Integration of Fuzzy Matching and Domain Rules for Identifying Bali's Indigenous Banjar-Based Addresses in Last-Mile Delivery Without Predefined Gazetteers
Received: 26 November 2025 | Revised: 23 December 2025 | Accepted: 29 December 2025 | Online: 9 February 2026
Corresponding author: Muhammad Isa Ansori
Abstract
Identifying residential addresses in regions that depend on culturally embedded locality markers presents a significant challenge for geocoding and last-mile logistics, particularly when such references are absent from administrative gazetteers. In Bali, shipment records frequently incorporate indigenous Banjar-based address components, which introduce ambiguity and diminish courier-routing accuracy. This study proposes a hybrid framework that integrates fuzzy matching with domain-specific rules to identify Banjar references from unstructured address texts without relying on predefined gazetteers. Three similarity algorithms, namely Levenshtein Distance, Partial Ratio, and Token Sort Ratio, were combined into a Hybrid Mix Score to generate robust candidate matches. Domain rules, including prefix normalization, Banjar-Village-District hierarchy validation, and semantic disambiguation filters, were applied to eliminate linguistically similar but geographically invalid candidates. Using 17,354 cleaned delivery records from Pos Indonesia, the hybrid framework significantly enhanced interpretation reliability, with approximately 95% of all addresses converging to a single Highest Valid Candidate (HVC). The final predictions were linked to verified geographic centroids, enabling operationally meaningful location references. The results demonstrate that combining multi-metric fuzzy similarity with contextual domain constraints provides an effective and reproducible solution for geocoding indigenous Banjar-based addresses in last-mile delivery environments that lack standardized gazetteers.
Keywords:
fuzzy matching, domain rules, indigenous addressing, Banjar-based address, address localization, last-mile deliveryDownloads
References
M. Khalid, M. M. Yousaf, and M. U. Sadiq, "Toward Efficient Similarity Search under Edit Distance on Hybrid Architectures," Information, vol. 13, no. 10, Sept. 2022, Art. no. 452. DOI: https://doi.org/10.3390/info13100452
V. Silva, A. Amaral, T. Fontes, V. Silva, A. Amaral, and T. Fontes, "Sustainable Urban Last-Mile Logistics: A Systematic Literature Review," Sustainability, vol. 15, no. 3, Jan. 2023. DOI: https://doi.org/10.3390/su15032285
P. Cruz et al., "Automatic Identification of Addresses: A Systematic Literature Review," ISPRS International Journal of Geo-Information, vol. 11, no. 1, Dec. 2021.
M. S. M. Rudwan and J. V. Fonou-Dombeu, "Hybridizing Fuzzy String Matching and Machine Learning for Improved Ontology Alignment," Future Internet, vol. 15, no. 7, June 2023, Art. no. 229. DOI: https://doi.org/10.3390/fi15070229
R. Santos, P. Murrieta-Flores, and B. Martins, "Learning to combine multiple string similarity metrics for effective toponym matching," International Journal of Digital Earth, vol. 11, no. 9, pp. 913–938, Sept. 2018. DOI: https://doi.org/10.1080/17538947.2017.1371253
L. Liang, Y. Chang, Y. Quan, and C. Wang, "A Hierarchy-Aware Geocoding Model Based on Cross-Attention within the Seq2Seq Framework," ISPRS International Journal of Geo-Information, vol. 13, no. 4, Apr. 2024, Art. no. 135. DOI: https://doi.org/10.3390/ijgi13040135
B. Kilic, O. C. Bayrak, F. Gülgen, and M. Uzar, "Explainable address matching in online geocoding: filter-based feature selection and ensemble classification," GeoInformatica, vol. 30, no. 1, June 2026, Art. no. 5. DOI: https://doi.org/10.1007/s10707-025-00562-y
P. Cruz, L. Vanneschi, M. Painho, and P. Rita, "Automatic Identification of Addresses: A Systematic Literature Review," ISPRS International Journal of Geo-Information, vol. 11, no. 1, Dec. 2021, Art. no. 11. DOI: https://doi.org/10.3390/ijgi11010011
Y. Quan, Y. Chang, L. Liang, Y. Qiao, and C. Wang, "A Novel Address-Matching Framework Based on Region Proposal," ISPRS International Journal of Geo-Information, vol. 13, no. 4, Apr. 2024, Art. no. 138. DOI: https://doi.org/10.3390/ijgi13040138
P. Li et al., "A Multi-Semantic Feature Fusion Method for Complex Address Matching of Chinese Addresses," ISPRS International Journal of Geo-Information, vol. 14, no. 6, June 2025, Art. no. 227. DOI: https://doi.org/10.3390/ijgi14060227
M. Zhang, X. Liu, J. Ma, Z. Zhang, Y. Qiu, and Z. Jiang, "Non-Standard Address Parsing in Chinese Based on Integrated CHTopoNER Model and Dynamic Finite State Machine," Applied Sciences, vol. 13, no. 17, Aug. 2023, Art. no. 9855. DOI: https://doi.org/10.3390/app13179855
J. Martinez-Gil and J. M. Chaves-Gonzalez, "Automatic design of semantic similarity controllers based on fuzzy logics," Expert Systems with Applications, vol. 131, pp. 45–59, Oct. 2019. DOI: https://doi.org/10.1016/j.eswa.2019.04.046
"Dashboard Operasi Pos Indonesia", MileApp, https://board.mile.app/.
"Badan Pusat Statistik Kabupaten Gianyar." https://gianyarkab.bps.go.id/id.
M. Abdul Rahman, M. Aamir Basheer, Z. Khalid, M. Tahir, and M. Uppal, "Last Mile Logistics: Impact of Unstructured Addresses on Delivery Times," The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol. XLVIII-4/W5-2022, pp. 3–8, Oct. 2022. DOI: https://doi.org/10.5194/isprs-archives-XLVIII-4-W5-2022-3-2022
U. Singh, D. Ravi Shankar, G. Bellala, and V. Goel, "Geo-Spatially Informed Models for Geocoding Unstructured Addresses," in Proceedings of the 31st International Conference on Computational Linguistics: Industry Track, Abu Dhabi, UAE, Jan. 2025, pp. 236–242.
S. Yoo, E. Jeon, J. Hyeon, and J. Cho, "Adaptive ensemble techniques leveraging BERT based models for multilingual hate speech detection in Korean and english," Scientific Reports, vol. 15, no. 1, June 2025, Art. no. 19844. DOI: https://doi.org/10.1038/s41598-025-88960-y
I. Gagliardi, M. T. Artese, I. Gagliardi, and M. T. Artese, "Ensemble-Based Short Text Similarity: An Easy Approach for Multilingual Datasets Using Transformers and WordNet in Real-World Scenarios," Big Data and Cognitive Computing, vol. 7, no. 4, Sept. 2023. DOI: https://doi.org/10.3390/bdcc7040158
N. Elmobark, "A Comparative Analysis of Python Text Matching Libraries: A Multilingual Evaluation of Capabilities, Performance and Resource Utilization," International Journal of Environment, Engineering and Education, vol. 7, no. 1, pp. 48–60, Apr. 2025. DOI: https://doi.org/10.55151/ijeedu.v7i1.188
B. Bouaita, A. Beghriche, A. Kout, and A. Moussaoui, "A New Approach for Optimizing the Extraction of Association Rules," Engineering, Technology & Applied Science Research, vol. 13, no. 2, pp. 10496–10500, Apr. 2023. DOI: https://doi.org/10.48084/etasr.5722
J. P. Buckley, B. P. Buckles, and F. E. Petry, "Processing noisy structured textual data using a fuzzy matching approach: application to postal address errors," Soft Computing, vol. 4, no. 4, pp. 195–205, Dec. 2000. DOI: https://doi.org/10.1007/s005000000054
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