Slamet Endro Prianto, Berlilana Berlilana, Rujianto Eko Saputro

Comparative Performance Analysis of Random Forest and Logistic Regression for Sentiment Classification of the Makan Bergizi Gratis Program on Platform X

Introduction

Comparative performance analysis of random forest and logistic regression for sentiment classification of the makan bergizi gratis program on platform x. Learn how Adiva Fashion Store implemented a Collaborative Filtering product recommendation system, boosting sales & customer satisfaction. Insights for small-to-medium e-commerce retailers.

Abstract

The rapid growth of e-commerce has made personalized product recommendations a crucial aspect of enhancing customer satisfaction and boosting sales. However, many small-to-medium-sized retail businesses, like Adiva Fashion Store, still rely on manual product selection through customer searches or seller recommendations, which often leads to challenges in meeting customer preferences. This study presents a case study of Adiva Fashion Store, where the Collaborative Filtering method was implemented to develop a personalized clothing product recommendation system. The item-based Collaborative Filtering approach was employed to calculate the similarity between products based on customer ratings and transaction history. These similarity values were then used to predict customer preferences for products that had not yet been purchased. The system was developed using the Waterfall methodology, which involved needs analysis, system design, implementation, testing, and maintenance. The results show that the recommendation system significantly improved the relevance of product suggestions, helping customers make better purchasing decisions and increasing sales effectiveness. This case study illustrates how data-driven recommendation systems can be effectively integrated into small-to-medium-sized retail environments, providing valuable insights for other businesses aiming to adopt similar strategies.

Review

This submission presents a significant disconnect between its stated title and the provided abstract. The title, "Comparative Performance Analysis of Random Forest and Logistic Regression for Sentiment Classification of the Makan Bergizi Gratis Program on Platform X", clearly indicates a study focused on natural language processing and machine learning for sentiment analysis within a specific social or government program context. In stark contrast, the abstract describes a case study on implementing a personalized clothing product recommendation system using Collaborative Filtering for a small-to-medium-sized retail business, Adiva Fashion Store. This fundamental inconsistency makes it impossible to assess the work as a coherent submission and requires immediate clarification from the authors. Assuming the abstract pertains to an intended paper, it outlines a relevant and practical application of data science in the retail sector, specifically for SMEs which often lack sophisticated systems. The chosen approach, item-based Collaborative Filtering, is a well-established method for product recommendations, and its application to a real-world case like Adiva Fashion Store offers potential value for businesses facing similar challenges in meeting customer preferences. The mention of the Waterfall methodology provides a structural overview of the development process. However, for a case study, the abstract would benefit from greater specificity regarding the evaluation metrics used to quantify the "significant improvement" in relevance and sales effectiveness, as well as details on the scale of data and the methodology for demonstrating increased "sales effectiveness." In its current form, this submission cannot proceed. The authors must urgently rectify the mismatch between the title and abstract. If the intent is to publish the work described in the abstract, a completely new and appropriate title must be formulated that accurately reflects the study on collaborative filtering for product recommendations. Conversely, if the title truly represents the research, a new abstract detailing sentiment classification, the 'Makan Bergizi Gratis' program, and the comparative analysis of Random Forest and Logistic Regression models must be provided. Once this critical inconsistency is resolved, a proper review of the actual research content can be conducted.

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