Skip To Main Content
Rahmad Ihza mahendra on Muck Rack

Rahmad Ihza mahendra

As seen in: arXiv, MDPI, Kompasiana, Dialnet - Universidad de La Rioja
Is this you? As a journalist, you can create a free Muck Rack account to customize your profile, list your contact preferences, and upload a portfolio of your best work. Claim your profile

Get in touch with Rahmad Ihza

Contact Rahmad Ihza, search articles and posts on X, monitor coverage, and track replies from one place.

Learn more about Muck Rack

Actions

Share this page

Is this you?

As a journalist, you can create a free Muck Rack account to customize your profile, list your contact preferences, and upload a portfolio of your best work.
Claim your profile

Articles

Towards Product Attributes Extraction in Indonesian e-Commerce Platform

|
By Rahmad Ihza mahendra, Indra Budi, Haryo Akbarianto Wibowo, Muhammad Rif’at
| Dialnet - Universidad de La Rioja
Ayuda Buscar en la ayuda Buscar en la ayuda Towards Product Attributes Extraction in Indonesian e-Commerce Platform Autores: Muhammad Rif’at, Rahmad Mahendra, Indra Budi, Haryo Akbarianto Wibowo Localización: Computación y Sistemas (CyS), ISSN 1405-5546, ISSN-e 2007-9737, Vol. 22, Nº. 4, 2018, págs. 1367-1375 Idioma: inglés Enlaces Texto completo Resumen Abstract: Product attribute extraction is an important task in e-commerce domain.
Open in Who Shared Issue with article?

IndoCulture: Exploring Geographically-Influenced Cultural Commonsense Reasoning Across Eleven Indonesian Provinces

|
By Rahmad Ihza mahendra, Nurul Aisyah, Timothy Baldwin
| arXiv
Open in Who Shared Issue with article?

We Know You Are Living in Bali: Location Prediction of Twitter Users Using BERT Language Model

|
By Rahmad Ihza mahendra, Lihardo Faisal Simanjuntak, Evi Yulianti, Lihardo Faisal
| MDPI
7. Discussion In general, when using machine learning algorithms, logistic regression can provide the best results compared to naïve Bayes and support vector machine. Compared to TF-IDF representation and classification using logistic regression, the use of BERT for word representation resulted in better accuracy and F1 scores. Table 6 shows some examples of predicting the location of tweets using IndoBERTweet in the majority vote scenario.
Open in Who Shared Issue with article?
See all 4 articles