Article Per Year (5 Year)
p-Index From 2020 - 2025
0.61
P-Index
This Author published in this journals
All Journal Journal of the Indonesian Mathematical Society BAREKENG: Jurnal Ilmu Matematika dan Terapan Jurnal Lebesgue : Jurnal Ilmiah Pendidikan Matematika, Matematika dan Statistika
Amanda, Nabila Tri
Unknown Affiliation
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Energy Engineering Mathematics Mechanical Engineering Physics Transportation

Published : 3 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 3 Documents
Search

 1 
ANALISIS KINERJA MODEL STACKING BERBASIS RANDOM FOREST DAN SVM DALAM KLASIFIKASI RUMAH TANGGA BERDASARKAN GARIS KEMISKINAN MAKANAN DI PROVINSI JAWA BARAT Ghiffary, Ghardapaty Ghaly; Amanda, Nabila Tri; Ardhani, Rizky; Sartono, Bagus; Firdawanti, Aulia Rizki
Jurnal Lebesgue : Jurnal Ilmiah Pendidikan Matematika, Matematika dan Statistika Vol. 5 No. 3 (2024): Jurnal Lebesgue : Jurnal Ilmiah Pendidikan Matematika, Matematika dan Statistik
Publisher : LPPM Universitas Bina Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46306/lb.v5i3.856

Abstract

The stacking method is an ensemble technique in machine learning that combines predictions from several base models to improve classification accuracy. This research applies the stacking method with two machine learning algorithms, namely Random Forest and Support Vector Machine (SVM) as base learners and logistic regression as a meta learner. This study aims to develop a classification model to identify households based on the food poverty line in West Java Province. The data used is KOR and household data in West Java Province sourced from the 2023 BPS National Socio-Economic Survey (Susenas). The variables used consisted of 24 independent variables with food poverty level as the response variable. Modeling was conducted using feature selection using Recursive Feature Elimination (RFE) and class imbalance handling using the ADASYN method. The results showed that the stacking model was superior to the single model with a balance accuracy of 0.81, sensitivity of 0.72, and specificity of 0.89. Feature importance analysis identified that calorie consumption, expenditure on cigarettes, meat and fruits, and expenditure on rice, eggs and other commodities contributed the most to the classification households based on the food poverty line in West Java Province.
COMPARISON OF SARIMA AND SARIMAX METHODS FOR FORECASTING HARVESTED DRY GRAIN PRICES IN INDONESIA Yulianti, Riska; Amanda, Nabila Tri; Notodiputro, Khairil Anwar; Angraini, Yenni; Mualifah, Laily Nissa Atul
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 19 No 1 (2025): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol19iss1pp319-330

Abstract

Harvested dry grain (HDG) is a vital commodity for rice availability and plays a strategic role in Indonesia’s agricultural economy. Farmers typically sell HDG to rice millers post-harvest, yet disparities between farm-level selling prices and consumer-level purchase prices. This price gap can lead to financial losses for farmers, highlighting the need for accurate forecasting can lead to potential losses for farmers. SARIMA models are effective in capturing seasonality and trends but often fail to incorporate external factors influencing the dependent variable, resulting in less accurate forecasts when such factors have significant impacts. SARIMAX models, however, can include exogenous variables like the government purchase price (GPP), which supports farmer income by establishing a price floor for HDG and directly influencing farm-level price dynamics. This study aims to compare the SARIMA and SARIMAX models in forecasting HDG prices at the farm level in Indonesia, using GPP as an exogenous variable. The dataset, obtained from Statistics Indonesia, covers January 2008 to March 2024, and the forecasting accuracy is measured using Mean Absolute Percentage Error (MAPE). The findings indicate that the best model is the SARIMAX model (1,1,1)(0,1,2)12, achieving a MAPE of 10.919%. The forecasted results show that HDG prices in 2024 are expected to remain stable, with only a gradual increase throughout the year.
ANALYSIS OF UNEMPLOYED YOUTH IN INDONESIA BY PANEL DATA REGRESSION WITH MODERATING VARIABLE Subanti, Sri; Amanda, Nabila Tri
Journal of the Indonesian Mathematical Society Vol. 30 No. 3 (2024): NOVEMBER
Publisher : IndoMS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22342/jims.30.3.1803.338-351

Abstract

Indonesia is entering the era of demographic bonus with the productive age dominating the population. Productive age is the main focus of the government in maximizing the demographic dividend, but Indonesia has the highest percentage of Not in employment, education or training (NEET) in Asia. NEET are people on 15 – 24 years old who do other activities outside of school, work or training. This study aims to analyze NEET in Indonesia using panel data with moderate regression analysis. The analysis of multiple linear regression is focused on the relationship between the independent and dependent variables without taking into other possible outcomes. By inserting a moderating variable, this study explores the relationship between the independent and dependent variables differently and aims to strengthen or weaken it. Under certain conditions, the relationship between the independent and dependent variables can be explained by the moderating variable. The research data used were obtained from the employment book and the website of BPS Indonesia, in the form of 34 cross section and 5 years time series data that tends to be stationary. The dependent variable is NEET with 5 independent variables including Human development index, the open unemployment rate, labor force participation rate, proportion of individuals who own phone, and proportion of informal employment. The moderating variable is the proportion of youth aged with ICT skills. The best model in regression analysis panel data is FEM with 4 significant independent variables and 92.75% of R-square. Moderating variable can moderates the relationship of NEET with its independent variables and increased the R-square to 94.19%.
Co-Authors Ardhani, Rizky Aulia Rizki Firdawanti Bagus Sartono Ghiffary, Ghardapaty Ghaly Mualifah, Laily Nissa Atul Riska Yulianti, Riska Sri Subanti Yenni Angraini