| Penulis/Author |
MUHAMMAD ICHWANDAR A (1); Ir. Adhistya Erna Permanasari, S.T., M.T., Ph.D. (2); Dr. Indriana Hidayah, S.T., M.T. (3); Mahfud Sholihin, Prof., Ph.D., Ak., CA., CPA (Aust) (4) |
| Abstrak/Abstract |
Waqf has an important role in the development
and increase in welfare. In addition to reducing dependence on
funds from the Indonesian government, waqf has also had a
significant impact on reviving the economy, especially since the
outbreak of the COVID-19 virus. The rapid advancement of
technology has also transformed waqf, one of which is that
people can donate waqf money online through several digital
applications. But so far, several advantages and disadvantages
are felt by application users. To make it easier to get
information based on user experience, we propose to develop a
model that can classify sentiments into positive, negative, and
neutral automatically. Text classification using word
embedding is the basis for getting the best performance results.
Bag of Word (BOW) is a word embedding model that is often
used, but this model is considered not optimal because it has
disadvantages such as dependence on certain languages.
Therefore, we suggest the fastText model minimizes
dependency on pre-processing words and use 2 classification
methods, namely SVM and KNN. This study aims to compare
the performance results using the fastText model with
conventional models that are often used, namely Bag of Word
(BOW) and Term Frequency – Inverse Document Frequency
(TF-IDF) to find the best accuracy value produced. Based on
this research, it can be interpreted that in general, the fastText
model can produce better performance than BOW and TF-
IDF. |
