Article Per Year (5 Year)
p-Index From 2021 - 2026
0.61
P-Index
This Author published in this journals
All Journal JOURNAL OF SCIENCE AND SOCIAL RESEARCH International Journal of Engineering, Science and Information Technology Journal Collabits
Elyandri Prasiwiningrum
Universitas Rokania
Astronomy Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Physics Social Sciences Transportation

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

Found 3 Documents
Search

 1 
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.
Electre Method Decision Support System for Concrete Type Selection in Building Structures Elyandri Prasiwiningrum; Rasna Rasna; Putu Doddy Heka Ardana; Fifto Nugroho; Qurrotul Aini
International Journal of Engineering, Science and Information Technology Vol 5, No 2 (2025)
Publisher : Malikussaleh University, Aceh, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52088/ijesty.v5i2.843

Abstract

Choosing the correct type of concrete in building construction is a crucial step that significantly affects the structure's quality, durability, and safety. Concrete, as the most widely used building material, has various types with different characteristics, such as compressive strength, tensile strength, modulus of elasticity, and durability. During this time, people who make concrete often ignore the strength of the concrete itself. They do not care what will happen if the manufacture of concrete is not by the recommended concrete construction. Concrete is the primary material for constructing a building such as a building. The quality of concrete is determined by its constituent materials, which include hydraulic cement, coarse aggregate, fine aggregate, water, and other additives. The concrete mix determines the strength of the concrete. If the building is built with unsuitable concrete, it will be quickly destroyed during natural disasters such as earthquakes. Based on this, the concrete type selection must be precise and accurate. Decision Support Systems are used in this research to provide additional input to decision-makers. Decision support systems can deliver maximum results by using algorithms or methods. The Electre method is one of the multicriteria decision-making methods based on ranking by pairwise comparisons of existing alternatives based on appropriate criteria. Overall, this research is expected to significantly improve the quality of decision-making in selecting concrete types, resulting in a safer, more durable, and more efficient building structure. The results obtained after inputting criteria values and alternative values are concrete types such as reinforced concrete, precast concrete, and lightweight concrete.
Co-Authors Ego Oktafanda Ersha Thoriq Ferdinansyah Fifto Nugroho Junadhi Junadhi Muhammad Roffi Putu Doddy Heka Ardana Qurrotul Aini Rafi Ramadhan Rasna