Article Per Year (5 Year)
p-Index From 2021 - 2026
1.8
P-Index
This Author published in this journals
All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Bulletin of Electrical Engineering and Informatics Abdimas Talenta : Jurnal Pengabdian Kepada Masyarakat International Journal of Supply Chain Management Journal of Saintech Transfer Indonesian Journal of Electrical Engineering and Computer Science Journal of Applied Data Sciences
Marischa Elveny, Marischa
Unknown Affiliation
Agriculture, Biological Sciences & Forestry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering Environmental Science Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice Social Sciences Transportation Other

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

Found 4 Documents
Search
Journal : IAES International Journal of Artificial Intelligence (IJ-AI)

 1 
An boosting business intelligent to customer lifetime value with robust M-estimation Elveny, Marischa; Y. Syah, Rahmad B.; M. Nasution, Mahyuddin K.
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 2: June 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v13.i2.pp1632-1639

Abstract

When a business concentrates too much on acquiring new clients rather than retaining old ones, mistakes are sometimes made. Each customer has a different value. Customer lifetime value (CLV) is a metric used to assess longterm customer value. Customer value is a key concern in any commercial endeavor. When there are variations in customer behavior, CLV forecasts the value of total customer income when the data distribution is not normal, and outliers are present. Robust M-estimation, a maximum likelihood type estimator, is used in this study to enhance CLV data. Through the minimization of the regression parameter from the residual value, robust Mestimation eliminates data outliers in customer metric data. With an accuracy of 94.15%, R-square is used to gauge model performance. This research shows that CLV optimization can be used as a marketing and sales strategy by companies.
An efficiency metaheuristic model to predicting customers churn in the business market with machine learning-based Y. Syah, Rahmad B.; Muliono, Rizki; Akbar Siregar, Muhammad; Elveny, Marischa
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 2: June 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v13.i2.pp1547-1556

Abstract

Metaheuristics is an optimization method that improves and completes a task in a short period of time based on its objective function. The goal of metaheuristics is to search the search space for the best solution. Machine learning detects patterns in large amounts of data. Machine learning encourages enterprise automation in a variety of areas in order to improve predictive ability without requiring explicit programming to make decisions. The percentage of customers who leave the company or stop using the service is referred to as churn. The purpose of this research is to forecast customer churn in the market business. Particle swam optimization (PSO) was used in this study as a metaheuristic method to provide a strategy to guide the search process for new customers and obtain parameters for processing by support vector regression (SVR). SVR predicts the value of a continuous variable by determining the best decision line to find the best value. The number of transactions, the number of periods, and the conversion value are the parameters that are visible. Efficiency models are added to improve prediction results through two optimizations: prediction flexibility and risk minimization. The findings demonstrate the effectiveness of prediction in reducing customer churn.
A novel approach to optimizing customer profiles in relation to business metrics Elveny, Marischa; Nasution, Mahyuddin K. M.; Zarlis, Muhammad; Efendi, Syahril; Syah, Rahmad B. Y.
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 1: March 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v13.i1.pp440-450

Abstract

Business is very closely related to customers. Each user owns the data, and the data is used to identify cross-selling opportunities for each customer. For example, the type of product or service purchased, the frequency of purchases, geographic location, and so on. By doing so, you can gain the ability to manage and analyze customer data, allowing you to create new opportunities in industries that were previously difficult to enter. The purpose of optimizing user profiles is to determine minimum or maximum business value and improve efficiency by determining user needs. In this study, multivariate adaptive regression spline (MARS) is a statistical model used to explain the relationship between the response variable and the predictor variable. Robust is used to find variable relationships to make predictions. To improve classification performance, the model is validated using a confusion matrix. The results show an accuracy value of 84.5%, with better time management (period management) reflected in the number of hours spent by merchants as well as discounts during that time period, which has a significant impact on any business. In addition, the distance between customers and merchants is also important, as customers prefer merchants who are closer to them to save time and transportation costs.
MobileChiliNet: convolutional neural network for chili leaves classification Rahman, Sayuti; Elveny, Marischa; Ramli, Marwan; Manurung, Dionikxon
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 5: October 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i5.pp3757-3770

Abstract

Chili pepper (Capsicum annuum) is an important crop in many countries, including Indonesia, which plays an important role in local economy and food production. To meet the high demand, effective agricultural management, especially the diagnosis and treatment of plant diseases, is essential. This study aims to improve the accuracy of chili leaf disease classification while reducing the computational cost so that it can be applied to low-cost smart farming systems. Through the development of the MobileChiliNet architecture, which is the result of pruning and fine-tuning of MobileNetV2, this model achieves the best accuracy, better than other CNNs such as ResNet50 and VGG16. Testing with various optimizers and learning rate schedulers shows that AdamW with PolynomialDecay provides the best performance by increasing the validation accuracy to 96.48%. This approach successfully reduces the computational complexity while maintaining high accuracy, so that it can be implemented in smart farming systems at a lower cost.
Co-Authors Efendi, Syahril Fauzan Nurahmadi Habib Satria Hartono Hartono Jaya, Ivan Lubis, Fahrurrozi Mahyuddin K. M Nasution Manurung, Dionikxon Marwan Ramli Muhammad Akbar Siregar Muhammad Rivai Muhammad Zarlis, Muhammad Nababan, Erna B Nasution, M.K.M. Nasution, Mahyuddin K.M Purnamasari, Fanindia Putri Nasution, Umaya Ramadhani M. O. Rahmad Syah, Rahmad Rahman, Sayuti Rana Fathinah Ananda Rizki Muliono Sajadin Sembiring Sri Melvani Hardi Syah, Rahmad B. Y. Syah, Rahmad B.Y Syah, Rahmad Bayu Y. Syah, Rahmad B.