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All Journal Scientific Journal of Informatics Abdi Laksana : Jurnal Pengabdian Kepada Masyarakat Journal of Soft Computing Exploration Journal of Advances in Information Systems and Technology Journal of Information System Exploration and Research Recursive Journal of Informatics Journal of Advances in Information Systems and Technology Jurnal Abdi Negeri Recursive Journal of Informatics
Efrilianda, Devi Ajeng
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Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Engineering Law, Crime, Criminology & Criminal Justice Library & Information Science Social Sciences Other

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Machine Learning Model Using Extreme Gradient Boosting (XGBoost) Feature Importance and Light Gradient Boosting Machine (LightGBM) to Improve Accurate Prediction of Bankruptcy Syafei, Risma Moulidya; Efrilianda, Devi Ajeng
Recursive Journal of Informatics Vol. 1 No. 2 (2023): September 2023
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/edp9qp23

Abstract

Abstract. Humans have limitations in processing and analyzing large amounts of data in a short time, including in terms of analyzing bankruptcy data. Bankruptcy data is one of the data that has complex information, so it requires technology that can assist in the process of analyzing and processing data more quickly and efficiently. Data science technology enables data processing and analysis on a large scale, using parallel processing techniques. Parallel processing can be implemented in machine learning models. Purpose: Using parallel processing techniques, data science technologies enable data processing and analysis at scale. Parallel processing can be implemented in machine learning models. Therefore, this study aims to implement a machine learning model using the Light Gradient Boosting Machine (LightGBM) classification algorithm which is optimized using Extreme Gradient Boosting (XGBoost) Feature Importance to increase the accuracy of bankruptcy prediction. Methods/Study design/approach: Bankruptcy prediction is carried out by applying LightGBM as a classification model and optimized using the XGBoost algorithm as a Feature Importance technique to improve model accuracy. the dataset used is the Taiwanese Bankruptcy dataset collected from the Taiwan Economic Journal for 1999 to 2009 and has 6,819 data. Taiwanese Bankruptcy is unbalanced data, so this study applies random oversampling. Result/Findings: The results obtained after going through the model testing process using the confusion matrix obtained an accuracy of the performance of LightGBM+XGBoost Feature Importance of 99.227%. Novelty/Originality/Value: So it can be concluded that the implementation of XGBoost Feature Importance can be used to improve LightGBM's performance in bankruptcy prediction.
Peningkatan Kompetensi Pekerja Muslim Indonesia Di Melaka Melalui Pelatihan Digital Marketing Untuk Mempersiapkan Peluang Usaha Pasca Selesai Kontrak Arief, Sandy; Mudrikah, Saringatun; Efrilianda, Devi Ajeng; Susanti, Anis; Azizatul Kholqiyah, Dewi
Abdi Laksana : Jurnal Pengabdian Kepada Masyarakat Vol 7 No 1 (2026): Abdi Laksana : Jurnal Pengabdian Kepada Masyarakat
Publisher : LPPM Universitas Pamulang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32493/abdilaksana.v7i1.57336

Abstract

Pelatihan digital marketing dan manajemen usaha bagi pekerja migran anggota Ikatan Pekerja Muslim Indonesia (IPMI) dilaksanakan pada 18 Mei 2025 di Dewan Mencari Court, Kuala Lumpur, diikuti 30 peserta dari berbagai cabang. Kegiatan ini bertujuan membekali peserta dengan keterampilan pemasaran digital, pengelolaan keuangan, dan perencanaan usaha berbasis data, sebagai persiapan pasca kontrak kerja. Metode community-based training dengan blended learning digunakan, mencakup tatap muka untuk materi dasar dan pembelajaran daring untuk pendalaman serta sertifikasi. Hasil evaluasi menunjukkan peningkatan pengetahuan sebesar 35%, dengan 90% peserta mampu menyusun rencana usaha dan 83% menguasai aplikasi pencatatan keuangan. Peserta juga menunjukkan perubahan mindset menjadi lebih berorientasi wirausaha dan mulai merancang usaha berbasis produk daerah asal. Pelatihan ini dinilai efektif dan relevan dengan kebutuhan, serta direkomendasikan untuk dilanjutkan dengan pendampingan berkelanjutan dan penguatan jejaring digital.
Co-Authors Adhirajasa, Danendra Yassar Alamsyah - Ali Djamhuri Anis Susanti Astuti, Clara Rosita Hani Yuli Azizatul Kholqiyah, Dewi Bachtiar, Hafid Fikri Budiman, Kholiq Dewi, Nuriana Rachmani Dianti, Erika Noor Erlita, Dea Hasaroh, Elsa Fadila Iwan junaedi Khoirunnisa, Oktaria Gina Mudrikah, Saringatun Muhammad Yusuf Mulyono Mulyono Pertiwi, Dwika Ananda Agustina Prasetya, Ananda Ramadhan, Hibban Daffa Sandy Arief Satrio Ardiansyah, Adi Syafei, Risma Moulidya Yahya Nur Ifriza Yusuf Wisnu Mandaya