVE NEURO-FUZZY INFERENCE SYSTEM),” Transient J. Ilm. Tek. Elektro, vol. 5, no. 3, pp. 255–261, 2017.

A. Wantoro, “KOMPARASI PERHITUNGAN PEMILIHAN MAHASISWA TERBAIK MENGGUNAKAN METODE PERHITUNGAN KLASIK DENGAN LOGIKA FUZZY MAMDANI & SUGENO,” J. Pendidik. Teknol. Dan Kejuru., vol. 15, no. 1, 2018.

F. M. Siregar, G. W. Nurcahyo, and S. Defit, “PREDIKSI HASIL UJIAN KOMPETENSI MAHASISWA PROGRAM PROFESI DOKTER (UKMPPD) DENGAN PENDEKATAN ANFIS,” J. RESTI (Rekayasa Sist. dan Teknol. Informasi), vol. 2, no. 2, pp. 554–559, 2018.

A. D. Tura, H. G. Lemu, H. B. Mamo, and A. J. Santhosh, “PREDICTION OF TENSILE STRENGTH IN FUSED DEPOSITION MODELING PROCESS USING ARTIFICIAL NEURAL NETWORK AND FUZZY LOGIC,” Prog. Addit. Manuf., vol. 8, no. 3, pp. 529–539, 2023.

A. Gani and A. Mujianto, “PREDIKSI KEKUATAN TARIK DAN BENDING KOMPOSIT SERAT TKKS MENGGUNAKAN ARTIFICIAL NEURO FAZZY INFERENCE SYSTEM ( ANFIS ),” vol. 3, no. 1, pp. 103–110, 2024.

A. M. de Almeida, M. K. Lenzi, and E. K. Lenzi, “A SURVEY OF FRACTIONAL ORDER CALCULUS APPLICATIONS OF MULTIPLE-INPUT, MULTIPLE-OUTPUT (MIMO) PROCESS CONTROL,” Fractal Fract., vol. 4, no. 2, p. 22, 2020.

S. A. N. Gupita, A. S. Aisjah, and S. Arifin, “PREDIKSI KADAR POLUTAN MENGGUNAKAN ADAPTIVE NEOURO-FUZZY INFERENCE SYSTEM (ANFIS) UNTUK PEMANTAUAN KUALITAS UDARA DI KOTA SURABAYA,” Surabaya Dep. Tek. Fis. Fak. Teknol. Ind. Inst. Teknol. Sepuluh Novemb., 2017.

U. Hani’ah, R. Arifudin, and E. Sugiharti, “IMPLEMENTASI ADAPTIVE NEURO-FUZZY INFERENCE SYSTEM (ANFIS) UNTUK PERAMALAN PEMAKAIAN AIR DI PERUSAHAAN DAERAH AIR MINUM TIRTA MOEDAL SEMARANG,” Sci. J. Informatics, vol. 3, no. 1, pp. 76–87, 2016.

U. Khasanah, D. C. R. Novitasari, and W. D. Utami, “ANALISIS PERAMALAN BEBAN LISTRIK JANGKA PENDEK MENGGUNAKAN METODE ADAPTIVE NEURO FUZZY INFERENCE SYSTEM: STUDI KASUS PT. PLN (PERSERO) AREA PENGATURAN DISTRIBUSI JAWA TIMUR,” J. Mat., vol. 1, no. 1, pp. 17–24, 2019.

N. Talpur, M. N. M. Salleh, and K. Hussain, “AN INVESTIGATION OF MEMBERSHIP FUNCTIONS ON PERFORMANCE OF ANFIS FOR SOLVING CLASSIFICATION PROBLEMS,” in IOP conference series: materials science and engineering, 2017, vol. 226, no. 1, p. 12103.

G. D. Santika, W. F. Mahmudy, and A. Naba, “ELECTRICAL LOAD FORECASTING USING ADAPTIVE NEURO-FUZZY INFERENCE SYSTEM,” Int. J. Adv. Soft Comput. Appl, vol. 9, no. 1, pp. 50–69, 2017.

L. Chen, T. Wu, Z. Wang, X. Lin, and Y. Cai, “A NOVEL HYBRID BPNN MODEL BASED ON ADAPTIVE EVOLUTIONARY ARTIFICIAL BEE COLONY ALGORITHM FOR WATER QUALITY INDEX PREDICTION,” Ecol. Indic., vol. 146, p. 109882, 2023.

A. Kamsyakawuni, W. Sholihah, and A. Riski, “PREDICTION SYSTEM FOR THE AMOUNT OF SUGAR PRODUCTION USING ADAPTIVE NEURO-FUZZY INFERENCE SYSTEM,” BAREKENG, vol. 18, no. 4, pp. 2597–2610, 2024, https://doi.org/10.30598/barekengvol18iss4pp2597-2610.

B. K. Tuban, KABUPATEN TUBAN DALAM ANGKA 2014. Tuban: BPS Kabupaten Tuban, 2014.

B. K. Tuban, KABUPATEN TUBAN DALAM ANGKA 2015. Tuban: BPS Kabupaten Tuban, 2015.

B. K. Tuban, KABUPATEN TUBAN DALAM ANGKA 2024. Tuban: BPS Kabupaten Tuban, 2024.

B. K. Tuban, KABUPATEN TUBAN DALAM ANGKA 2016. Tuban: BPS Kabupaten Tuban, 2016.

B. K. Tuban, KABUPATEN TUBAN DALAM ANGKA 2017. Tuban: BPS Kabupaten Tuban, 2017.

B. K. Tuban, KABUPATEN TUBAN DALAM ANGKA 2018. Tuban: BPS Kabupaten Tuban, 2018.

B. K. Tuban, KABUPATEN TUBAN DALAM ANGKA 2019. Tuban: BPS Kabupaten Tuban, 2019.

B. K. Tuban, KABUPATEN TUBAN DALAM ANGKA 2020. Tuban: BPS Kabupaten Tuban, 2020.

B. K. Tuban, KABUPATEN TUBAN DALAM ANGKA 2021. Tuban: BPS Kabupaten Tuban, 2021.

B. K. Tuban, KABUPATEN TUBAN DALAM ANGKA 2022. Tuban: BPS Kabupaten Tuban, 2022.

B. K. Tuban, KABUPATEN TUBAN DALAM ANGKA 2023. Tuban: BPS Kabupaten Tuban, 2023.

D. Gupta and A. K. Ahlawat, “TAXONOMY OF GUM AND USABILITY PREDICTION USING GUM MULTISTAGE FUZZY EXPERT SYSTEM.,” Int. Arab J. Inf. Technol., vol. 16, no. 3, pp. 357–363, 2019.

M. Ashfaq, “A TRIBUTE TO FATHER OF FUZZY SET THEORY AND FUZZY LOGIC (Dr. Lotfi A. Zadeh),” J. Swarm. Intel. Evol. Comput, vol. 7, no. 2, 2018.

E. F. Ma’rif and A. M. Abadi, “FUZZY APPLICATION (MAMDANI METHOD) IN DECISION-MAKING ON LED TV SELECTION,” BAREKENG J. Ilmu Mat. dan Terap., vol. 18, no. 2, pp. 1117–1128, 2024, https://doi.org/10.30598/barekengvol18iss2pp1117-1128.

N. Walia, H. Singh, and A. Sharma, “ANFIS: ADAPTIVE NEURO-FUZZY INFERENCE SYSTEM-A SURVEY,” Int. J. Comput. Appl., vol. 123, no. 13, 2015.

M. Öztürk, “A MODIFIED ANFIS SYSTEM FOR AERIAL VEHICLES CONTROL,” 2022.

Published
2025-07-01
How to Cite
[1]
M. Muaziza, A. Z. Arifin, and S. Putro, “PREDICTION OF ECONOMIC GROWTH RATE OF TUBAN REGENCY WITH ADAPTIVE NEURO-FUZZY INFERENCE SYSTEM ALGORITHM”, BAREKENG: J. Math. & App., vol. 19, no. 3, pp. 1699-1710, Jul. 2025.
Issue
Vol 19 No 3 (2025): BAREKENG: Journal of Mathematics and Its Application
Section
Articles

Copyright (c) 2025 Maya Muaziza, Ahmad Zaenal Arifin, Suzatmo Putro

Creative Commons License

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

Authors who publish with this Journal agree to the following terms:

  1. Author retain copyright and grant the journal right of first publication with the work simultaneously licensed under a creative commons attribution license that allow others to share the work within an acknowledgement of the work’s authorship and initial publication of this journal.
  2. Authors are able to enter into separate, additional contractual arrangement for the non-exclusive distribution of the journal’s published version of the work (e.g. acknowledgement of its initial publication in this journal).
  3. Authors are permitted and encouraged to post their work online (e.g. in institutional repositories or on their websites) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published works.