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Made Hanindia Prami Swari
Informatika, Universitas Pembangunan Nasional Veteran Jawa Timur
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Journal : JOIV : International Journal on Informatics Visualization

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Implication of ICWFPSO as Optimization Neural Network Algorithm on Sales Forecasting System Swari, Made Hanindia Prami; Rizki, Agung Mustika; Satwika, I Kadek Susila; Handika, I Putu Susila
JOIV : International Journal on Informatics Visualization Vol 8, No 3-2 (2024): IT for Global Goals: Building a Sustainable Tomorrow
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.8.3-2.3134

Abstract

Predictive systems play a crucial role in a company's operations and strategy by aiding in more informed and data-driven decision-making and more effective planning and budgeting. It is possible to develop an intelligent system to perform forecasting. Neural networks offer significant advantages in forecasting systems due to their flexible modeling capabilities. However, this algorithm's fundamental weakness is the slow convergence rate and being trapped in a local minimum. To overcome it, this research is conducted to optimize the NN algorithm using the ICWFPSO to produce a forecasting algorithm with high accuracy and faster execution time using real e-commerce sales data for the past 7 years.  Algorithm performance testing tests the Mean Absolute Error (MAE) value of the forecasting system using three scenarios: the NN forecasting algorithm, the NN optimized with ICWFPSO on the weight value, and the same scheme. Still, the optimized value is the hyperparameter value.  ICWPSO has been shown to enhance the performance of PSO by tuning the inertia weight dynamically, which helps balance exploration and exploitation during the optimization process. The best prediction result is obtained when optimizing the hyperparameters using the ICWFPSO optimization technique compared to using traditional NN or optimizing weight value with ICWFPSO with the MAE value of 245.32958984375, and the best performance is obtained at iterations below 100. Further, gradient-based optimization methods might be generally preferred for their efficiency and effectiveness in handling large-scale neural network training.
Co-Authors Agung Mustika Rizki Agung Mustika Rizki, Agung Mustika Aileena Solicitor Costa Rica El Chidtian Akbar, Fawwaz Ali Andreas Nugroho Sihananto Andreas Nugroho Sihanto Anggraini Puspita Sari Ani Dijah Rahajoe Arifan, Miftakhul Askara Raditya Aurora Prameswaty, Almira Azhari SN Basuki Rahmat Damayanti, Alfina Diyasa, I Gede Susrama Mas Dwi Wahyuningtyas Eva Yulia Puspaningrum Faisal Muttaqin Faisal Muttaqin Fetty Tri Anggraeny Firmansyah Firdaus Anhar Firza Prima Aditiawan Gilang Gema Ramadhan Handika, I Putu Susila Henni Endah Wahanani Hindrayani, Kartika Maulida Hutagaol, LeonHoss I Gede Winaya I Gusti Ngurah Agung Mahendra I Kadek Susila Satwika I Kadek Susila Satwika I Nyoman Sujana I Putu Susila Handika I Putu Susila Handika I WAYAN SUDIARSA Ika Nur Habibah Jannatul Firdaus Joni Bastian Joni Bastian Julastri, Bregsi Atingsari Kevin Santosa, Mochammad Lintang Perdana Rochmat Sugiharto Mandyartha, Eka Prakarsa Martoni Martoni Maulana, Hendra Muhammad Farhan Maulana Muhammad Hakam Fardana Muhammad Rifki Bahrul Ulum Muhammad Syafril Hidayat Muttaqin, Faisal Muttaqin, Faisal Nabila Rizky Amali Putri Ngurah Agus Sanjaya ER Nine Alvariqati Varqa Ansori Nugroho Sihananto, Anderas Permana, Eriko Indra Phitria, Shaum Pratama Wirya Atmaja Pratiwi, Nisa Prismahardi Aji Riyantoko Putra, Chrystia Aji Rabbani, Rafi Rahel Widya Arianti Rahel Widya Arianti Rahmadsyach, Mochammad Taufiq Retno Mumpuni Risnaldy Novendra Irawan Satria, Vinza Hedi Satwika, I Kadek Susila Sugiarto Sukandar, Ivan Christopher Syaifullah Jauharis Saputra, Wahyu Tasya Ardhian Nisaa' Tentra Olivia Tresna Maulana Fahrudin Tresna Maulana Fahrudin Ulummuddin, Ikhya Wahyu Syaifullah Jauharis Saputra Wariyanti Nugroho Putri Wayan Gede Suka Parwita Welda Wirya Atmaja, Pratama Yuniar Purbasari, Intan