Analisis Sentimen Twitter Menggunakan Ensemble Feature dan Metode Extreme Learning Machine (ELM) (Studi Kasus: Samsung Indonesia) - Alqis Rausanfita; Putra Pandu Adikara; Sigit Adinugroho (Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer, 2018)
Business activity is very crucial and has a real impact on organizational growth and ROI (Return Of Investment) is to understand and respond appropriately sentiment from customers by conducting a sentiment analysis. The sentiment analysis can be a guide to evaluate a company's product, service, reputation, brand reputation, and the company can be a market leader supported by a very emotional customer condition so that disappointing products / services will lose the customer's commitment even customers will find it difficult to recover customer experience if a company does not care about customer sentiment. Based on the explanation, this research is done using ensemble feature and Extreme Learning Machine for Twitter sentiment analysis. The data used in this research is 72 tweets with the ratio of the amount of training and testing data 70:30 where the amount of data per class is balanced. Prior to the classification of data is done preprocessing, weighting ensemble feature, and weighting the word. The result of this research is get the best hidden neuron number as much as 5000, best activation function is sigmoid bipolar, and ensemble feature influence to accuracy result. Twitter sentiment analysis using ensemble feature and Extreme Learning Machine method in Samsung Indonesia case study did not get high accuracy. Accuracy in getting only amounted to 42.857 percent. The low accuracy caused by sparse data matrix resulting in overfitting which then resulted in low classification results.