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All Journal Techno.Com: Jurnal Teknologi Informasi CALYPTRA : Jurnal Ilmiah Mahasiswa Universitas Surabaya Telematika : Jurnal Informatika dan Teknologi Informasi Jurnal Teknologi Informasi dan Ilmu Komputer Jurnal Edukasi dan Penelitian Informatika (JEPIN) Journal of Information Systems Engineering and Business Intelligence Jurnal ELTIKOM : Jurnal Teknik Elektro, Teknologi Informasi dan Komputer JOIV : International Journal on Informatics Visualization Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Jurnal Informatika dan Multimedia Indonesian Journal of Information System J-SAKTI (Jurnal Sains Komputer dan Informatika) Keluwih: Jurnal Sains dan Teknologi Jurasik (Jurnal Riset Sistem Informasi dan Teknik Informatika) Teknika J-SAKTI (Jurnal Sains Komputer dan Informatika) Jurnal Informatika dan Multimedia
Mohammad Farid Naufal, Mohammad Farid
Jurusan Teknik Informatika, Universitas Surabaya, Surabaya, Jawa Timur
Aerospace Engineering Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Energy Engineering Environmental Science Industrial & Manufacturing Engineering Mechanical Engineering

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Journal : Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi)

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Analisis Perbandingan Algoritma Klasifikasi MLP dan CNN pada Dataset American Sign Language Mohammad Farid Naufal; Sesilia Shania; Jessica Millenia; Stefan Axel; Juan Timothy Soebroto; Rizka Febrina P.; Mirella Mercifia
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 5 No 3 (2021): Juni 2021
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (501.161 KB) | DOI: 10.29207/resti.v5i3.3009

Abstract

People who have hearing loss (deafness) or speech impairment (hearing impairment) usually use sign language to communicate. One of the most basic and flexible sign languages ​​is the Alphabet Sign Language to spell out the words you want to pronounce. Sign language uses hand, finger, and face movements to speak the user's thoughts. However, for alphabetical sign language, facial expressions are not used but only gestures or symbols formed using fingers and hands. In fact, there are still many people who don't understand the meaning of sign language. The use of image classification can help people more easily learn and translate sign language. Image classification accuracy is the main problem in this case. This research conducted a comparison of image classification algorithms, namely Convolutional Neural Network (CNN) and Multilayer Perceptron (MLP) to recognize American Sign Language (ASL) except the letters "J" and "Z" because movement is required for both. This is done to see the effect of the convolution and pooling stages on CNN on the resulting accuracy value and F1 Score in the ASL dataset. Based on the comparison, the use of CNN which begins with Gaussian Low Pass Filtering preprocessing gets the best accuracy of 96.93% and F1 Score 96.97%
Sentiment Analysis of ChatGPT on Indonesian Text using Hybrid CNN and Bi-LSTM Prasetyo, Vincentius Riandaru; Naufal, Mohammad Farid; Wijaya, Kevin
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 9 No 2 (2025): April 2025
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v9i2.6334

Abstract

This study explores sentiment analysis on Indonesian text using a hybrid deep learning approach that combines Convolutional Neural Networks (CNN) and Bidirectional Long Short-Term Memory (Bi-LSTM). Due to the complex linguistic structure of the Indonesian language, sentiment classification remains challenging, necessitating advanced methods to capture both local patterns and sequential dependencies. The primary objective of this research is to improve sentiment classification accuracy by leveraging a hybrid model that integrates CNN for feature extraction and Bi-LSTM for contextual understanding. The dataset consists of 800 manually labeled samples collected from social media platforms, preprocessed using case folding, stop word removal, and lemmatization. Word embeddings are generated using the Word2Vec CBOW model, and the classification model is trained using a hybrid architecture. The best performance was achieved with 32 Bi-LSTM units, a dropout rate 0.5, and L2 regularization, which was evaluated using Stratified K-Fold cross-validation. Experimental results demonstrate that the hybrid model outperforms conventional deep learning approaches, achieving 95.24% accuracy, 95.09% precision, 95.15% recall, and 95.99% F1 score. These findings highlight the effectiveness of hybrid architectures in sentiment analysis for low-resource languages. Future work may explore larger datasets or transfer learning to enhance generalizability.
Co-Authors Alvin Eka Putra Aminulloh, Septian Wijaya Ang Alexander Yoshua Arifin, Theofilus Aryo Budilaksono Astri Agustina Arius Daniel Cristianindra R. David Sugiarto Derry Alim Fernando Fania Alya Miranti Farrel Arghya Tito Prayoga Felix Handani Ferdiana Kusuma, Selvia Jeremy Owen Lieyanto Jessica Millenia Joko Siswantoro Joseph Kristiano Juan Timothy Soebroto Kansil, Christian Nathaneil Kevin Christian Tanus Kevin Wijaya, Kevin Kusuma, Selvia Ferdiana Mirella Mercifia Mohd Sanusi Bin Azmi Muhammad Ghifari Kusuma Wicaksono Nicky Setyawan Dinata Njoto Benarkah Prasetyo, Daniel Hary Putra, Putu Aditya Riva Ramadhan, Firman Herda Raynaldy Valentino Sukiwun Renatasiva, Billy Rizka Febrina P. Selvia F Kusuma, Selvia F Selvia Ferdiana Kusuma Selvia Ferdiana Kusuma Selvia Ferdiana Kusuma Selvia Ferdiana Kusuma Sesilia Shania Sesilia Shania Soebroto, Juan Timothy Solichul Huda Solichul Huda, Solichul Stefan Axel Subrata, - Susanto, Alvin Fernando Vernolyo, Panji Yumadana Vincentius Riandaru Prasetyo Vincentius Riandaru Prasetyo Wibisono, Yudistira Rahadian Wirjawan, Hans Wisnu Aria Yustisia Yohanes Albert Lauwoto Zefanya Ardya Prayuska