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All Journal Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) JURNAL MEDIA INFORMATIKA BUDIDARMA
Widi Widayat
Institut Teknologi Telkom Purwokerto, Purwokerto
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering

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Comparative Study of VGG16 and MobileNetV2 for Masked Face Recognition Faisal Dharma Adhinata; Nia Annisa Ferani Tanjung; Widi Widayat; Gracia Rizka Pasfica; Fadlan Raka Satura
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol 7, No 2 (2021): August
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v7i2.20758

Abstract

Indonesia is one of the countries affected by the coronavirus pandemic, which has taken too many lives. The coronavirus pandemic forces us to continue to wear masks daily, especially when working to break the chain of the spread of the coronavirus. Before the pandemic, face recognition for attendance used the entire face as input data, so the results were accurate. However, during this pandemic, all employees use masks, including attendance, which can reduce the level of accuracy when using masks. In this research, we use a deep learning technique to recognize masked faces. We propose using transfer learning pre-trained models to perform feature extraction and classification of masked face image data. The use of transfer learning techniques is due to the small amount of data used. We analyzed two transfer learning models, namely VGG16 and MobileNetV2. The parameters of batch size and number of epochs were used to evaluate each model. The best model is obtained with a batch size value of 32 and the number of epochs 50 in each model. The results showed that using the MobileNetV2 model was more accurate than VGG16, with an accuracy value of 95.42%. The results of this study can provide an overview of the use of transfer learning techniques for masked face recognition.
Analisis Sentimen Evaluasi Terhadap Pengajaran Dosen di Perguruan Tinggi Menggunakan Metode LSTM Muhammad Afrizal Amrustian; Widi Widayat; Arif Muhammad Wirawan
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 6, No 1 (2022): Januari 2022
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v6i1.3527

Abstract

Education in Indonesia is divided into several levels, from elementary education to university education. At the university education level, lecturers are asked to not only teach material but also emphasize to students that students have an important role for the future.  Due the students are considered as adults to make the decisions and take a responsibility for those decisions. During a pandemic, teaching activities are carried out online, in order the teaching activities run well, the evaluation from students is needed. Considering that students are one of the important elements in university education. In this study, sentiment analysis was carried out on the evaluation of teaching by students. The data used in this study amounted to 2280 data with the number of words in the evaluation text ranging from 3 to 50 words. The LSTM method is the method used in this study, and the results of the accuracy of using the LSTM method are 91.08%. With the analysis carried out, lecturers can improve their teaching methods based on the results of the evaluation analysis.
Analisis Sentimen Movie Review menggunakan Word2Vec dan metode LSTM Deep Learning Widi Widayat
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 5, No 3 (2021): Juli 2021
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v5i3.3111

Abstract

The increasing number of internet users is directly in line with the increasing number of data on the internet that is available for analysis, especially data in text form. The availability of this text data encourages a lot of sentiment analysis research. However, it turns out that the availability of abundant text data is also one of the challenges in sentiment analysis research. Datasets that consist of long and complex text documents require a different approach. In this study, LSTM was chosen to be used as a sentiment classification method. This research uses a movie review dataset that consists of 25,000 review documents, with an average length per review is 233 words. The research uses CBOW and Skip-Gram methods on word2vec to form a vector representation of each word (word vector) in the corpus data. Several dimensions of the word vector was used in this research, there are 50, 60, 100, 150, 200, and 500, this tuning parameter is used to determine their effect on the resulting accuracy. The best accuracy around 88.17% is obtained at the word vector 100 dimension and the lowest accuracy is 85.86% at the word vector 500 dimension.
Co-Authors Arif Wirawan Muhammad Fadlan Raka Satura Faisal Dharma Adhinata Gracia Rizka Pasfica Muhammad Afrizal Amrustian Nia Annisa Ferani Tanjung