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All Journal Jurnal Teknologi dan Sistem Komputer The Journal of Indonesia Sustainable Development Planning (JISDeP)
Kurniawan, Bayu Dwi
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Computer Science & IT Economics, Econometrics & Finance Electrical & Electronics Engineering Energy Environmental Science Law, Crime, Criminology & Criminal Justice Social Sciences

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Perbandingan Metode Ensemble Machine Learning untuk Klasifikasi Tenaga Kerja di Indonesia dengan Random Forest, XGBoost, dan CatBoost Kurniawan, Bayu Dwi; Wijayanto, Arie Wahyu
Jurnal Teknologi dan Sistem Komputer [IN PRESS] Volume 10, Issue 4, Year 2022 (October 2022)
Publisher : Department of Computer Engineering, Engineering Faculty, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/jtsiskom.2022.14031

Abstract

Survei Angkatan Kerja Nasional (Sakernas) adalah survei periodik yang besar sehingga membutuhkan pengolahan data  kompleks serta validasi benar untuk menjaga kualitas data. Salah satu pertanyaan Sakernas yang pengisian dan validasinya secara manual yaitu lapangan pekerjaan utama. Untuk memberikan validasi, Machine Learning dapat diterapkan dengan memanfaatkan informasi pada isian lain. Penelitian ini menggunakan metode Random Forest, XGBoost, dan CatBoost untuk klasifikasi lapangan pekerjaan utama pada Sakernas Agustus 2019. Berdasarkan hasil, ketiga model memiliki performa yang hampir sama baik dari presisi, recall, dan f1 yaitu untuk sektor primer dan tersier diatas 90 % dan sektor sekunder sebesar 80%. Model dari Random Forest, XGBoost, dan CatBoost memiliki akurasi sebesar 91,80%; 90,88%; dan 91,84%. Nilai Area Under Curve (AUC) dari ketiga model relatif tinggi dengan CatBoost memiliki nilai tertinggi pada klasifikasi sektor primer, sekunder, dan tersier masing-masing sebesar 1,00; 0,97; dan 0,98.
Youth, Agriculture, and Food Security: Understanding the Farmer Regeneration Challenge in Sumatra Nurarifin, Nurarifin; Kurniawan, Bayu Dwi
The Journal of Indonesia Sustainable Development Planning Vol 6 No 1 (2025): April
Publisher : Pusbindiklatren Bappenas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46456/jisdep.v6i1.650

Abstract

Agriculture plays a major role in Sumatra’s economy, however, younger generations lack interest in the agriculture sector. This will affect farmer regeneration, thus threatening food security. Currently, comprehensive analyses that reveal the level of farmer regeneration remain scarce. This study seeks to examine the level of farmer regeneration by understanding the determinants of an individual to be a farmer. In addition, we aim to assess the impact of young farmers on food security by utilizing the National Labor Force Survey (Sakernas) from 2018 to 2022. Logistic regression is used to examine how individual characteristics influence the likelihood of being employed as a farmer. The result of the study shows that only a very limited proportion of farmers' descendants in Sumatra choose to carry on their family farming business. Typically, younger individuals, women, individuals with at least a high school degree, and those who attended training, have migrated and adopted digital technology, tend to be more reluctant to become farmers. The analysis also highlights that promoting opportunities for young farmers and lowering the prevalence of undernourishment has a favourable effect on food security. To address the low percentage of younger individuals choosing farming, policies should focus on attracting educated youth through targeted training and incentives. Enhancing digital access and modernizing agriculture can also improve productivity and food security by reducing undernourishment.
Co-Authors Arie Wahyu Wijayanto Nurarifin, Nurarifin