Article Per Year (5 Year)
p-Index From 2020 - 2025
0.408
P-Index
This Author published in this journals
All Journal JURNAL AKUNTANSI, EKONOMI dan MANAJEMEN BISNIS Journal of Mathematics, Computation and Statistics (JMATHCOS)
I Gusti Ngurah, Sentana Putra
Unknown Affiliation
Economics, Econometrics & Finance Mathematics

Published : 2 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search

 1 
Evaluasi Efektivitas Promosi Makanan Cepat Saji Menggunakan Pendekatan Hibrida Metode Non-Parametrik Robust dan Simulasi Monte Carlo Berdasarkan Segmentasi Ukuran Pasar I Gusti Ngurah, Sentana Putra
Jurnal Akuntansi, Ekonomi dan Manajemen Bisnis Vol. 13 No. 1 (2025): Jurnal Akuntansi, Ekonomi dan Manajemen Bisnis - Juli 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaemb.v13i1.9313

Abstract

The fast food industry is highly competitive, requiring effective promotional strategies to drive sales and maintain customer loyalty. This study evaluates the effectiveness of fast food promotions using a hybrid approach combining robust non-parametric methods, Random Forest, and Monte Carlo simulation. The analysis focuses on segmenting the market by Market Size (Large, Medium, Small) to identify the most impactful promotional strategies for each segment. Non-parametric A/B testing using the Kruskal-Wallis test revealed significant differences in sales across promotions, with Promotion 1 emerging as the most effective overall. The Random Forest model highlighted LocationID as the most critical factor influencing sales, particularly in Large markets. Monte Carlo simulation further demonstrated that Promotion 1 yields the highest Expected Monetary Value (EMV), making it the optimal choice for long-term sales growth. The findings emphasize the importance of tailoring promotional strategies to specific market segments, considering factors such as location, timing, and store history. This study provides actionable insights for businesses to optimize promotional campaigns, enhance sales performance, and achieve sustainable growth in the fast food industry.
Performance Comparison of Random Forest and XGBoost Optimized with Cuckoo Search Algorithm for Coconut Milk Adulteration Detection Using FTIR Spectroscopy I Gusti Ngurah, Sentana Putra; Kusman Sadik; Agus Mohamad Soleh; Cici Suhaeni
Journal of Mathematics, Computations and Statistics Vol. 8 No. 2 (2025): Volume 08 Nomor 02 (Oktober 2025)
Publisher : Jurusan Matematika FMIPA UNM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35580/jmathcos.v8i2.7817

Abstract

Coconut milk has emerged as a strategic food commodity in the global tropical region, with market demand growing at 7.2% per annum since 2021. This increasing demand has led to sophisticated adulteration practices, including dilution with water. Such adulteration not only reduces the nutritional value but also poses serious health risks, including food poisoning and allergic reactions. This study developed an innovative detection method combining Fourier Transform Infrared (FTIR) spectroscopy with a sophisticated machine learning algorithm. We analyzed 719 coconut milk samples (wavelength range 2500-4000 nm) consisting of traditional market products and instant commercial products. This study aims to develop an FTIR-based coconut milk adulteration detection model by optimizing RF and XGBoost parameters using CSA and evaluating the comparative performance of the two models in identifying different types of adulterants. The spectral data underwent rigorous preprocessing using a combination of Standard Normal Variate (SNV) and Savitzky-Golay (SG) techniques to overcome the effects of noise and light scattering, which significantly improved feature extraction. The results show that CSA-optimized XGBoost achieves superior performance with 92% accuracy and 91% F1 score, outperforming Random Forest in all evaluation metrics. The model shows particular strength in precision (98%), indicating its outstanding ability to minimize false positives in adulteration detection. Stability tests through 30 experimental repetitions reveal that the combination of XGBoost+CSA maintains consistent performance with minimal variance, confirming its reliability for industrial applications. Comparative analysis shows that the combination of SNV+SG preprocessing improves the accuracy of the baseline model by 9-12%, while CSA optimization provides an additional performance improvement of 10-15%. This research makes significant contributions to food science and safety. This study demonstrates the effectiveness of CSA in optimizing spectroscopic models, achieving 19.5% higher precision. The combination of SNV+SG preprocessing improves the baseline accuracy by 9-12%, while CSA optimization provides an additional performance improvement of 10-15%. This study not only provides a rapid and non-destructive adulteration detection solution but also proves the effectiveness of the CSA approach in optimizing the spectroscopic model. These findings have important implications for strengthening food safety regulations and developing real-time quality control systems in the coconut milk industry.
Co-Authors Agus Mohamad Soleh Cici Suhaeni Kusman Sadik