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All Journal International Journal of Electrical and Computer Engineering Jurnal Ilmiah Informatika Komputer Jurnal Psikologi Jurnal Ilmiah Teknologi dan Rekayasa IPTEK Journal of Proceedings Series Semantik Jurnal Ilmiah KOMPUTASI Applied Information System and Management ILKOM Jurnal Ilmiah RESOLUSI : REKAYASA TEKNIK INFORMATIKA DAN INFORMASI
Fitrianingsih Fitrianingsih
Fakultas Teknik-Jurusan Teknik Informatika , Universitas Gunadarma
Religion Humanities Automotive Engineering Computer Science & IT Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Materials Science & Nanotechnology Social Sciences Other

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A Comparative Study of Machine Learning Models for Fashion Product Demand Prediction: Exploring Algorithms, Data Splitting, and Feature Engineering Mardiah, Reviana Siti; Fitrianingsih, Fitrianingsih
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.v8i1.45600

Abstract

The fashion industry faces challenges in accurately predicting demand due to inherent uncertainty, leading to suboptimal inventory and financial losses. Machine learning (ML) offers a robust solution by analyzing large and complex data, identifying non-linear patterns, and providing more accurate predictions than conventional methods that rely on limited factors.  This research aims to compare and evaluate the performance of six different ML models—XGBoost, SVM, RF, GBM, KNN, and NN, considering the influence of feature engineering and various data split ratios on predicting fashion product demand. KNN and NN were included due to distinct modeling approaches and competitive capabilities in identifying local and non-linear patterns across numerical, categorical, and time series data.  Techniques such as feature extraction and selection and various data split ratios (70:30, 80:20, 90:10) were used.  Using Adidas sales data, the models were evaluated based on Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE). The results indicate that the XGBoost-based model with feature engineering consistently outperforms the other models across all data split ratios. Particularly, XGBoost with feature engineering at a data split ratio of 90:10 achieved the best performance with an RMSE of 4.46 and an MAE of 1.51. Analyzing model performance shows that the predictive ability of ML models is influenced by the implementation of feature engineering and the selection of the data split ratio. These results demonstrate the potential of using feature-engineered XGBoost models and optimized data ratios to mitigate the risk of stockouts or overstocks, and reduce financial losses and environmental waste.
Co-Authors Agushinta , Dewi Andri Hanafi Andri Hanafi Betty Suswati Cahyawati Diah Kusumarini Debyo Saptono Dody Pernadi Dody Pernadi Eka Patriya Elfitrin Syahrul Ernastuti Ernastuti Hermas Yuda Pamungkas Ihsan Jatnika Khairunnisa, Aprillia Intan Mardiah, Reviana Siti Quroyzhin Kartika Rini Rini Arianty Rodiah Rodiah Sarifuddin Madenda Suryarini Widodo Susetianingtias, Diana Tri Tri Mardhika Sampurna Tri Wahyu R.N