Article Per Year (5 Year)
p-Index From 2021 - 2026
1.745
P-Index
This Author published in this journals
All Journal BAREKENG: Jurnal Ilmu Matematika dan Terapan JURNAL PENDIDIKAN TAMBUSAI Jurnal Matematika UNAND Jurnal Sakinah : Journal of Islamic and Social Studies Fruitset Sains : Jurnal Pertanian Agroteknologi TOFEDU: The Future of Education Journal Verdict: Journal of Law Science Jurnal Teknologi Rekayasa Informasi dan Komputer
Ridho Saputra, Ridho
Unknown Affiliation
Religion Agriculture, Biological Sciences & Forestry Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Education Energy Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mathematics Mechanical Engineering Physics Social Sciences Transportation Other

Published : 9 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 1 Documents
Search
Journal : Jurnal Matematika UNAND

 1 
Locally Weighted KNN-Based Fuzzy Regression for Property Valuation under Market Uncertainty Yozza, Hazmira; Efendi, Riswan; Samat, Nor Azah; Rahmi, Izzati; Saputra, Ridho
Jurnal Matematika UNAND Vol. 15 No. 2 (2026)
Publisher : Departemen Matematika dan Sains Data FMIPA Universitas Andalas Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25077/jmua.15.2.163-178.2026

Abstract

Accurate evaluation of house valuation is crucial. Misestimation of house prices creates serious consequences for a variety of stakeholders. House prices can be modeled as a function of their constituent attributes. House prices are fuzzy due to negotiation and unpredictable market conditions. This study aims to develop rules to predict triangular fuzzy numbers of price for a given new house by implementing locally weighted KNN-based fuzzy regression, and to compare its performance with possibilistic fuzzy regression. The dataset used is the house valuation dataset. Data is examined using the modified Cheng and Lee k-nearest neighbor fuzzy regression and Tanaka’s possibilistic fuzzy regression. It is found that the modified Cheng and Lee k-nearest neighbor fuzzy regression outperforms possibilistic fuzzy regression in predicting the triangular fuzzy number of the house prices. The best performance is achieved when data is trained using the modified Cheng and Lee k-nearest neighbor fuzzy regression using: = 29 nearest neighbors, Minkowski distance with exponent parameter = 1.6 and an unequal weighting scheme with r = 1.
Co-Authors Adrian, Rama Almuhayar, Mawanda Amri Amri Asep Suherman Aswandi Aswandi, Aswandi Chaniago, Almunazza Kani Devianto, Dodi Edra Satmaidi Efendi, Riswan Farsyak, Varik Hazmira Yozza Izzati Rahmi HG Mahany, Chalisa Neyasyah, Muhammad Syirazi Nisa, Alvi Khairin Ramadhan, Akbar Syam Ramadhani, Nia Samat, Nor Azah Septaria, Ema Siregar, Faiq Raihan Zaky Yofita Sandra