Sentimen Analisis Layanan Produk Indihome menggunakan Information Gain dan Metode K-Nearest Neighbor - Atika Anggraeni; Imam Cholissodin; Marji Marji (Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer, 2021)
The development of the times from time to time increasingly influences the digital era in various parts of the world, because the development of this era requires an internet network. Users who need an internet network come from various groups, from students to workers. The increasing of internet network labor every year so that it can generate profits for internet service providers, one of which is PT. Telekomunikasi Indonesia, Tbk (Telkom). Users on the internet network provided by this company are people throughout Indonesia. Because of this, it is possible that there are encouraging suggestions or complaints from customers. In this study using responses from the public in the form of positive comments and negative comments. To find out whether these comments are positive or negative, you must carry out several stages of analysis to get the final result. The steps taken are pre-processing, Information Gain feature selection, term weighting and classification of the K-Nearest Neighbor (KNN) algorithm. This study uses 480 training data and 120 test data. This study obtained the highest accuracy value of 86.67%, with a precision of 94.44%, a recall of 81.73%, and f-measure of 91,30%.