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All Journal INDONESIAN JOURNAL OF APPLIED PHYSICS Jurnal Akuntansi
Affix Mareta
Universitas Tidar
Astronomy Economics, Econometrics & Finance Energy Materials Science & Nanotechnology Physics

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Machine Learning-Based Cow Milk Quality Classification using Recursive Feature Elimination Cross-Validation Damar Wicaksono; Affix Mareta; Ardy Erdiyanto; Nuzula Afianah; Rafly Ramadhani
INDONESIAN JOURNAL OF APPLIED PHYSICS Vol 14, No 2 (2024): October
Publisher : Department of Physics, Sebelas Maret University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.13057/ijap.v14i2.85064

Abstract

Milk quality is of paramount importance as it directly impacts consumer health and well-being. High-quality milk is rich in essential nutrients such as calcium, protein, and vitamins, contributing to overall nutrition. Moreover, ensuring milk quality is vital for preventing the transmission of diseases and contaminants through dairy products. Therefore, research in this field is essential to guaranteeing the safety and nutritional value of milk consumed by individuals of all ages. In this paper, the design of machine learning-based grade measuring devices with recursive feature elimination with cross-validation (RFECV) is carried out as a guide in the design of a milk grade detection system. The milk is rated as low, medium, or high based on these criteria. The sensors will gather this information from the milk with the aid of the microcontroller. The algorithms utilized in this study and the results obtained from K-Nearest Neighbors (KNN) combined with the RFECV algorithm have a higher accuracy value: 17,20% better than the support vector machine (SVM) model, 25.37% better than the single K-Nearest Neighbors (KNN), and 26.37% better than the random forest (RF) model trained without RFECV. Using seven input features (pH, temperature, taste, odor, fat, turbidity, and color), the proposed model produces 96.27% accuracy.
Moral Injury as Mediating Variable between Client Pressure and Auditor Professional Judgment Masculine Muhammad Muqorobin; Affix Mareta; Beta Estri Adiana; Ahmad Abdul Aziz; Alex Johanes Simamora
Jurnal Akuntansi Vol. 30 No. 2 (2026): May-August 2026
Publisher : Fakultas Ekonomi dan Bisnis Universitas Tarumanagara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24912/ja.v30i2.3794

Abstract

This study aims to examine the effect of client pressure on auditors’ professional judgment with moral injury as a mediating variable. The study uses a quantitative approach through a questionnaire survey distributed to 256 auditors working in Indonesian public accounting firms. The data are analyzed using Structural Equation Modeling (SEM) to test the relationships among variables. The results show that client pressure has a positive effect on auditors’ moral injury. Furthermore, moral injury has a negative effect on auditors’ professional judgment. The findings also indicate that moral injury mediates the relationship between client pressure and professional judgment, suggesting that the impact of client pressure on audit decisions operates not only directly but also through auditors’ internal psychological mechanisms. This research introduces the concept of moral injury into the auditing context as a psychological mechanism explaining how client pressure can undermine the quality of auditors' professional judgment.
Co-Authors Ahmad Abdul Aziz Alex Johanes Simamora Ardy Erdiyanto Beta Estri Adiana Damar Wicaksono Masculine Muhammad Muqorobin Nuzula Afianah Rafly Ramadhani