Model Klasifikasi Machine Learning untuk Prediksi Ketepatan Penempatan Karir
The complexity of the job market requires individuals and organizations to understand the trends and needs of the world of work. One of the main challenges is the right career placement. That is becoming increasingly popular is the use of Machine Learning algorithms in the decision-making process....
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | Indonesian |
| Published: |
STMIK Palangkaraya
2024-03-01
|
| Series: | Jurnal Saintekom |
| Subjects: | |
| Online Access: | https://ojs.stmikplk.ac.id/index.php/saintekom/article/view/512 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850199943183073280 |
|---|---|
| author | Hendri Mahmud Nawawi Agung Baitul Hikmah Ali Mustopa Ganda Wijaya |
| author_facet | Hendri Mahmud Nawawi Agung Baitul Hikmah Ali Mustopa Ganda Wijaya |
| author_sort | Hendri Mahmud Nawawi |
| collection | DOAJ |
| description | The complexity of the job market requires individuals and organizations to understand the trends and needs of the world of work. One of the main challenges is the right career placement. That is becoming increasingly popular is the use of Machine Learning algorithms in the decision-making process. ML classification models such as Random Forest, Decision Tree, Naïve Bayes, KNN, and SVM have demonstrated their potential in uncovering hidden patterns from data, including a person's educational history, work experience and interests. In this research, the application of the ML classification model is aimed at predicting career placement. From the data sample used of 215, this research evaluates the effectiveness of various ML models in the context of career placement. As a result, the Random Forest Model is superior to other proposed models with an accuracy value of 87% and an AUC/ROC value of 0.93 which indicates a very good classification value. Meanwhile, the SVM model with Linear Kernel shows the lowest performance with an accuracy value of 67%. Apart from getting information on the best accuracy and AUC/ROC values, the results of this research found that the 'ssc_presentage' attribute (high school exam percentage) is an important factor in career placement decisions. |
| format | Article |
| id | doaj-art-f5b3058eeab7405997e49e7eea7c8fac |
| institution | OA Journals |
| issn | 2088-1770 2503-3247 |
| language | Indonesian |
| publishDate | 2024-03-01 |
| publisher | STMIK Palangkaraya |
| record_format | Article |
| series | Jurnal Saintekom |
| spelling | doaj-art-f5b3058eeab7405997e49e7eea7c8fac2025-08-20T02:12:29ZindSTMIK PalangkarayaJurnal Saintekom2088-17702503-32472024-03-01141132510.33020/saintekom.v14i1.512458Model Klasifikasi Machine Learning untuk Prediksi Ketepatan Penempatan KarirHendri Mahmud Nawawi0Agung Baitul Hikmah1Ali Mustopa2Ganda Wijaya3Universitas Nusa MandiriUniversitas Bina Sarana InformatikaUniversitas Bina Sarana InformatikaUniversitas Nusa MandiriThe complexity of the job market requires individuals and organizations to understand the trends and needs of the world of work. One of the main challenges is the right career placement. That is becoming increasingly popular is the use of Machine Learning algorithms in the decision-making process. ML classification models such as Random Forest, Decision Tree, Naïve Bayes, KNN, and SVM have demonstrated their potential in uncovering hidden patterns from data, including a person's educational history, work experience and interests. In this research, the application of the ML classification model is aimed at predicting career placement. From the data sample used of 215, this research evaluates the effectiveness of various ML models in the context of career placement. As a result, the Random Forest Model is superior to other proposed models with an accuracy value of 87% and an AUC/ROC value of 0.93 which indicates a very good classification value. Meanwhile, the SVM model with Linear Kernel shows the lowest performance with an accuracy value of 67%. Apart from getting information on the best accuracy and AUC/ROC values, the results of this research found that the 'ssc_presentage' attribute (high school exam percentage) is an important factor in career placement decisions.https://ojs.stmikplk.ac.id/index.php/saintekom/article/view/512machine learningrandom forestjob placementclassificationprediction |
| spellingShingle | Hendri Mahmud Nawawi Agung Baitul Hikmah Ali Mustopa Ganda Wijaya Model Klasifikasi Machine Learning untuk Prediksi Ketepatan Penempatan Karir Jurnal Saintekom machine learning random forest job placement classification prediction |
| title | Model Klasifikasi Machine Learning untuk Prediksi Ketepatan Penempatan Karir |
| title_full | Model Klasifikasi Machine Learning untuk Prediksi Ketepatan Penempatan Karir |
| title_fullStr | Model Klasifikasi Machine Learning untuk Prediksi Ketepatan Penempatan Karir |
| title_full_unstemmed | Model Klasifikasi Machine Learning untuk Prediksi Ketepatan Penempatan Karir |
| title_short | Model Klasifikasi Machine Learning untuk Prediksi Ketepatan Penempatan Karir |
| title_sort | model klasifikasi machine learning untuk prediksi ketepatan penempatan karir |
| topic | machine learning random forest job placement classification prediction |
| url | https://ojs.stmikplk.ac.id/index.php/saintekom/article/view/512 |
| work_keys_str_mv | AT hendrimahmudnawawi modelklasifikasimachinelearninguntukprediksiketepatanpenempatankarir AT agungbaitulhikmah modelklasifikasimachinelearninguntukprediksiketepatanpenempatankarir AT alimustopa modelklasifikasimachinelearninguntukprediksiketepatanpenempatankarir AT gandawijaya modelklasifikasimachinelearninguntukprediksiketepatanpenempatankarir |