Article Per Year (5 Year)
p-Index From 2020 - 2025
0.23
P-Index
This Author published in this journals
All Journal International Journal of Applied Sciences and Smart Technologies
Ngudiantoro Ngudiantoro
Sriwijaya University
Computer Science & IT Energy Engineering Industrial & Manufacturing Engineering

Published : 1 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 1 Documents
Search

 1 
Binary Logistic Regression Modeling on Net Income of Pagar Alam Coffee Farmers Ngudiantoro Ngudiantoro; Irmeilyana Irmeilyana; Mukhlizar Nirwan Samsuri
International Journal of Applied Sciences and Smart Technologies Volume 02, Issue 02, December 2020
Publisher : Universitas Sanata Dharma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24071/ijasst.v2i2.2734

Abstract

Pagar Alam Coffee is a Besemah coffee originating from the Smallholder Plantation in South Sumatra, Indonesia. The majority of Pagar Alam coffee farming is a hereditary business. Coffee farmers' income is very dependent on coffee production, production costs, and coffee prices. This study aims to obtain a probability model of Pagar Alam coffee farmers income based on the factors that influence it. The independent variables studied were the number of dependents, economic conditions, number of trees, age of trees, frequency of fertilizer used, frequency of pesticide used, production at harvest time, production outside harvest time, number of women workers outside the family, minimum price of coffee, maximum price of coffee, farmers' gross income, and land productivity. Modeling used binary logistic regression method on 179 respondents. There were three methods used, i.e. enter method, forward and backward methods. The model using enter method results the greatest prediction accuracy which is 87.7%. The factors that have a significant influence on the net income of Pagar Alam coffee farmers are gross income, land productivity, and the number of women workers from outside the family. The most influential variable is gross income.
Co-Authors Irmeilyana Mukhlizar Nirwan Samsuri