Hosting Customer Clustering Based On Log Web Server Using K-Means Algorithm

To compete in global industries, a company must have a good business strategy. Especially for domain and hosting company that has many competitors there. The business strategy could be found with hosting customer behavior based on log web server analytics. The most important log web server associated with customer access is recorded in the access.log file. Potential customers were read from access activity in the form of request method /pesan on access.log. One of popular method for data mining from log server is Clustering with K-Means Algorithm. This algorithm was chosen because K-Means has a fast execution time, easy to implement, and good for a big numeric data. The evaluation technique determining the optimal value of K is used Elbow Method and the Calinski Harabasz Index. K-Means algorithm can be used to determine the pattern of hosting customers based log web server. The results of this research indicate that the clustering process based on web server log with K-Means Algorithm can be used to know the pattern of customer hosting. There are total 5 clusters for data by week and data access time. The pattern of hosting customers that are formed in ordering a succession of clusters 1,2,3,4,0. The most ordered areas are Jakarta in cluster 1, Bandung Semarang, Surabaya on cluster 2 and Medan, Tangerang, Malang, Yogyakarta on cluster 3. The frequency of booking is mostly done at the beginning of the month at 12.00 - 23.59. This customer behavior could be a reference to know the best business strategy to expand the marketing in cluster 4 and 0 and help any other stakeholder for making some policy to develop the company

Enhancing Indonesian customer complaint analysis: LDA topic modelling with BERT embeddings

Social media data can be mining for recommended systems to know the best trends or patterns. The customers have the freedom to ask questions about the product, tell their demands, and convey their complaints through social media. By mining social media data, companies can gain valuable insights into customer preferences, opinions, and sentiments. This information can be utilized to improve products and services, tailor marketing strategies, and enhance overall customer satisfaction. Topic modelling is a text mining technique that extracts the content from the raw and unlabelled data. Latent Dirichlet Allocation is popular for topic modelling research cause flexible and adaptive. But that method has issues with sparsity, performs poorly when documented in the short text and there is no correlation between topics that are actually important in text data. BERT is Bidirectional Encoder Representations from Transformer is designed to pre-train deep bidirectional representations from unlabelled text. The result of this research proves that Latent Dirichlet Allocation and BERT can be arranged on the topic of Indonesian customer complaints. BERT-Base Multilingual Cased and LDA have the highest coherence score. The combination of BERT-Base Multilingual Uncased and LDA has the highest silhouette score. BERT Multilingual are potential for improving the LDA method for Indonesian customer complaints topic modelling