Job Vacancy Recommendation System using JACCARD Method On Graph Database
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 consisti...
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| Format: | Article |
| Language: | English |
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LPPM ISB Atma Luhur
2025-07-01
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| Series: | Jurnal Sisfokom |
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| Online Access: | https://jurnal.atmaluhur.ac.id/index.php/sisfokom/article/view/2387 |
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| author | Saiful Riza Wahyu Fuadi Yesy Afrillia |
| author_facet | Saiful Riza Wahyu Fuadi Yesy Afrillia |
| author_sort | Saiful Riza |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-4fe7b84fcd714aefaa2ecdbe5378af1e |
| institution | Kabale University |
| issn | 2301-7988 2581-0588 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | LPPM ISB Atma Luhur |
| record_format | Article |
| series | Jurnal Sisfokom |
| spelling | doaj-art-4fe7b84fcd714aefaa2ecdbe5378af1e2025-08-20T03:56:05ZengLPPM ISB Atma LuhurJurnal Sisfokom2301-79882581-05882025-07-0114334935610.32736/sisfokom.v14i3.23872050Job Vacancy Recommendation System using JACCARD Method On Graph DatabaseSaiful Riza0https://orcid.org/0009-0006-9932-1032Wahyu Fuadi1Yesy Afrillia2Universitas MalikussalehMalikussaleh UniversityMalikussaleh UniversityIn 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.https://jurnal.atmaluhur.ac.id/index.php/sisfokom/article/view/2387graph databasejaccard coefficientneo4jrecommendation system |
| spellingShingle | Saiful Riza Wahyu Fuadi Yesy Afrillia Job Vacancy Recommendation System using JACCARD Method On Graph Database Jurnal Sisfokom graph database jaccard coefficient neo4j recommendation system |
| title | Job Vacancy Recommendation System using JACCARD Method On Graph Database |
| title_full | Job Vacancy Recommendation System using JACCARD Method On Graph Database |
| title_fullStr | Job Vacancy Recommendation System using JACCARD Method On Graph Database |
| title_full_unstemmed | Job Vacancy Recommendation System using JACCARD Method On Graph Database |
| title_short | Job Vacancy Recommendation System using JACCARD Method On Graph Database |
| title_sort | job vacancy recommendation system using jaccard method on graph database |
| topic | graph database jaccard coefficient neo4j recommendation system |
| url | https://jurnal.atmaluhur.ac.id/index.php/sisfokom/article/view/2387 |
| work_keys_str_mv | AT saifulriza jobvacancyrecommendationsystemusingjaccardmethodongraphdatabase AT wahyufuadi jobvacancyrecommendationsystemusingjaccardmethodongraphdatabase AT yesyafrillia jobvacancyrecommendationsystemusingjaccardmethodongraphdatabase |