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Enhancing E-Commerce Customer Satisfaction: A Multi-Method Approach Using Sentiment Analysis, IPA, and PGCV

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The advancement of information technology has enabled rapid expansion in e-commerce, establishing one of the platforms as a leading company in Indonesia. Subsequently, the authors name the e-commerce platform as XZ to preserve the anonymity of the research subject. This research intends to evaluate consumer sentiment regarding XZ’s e-service quality utilizing opinion mining based in the Support Vector Machine (SVM) method, while also determining enhancement priorities through Importance Performance Analysis (IPA) and Potential Gain in Customer Value (PGCV). The dataset comprises 50,000 data from the Google Play Store, categorized into positive and negative comments. The findings reveal that factors including the promptness in addressing customer complaints, the effectiveness of refund procedures, and XZ’s reliability in delivering promotional commitments, including shipping discount vouchers, are primary customer concerns. The IPA technique indicated that key service components should be emphasized to improve customer satisfaction, particularly attributes such as P4, P13, and P14. The PGCV methodology strengthened these results by determining the enhancement opportunities that would most significantly elevate service value, particularly attribute P4 associated with the promptness of addressing customer complaints. Also, P13 regarding the effectiveness of refund procedures for incorrectly shipped or defective products, and lastly P14 pertaining to XZ’s reliability in delivering on promotional commitments such as shipping discount vouchers. The results of this study have the potential to provide a benchmark for the businesses in formulating more efficient service enhancement plans and assist in advancing the field of quality management within the e-commerce industry.

A Comparative Machine Learning Approach for Usability Evaluation of Localized Software in Afghanistan

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Usability is one of the most important software quality attributes in determining user satisfaction, productivity and system acceptance. Afghanistan is a multilingual country, and usability problems with localized software applications may occur due to linguistic diversity, cultural differences, and scarce resources for evaluating the usability of software applications. These traditional usability assessment techniques, such as heuristic evaluation and laboratory testing, are difficult to implement in a capacity-constrained environment, as they require significant time, expertise and funds. The study suggests an automatic usability evaluation framework by machine learning techniques for localized software systems in Afghanistan. Data were gathered from ISO 9241-11 usability framework-based structured questionnaires from users of localized software applications. After collecting the responses, they were subjected to a pre-processing procedure involving cleaning, encoding and normalizing. Three supervised machine learning algorithms Decision Tree (DT), K-Nearest Neighbors (KNN) and Support Vector Machine (SVM) were trained and tested with 80:20 ratio of training and test data. Model performance was evaluated using the accuracy, precision, recall, F1-score and confusion matrix analysis. Experimental results indicate that the highest accuracy of 91.4% is obtained by DT followed by SVM with 90.0% accuracy and KNN with 88.7%. The results show that the Decision Tree is a good model for balancing the prediction accuracy and interpretation of local software environments. The proposed framework has been designed to be both scalable and cost-effective, and can be used to complement existing usability evaluation methods, and can also be used to aid in software quality improvement efforts in multilingual and developing country settings.

Comparative Analysis of Machine Learning Algorithms for Used Car Price Prediction

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After 2021, over 90 million passenger automobiles were produced, marking a significant increase in auto production. This growth has led to a flourishing used car market, which has become a highly lucrative sector. One of the most critical and fascinating areas of research within this market is automobile price prediction. Accurate price prediction models can greatly benefit buyers, sellers, and businesses in the used car industry. This paper presents a detailed comparative analysis of two supervised machine learning models: K-Nearest Neighbour and Support Vector Machine regression techniques, to predict used car prices. We utilized a comprehensive dataset of used cars sourced from the Kaggle website for training and testing our models. The K Nearest Neighbour algorithm is known for its simplicity and effectiveness in regression tasks. On the other hand, the Support Vector Machine regression technique uses a different approach, finding the optimal hyperplane that best fits the data. Both methods have their strengths and weaknesses, which we explored in this study. Our results indicated that both KNN and SVM models performed well in predicting used car prices, but with slight variations in accuracy.  Consequently, the suggested models fit as the optimum models and have an accuracy of about 83 percent for KNN and 80 percent for SVM. The results indicate that the KNN model slightly outperforms the SVM model in predicting used car prices.