Multiple Attribute Hierarchical Fuzzy Logic System with Quartile-Based Consequent Calibration for Rainfall Forecasting in Palembang City

Kanda Januar Miraswan; Meylani Utari; Yesinta Florensia

doi:10.52436/1.jutif.2026.7.4.5524

Authors

Kanda Januar Miraswan Informatics Engineering, Faculty of Computer Science, Universitas Sriwijaya, Indonesia
Meylani Utari Computerized Accounting, Faculty of Computer Science, Universitas Sriwijaya, Indonesia
Yesinta Florensia Informatics Management, Faculty of Computer Science, Universitas Sriwijaya, Indonesia

DOI:

https://doi.org/10.52436/1.jutif.2026.7.4.5524

Keywords:

Fuzzy Inference System, Mamdani Fuzzy, Multiple Attribute Hierarchical Fuzzy Logic System, Rainfall Prediction, Sugeno Fuzzy

Abstract

Global climate change is making weather conditions in Indonesia, particularly in Palembang City, increasingly difficult to predict. The unexpected high-intensity rainfall frequently triggers hydrometeorological disasters, leading to threats to human safety and substantial economic losses. Consequently, reliable rainfall forecasting systems are urgently needed to support early warning, disaster mitigation, and operational planning across critical sectors. This research aims to design an accurate rainfall forecasting model for Palembang City by applying the Multiple Attribute Hierarchical Fuzzy Logic System (HFLS) with quartile-based consequent calibration. The method integrates the strengths of both Sugeno and Mamdani fuzzy inference models within a hierarchical structure to manage data uncertainty more effectively and reduce rule complexity. Unlike conventional fuzzy rainfall models that rely on fixed rainfall intensity categories, this study introduces a quartile-based calibration of Sugeno consequent values derived from local rainfall data. The model is developed using daily meteorological variables such as temperature, humidity, sunshine duration, and wind characteristics, obtained from the BMKG station in Palembang. Two configurations of consequent values are tested, one using conventional rainfall intensity categories and the other using quartile-based local data distribution derived from empirical rainfall observations. The experimental results show that the quartile-based configuration significantly improves accuracy, achieving MAE of 12.26 mm and RMSE of 25.85 mm, outperforming the conventional configuration (with MAE of 18.89 mm and RMSE of 28.29 mm). These findings highlight the importance of integrating hierarchical fuzzy architecture with locally calibrated consequent values to enhance rainfall prediction performance in tropical regions characterized by highly skewed rainfall distributions.

Downloads

Download data is not yet available.

References

D. Desmonda, T. Tursina, M.A. Irwansyah, “Prediksi Besaran Curah Hujan Menggunakan Metode Fuzzy Time Series,” Journal of Information Systems and Technology (JUSTIN), vol. 6, no. 4, pp. 145–149, 2018, doi: 10.26418/justin.v6i4.27036.

C. Peláez-Rodríguez, J. Pérez-Aracil, C.M. Marina, L. Prieto-Godino, C. Casanova-Mateo, P.A. Gutiérrez, S. Salcedo-Sanz, “A general explicable forecasting framework for weather events based on ordinal classification and inductive rules combined with fuzzy logic,” Knowledge-Based Systems, vol. 291, pp. 111556, 2024, doi: 10.1016/j.knosys.2024.111556.

M. Yusuf, A. Setyanto, K. Aryasa, “Analisis Prediksi Curah Hujan Bulanan Wilayah Kota Sorong Menggunakan Metode Multiple Regression,” Journal of Computer Science and Informatics (J-SAKTI), vol. 6, no. 1, pp. 405–417, 2022, doi: 10.30645/j-sakti.v6i1.455.

A. Prasetiyo, H. Husain, S. Subaer, M. Arsyad, P. Palloan, “Pemanfaatan Metode Fuzzy Logic dalam Memprakirakan Hujan (2025–2030) di Stasiun Meteorologi Kelas I Sultan Hasanuddin,” Buletin GAW Bariri (BGB), vol. 6, no. 1, pp. 27–37, 2025, doi: 10.31172/bgb.v6i1.146.

J. Badriyah, A. Fariza, T. Harsono, “Rainfall Prediction Using Long Short Term Memory,” Jurnal Media Informatika Budidarma, vol. 6, no. 3, pp. 1297–1303, 2022, doi: 10.30865/mib.v6i3.4008.

S. Zikri, W.R. Hasibuan, “Rainfall Prediction Analysis Using the Fuzzy Time Series Method in Medan City,” Journal of Artificial Intelligence and Engineering Applications, vol. 4, no. 1, pp. 575–579, 2024, doi: 10.59934/jaiea.v4i1.685.

Z. Julisman, E. Erlin, “Prediksi Tingkat Curah Hujan di Kota Pekanbaru menggunakan Logika Fuzzy Mamdani,” SATIN - Sains Dan Teknologi Informasi, vol. 3, no. 1, pp. 65–72, 2018, doi: 10.33372/stn.v3i1.355.

D.R. Damayanti, S. Wicaksono, M.F.A. Hakim, J. Jumanto, S. Subhan, Y.N. Ifriza, “Rainfall prediction in Blora regency using Mamdani’s fuzzy inference system,” Journal of Soft Computing Exploration, vol. 3, no. 1, pp. 62–69, 2022, doi: 10.52465/joscex.v3i1.69.

N. Setyanugraha, S.A. Aziz, I.W. Harmoko, F. Fianti, “Study of a Weather Prediction System Based on Fuzzy Logic Using Mamdani and Sugeno Methods,” Physics Communications, vol. 6, no. 2, pp. 61–70, 2022, doi: 10.15294/physcomm.v6i2.39703.

Y. Andrian, E. Ningsih, “Prediksi Curah Hujan di Kota Medan Menggunakan Metode Backpropagation Neural Network,” Proceedings of the National Informatics Seminar (SNIf), vol. 1, pp. 184–189, 2014.

T. Anuradha, P.S.G. Aruna Sri Formal, J. RamaDevi, “Hybrid model for rainfall prediction with statistical and technical indicator feature set,” Expert Systems with Applications, vol. 249, pp. 123260, 2024, doi: 10.1016/j.eswa.2024.123260.

S. Asiah, W. Riana, D.C. Purba, M.I. Azharsum, V.A. Elyakim P., “Annual Rainfall Prediction in Indonesia Using A Hybrid Artificial Neural Network and Fuzzy Algorithm Model,” JOMLAI: Journal of Machine Learning and Artificial Intelligence, vol. 4, no. 2, pp. 99–105, 2025, doi: 10.55123/jomlai.v4i2.5964.

E.D.S. Mulyani, I. Septianingrum, N. Nurjanah, R. Rahmawati, S. Nurhasani, K. Milky R.K., “Prediksi Curah Hujan di Kabupaten Majalengka dengan Menggunakan Algoritma Regresi,” E-JURNAL JUSITI: Journal of Information Systems and Information Technology, vol. 8, no. 1, pp. 67–77, 2019, doi: 10.36774/jusiti.v8i1.602.

C.D. Usman, A.P. Widodo, K. Adi, R. Gernowo, “Rainfall prediction model in Semarang City using machine learning,” Indonesian Journal of Electrical Engineering and Computer Science, vol. 30, no. 2, pp. 1224–1231, 2023, doi: 10.11591/ijeecs.v30.i2.pp1224-1231.

S. Nurhayati, I. Immanudin, “Penerapan Logika Fuzzy Mamdani Untuk Prediksi Pengadaan Peralatan Rumah Tangga Rumah Sakit,” Komputika: Journal of Computer Systems, vol. 8, no. 2, pp. 81–87, 2019, doi: 10.34010/komputika.v8i2.2254.

T. Tundo, S. Saifullah, “Fuzzy Inference System Mamdani dalam Prediksi Produksi Kain Tenun Menggunakan Rule Berdasarkan Random Tree,” Jurnal Teknologi Informasi dan Ilmu Komputer (JTIIK), vol. 9, no. 3, pp. 443–452, 2022, doi: 10.25126/jtiik.2022924212.

K. Putriyani, T. Wahyuningrum, Y.D. Prasetyo, “Prediksi Jumlah Produksi Akibat Penyebaran Covid-19 Menggunakan Metode Fuzzy Takagi-Sugeno,” RESTI Journal (System and Information Technology Engineering), vol. 5, no. 2, pp. 220–230, 2021, doi: 10.29207/resti.v5i2.2973.

I. Wahyuni, F. Ahda, “Pemodelan Fuzzy Inference System Tsukamoto Untuk Prediksi Curah Hujan Studi Kasus Kota Batu,” Jurnal Institut Teknologi dan Bisnis Asia Malang, vol. 12, no. 2, pp. 115–124, 2018, doi: 10.32815/jitika.v12i2.260.

V.M. Nasution, G. Prakarsa, “Perancangan Aplikasi Fuzzy Logic Untuk Prediksi Kasus Positif Covid-19 Menggunakan Metode Tsukamoto,” Jurnal Media Informatika Budidarma, vol. 5, no. 4, pp. 1642–1651, 2021, doi: 10.30865/mib.v5i4.3338.

V. Kumar, A. Kumar, “Integration of Fuzzy Clustering and Stochastic Models for Short-Term Rainfall Forecasting,” Advances in Climate Change Research, vol. 2, no. 5, pp. 1526–1532, 2025.

D.L. Rahakbauw, F.J. Rianekuay, Y.A. Lesnussa, “Penerapan Metode Fuzzy Mamdani Untuk Memprediksi Jumlah Produksi Karet (Studi Kasus: Data Persediaan Dan Permintaan Produksi Karet Pada PTP Nusantara XIV (Persero) Kebun Awaya, Teluk Elpaputih, Maluku-Indonesia),” Jurnal Ilmiah Matematika dan Terapan, vol. 16, no. 1, pp. 51–59, 2019, doi: 10.22487/2540766x.2019.v16.i1.12764.

S. Hajar, M. Badawi, Y.D. Setiawan, M.N.H. Siregar, A.P. Windarto, “Prediksi Perhitungan Jumlah Produksi Tahu Mahanda dengan Teknik Fuzzy Sugeno,” Jurnal Sains Komputer & Informatika (J-SAKTI), vol. 4, no. 1, pp. 210–219, 2020, doi: 10.30645/j-sakti.v4i1.200.

S. Kamthan, H. Singh, “Hierarchical Fuzzy Logic Systems,” Journal of The Institution of Engineers (India): Series, vol. 103, pp. 1–9, 2022, doi: 10.1007/s40031-022-00728-4.

D.L. Rahakbauw, “Penerapan Logika Fuzzy Metode Sugeno Untuk Menentukan Jumlah Produksi Roti Berdasarkan Data Persediaan Dan Jumlah Permintaan,” BAREKENG: Journal of Mathematical and Applied Sciences, vol. 9, no. 2, pp. 121–134, 2015, doi: 10.30598/barekengvol9iss2pp121-134.

M.E. Irawan, Herviana, “Implementasi Logika Fuzzy dalam Menentukan Jurusan Bagi Siswa Baru Sekolah Menengah Kejuruan (SMK) Negeri 1 Air Putih,” Jurnal Teknologi Informasi (JurTI), vol. 2, no. 2, pp. 129–137, 2018, doi: 10.36294/jurti.v2i2.427.

M. Irfan, L.P. Ayuningtias, Jumadi, “Analisa Perbandingan Logic Fuzzy Metode Tsukamoto, Sugeno dan Mamdani,” Jurnal Teknik Informatika, vol. 10, no. 1, pp. 9–16, 2018, doi: 10.15408/jti.v10i1.6810.

M. Silver, T. Svoray, A. Karnieli, E. Fredj, “Improving weather radar precipitation maps: A fuzzy logic approach,” Atmospheric Research, vol. 234, 2020.

T. Zhao, Y. Zhu, X. Xie, “Topology structure optimization of evolutionary hierarchical fuzzy systems,” Expert Systems with Applications, vol. 236, pp. 121276, 2024, doi: 10.1016/j.eswa.2023.121857.

F. Yin, H.-K. Lam, D. Watson, “Hierarchical Fuzzy Model-Agnostic Explanation: Framework, Algorithms, and Interface for XAI,” IEEE Transactions on Fuzzy Systems, 2024, doi: 10.1109/TFUZZ.2024.3485212.

D.T.T. Loan, N.D. Tho, N.H. Nghia, V.D. Chien, T.A. Tuan, “Analyzing Students’ Performance Using Fuzzy Logic and Hierarchical Linear Regression,” International Journal of Modern Education and Computer Science (IJMECS), vol. 16, no. 1, pp. 1–10, 2024, doi: 10.5815/ijmecs.2024.01.01.

Y. Shui, L. Dong, Y. Zhang, C. Sun, “Dynamic Event-Based Hierarchical Fuzzy Prescribed Performance Control for Underactuated Systems With Uncertain Dead Zone,” IEEE Transactions on Fuzzy Systems, 2024, doi: 10.1109/TFUZZ.2024.3481633.

J. Cao, T. Zhou, Z. Shaohua, S. Lam, G. Ren, Z. Yuanpeng, et al., “Fuzzy inference system with interpretable fuzzy rules: Advancing explainable artificial intelligence for disease diagnosis—A comprehensive review,” Information Sciences, vol. 671, pp. 120543, 2024, doi: 10.1016/j.ins.2024.120212.

A.R. Wardani, Y.N. Nasution, F.D.T. Amijaya, “Aplikasi logika Fuzzy dalam mengoptimalkan produksi minyak kelapa sawit di PT Waru Kaltim Plantation,” Informatika Mulawarman: Jurnal Ilmiah Ilmu Komputer, vol. 12, no. 2, pp. 94–103, 2017, doi: 10.30872/jim.v12i2.651.

N.M. Arfiana, E. Alisah, D. Ismiarti, “Penerapan metode Fuzzy Time Series Chen orde tinggi pada peramalan hasil penjualan,” Jurnal Riset Mahasiswa Matematika, vol. 1, no. 6, pp. 273–282, 2022, doi: 10.18860/jrmm.v1i6.14561.

R.S. Prihatin, E. Zuliarso, “Rainfall Prediction using the SARIMAX and LSTM Methods in Semarang City,” INOVTEK Polbeng - Seri Informatika, vol. 10, no. 3, pp. 1391–1401, 2025, doi: 10.35314/6sg7m889.

A. Indryani, U. Khaira, M.F. Putri, “Rainfall Prediction Using Long Short-Term Memory Method (Case Study: Jambi City),” Jurnal Pepadun, vol. 6, no. 1, pp. 57–70, 2025, doi: 10.23960/pepadun.v6i1.256.