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All Journal TELKOMNIKA (Telecommunication Computing Electronics and Control) Proceeding of the Electrical Engineering Computer Science and Informatics
Dwi H Widyantoro
Institut Teknologi Bandung
Computer Science & IT Electrical & Electronics Engineering

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Factors Influencing User’s Adoption of Conversational Recommender System Based on Product Functional Requirements Z.K. Abdurahman Baizal; Dwi H Widyantoro; Nur Ulfa Maulidevi
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 14, No 4: December 2016
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v14i4.4234

Abstract

Conversational recommender system (CRS) helps customers get products fitted their needs by repeated interaction mechanisms. When customers want to buy products having many and high tech features (e.g., cars, smartphones, notebook, etc.), most users are not familiar with product technical features. The more natural way to elicit customers’ needs is by asking what they really want to use with the product they want (we call as product functional requirements). In this paper, we analyze four factors, e.g., perceived usefulness, perceived ease of use, trust and perceived enjoyment  associated to user’s intention to adopt the interaction model (in CRS) based on product functional requirements. Result of experiment using technology acceptance model (TAM) indicates that, for users who aren’t familiar with technical features, perceives usefulness is a main factor influencing users’ adoption. Meanwhile, perceived enjoyment plays a role on user’s intention to adopt this interaction model, for users who are familiar with technical features of product.
Feature Expansion for Sentiment Analysis in Twitter Erwin B. Setiawan; Dwi H Widyantoro; Kridanto Surendro
Proceeding of the Electrical Engineering Computer Science and Informatics Vol 5: EECSI 2018
Publisher : IAES Indonesia Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (272.121 KB) | DOI: 10.11591/eecsi.v5.1660

Abstract

The community's need for social media is increasing, since the media can be used to express their opinion, especially the Twitter. Sentiment analysis can be used to understand public opinion a topic where the accuracy can be measured and improved by several methods. In this paper, we introduce a hybrid method that combines: (a) basic features and feature expansion based on Term Frequency-Inverse Document Frequency (TF-IDF) and (b) basic features and feature expansion based on tweet-based features. We train three most common classifiers for this field, i.e., Support Vector Machine (SVM), Logistic Regression (Logit), and Naïve Bayes (NB). From those two feature expansions, we do notice a significant increase in feature expansion with tweet-based features rather than based on TF-IDF, where the highest accuracy of 98.81% is achieved in Logistic Regression Classifier.
Co-Authors Erwin B. Setiawan Kridanto Surendro Nur Ulfa Maulidevi ZK Abdurahman Baizal