cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
Reza Maulana
Contact Email
saya@rezamaulana.com
Phone
+6282330060777
Journal Mail Official
insypro@uin-alauddin.ac.id
Editorial Address
Jl.H.M.Yasin Limpo No. 36 Samata, Gowa, Sulawesi Selatan Building D, 4th floor
Location
Kab. gowa,
Sulawesi selatan
INDONESIA
Jurnal INSYPRO (Information System and Processing)
Published by Universitas Islam Negeri Alauddin Makassar
ISSN : 2597419X     EISSN : 2579468X     DOI : https://doi.org/10.24252/insypro
Core Subject : Science, Engineering,
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering
Jurnal Insypro adalah jurnal yang bergerak di bidang Sistem Informasi, hadir untuk diharapkan mampu mengembangkan riset pada bidang sistem informasi di Indonesia dan dunia internasional secara umum
Arjuna Subject : Ilmu Komputer (semua kategori) - Sistem Informasi
Articles 12 Documents
 1   2 
Search results for , issue "Vol 9 No 2 (2024)" : 12 Documents clear
Sistem Deteksi Ekspresi Wajah Berbasis Convolutional Neural Network (CNN) Untuk Pengenalan Emosi Manusia Wibawa. Ar, Arya; Irhamna Rachman, Fahrim; Yusliana Bakti, Rizki; Wahyuni, Titin
Jurnal INSYPRO (Information System and Processing) Vol 9 No 2 (2024)
Publisher : Prodi Sistem Informasi UIN Alauddin

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24252/insypro.v9i2.51360

Abstract

The development of human facial expression detection systems has become a growing research topic, particularly in efforts to create applications capable of automatically understanding and responding to human emotions. This research aims to develop and evaluate a human facial expression detection system using the Convolutional Neural Network (CNN) method. The dataset used consists of facial images with various expressions sourced from diverse origins. The data undergoes several preprocessing stages, including normalization, augmentation, and splitting into training and test sets. This study employs several CNN architectures to identify emotions such as happy, sad, angry, and scared. Testing is conducted using various parameters, including training and test data splits, as well as different CNN architectures. The results show that the CNN model can achieve over 90% accuracy on training data, with the best performance on the "Happy" emotion, achieving an f1-score of 0.93. However, there is a decrease in accuracy on validation data, with an overall average accuracy of 78%, indicating challenges in model generalization. Additionally, the "Sad" emotion has the lowest recall of 0.49, indicating the need for model improvement in classifying specific emotions. This study contributes to the development of CNN-based facial expression detection systems, but further exploration of more complex architectures, evaluation with diverse datasets, and real-time testing are needed to improve system performance.
PENGGUNAAN WORD EMBEDDING WORD2VEC DALAM PENGEMBANGAN MODEL CNN STUDY KASUS ANALISIS SENTIMEN TEMPAT WISATA MAKASSAR Wahyuni, Titin; Anas, Lukman; Arvianda
Jurnal INSYPRO (Information System and Processing) Vol 9 No 2 (2024)
Publisher : Prodi Sistem Informasi UIN Alauddin

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24252/insypro.v9i2.51327

Abstract

This research aims to evaluate the effect of applying the Word Embedding Word2Vec technique on the accuracy of the Convolutional Neural Network (CNN) model in sentiment analysis of tourist attraction reviews in Makassar. Sentiment analysis is the process of identifying and classifying emotions or opinions contained in text, whether positive, negative, or neutral. The research dataset consists of 4500 tourist attraction reviews taken from Google Maps. The data was then processed using the Word2Vec technique to generate vector representations of the words in the reviews. These vectors were used as input to the CNN model for sentiment classification. The study employed three data splitting scenarios, namely 90:10, 80:20, and 70:30, for training and testing the model. The results showed that the application of Word2Vec in the CNN model improved sentiment prediction accuracy. The CNN model with Word2Vec achieved an accuracy of 79%, while the CNN model without Word2Vec only reached an accuracy of 74%. This indicates that the use of Word2Vec can enhance the performance of the model in classifying sentiment in tourist attraction reviews.

Page 2 of 2 | Total Record : 12

 1   2 

Filter by Year

2024 2024


Reset
Filter By Issues
All Issue Vol 10 No 2 (2025) Vol 10 No 1 (2025) Vol 9 No 2 (2024) Vol 9 No 1 (2024) Vol 8 No 2 (2023) Vol 8 No 1 (2023) Vol 7 No 2 (2022) Vol 7 No 1 (2022) Vol 6 No 2 (2021) Vol 6 No 1 (2021) Vol 5 No 2 (2020) Vol 5 No 1 (2020) Vol 4 No 2 (2019) Vol 4 No 1 (2019) Vol 3 No 2 (2018) Vol 3 No 1 (2018) Vol 2 No 2 (2017) Vol 2 No 1 (2017) Vol 1 No 1 (2016) More Issue