Job Vacancy Recommendation System using JACCARD Method On Graph Database

Authors

DOI:

https://doi.org/10.32736/sisfokom.v14i3.2387

Keywords:

Graph Database, Jaccard Coefficient, Neo4j, Recommendation System

Abstract

In the rapidly evolving digital era, recommendation systems play a crucial role in helping users discover relevant information aligned with their preferences. PT Nirmala Satya Development, a company engaged in psychology and human resource development, faces challenges in utilizing big data consisting of 500 applicants, 500 job postings, and 500 job applications to generate accurate and relevant job recommendations. This study develops a job recommendation system using the Jaccard Coefficient method to measure similarity between users based on their job application history, implemented within a Neo4j graph database. The system models the relationships between entities through nodes and edges, allowing dynamic analysis using the Cypher Query Language. Testing on 237 users demonstrated that the majority received at least one relevant recommendation, with recall values often reaching 1.0, especially among users who had a single job target. The system achieved precision values ranging from 10% to 20%, which is considered acceptable given that ten recommendations are generated per user. The highest F1-score reached 0.33, although some users received F1 = 0 due to limited application history or unique preferences. Overall, the system effectively delivers personalized and efficient job recommendations, particularly for active users. This research also proves that combining the Jaccard Coefficient with a graph database structure is a powerful approach to representing and analyzing complex relationships between users and job postings in a modern recruitment platform.

Author Biographies

Saiful Riza, Universitas Malikussaleh

Informatics Engineering

Wahyu Fuadi, Malikussaleh University

Informatics Engineering

Yesy Afrillia, Malikussaleh University

Informatics Engineering

References

A. A. P. Devi and D. B. Tonara, “Rancang Bangun Recommender System dengan Menggunakan Metode Collaborative Filtering untuk Studi Kasus Tempat Kuliner di Surabaya,” Jurnal Informatika Dan Sistem Informasi, pp. 102–110, 2015.

F. A. Ajipradana, “Sistem Rekomendasi Film Menggunakan Algoritma Itesm-Based Collaborative Filtering Dan Basis Data Graph,” Jurnal Univsitas Diponegoro, 2017.

Y. Liang, “Research on Personalized Recommendation System for Graph Database,” in 2018 International Conference on Network, Communication, Computer Engineering (NCCE 2018), Atlantis Press, 2018, pp. 1019–1023.

D. Fernandes and J. Bernardino, “Graph Databases Comparison: AllegroGraph, ArangoDB, InfiniteGraph, Neo4J, and OrientDB.,” Proceedings of the 7th International Conference on Data Science, vol. 18, pp. 373–380, 2018.

A. Ilic and M. Kabiljo, “Recommending items to more than a billion people.” Accessed: Oct. 13, 2024. [Online]. Available: https://engineering.fb.com/2015/06/02/core-infra/recommending-items-to-more-than-a-billion-people/

W. Fuadi, M. Maryana, and U. Zahara, “Sistem penerjemahan kitab pelajaran akhlak ke dalam bahasa Indonesia menggunakan metode Euclidean Distance,” TECHSI-Jurnal Teknik Informatika, vol. 11, no. 1, pp. 92–103, 2019.

S. Pawestri and Y. Suyanto, “Analisis Perbandingan Metode Similarity untuk Kemiripan Dokumen Bahasa Indonesia pada Deteksi Kemiripan Teks Bahasa Indonesia,” JURNAL MEDIA INFORMATIKA BUDIDARMA, vol. 3, no. 8, pp. 1440–1450, 2024.

W. Hidayat, E. Utami, A. F. Iskandar, A. D. Hartanto, and A. B. Prasetio, “Perbandingan Performansi Model pada Algoritma K-NN terhadap Klasifikasi Berita Fakta Hoaks Tentang Covid-19,” Edumatic: Jurnal Pendidikan Informatika, vol. 5, no. 2, pp. 167–176, 2021.

I. M. Siregar, D. Pratama, and C. Himawan, “Penggunaan Jaccard Similarity Coefficient dalam Optimasi Proses Rekrutmen Karyawan Berbasis Profil dan Kompetensi,” SINTECH (Science and Information Technology) Journal, vol. 7, no. 2, pp. 101–111, 2024.

J. Florentina and H. C. Kurniawan, “Perbandingan Penerapan Relational Database Dan Graph Database Dalam Sistem Rekomendasi Film,” Jurnal Telematika, vol. 18, no. 2, pp. 94–103, 2023.

A. A. Ristias, E. D. Wahyuni, and S. F. A. Wati, “Komparasi Kinerja Metode Cosine dan Jaccard Similarity dalam Content-Based Recommendation Systems (CBRS) pada Aplikasi Eventhings,” Jurnal Informatika dan Teknik Elektro Terapan, vol. 12, no. 3, 2024.

L. Sebastia, I. Garcia, E. Onaindia, and C. Guzman, “e-Tourism: a tourist recommendation and planning application,” International Journal on Artificial Intelligence Tools, vol. 18, no. 05, pp. 717–738, 2009.

B. Sarwar, G. Karypis, J. Konstan, and J. Riedl, “Item-based collaborative filtering recommendation algorithms,” in Proceedings of the 10th international conference on World Wide Web, 2001, pp. 285–295.

S. Sunardi, A. Yudhana, and I. A. Mukaromah, “Implementasi Deteksi Plagiarisme Menggunakan Metode N-Gram Dan Jaccard Similarity Terhadap Algoritma Winnowing,” Transmisi: Jurnal Ilmiah Teknik Elektro, vol. 20, no. 3, pp. 105–110, 2018.

Wikipedia, “Graph database,” wikipedia.org. Accessed: May 15, 2025. [Online]. Available: https://upload.wikimedia.org/wikipedia/commons/3/3a/GraphDatabase_PropertyGraph.png.

N. Francis et al., “Cypher: An evolving query language for property graphs,” in Proceedings of the 2018 international conference on management of data, 2018, pp. 1433–1445.

C. L. Rujiani, E. R. Syahputra, and S. D. Andriana, “Implementation Of Application Programming Interface (API) Using Representational State Transfer (REST) Architecture For Development E-Learning Unhar Medan,” INTERNATIONAL JOURNAL OF DATA SCIENCE AND VISUALIZATION (IJDSV), vol. 1, no. 1, 2023.

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Published

2025-07-27

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