Analisis Kinerja Naïve Bayes dalam Klasifikasi Produk Terlaris Berdasarkan Data Penjualan dan Interaksi Pengguna pada Toko Babygear.Project

A descriptive model of the trade-show budgeting decision process

For the purpose of improvement of the sales, confirming the influence of price to the sales and customer satisfaction of the product is important. The most suitable price should be determined from the view point of customers, and it is extremely important to implement a high quality product corresponding to the real need of customers. It may have close relationship between cost and an expense to implement the individual inherent attribute of system product. Also, it may have close relationship between production cost and price of product. For the purpose of improvement of the customer satisfaction for quality of system product, the method of quantitative quality requirement and evaluation based on the ISO/IEC9126 quality model that includes six quality characteristics is widely recognized. In the precedent study, I have introduced the requirements definition method for the quality of system product based on the system quality model defined in ISO/IEC9126 and proposed the effectiveness of it statistically. In the previous study, I have also confirmed the relationship between inherent attributes of the product and quantitative result of the measured value of total customer satisfaction from the view point of six quality characteristics statistically. I performed the development of the prediction model to estimate the total customer satisfaction for the system product from the view point of inherent attribute of the product. And, I have proposed the effectiveness of application of the estimated prediction model and possibility of improvement of the total customer satisfaction of a system product. Based on the result of previous study, in this paper, I propose the result of investigation of influence of price to customer satisfaction, and the possibility of application of estimated prediction model for improvement of the total customer satisfaction of system product based on the price of product. Also, based on the result of investigation of relationship among price and inherent attributes of product, I propose the possibility of application of estimated prediction model and improvement of the price of system product from the view point of inherent attributes of product.

PurposeThe purpose of this paper is to understand how two online marketing orientations of land-based retailers in product category and price could change retail brand attitude when retail brand familiarities differ.Design/methodology/approachThis paper proposes a research model with two orientations in product category and price as antecedents of retail brand attitude change and retail brand familiarity as a moderator. Empirical data were collected from 684 shoppers across three land-based retailers to test the research hypotheses.FindingsBoth orientations in product category and price can improve customers’ retail brand attitudes. Retail brand familiarity plays a significant moderator in some of the situations. Online-offline product category congruence and online-prototypical price congruence have significantly positive effects on retail brand attitude change whether retail brand familiarity is high or low. The effect of online-offline price congruence is significant only among high-familiarity customers, while the effect of online-prototypical product category congruence is found to be significant only among low-familiarity customers.Research limitations/implicationsThe study identifies the moderating effects of retail brand familiarity on the relationships between two online marketing orientations in product category and price and retail brand attitude change. Based on the moderating effects, this study will help researchers to better understand the effectiveness of two online marketing orientations subject to varying degrees of retail brand familiarity in a multichannel retailing context.Practical implicationsThe findings of this study can guide land-based retailers to focus on the right orientations in product category and price to improve customers’ attitudes toward the retail brand when existing or new customers are targeted.Originality/valueThis study provides a first study to empirically assess the change in retail brand attitude prompted by homogenous and prototypical orientations in product category and price and subject to varying degrees of retail brand familiarity. Overall, the results offer insights of how land-based retailers could manage their overall performance by designing more effective online product category and pricing strategies for existing or new customers.

The development of electric vehicles in Indonesia has sparked various opinions from the public, which are often shared on social media, especially X. These opinions need to be analyzed to understand how the public views the policies and implementation of environmentally friendly vehicles. This study aims to examine public sentiment toward electric vehicles by comparing two types of text classification algorithms, namely Naïve Bayes and Support Vector Machine (SVM), using the Term Frequency–Inverse Document Frequency (TF-IDF) approach. The data used is Indonesian-language tweets collected through a crawling process, which then undergoes several pre-processing stages such as cleaning, case folding, normalization, tokenizing, stopword removal, and stemming. After that, the data was labeled for sentiment into three categories: positive, negative, and neutral, before being processed using a classification algorithm. To evaluate the model's performance, a confusion matrix was used, which shows the algorithm's performance based on accuracy, precision, recall, and F1-score values. The research results show that the Naïve Bayes algorithm has better results with an accuracy of 92%, while SVM achieves an accuracy of 76%. Therefore, the Naïve Bayes algorithm is considered more suitable for analyzing the sentiment of tweets related to electric vehicles in Indonesia.

Online shopping or internet shopping is increasing day by day. With the advancement of modern technology, the online market is growing in a vast way. People nowadays prefer online shopping because it saves time, energy, and money. Because of the blessing of the internet that online shopping has made its debut which also affects the common citizens for online shopping. So, for the emerging growth of the online market, it is necessary to find out the behavior of online shopping and customer satisfaction. Safety, trust, product quality plays an important role in customer satisfaction. In this study, we examined customer online shopping satisfaction its impact. The quality of the product, the price of the product compared to the local market, the policy of return, timely delivery of the product are also essential elements of online shopping. By analyzing all these factors we have tried to find the customer behavior and satisfaction with online shopping. In our study, we used a machine learning method to search for the result. We used 40 thousand data to find out the accuracy of our work and to analyze customer shopping satisfaction. We use Naïve Bayes, Apiorir, Decision Tree, and Random Forest classification algorithms for this analysis. We have got our best result by using Apiorir algorithm (88% accuracy) and Naïve Bayes algorithm (87% accuracy). We have also focused on customer behavior and interest in online shopping. Our study can help to develop the business intelligence and satisfaction enhancement about E-commerce.

Abstract. 
 Purpose: This study aims to apply feature selection particle swarm optimization (PSO) and gain ratio to the naïve Bayes algorithm and gauging the level of accuracy before and after applying PSO feature selection and gain ratio to the naïve Bayes algorithm in the diagnosis of heart disease.Methods/Study design/approach: Data collection is done by using taking the Cleveland dataset obtained from the UCI machine learning repository. The data used in this study were 303 samples. The data is processed using the preprocessing stage. The naïve Bayes algorithm is used for a classifier, while PSO and gain ratio for feature selection.Result/Findings: The results of the study revealed that the classification accuracy of the naïve Bayes algorithm without the application of feature selection in the Cleveland dataset is 86.88%, while the results of the classification accuracy of the naïve Bayes algorithm after applying PSO and gain ratio in the Cleveland dataset is 93.44%. Application of PSO and gain ratio as feature selection algorithms can improve classification accuracy by 6.56%.Novelty/Originality/Value: This study combines the PSO feature selection and gain ratio on the naïve Bayes algorithm using the Cleveland dataset. The research model that was carried out was enriched by carrying out the preprocessing stages, namely data cleaning, changing the number of class labels, data normalization, and data discretization. This study shows that using a combination of the PSO feature selection algorithm and the gain ratio gives better accuracy to the naïve Bayes algorithm in diagnosing heart disease.

The purpose of this study was to measure customer satisfaction at commercial banks. This study used a survey-based approach with quantitative descriptive analysis techniques, obtained multiple population samples from multiple private banks in his three provinces of Indonesia, and used questionnaires as data collection tools. I'm here. Respondents were selected using probabilistic sampling and systematic sampling techniques. Samples were randomly selected as reference data using interval measurements. The results showed that: Product quality has a positive effect on customer satisfaction. Product pricing has a significant impact on customer satisfaction. Service quality has a great impact on customer satisfaction. Customer satisfaction has a significant impact on purchasing decisions. Customer satisfaction influences purchase decisions for product quality, price and service quality. Limitations of this study and recommendations for further research are discussed below.

This study is a quantitative study that aims to examine the factors that influence customer loyalty with customer satisfaction as a moderating variable in the perspective of Islamic economics. These factors are product quality, business location, product price, service quality and brand image on customer loyalty with customer satisfaction as a moderating variable in Raos Kopi Tulungagung. The population of this study amounted to 215 people who were taken from customers of Raos Kopi Tulungagung. The research sample was determined by simple random sampling technique, namely taking samples from the entire population. The resource obtained from interviews, questionnaires and documentation. The resource analysis method used is Moderating Regression Analysis (MRA). This study shows Results (1). Product quality has no significant effect on customer loyalty, (2). Business location, product price, service quality and brand image have a significant effect on customer loyalty, (3). Product quality, business location and service quality have no significant effect on customer satisfaction, (4). Product prices and brand image have a significant effect on customer satisfaction, (5). Product quality, product price, service quality, product quality on customer satisfaction, product price on customer satisfaction and service quality on customer satisfaction have no significant effect on customer loyalty. Factors that influence customer loyalty with customer satisfaction as moderating variables in the Islamic perspective are business location, brand image, business location on customer satisfaction, brand image on customer satisfaction have a significant effect on customer loyalty with customer satisfaction as a moderating variable on Raos Kopi Tulungagung. This research is expected to be a reference and evaluation for Muslim business actors in improving their business.

Private labels hold a substantial share of consumers’ wallets and their popularity is still growing as they spread into various product categories and quality tiers. To determine the right branding strategy, in terms of offering uniform or different private-label brands across product categories, retailers have to know whether consumers use their private-label experience across product categories and private-label tiers. Therefore, we examine different determinants of consumers’ consideration sets. We apply proneness for certain private-label tiers, product categories purchased, purchase frequency, and variety seeking as internal determinants, which contribute to consumers’ knowledge and experience with private labels. Further, we use consumers’ price consciousness and promotion sensitivity as external determinants, which the retailer can use to influence consumers’ consideration sets in the short run. Our analyses are based on large-scale loyalty program data for a period of 24 months. In particular, we use the first 12 months to derive the determinants of consumers’ share of wallets regarding different private-label quality tiers in the second half of the sample. We conduct our analyses for 12 different product categories and aggregate the results by using meta-analytic techniques. Notably, some determinants show dissimilar effects across product categories (e.g., price consciousness and promotion sensitivity), while others (e.g., private-label proneness) are rather similar. We find that consumers’ general proneness for certain private-labels tiers leads to a propensity to purchase them in a specific category and in adjacent quality tiers. Further, we reveal that product category characteristics moderate the determinants of private-label share.

This research analyzes the sentiment reviews of the SIREKAP application on the Google Play Store using two machine learning algorithms, namely Naïve Bayes and Support Vector Machine (SVM). The dataset used consists of 19,925 reviews that have gone through preprocessing stages, including text cleaning, stopword removal, stemming, and tokenization. To overcome data imbalance, oversampling and undersampling techniques were applied. Furthermore, TF-IDF is used for feature extraction, converting text into numerical representation. The dataset is divided into 80% training data (15,940 data) and 20% test data (3,985 data). The results show that oversampling provides better performance than undersampling. In the oversampling method, the SVM algorithm achieved the highest accuracy of 95%, with consistent precision, recall, and F1-score values across all sentiment classes. The Naïve Bayes algorithm also performed quite well, with an accuracy of 77% on the oversampled data. In contrast, in the undersampling method, both algorithms have the same accuracy of 61%. This study confirms that the combination of oversampling technique and SVM algorithm is the best approach to handle imbalanced data and provides important insights into user perception of the SIREKAP application.

The objective of the study is to analyse the impact of e-service quality attributes on customer satisfaction and on purchase intention and purchase frequency of electronic gadgets and home appliances in Bangalore, India using the service quality (SERVQUAL) model. The e-service quality attributes chosen for the study are empathy, information, security, reliability, user interface, responsiveness, fulfillment, and personalization. This paper extends existing research by quantifying the impact of chosen attributes of e-service quality on customer satisfaction and purchase intentions, and on purchase frequency. 120 responses were collected for the analysis through an online questionnaire using convenience sampling. A diverse mix of respondents who are proficient in digital platform navigation and online purchases were chosen for the study. SPSS and R studio was applied to analyze the data. The techniques used for the study to test the hypothesis and to understand the relationship between the e-service quality attributes on customer satisfaction and purchase intention are descriptive statistics, chi-square, correlation, logistic regression, decision tree, SEM, and k-fold cross-validation. The results indicate that personalization, and fulfillment have a significant impact on customer purchase intention, the attributes fulfillment and information are key attributes for purchase frequency. From SEM analysis it is evident that all the chosen e-service quality attributes have a positive impact on customer satisfaction and it is significant, which in turn enhances purchase intention and purchase frequency. From decision tree analysis it is evident that customers aged 20-40 prefer buying products after verifying seller information on shopping websites. The results of the study provide valuable insights into the e-commerce sector to make better policies and strategies to enhance customer satisfaction and purchase intention.

This study aims to determine the extent to which product quality, price, promotion, and service quality significantly influence customer satisfaction at Es Teh Indonesia in Bogor. A total of 430 respondents participated in this study, with questionnaires distributed using a non-probability sampling technique in a descriptive quantitative research design. Data were analyzed using SPSS 29 software, applying multiple linear regression with an error value of α = 0.05. The results indicate that (1) customer satisfaction is positively and significantly influenced by product quality, (2) price significantly and favorably affects customer satisfaction, (3) promotion significantly and positively impacts customer satisfaction, and (4) service quality also has a positive and significant impact on customer satisfaction. Furthermore, the study finds that product quality, price, promotion, and service quality collectively have a positive and significant influence on customer satisfaction at Es Teh Indonesia in Bogor. These findings underscore the importance of each marketing element in fostering customer loyalty and satisfaction. The research has significant implications for Es Teh Indonesia’s strategy to enhance its product offerings, improve service quality, and design effective promotional campaigns. These results can guide the company's marketing practices to achieve a competitive advantage in the local beverage market

In an online shopping platform, a detailed categorization of the products greatly enhances user navigation. Online retailers also benefit from well-defined product categories as various sales and marketing operations such as special discounts and promotions can be easily done over a set of product categories. Furthermore, incorrect and subjective product categories suggested by an operator can be more easily identified thanks to an automated classification system. In this study, we investigate the task of classifying grocery product categories using product titles. We employ a wide variety of text classification models for this task, including traditional machine learning and deep learning models as well as state-of-the-art transformer models. In our analysis, we specifically focus on the generalizability of the trained classification models to the products of other online retailers, the dynamic masking of infeasible subcategories for pretrained language models, and the impact of incorporating different word embeddings. We observe that the deep learning models and the transformers significantly outperform traditional text classification methods such as XGBoost and SVM, and achieve excellent prediction performance exceeding 90% accuracy and F1-score values. We lastly explore the failure cases where a product is misclassified, and make recommendations for future studies to improve the prediction performance.

In the research, the researcher evaluates analysis sentiment from review patients about services at the Az-Zainiyah clinic with the use Naïve Bayes method in Natural Language Processing (NLP). The dataset used consists of from review grouped patients become three categories of sentiment: positive, neutral, and negative. The Naïve Bayes model was trained and tested. To test its performance in classifying sentiment review patients. Research results show that the Naïve Bayes model achieves accuracy by 96%, Good macro average or weighted average shows high precision, recall, and f1-score values, around 0.97 and 0.96, respectively. These results show the effectiveness of the model in identifying sentiment review patients with high accuracy. Findings This gives valuable insights for increased quality services at the Az-Zainiyah clinic based on bait come back from patients, who in turn can increase satisfaction and experience patient.

This study aims to examine the role of product characteristics and price discounts in building customer satisfaction and loyalty. The study is motivated by the growing competition among café businesses, where customer retention and satisfaction are crucial for business sustainability. This research explores whether product features and price discounts can effectively shape customer loyalty, either directly or through the mediating role of customer satisfaction. Using a quantitative approach, data were collected through purposive sampling involving 114 respondent who had previously visited MoMoo Café, located in a popular culinary tourism area. The analysis was conducted using Partial Least Squares Structural Equation Modeling (PLS-SEM) to test the proposed hypotheses. The results show that price discounts do not have a direct and significant effect on customer loyalty. However, discounts were found to significantly influence customer satisfaction, which in turn significantly affects customer loyalty. This indicates that although discounts do not directly create loyal customers, they enhance satisfaction through the perception of value received, which subsequently drives loyalty. In contrast, product characteristics were found to have no significant influence, either directly on loyalty or on customer satisfaction. These findings highlight the importance of customer satisfaction as a strategic mediator in the formation of loyalty. Therefore, businesses operating in tourist areas should not rely solely on price promotions but should focus on creating a satisfying customer experience. Enhancing service quality, ambiance, and emotional engagement with the brand can result in more sustainable loyalty than price-based incentives alone. This study contributes to the consumer behavior literature in the tourism context by emphasizing the indirect role of price discounts and the central position of satisfaction in building long-term customer relationships.