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All Journal TEKNIK INFORMATIKA Jurnal Simetris Jurnal S2 Pendidikan Matematika Prosiding SNATIF JURNAL MANAJEMEN (EDISI ELEKTRONIK) Simtek : Jurnal Sistem Informasi dan Teknik Komputer Jurnal Informa: Jurnal Penelitian dan Pengabdian Masyarakat TIN: TERAPAN INFORMATIKA NUSANTARA Infotech: Journal of Technology Information International Journal of Community Service Simpatik: Jurnal sistem Informasi dan Informatika Buletin Sistem Informasi dan Teknologi Islam Journal of Management and Digital Business Duta Abdimas: Jurnal Pengabdian Masyarakat Innovative: Journal Of Social Science Research SmartComp CSRID Jurnal Sosialita: Jurnal Kajian Sosial dan Pendidikan
Nurmalitasari Nurmalitasari
Prodi Magister Pendidikan Matematika Program Pascasarjana UNS
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Journal : TEKNIK INFORMATIKA

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Hybrid Logistic Super Newton Model for Predicting Small Sample Size Data Nurmalitasari, Nurmalitasari; Awang Long, Zalizah; Nurchim, Nurchim
JURNAL TEKNIK INFORMATIKA Vol. 18 No. 1: JURNAL TEKNIK INFORMATIKA
Publisher : Department of Informatics, Universitas Islam Negeri Syarif Hidayatullah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15408/jti.v18i1.43929

Abstract

Logistic regression is a model commonly used for predicting data with large sample sizes. However, in real-world scenarios, many cases involve small datasets that need to be addressed using logistic regression. The aim of this research is to develop a hybrid logistic regression model to address issues with small sample sizes by combining the Newton Raphson and Super Cubic methods. This hybrid model is applied to predict student dropout at Universitas Duta Bangsa Surakarta. The performance of the hybrid model is evaluated using two main metrics: the convergence of the parameter approximation to measure the precision of parameter estimation, and the ROC curve to assess prediction accuracy. Experimental results show that the Hybrid Logistic Super Newton model outperforms the logistic regression Newton Raphson model, requiring only three iterations to converge, thus improving computational efficiency. Moreover, this model achieves higher accuracy, with an AUC of 0.8833. These findings suggest that the developed model has the potential to be applied in various fields, such as healthcare, finance, and others, offering an effective solution for accurate, real-time predictive analytics. Further research could focus on optimizing the model’s computational efficiency and exploring its application in other domains with small dataset challenges, such as healthcare and finance.
Co-Authors Adenta Sari, Nur Avia Al Mustofa, Muhammad Hafizh Alliya, Sania Qorrii Atina, Vihi Awang Long, Zalizah Dwi Hartanti Eko Purwanto Hasanah, Herliyani Imam Sujadi Irawan, Ridwan Dwi JOKO SUSANTO Laksono, Ardean Risqi Miftakhul Jannah, Ulfarida Nugroho Arif Sudibyo Nugroho, Mohammad Yusuf Nurchim Nurchim Permatasari, Hanifah Pipin Widyaningsih Rifki Hidayat Sajad, Abdullah Sari, Nur Avia Adenta Setiawati, Neha Poetri Singgih Purnomo Syawaludin, M Ainur Taufik Hidayat Tri Atmojo Kusmayadi Tri Wulandari, Rahmawati Desi Utami, Maulinda Devi