Implementasi Deep Learning untuk Entity Matching pada Dataset Obat (Studi Kasus K24 dan Farmaku)


  • Rivanda Putra Pratama Departemen Sistem Informasi, Institut Teknologi Sepuluh Nopember
  • Rahmat Hidayat Departemen Sistem Informasi, Institut Teknologi Sepuluh Nopember
  • Nisrina Fadhilah Fano Departemen Sistem Informasi, Institut Teknologi Sepuluh Nopember
  • Adam Akbar Departemen Sistem Informasi, Institut Teknologi Sepuluh Nopember
  • Nur Aini Rakhmawati Departemen Sistem Informasi, Institut Teknologi Sepuluh Nopember



Entity Matching, Deep Learning, DeepMatcher, Dataset, Hybrid


Data processing speed in companies is important to speed up their analysis. Entity matching is a computational process that companies can perform in data processing. In conducting data processing, entity matching plays a role in determining two different data but referring to the same entity. Entity matching problems arise when the dataset used in the comparison is large. The deep learning concept is one of the solutions in dealing with entity matching problems. DeepMatcher is a python package based on a deep learning model architecture that can solve entity matching problems. The purpose of this study was to determine the matching between the two datasets with the application of DeepMatcher in entity matching using drug data from and The comparison model used is the Hybrid model. Based on the test results, the Hybrid model produces accurate numbers, so that the entity matching used in this study runs well. The best accuracy value of the 10th training with an F1 value of 30.30, a precision value of 17.86, and a recall value of 100.


Abdullah, S. M. S. A., Ameen, S. Y. A., M. Sadeeq, M. A., & Zeebaree, S. (2021). Multimodal Emotion Recognition using Deep Learning. Journal of Applied Science and Technology Trends, 2(02), 52–58.

Akbar, A., Fano, N. F., Pratama, R. P., Hidayat, R., & Rakhmawati, N. A. (2021). Dataset Obat Untuk Penelitian Entity Matching.

Arsi, P., Wahyudi, R., & Waluyo, R. (2021). Optimasi SVM Berbasis PSO pada Analisis Sentimen Wacana Pindah Ibu Kota Indonesia. Jurnal RESTI (Rekayasa Sistem Dan Teknologi Informasi), 5(2), 231–237.

Chen, C., Golshan, B., Halevy, A., Tan, W., & Doan, A. (2018). BigGorilla: An Open-Source Ecosystem for Data Preparation and Integration. IEEE Data Eng. Bull., 41, 10–22.

Christophides, V., Efthymiou, V., & Stefanidis, K. (2015). Entity Resolution in the Web of Data. Synthesis Lectures on the Semantic Web: Theory and Technology, 5(3), 1–122.

Fu, C., Han, X., Sun, L., Chen, B., Zhang, W., Wu, S., & Kong, H. (2019). End-to-End Multi-Perspective Matching for Entity Resolution. Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence, 4961–4967.

Garreta, R., & Moncecchi, G. (2013). Learning Scikit-Learn: Machine Learning in Python. Packt Publishing Ltd.

Hardi, W. (2006). Mengukur kinerja search engine : sebuah eksperimentasi penilaian precision and recall untuk informasi ilmiah bidang ilmu perpustakaan dan informasi [Search Engines performance evaluation: an experimental the value of precision and recall for scientific information in LIS field.]. In Visi Pustaka [National Library of Indonesia]. Perpustakaan Nasional RI [National Library of Indonesia].


Kasai, J., Qian, K., Gurajada, S., Li, Y., & Popa, L. (2019). Low-resource Deep Entity Resolution with Transfer and Active Learning. Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, 5851–5861.

Li, Y., Li, J., Suhara, Y., Doan, A., & Tan, W.-C. (2020). Deep entity matching with pre-trained language models. Proceedings of the VLDB Endowment, 14(1), 50–60.

Mudgal, S., Li, H., Rekatsinas, T., Doan, A., Park, Y., Krishnan, G., Deep, R., Arcaute, E., & Raghavendra, V. (2018). Deep Learning for Entity Matching. Proceedings of the 2018 International Conference on Management of Data, 19–34.

Powers, D. M. W. (2020). Evaluation: from precision, recall and F-measure to ROC, informedness, markedness and correlation. International Journal of Machine Learning Technology, 2(1), 37–63.

Rule, A., Tabard, A., & Hollan, J. D. (2018). Exploration and Explanation in Computational Notebooks. Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems, 1–12.

Thirumuruganathan, S., Tang, N., Ouzzani, M., & Doan, A. (2020). Data Curation with Deep Learning. Proceedings of the 23rd International Conference on Extending Database Technology (EDBT), 277–286.

Yuan, Q., Shen, H., Li, T., Li, Z., Li, S., Jiang, Y., Xu, H., Tan, W., Yang, Q., Wang, J., Gao, J., & Zhang, L. (2020). Deep learning in environmental remote sensing: Achievements and challenges. Remote Sensing of Environment, 241, 111716.

Zhao, C., & He, Y. (2019). Auto-EM: End-to-end Fuzzy Entity-Matching using Pre-trained Deep Models and Transfer Learning. The World Wide Web Conference on - WWW ’19, 2413–2424.




How to Cite

Pratama, R. P., Hidayat, R., Fano, N. F., Akbar, A., & Rakhmawati, N. A. (2021). Implementasi Deep Learning untuk Entity Matching pada Dataset Obat (Studi Kasus K24 dan Farmaku). JISKA (Jurnal Informatika Sunan Kalijaga), 6(3), 130–138.