Article Per Year (5 Year)
p-Index From 2021 - 2026
0.61
P-Index
This Author published in this journals
All Journal Indo-Fintech Intellectuals: Journal of Economics and Business Jurnal Software Engineering and Computational Intelligence Jurnal Ilmiah Betrik : Besemah Teknologi Informasi dan Komputer
Nina Dwi Putriani
Universitas Serelo
Humanities Computer Science & IT Economics, Econometrics & Finance Social Sciences

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

Found 3 Documents
Search

 1 
Analisis Penggunaan AI dalam Keberhasilan Customer Experience Pengguna Aplikasi E-Commerce Shopee Arief Zikry; Muhammad Bitrayoga; Siska Yulia Defitri; Akhmad Dahlan; Nina Dwi Putriani
Indo-Fintech Intellectuals: Journal of Economics and Business Vol. 4 No. 3 (2024): Indo-Fintech Intellectuals: Journal of Economics and Business
Publisher : Lembaga Intelektual Muda (LIM) Maluku

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54373/ifijeb.v4i3.1387

Abstract

E-commerce has experienced rapid growth with the adoption of digital technology particularly in Southeast Asia. AI enables Shopee to provide personalized product recommendations, responsive customer service, and secure shopping experiences. This study analyzes the use of Artificial Intelligence (AI) technology in enhancing user experience on the Shopee e-commerce platform. The research employs a mixed-method approach involving 400 Shopee users in Indonesia. Product recommendation and user experience personalization both significantly increase user satisfaction. User experience personalization has a greater influence than product recommendations in increasing user satisfaction. The implications of this study suggest that AI not only enhances Shopee's operational efficiency but also strengthens customer satisfaction and loyalty, making it a key element in maintaining competitiveness in today's competitive e-commerce industry
Optimasi Hyperparameter WOA-SVM pada Citra Daun Kopi Terpupuk NPK Agustian Prakarsya; Nina Dwi Putriani; Yusi Nurmala Sari; Firza Septian
BETRIK Vol. 16 No. 02 (2025): Jurnal Ilmiah BETRIK : Besemah Teknologi Informasi dan Komputer
Publisher : PPPM Institut Teknologi Pagar Alam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36050/zrj1e094

Abstract

This study aims to analyze the impact of Whale Optimization Algorithm (WOA) optimization on the performance of Support Vector Machine (SVM) in classifying images of coffee leaves treated with NPK fertilizer. WOA is employed to find the optimal combination of SVM parameters to improve classification accuracy. The dataset consists of coffee leaf images that have undergone feature extraction based on color and texture. Performance evaluation was conducted using a confusion matrix, classification report, and heatmap visualization. The results show that the SVM model optimized with WOA performs better than the non-optimized SVM. Specifically, the non-optimized SVM achieved a precision of 0.82, recall of 0.81, and F1-score of 0.81. After optimization with WOA, the model’s precision increased to 0.90, recall to 0.88, and F1-score to 0.87. This study demonstrates that metaheuristic approaches like WOA can significantly enhance the performance of classification algorithms in the context of digital image processing. The findings have practical implications for early detection of plant quality through image-based analysis in technology-driven agriculture
Identification of Determinants of Inclusive Economic Growth Using the Metaheuristic Whale Optimization Algorithm Approach Firza Septian; Nina Dwi Putriani
Jurnal Software Engineering and Computational Intelligence Vol 3 No 01 (2025)
Publisher : Informatics Engineering, Faculty of Computer Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36982/jseci.v3i01.5396

Abstract

Inclusive economic growth demands the identification of key factors that drive equitable improvements in regional welfare. However, the complex interrelationships among social, economic, and demographic variables make traditional approaches insufficient for handling high-dimensional data. This study introduces an innovative approach by combining the Whale Optimization Algorithm (WOA) for feature selection with a Random Forest Regressor model to predict Gross Regional Domestic Product (GRDP) per capita as the main indicator of regional prosperity. The dataset consists of 210 regional observations and 18 independent variables. Feature selection using WOA was guided by minimizing the mean squared error (MSE), resulting in the identification of the 8 most relevant features. The retrained Random Forest model on the selected features achieved a high prediction performance, with an R² value of 0.9938 and a low RMSE. Furthermore, GRDP values were categorized into three regional welfare classes (Low, Medium, High), and the classification yielded 97.92% accuracy with high precision, recall, and F1-scores across all classes. These findings demonstrate that combining metaheuristic optimization and machine learning enables efficient and accurate identification of the key determinants of inclusive economic growth. The results provide valuable insights for formulating more targeted regional development policies.
Co-Authors Agustian Prakarsya Akhmad Dahlan Arief Zikry Firza Septian Muhammad Bitrayoga Siska Yulia Defitri Yusi Nurmala Sari