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All Journal JOURNAL OF SCIENCE AND SOCIAL RESEARCH Journal Collabits
Elyandri Prasiwiningrum
Universitas Rokania
Computer Science & IT Economics, Econometrics & Finance Education Engineering Social Sciences

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Comparative Analysis of Arima and Facebook Prophet Algorithms for E-Commerce Product Sales Forecasting Ersha Thoriq Ferdinansyah; Muhammad Roffi; Rafi Ramadhan; Elyandri Prasiwiningrum
Journal Collabits Vol. 3 No. 1 (2026)
Publisher : Journal Collabits

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22441/collabits.v3i1.37651

Abstract

Uncertainty in market demand poses a fundamental challenge in e-commerce supply chain management. This study evaluates the accuracy of daily sales forecasting for the "Set" product category in the Amazon Sales Report dataset by comparing the traditional ARIMA model with the modern additive Facebook Prophet model. Inventory management in e-commerce is often hindered by unpredictable demand fluctuations, which are difficult to forecast manually. The findings reveal that Prophet outperforms ARIMA, achieving a mean absolute error (MAE) of 35.412 and a root mean square error (RMSE) of 48.723, corresponding to an 18.82% improvement in forecasting efficiency. Prophet’s ability to capture weekly seasonal patterns demonstrates its suitability as a more reliable approach for operational stock management.
SMART TOUR: SUSTAINABLE TOURISM MANAGEMENT AND RECOMMENDATION SYSTEM IN ROKAN HULU USING LONG SHORT-TERM MEMORY Elyandri Prasiwiningrum; Ego Oktafanda; Junadhi
JOURNAL OF SCIENCE AND SOCIAL RESEARCH Vol. 9 No. 2 (2026): April 2026
Publisher : Smart Education

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54314/kpzth108

Abstract

Rokan Hulu Regency possesses significant natural and cultural tourism potential; however, tourist visitation rates remain relatively low due to the lack of integrated destination information and concerns regarding environmental sustainability. This study aims to design and develop SMART TOUR, a sequence-prediction–based tourism recommendation system utilizing a deep learning approach. The system is designed to learn tourist travel patterns based on their visitation history by employing a dual-input Long Short-Term Memory (LSTM) model that integrates location and activity data. To address the limitations of real-world data, a synthetic dataset was constructed in a realistic manner by considering tourist preferences, inter-location distances, visit duration, and the popularity of tourist attractions. Experimental results indicate that the proposed recommendation system achieves competitive performance, with a Top-1 Accuracy of 39.7%, Top-3 Accuracy of 76.2%, and Top-5 Accuracy reaching 92.4%. Evaluation was conducted using the Top-K Accuracy approach, which is considered most relevant in the context of recommendation systems as it reflects user behavior in selecting from multiple alternatives. This study demonstrates that integrating LSTM models with predictive techniques can enhance the quality of tourism recommendations and support the development of more environmentally friendly and sustainable tourism practices. SMART TOUR is expected to serve as an innovative solution to strengthen the tourism appeal of Rokan Hulu and empower local communities through the adoption of technology.
Co-Authors Ego Oktafanda Ersha Thoriq Ferdinansyah Junadhi Junadhi Muhammad Roffi Rafi Ramadhan