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All Journal JURNAL MEDIA INFORMATIKA BUDIDARMA JURIKOM (Jurnal Riset Komputer)
Alisia Silver Stone
Universitas Sriwijaya, Palembang
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering

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Pengaruh Customer Relationship Management (CRM) Terhadap Loyalitas Pelanggan Pada Apotek Mega Mulia Ali Ibrahim; Tanti Hidayah; Alisia Silver Stone; Yona Saymona; Tea Anggelah; Siti Raisah Adilah
JURIKOM (Jurnal Riset Komputer) Vol 8, No 6 (2021): Desember 2021
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v8i6.3682

Abstract

In order to increase consumers, the pharmacy strategy is to provide services that match their needs and protect good ties with customers so that customer loyalty will be formed. To analyze the influence of aspects of customer identification, customer differentiation, budget interaction, and customization on customer loyalty at Apotek Mega Mulia is the aim of this research. Quantitative description with survey approach is the type of research in this research. The non-random method with accidental sampling was chosen by the researcher as a method of collecting information. Multiple liner is the analysis used. Based on the results of the information analysis, the data obtained by the value of Fcount 17.084 > Ftable 2.45. This shows that customer loyalty is significantly influenced by all factors. Of these four aspects, the calculated value for the customer differentiation aspect is 3,213 which is very large compared to the calculated value for the customer identification aspect of 2,283, customer interaction is 2,765, and customization is 2,226. The conclusion is that the aspects of Customer Relationship Management (CRM) are customer identification, customer differentiation, customer interaction, customization which together have a significant impact on customer loyalty at Mega Mulia pharmacies
Analisis Sentiment Pelanggan Terhadap Penilaian Produk Pada Toko Online Shop Amreta Menggunakan Metode Naïve Bayes Classification Alisia Silver Stone; Fathoni Fathoni
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 6, No 3 (2022): Juli 2022
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v6i3.4436

Abstract

Sentiment analysis or opinion mining is an analysis that aims to see the sentiment of people or groups regarding certain entities. The sentiments expressed by society can be in positive, negative and neutral form. One media that can be given an opinion by the public is in the e-commerce application,  namely the shopee application, shopee has a comment or assessment feature on the product that has been purchased. Toko which was used as a sample of researchersis an amreta online shop store  , based on the results of the identification of the problem, it was found that the fact was that many comments did not match the stars given so it can be said that the rating cannot represent that the store's performance is good or not. Therefore, to increase the profit of shop work, the amreta still needs to evaluate the store. In conducting an evaluation, the store needs to classify positive, negative or neutral comments. Analysis of customer sentiment towards product assessments in amreta online shop stores using the naive bayes classification method. The use of test data in this study was obtained from the sentiment of amreta online shop consumers as much as 2014 data,then the data was processed through  the data cleaning process  resulting in net data of 1899 data. Furthermore, the data preprocessing process is divided into 3 stages, namely Tokenize Data, Transform case and Stopword removal. After that, the analysis of data for the automatic labeling stage using Text Vectorize from the process obtained data division into 3 data groups of 71% or 1343 positive data, 3% or 52 negative data and 26% or 504 neutral data.  furthermore, it is processed using rapidminer tools while for operators in the form of algorithms using the Sentiment Naïve Bayes Classification model  through automatic calculations.  The results of the study can be concluded that the test data obtained have an accuracy level of 97.16% using the Naive Bayes Classification model.
Co-Authors Ali Ibrahim Fathoni - Siti Raisah Adilah Tanti Hidayah Tea Anggelah Yona Saymona