Article Per Year (5 Year)
p-Index From 2020 - 2025
0.562
P-Index
This Author published in this journals
All Journal Applied Information System and Management Journal of Applied Data Sciences
Yamin, Fadhilah Bt Mat
Unknown Affiliation
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Languange, Linguistic, Communication & Media

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

Found 3 Documents
Search

 1 
Price Prediction of Aglaonema Ornamental Plants Using the Long Short-Term Memory (LSTM) Algorithm Sugiarti, Yuni; Suroso, Arif Imam; Hermadi, Irman; Sunarti, Euis; Yamin, Fadhilah Bt Mat
Journal of Applied Data Sciences Vol 6, No 2: MAY 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v6i2.640

Abstract

The Aglaonema ornamental plant is a horticultural commodity with high economic value and promising prospects. It is well known for its attractive leaf variations, earning it the nickname "Queen of Leaves." However, unpredictable price fluctuations make investing in Aglaonema speculative and high-risk. This research aims to predict the price of Aglaonema over the next five years using the Long Short-Term Memory (LSTM) algorithm. LSTM is considered superior to other algorithms in handling time series data. The model's performance was evaluated using Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and Mean Absolute Error (MAE) on a weekly Aglaonema price dataset covering the period from January 2012 to December 2023. The results demonstrate that the LSTM algorithm can predict Aglaonema prices with high accuracy, as indicated by the following metrics: MSE: 0.005 – Represents the average squared difference between predicted and actual prices. A lower MSE indicates higher model accuracy. RMSE: 0.07-RMSE provides a more interpretable error measurement as it retains the same units as the original data. A low RMSE signifies that the model's predictions closely align with actual values. MAE: 0.04 – Measures the absolute average difference between predicted and actual prices. A lower MAE value reflects a smaller prediction error. Thus, this research makes a significant contribution to the development of a machine learning-based price prediction system for the ornamental plant industry.
Challenges of Implementing Knowledge Management Systems in Agribusiness for Aglaonema Farmers Sugiarti, Yuni; Suroso, Arif Imam; Hermadi, Irman; Sunarti, Euis; Yamin, Fadhilah Bt Mat
Applied Information System and Management (AISM) Vol 8, No 1 (2025): Applied Information System and Management (AISM)
Publisher : Depart. of Information Systems, FST, UIN Syarif Hidayatullah Jakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15408/aism.v8i1.44074

Abstract

Aglaonema plants have become an important element in people's lives, serving as home decorations and sources of income. Aglaonema cultivation is increasingly popular, opening up business opportunities for farmers. This plant is not just a hobby but a horticultural commodity with high economic value. This research aims to analyze the difficulties faced by Aglaonema farmers, from seedling to marketing. This research uses a constructivist paradigm and qualitative methods, involving 20 informants from among farmers, traders, buyers, extension workers, and farmer organizations. Data were collected through interviews, focus group discussions, and observations, with triangulation to validate the information. Data analysis was conducted using the NVivo application. The research results show that the main difficulties faced by Aglaonema farmers include unpredictable price fluctuations, limited varieties, weak communication among farmers, traditional seedling methods, and a lack of information about seed supply. These findings emphasize the need for information sharing between farmers and stakeholders to improve productivity and quality in agribusiness, in line with consumer demands and technological advancements, which can be implemented through a knowledge management system platform in the Aglaonema agribusiness supply chain.
Challenges of Implementing Knowledge Management System Agribusiness for Aglaonema Farmers Sugiarti, Yuni; Suroso, Arif Imam; Hermadi, Irman; Sunarti, Euis; Yamin, Fadhilah Bt Mat
Applied Information System and Management (AISM) Vol. 8 No. 1 (2025): Applied Information System and Management (AISM)
Publisher : Depart. of Information Systems, FST, UIN Syarif Hidayatullah Jakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15408/aism.v8i1.44074

Abstract

Aglaonema plants have become an important element in people's lives, serving as home decorations and sources of income. Aglaonema cultivation is increasingly popular, opening up business opportunities for farmers. This plant is not just a hobby but a horticultural commodity with high economic value. This research aims to analyze the difficulties faced by Aglaonema farmers, from seedling to marketing. This research uses a constructivist paradigm and qualitative methods, involving 20 informants from among farmers, traders, buyers, extension workers, and farmer organizations. Data were collected through interviews, focus group discussions, and observations, with triangulation to validate the information. Data analysis was conducted using the NVivo application. The research results show that the main difficulties faced by Aglaonema farmers include unpredictable price fluctuations, limited varieties, weak communication among farmers, traditional seedling methods, and a lack of information about seed supply. These findings emphasize the need for information sharing between farmers and stakeholders to improve productivity and quality in agribusiness, in line with consumer demands and technological advancements, which can be implemented through a knowledge management system platform in the Aglaonema agribusiness supply chain.
Co-Authors Arif Imam Suroso Euis Sunarti Irman Hermadi Yuni Sugiarti, Yuni