Article Per Year (5 Year)
p-Index From 2019 - 2024
1.784
P-Index
This Author published in this journals
All Journal Jurnal Buana Informatika JUTI: Jurnal Ilmiah Teknologi Informasi Telematika Seminar Nasional Informatika (SEMNASIF) Sistemasi: Jurnal Sistem Informasi Matrik : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer Jurnal Nasional Pendidikan Teknik Informatika (JANAPATI) Progresif: Jurnal Ilmiah Komputer PROGRES PENDIDIKAN Universitas Muhammadiyah Yogyakarta Undergraduate Conference Proceeding Jurnal INFOTEL
Vynska Amalia Permadi
Universitas Pembangunan Nasional Veteran Yogyakarta
Agriculture, Biological Sciences & Forestry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Dentistry Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Library & Information Science Mathematics Physics Public Health Social Sciences Other

Published : 12 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 1 Documents
Search
Journal : Jurnal Nasional Pendidikan Teknik Informatika (JANAPATI)

 1 
Demand Forecasting for Improved Inventory Management in Small and Medium-Sized Businesses Dian Indri Purnamasari; Vynska Amalia Permadi; Asep Saepudin; Riza Prapascatama Agusdin
Jurnal Nasional Pendidikan Teknik Informatika : JANAPATI Vol. 12 No. 1 (2023)
Publisher : Prodi Pendidikan Teknik Informatika Universitas Pendidikan Ganesha

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23887/janapati.v12i1.57144

Abstract

Small and medium-sized businesses are constantly seeking new methods to increase productivity across all service areas in response to increasing consumer demand. Research has shown that inventory management significantly affects regular operations, particularly in providing the best customer relationship management (CRM) service. Demand forecasting is a popular inventory management solution that many businesses are interested in because of its impact on day-to-day operations. However, no single forecasting approach outperforms under all scenarios, so examining the data and its properties first is necessary for modeling the most accurate forecasts. This study provides a preliminary comparative analysis of three different machine learning approaches and two classic projection methods for demand forecasting in small and medium-sized leathercraft businesses. First, using K-means clustering, we attempted to group products into three clusters based on the similarity of product characteristics, using the elbow method's hyperparameter tuning. This step was conducted to summarize the data and represent various products into several categories obtained from the clustering results. Our findings show that machine learning algorithms outperform classic statistical approaches, particularly the ensemble learner XGB, which had the least RMSE and MAPE scores, at 55.77 and 41.18, respectively. In the future, these results can be utilized and tested against real-world business activities to help managers create precise inventory management strategies that can increase productivity across all service areas.
Co-Authors Agusdin, Riza Prapascatama Alfriska Deviane Puspita Aliza Hanum Anggani Asep Saepudin Dian Indri Purnamasari Edwina Ayu Christy Frans Richard Kodong Herlina Jayadianti Juwairiah Juwairiah Mangaras Yanu Florestiyanto Muhamad Azam Fuadi Partoyo Partoyo Rezky Putratama Raharjo Santosa, Budi Santoso, Bagus Jati Sylvert Prian Tahalea Tahalea, Sylvert Prian Tohari Ahmad Wilis Kaswidjanti