K-Means is a simple clustering algorithm that has the ability to throw large amounts of data, partition datasets into several clusters k. The algorithm is quite easy to implement and run, relatively fast and efficient. Another division of K-Means still has several weaknesses, namely in determining the number of clusters, determining the cluster center. The results of the cluster formed from the K-means method is very dependent on the initiation of the initial cluster center value provided. This causes the results of the cluster to be a solution that is locally optimal. This research was conducted to overcome the weaknesses in the K-Means algorithm, namely: improvements to the K-Means algorithm produce better clusters, namely the application of Sum Of Squared Error (SSE) to help K-Means Clustering in determining the optimum number of clusters, From this modification process, it is expected that the cluster center obtained will produce clusters, where the cluster members have a high level of similarity. Improving the performance of the K-Means cluster will be applied to determining the number of clusters using the elbow method.

Purpose:This research aims to mine review data on one of the e-commerce sites which ultimately produces clusters using the K-Means Clustering algorithm that can help potential customers to make a decision before deciding to buy a product or service. Design/methodology/approach: By using Octoparse we mine opinion or comment data in the form of customer online reviews, after getting the data we group the data using the k-emans clustering methode to obtain cluster Findings: Cluster Analysys can can help potential customers to make a decision before deciding to buy a product or service Research limitations/implications: WWW.Lazada.Com Practical implications: State your implication here. Originality/value: Paper type: This paper can be categorized as case study paper    

Technological developments have made changes in people's lifestyles, namely changes in the behavior of people who had shopped directly or offline to online. Many benefits are obtained from shopping online, namely the many conveniences offered by shopping online, besides that there are also many disadvantages of shopping online, namely the many risks in using e-commerce facilities, namely the problem of product or service quality, safety in payments, fraud. This research aims to mine review data on one of the e-commerce sites which ultimately produces clusters using the K-Means Clustering algorithm that can help potential customers to make a decision before deciding to buy a product or service

Sistem pencarian menjadi salah satu fitur yang sangat diperlukan pada sebuah aplikasi atau website. Dengan membandingkan 2 algoritma yang sering digunakan yaitu Binary Search dan algoritma Regular Search Expression dalam suatu sistem pencarian sederhana adalah permasalahan yang akan dibahas dalam jurnal ini. Analisa kedua algoritma dilakukan untuk menyelesaikan permasalahan dalam sistem pencarian, sehingga algoritma pencarian dapat diterapkan lebih tepat dan efektif lagi. Hasil penelitian membuktikan bahwa Binary search memiliki kelebihan dalam melakukan pencarian pada data berjumlah besar dengan keadaan terurut serta memiliki iterasi yang lebih efektif. Sedangkan Regular Expression Search memiliki kelebihan dalam melakukan pencarian yang tidak diketahui secara lengkap mengenai hasil dan kunci, selain itu algoritma ini juga memungkinkan untuk melakukan pencarian berdasarkan pola tertentu pada data.

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