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All Journal Sinkron : Jurnal dan Penelitian Teknik Informatika Jurnal JTIK (Jurnal Teknologi Informasi dan Komunikasi)
Syahputra, Akhmal Angga
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management

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Application of the XGBoost Model with Hyperparameter Tuning for Industry Classification for Job Applicants Syahputra, Akhmal Angga; Rujianto Eko Saputro
Sinkron : jurnal dan penelitian teknik informatika Vol. 8 No. 3 (2024): Research Artikel Volume 8 Issue 3, July 2024
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v8i3.13840

Abstract

The development of technology and changes in job market dynamics have created new challenges in aligning education with industry needs. In this research, the XGBoost model with hyperparameter tuning was applied for industry classification on job applicant data taken from the Kaggle dataset LinkedIn Job Postings in 2023. This dataset consists of 23 attributes with a total of 33,085 job vacancy data points. The experimental results show that both the model without hyperparameter tuning and with GridSearchCV produce the same classification accuracy, which is 0.89 or 89%, with stable precision, recall, and F1-Score values. The best parameters found in this study are colsample_bytree = 1.0, learning_rate = 0.3, max_depth = 6, min_child_weight = 1, n_estimators = 100, and subsample = 1.0. However, cross-validation using k-fold shows a significant increase in accuracy to 0.90, or 90%. This finding confirms that the use of cross-validation can improve the performance estimation of the model more accurately and robustly by utilizing all available data for training and testing. Moreover, the implementation of cross-validation demonstrates the importance of leveraging all data points to enhance model reliability and robustness. Future research can explore alternative hyperparameter tuning methods and apply the model to larger datasets to further validate the generalizability and reliability of the XGBoost model in different application contexts. Thus, this study underscores the significance of rigorous model evaluation techniques in achieving high-performing machine learning models
Implementasi Data Mining untuk Clustering Lowongan Pekerjaan Menggunakan Metode Algoritma K-Means Mubarok, Rifqi; Syahputra, Akhmal Angga; Permana, Abdillah Teguh; Sholiah, Lifa; Tarwoto
Jurnal JTIK (Jurnal Teknologi Informasi dan Komunikasi) Vol 9 No 2 (2025): APRIL-JUNE 2025
Publisher : Lembaga Otonom Lembaga Informasi dan Riset Indonesia (KITA INFO dan RISET)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/jtik.v9i2.3438

Abstract

The development of digital technology has transformed the way businesses recruit employees online. This study aims to create an interactive dashboard that facilitates job seekers and companies, using clustering methods with the K-Means algorithm to analyze job posting data in the United States. The data from the Kaggle LinkedIn Job Postings 2023 dataset, consisting of 33,000 records, is processed using the CRISP-DM phases: business understanding, data understanding, data preparation, modeling, evaluation, and deployment. The clustering analysis results in four job categories: low-mid-level general jobs, high-level executive jobs, time-based jobs, and mid-high-level professional jobs. Model evaluation shows good clustering quality with a Silhouette Coefficient of 0.78 and a Davies-Bouldin Index of 0.55. The developed dashboard helps companies plan recruitment and job seekers find positions matching their skills and salary expectations. The practical contribution of this study is modernizing the recruitment process, assisting companies and recruitment agencies in screening candidates more efficiently, and improving job matching through deeper data analysis.
Implementasi Data Mining untuk Clustering Lowongan Pekerjaan Menggunakan Metode Algoritma K-Means Mubarok, Rifqi; Syahputra, Akhmal Angga; Permana, Abdillah Teguh; Sholiah, Lifa; Tarwoto
Jurnal JTIK (Jurnal Teknologi Informasi dan Komunikasi) Vol 9 No 2 (2025): APRIL-JUNE 2025
Publisher : Lembaga Otonom Lembaga Informasi dan Riset Indonesia (KITA INFO dan RISET)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/jtik.v9i2.3438

Abstract

The development of digital technology has transformed the way businesses recruit employees online. This study aims to create an interactive dashboard that facilitates job seekers and companies, using clustering methods with the K-Means algorithm to analyze job posting data in the United States. The data from the Kaggle LinkedIn Job Postings 2023 dataset, consisting of 33,000 records, is processed using the CRISP-DM phases: business understanding, data understanding, data preparation, modeling, evaluation, and deployment. The clustering analysis results in four job categories: low-mid-level general jobs, high-level executive jobs, time-based jobs, and mid-high-level professional jobs. Model evaluation shows good clustering quality with a Silhouette Coefficient of 0.78 and a Davies-Bouldin Index of 0.55. The developed dashboard helps companies plan recruitment and job seekers find positions matching their skills and salary expectations. The practical contribution of this study is modernizing the recruitment process, assisting companies and recruitment agencies in screening candidates more efficiently, and improving job matching through deeper data analysis.
Co-Authors Mubarok, Rifqi Permana, Abdillah Teguh Rujianto Eko Saputro Sholiah, Lifa Tarwoto