CLASSIFICATION OF CUSTOMER COMPLAINTS ON INSTAGRAM COMMENTS USING NAÏVE BAYES ALGORITHM WITH N-GRAM FEATURE EXTENSION

Customer complaints about the company can be used as a form of self-evaluation and performance that has been carried out by the company, based on customer complaints the company can find out the weaknesses that exist in the company and fix them. The forms of submitting customer complaints are very diverse, currently not only by telephone, but customers also submit suggestions or complaints, customers can submit suggestions or complaints via electronic mail or e-mail or forums in cyberspace that are indeed created by product-producing companies to accommodate various complaints, suggestions, and direct criticism from consumers, especially social media that are free to express opinions on the delivery services used. Instagram is a social media that is more inclined towards images and on the other hand, has captions and comments text, a study is needed for the problem of customer complaints from shipping service users on an Instagram account of a delivery service company. Based on this background, a solution is needed in solving problems for text mining classification using Naïve Bayes with SMOTE techniques and N-Gram feature extraction with the usual process for text mining so that it can produce Naïve Bayes and SMOTE accuracy with an accuracy of 88.54%, before implementation. N-Gram and the accuracy rate increased by 1.44% after the N-Gram Term was applied to 89.98% by using a dataset of 776 Instagram comment text records that had to preprocess text

Workshop Pemanfaatan Market Place Dalam Menunjang Pemasaran Produk Pada Komunitas Mersi Fans Club Dimasa Pandemik Covid-19

PT Radio Swara Mersidiona atau yang lebih dikenal dengan nama Radio MeRsi yang dipercaya dalam dunia penyiaran, selalu memberikan kepuasan bagi pendengar. Sejak tahun 1971 radio mersi tetap konsisten dalam musik yang disugukan yaitu dangdut sehingga memiliki banyak penggemar setia sehingga terbentuklah komunitas yang bernama Mersi Fans Club (MFC). Permasalahan yang dihadapi oleh komunitas Mersi Fans Club (MFC) diantaranya para anggota Komunitas Mersi Fans Club l belum terlalu memahami bagaimana Pemanfaatan Market Place Dalam Menunjang Pemasaran Produk. Belum dapat mengimplementasikan pemasaran produk mereka pada Market Place. Juga dimasa pademik Covid-19 ini, kegiatan tatap muka juga menjadi kendala melanggar protokol kesehatan. Sebagai solusi kegiatan pengabdian masyarakat ini dengan mengadakan kegiatan Workshop secara daring dengan menggunakan fasilitas Zoom meeting. Hasil dari kegiatan ini, diharapkan dapat memahami dengan terus menurus dicoba dan mempraktekan yang sudah didapatkan, sekaligus dengan membuka komunkasi dengan WA Group

IMPLEMENTASI ASSOCIATION RULES MENENTUKAN POLA PEMILIHAN MENU DI THE GADE COFFEE & GOLD MENGGUNAKAN ALGORITMA APRIORI

ABSTRACT The Gade Coffee & Gold produces a lot of sales transaction data every day that is stored in the database, but this data has not been maximized in conducting analysis to produce new knowledge, based on this problem it is necessary to carry out an analysis using a data mining approach and applying association techniques. Data mining is able to analyze data into information by applying association techniques to find several purchasing patterns that are useful to assist companies in the process of making business decisions such as determining product cross-selling, determining promotional programs, and so on. This study aims to determine the pattern of combinations of food and drinks ordered by customers by applying the Apriori method based on sales transaction data for the month of September 2022. The results show that there are 16 association rules with the highest support value which is 6.8% with a confidence value of 85 .7% and the Lift value is 111%, with the rule formed that if a customer buys an Almond Croisant product, there is a chance that the customer will also buy Van Lenning – Iced products. Keywords: Data Mining, KDD, Association, Apriori   ABSTRAK The Gade Coffee & Gold setiap harinya menghasilkan banyak data transaksi penjualan yang tersimpan dalam basis data, namun data tersebut  belum dimaksimalkan dalam melakukan analisa untuk dapat menghasilkan suatu pengetahuan baru, berdasarkan masalah tersebut perlu dilakukan sebuah analisa dengan menggunakan pendekatan data mining serta menerapkan teknik asosiasi. Data mining mampu menganalisa data menjadi sebuah informasi dengan menerapkan teknik asosiasi dapat menemukan beberapa pola pembelian yang berguna untuk membantu perusahaan dalam proses pengambilan keputusan bisnis seperti menentukan cross-selling produk, menentukan program promosi, dan sebagainya. Penelitian ini bertujuan untuk menentukan pola kombinasi dari makanan dan minuman yang dipesan oleh pelanggan dengan menerapkan metode Apriori  berdasarkan data transaksi penjualan periode bulan September 2022.  Hasil penelitian menunjukkan sebanyak 16 aturan asosiasi dengan nilai support tertinggi adalah 6,8% dengan nilai confidence sebesar 85,7% dan nilai Lift 111%, dengan aturan yang terbentuk yaitu apabila pelanggan membeli produk Almond Croisant peluang pelanggan juga membeli produk Van Lenning – Iced. Kata Kunci: Data Mining, KDD, Asosiasi, Aprior

Analisis Sentimen Pengguna Marketplace Bukalapak dan Tokopedia di Twitter Menggunakan Machine Learning

      A collection of tweets from Twitter users about Marketplace Bukalapak and Tokopedia can be used as a sentiment analysis. The data obtained is processed using data mining techniques, in which there is a process of mining the text, tokenize, transformation, classification, stem, etc. Then calculated into three different algorithms to be compared, the algorithm used is the Decision Tree, K-NN, and Naïve Bayes Classifier with the aim of finding the best accuracy. Rapidminer application is also used to facilitate writers in processing data. The highest results from this study are Decision Tree algorithm with 82% accuracy, 81.95% precision and 86% recall