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All Journal Kajian Ilmiah Mahasiswa Manajemen CAUCHY: Jurnal Matematika Murni dan Aplikasi Jurnal Riset Mahasiswa Matematika
Andy Irawan
Staff Pengajar Jurusan Sosial Ekonomi Pertanian Fak. Pertanian UNIB
Decision Sciences, Operations Research & Management Mathematics

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PENGARUH KARAKTERISTIK INDIVIDU DAN KARAKTERISTIK KERJA TERHADAP ORGANIZATIONAL CITIZENSHIP BEHAVIORS DENGAN KEPUASAN KERJA SEBAGAI MEDIATOR PADA EVENT ORGANIZER DI SURABAYA Irawan, Andy
Kajian Ilmiah Mahasiswa Manajemen Vol 1, No 2 (2012)
Publisher : Kajian Ilmiah Mahasiswa Manajemen

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (82.946 KB)

Abstract

This study aims to identify the influence of individual characteristics and job characteristics on job satisfaction and job satisfaction influences on organizational citizenship behaviors and job satisfaction in the ability to identify the mediating influence of job characteristics on organizational citizenship behaviors.Design used in this study is causal design. Exogenous variables in this study are individual characteristics and job characteristics whereas endogenous variables are job satisfaction and organizational citizenship behaviors. Sample number of 150 samples, but there are 3 of respondents had outliers so the number of samples for further research are 147 respondents. Data analysis techniques using strucutural equation model. The findings in this study suggests that individual characteristics and job characteristics proved to have a significant effect on job satisfaction. Job satisfaction is a significant effect on organizational citizenship behaviors and job satisfaction shown to mediate the effect of job characteristics on organizational citizenship behaviors.
Deep Neural Network-Based Student Performance Prediction with Hessian-Free Optimization Irawan, Andy; Abidin, Zainal; Jamhuri, Mohammad
Jurnal Riset Mahasiswa Matematika Vol 5, No 4 (2026): Jurnal Riset Mahasiswa Matematika
Publisher : Universitas Islam Negeri Maulana Malik Ibrahim Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18860/jrmm.v5i4.37951

Abstract

Predicting student graduation predicates is important for academic monitoring and timely intervention in higher education. This study investigates graduation predicate prediction using deep neural networks under three feature-group settings: academic-only, non-academic-only, and combined academicnon-academic features. A multilayer perceptron with three hidden layers was trained using SGD with momentum, RMSProp, Adam, and a damped Hessian-free optimization procedure. Two tasks were considered: a four-class graduation predicate classification task and a binary risk-screening task in which Sufficient was treated as the positive risk class. The results show that the combined feature group achieved the best multiclass performance, with an accuracy of 0.8478 and a weighted F1-score of 0.8274. Hessian-free optimization consistently produced the best results across all feature-group scenarios, with the clearest gain appearing in the non-academic-only setting. In the additional risk-screening analysis, non-academic variables provided meaningful but limited predictive signal, and Major emerged as the strongest individual predictor. These findings show that combining academic and non-academic information improves graduation predicate prediction and that Hessian-free optimization is an effective training strategy for deep neural classification in educational data.
A Damped Hessian-Free Newton--Conjugate Gradient Method for Weighted Multiclass Neural Classification Irawan, Andy; Abidin, Zainal; Jamhuri, Mohammad
CAUCHY: Jurnal Matematika Murni dan Aplikasi Vol 11, No 1 (2026): CAUCHY: JURNAL MATEMATIKA MURNI DAN APLIKASI
Publisher : Mathematics Department, Universitas Islam Negeri Maulana Malik Ibrahim Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18860/cauchy.v11i1.40243

Abstract

This study presents a deterministic damped Hessian-free Newton--CG method for weighted multiclass neural classification. The method is built from a weighted categorical cross-entropy objective, a damped local quadratic model, and a matrix-free curvature representation through Hessian--vector products. The search direction is computed by an inexact conjugate gradient solve, while Armijo backtracking and adaptive damping are used to improve stability. The method is implemented for the classification of academic predicate categories using preprocessed student data with mixed categorical and numerical features. Its numerical behavior is compared with SGD with momentum, RMSProp, and Adam under the same loss, initialization, and network architecture. The proposed method is computationally feasible, attains the best overall weighted test-set performance among the compared methods, and exhibits a distinct optimization trajectory driven by curvature-informed updates. These results show that a damped Hessian-free formulation provides a mathematically transparent, reproducible, and practically competitive framework for second-order optimization in multiclass neural classification.
Co-Authors Mohammad Jamhuri Zainal Abidin