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All Journal Jurnal Pendidikan Fisika FKIP UM Metro JAIS (Journal of Applied Intelligent System) ANDHARUPA Phenomenon : Jurnal Pendidikan MIPA Jurnal Ilmiah Teknik Mesin PYTHAGORAS: Jurnal Program Studi Pendidikan Matematika JIPF (Jurnal Ilmu Pendidikan Fisika) Acta Aquatica: Aquatic Sciences Journal Turbo : Jurnal Program Studi Teknik Mesin Jusikom : Jurnal Sistem Komputer Musirawas Applied Industrial Engineering Journal Jurnal Riset Teknologi dan Inovasi Pendidikan (Jartika) PROSIDING SEMINAR NASIONAL PROGRAM PASCASARJANA Jurnal Pendidikan Fisika dan Teknologi Parole (Jurnal Pendidikan Bahasa dan Sastra Indonesia) M A T H L I N E : Jurnal Matematika dan Pendidikan Matematika JPPS (Jurnal Penelitian Pendidikan Sains) Jurnal Metaedukasi : Jurnal Ilmiah Pendidikan Journal of Energy and Electrical Engineering (JEEE) Abdimasku : Jurnal Pengabdian Masyarakat BERNAS: Jurnal Pengabdian Kepada Masyarakat Physics Communication Jurnal Health Sains Edu Masda Journal Industrika : Jurnal Ilmiah Teknik Industri Pendagogia: Jurnal Pendidikan Dasar International Journal of Economy, Education and Entrepreneurship (IJE3) SCIENCE : Jurnal Inovasi Pendidikan Matematika dan IPA Pillar of Physics Education : Jurnal Berkala Ilmiah Pendidikan Fisika ARMATUR : Artikel Teknik Mesin dan Manufaktur Sibatik Journal : Jurnal Ilmiah Bidang Sosial, Ekonomi, Budaya, Teknologi, Dan Pendidikan Share: Jurnal Ekonomi dan Keuangan Islam Jurnal Interkom : Jurnal Publikasi Ilmiah Bidang Teknologi Informasi dan Komunikasi Jurnal Teknologi Sistem Informasi Jurnal Nasional Teknik Elektro dan Teknologi Informasi Jurnal Locus Penelitian dan Pengabdian Makara Journal of Science Pharmaceutical Science Journal Gotong Royong Edusiana: Jurnal Ilmu Pendidikan
Aripin
Universitas Dian Nuswantoro
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Journal : Jurnal Nasional Teknik Elektro dan Teknologi Informasi

 1 
Pemetaan Emosi Dominan pada Kalimat Majemuk Bahasa Indonesia Menggunakan Multinomial Naïve Bayes Wisnu Agastya; Aripin
Jurnal Nasional Teknik Elektro dan Teknologi Informasi Vol 9 No 2: Mei 2020
Publisher : Departemen Teknik Elektro dan Teknologi Informasi, Fakultas Teknik, Universitas Gadjah Mada

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1248.65 KB) | DOI: 10.22146/jnteti.v9i2.157

Abstract

This study aimed at mapping Indonesian sentences into emotion classes based on the classification process in those sentences. The results of emotion mapping can be applied in various fields, such as production of animated films and games, analysis of facial expressions, human-computer interactions, and development of other expressive virtual characters, specifically to produce facial expressions that match the spoken sentences. The method used for the emotion mapping process was the text classification using multinomial naïve Bayes model that was accompanied by dominant boundary equation. Multinomial naïve Bayes model in the text classification is used to determine the types and the emotional intensity of Indonesian sentences, whereas dominant boundary equation iss used to determine the threshold in order to identify the dominant classes. The emotion classes used as references are six basic emotion classes according to Paul Ekman, i.e., happiness, sadness, anger, fear, disgust, and surprise. The experiment on the process of mapping emotions used Indonesian single and compound sentences. The experimental results show that the text classification using multinomial naïve Bayes model accompanied by dominant boundary equation can map compound sentences into several classes of dominant emotions.
Ekstraksi Emosi Majemuk Kalimat Bahasa Indonesia Menggunakan Convolutional Neural Network Aripin; Wisnu Agastya; Hanny Haryanto
Jurnal Nasional Teknik Elektro dan Teknologi Informasi Vol 10 No 2: Mei 2021
Publisher : Departemen Teknik Elektro dan Teknologi Informasi, Fakultas Teknik, Universitas Gadjah Mada

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1359.277 KB) | DOI: 10.22146/jnteti.v10i2.1051

Abstract

Facial expressions can strengthen the information conveyed in interactive communication. In the field of developing virtual characters specifically for facial characters, facial expressions are needed to animate a facial virtual character to make it look natural like a human. One type of emotional expression is a compound emotional expression, which is a combination of two or more basic emotions. For example, the expression of disappointed emotions is a combination of anger and sadness. Facial expressions can appear due to emotional stimulation, one of which is the meaning of the sentence. This research aims to extract emotional data from Indonesian sentences using the multi-label classification process of the CNN model so as to produce compound facial expressions that are applied in virtual character animation. The basic emotion classes used in the classification process are anger, disgust, fear, happiness, sadness, and surprise. Based on the experimental results, the CNN model can produce an accuracy of 94.5% with the composition of training data and test data is 8: 2. The classification process result shows that each sentence can produce more than one basic emotion class that forms compound expressions. The results of the visualization of compound expressions for each sentence can represent compound expressions.
Mengoptimalkan Akurasi pada Klasifikasi Emosi Majemuk Berdasarkan Semantik Kalimat Menggunakan XLM-RoBERTa Aripin; Steven Adi Santoso; Hanny Haryanto
Jurnal Nasional Teknik Elektro dan Teknologi Informasi Vol 12 No 1: Februari 2023
Publisher : Departemen Teknik Elektro dan Teknologi Informasi, Fakultas Teknik, Universitas Gadjah Mada

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/jnteti.v12i1.6084

Abstract

There are six basic emotions; they are anger, sadness, happiness, disgust, surprise, and fear. A combination of basic emotions creates a new type of emotion called a compound emotion. The examples of applying these compound emotions are in chatbots, translations, and text summarization. Several research on classifying these emotions based on Indonesian texts have used traditional models such as multinomial naïve Bayes, support vector machine (SVM), k-nearest neighborhood, and term frequency–inverse document frequency (TF-IDF). The previous research have a massive drawback, primarily on their less optimized performances. The models used could only classify things with the available data; thus, the text processing is required that results in a longer training time for larger This research aims to solve the issue from the previous research by using cross-lingual language model-robustly optimized bidirectional encoder representations from transformers approach (XLM-RoBERTa) model to classify compound emotions based on the semantics or meaning in words and sentences. The XLM-RoBERTa is a transformer model that can identify the meaning of a word from its attention mechanism and represent it as a vector to know the usage and position in a sentence. It is also a method to understand the meaning of a specific word. Using the attention mechanism, the model used the word position to recognize the sentence pattern and classify them even further to know the pattern and sequence to understand the semantics. The experiment result showed that the model could classify Indonesian texts into basic and compound emotion classes with an accuracy of up to 95.56%. This result is much higher than using traditional models to classify the compound emotion classes.
Optimasi Klasifikasi Fonem Menggunakan Backpropagation Neural Network dan Principal Component Analysis Clara Maria Livia Suitela; Aripin; Erika Dina Permata; Muzalfa Nakiatun Niza; Naeli Laelal Khiaroh
Jurnal Nasional Teknik Elektro dan Teknologi Informasi Vol 12 No 1: Februari 2023
Publisher : Departemen Teknik Elektro dan Teknologi Informasi, Fakultas Teknik, Universitas Gadjah Mada

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/jnteti.v12i1.5674

Abstract

A phoneme is the smallest sound in a sentence that has no meaning but plays the most important role in meaning formation. Phoneme identification from a video that shows an actor speaking Indonesian sentences is an important part of developing visual-to-text applications. This application can translate mouth movements from a video into a series of Indonesian texts so that it can facilitate communication for the deaf. This study aims to optimize the performance of the classification process on image data, including as many as 32 phonemes from video extraction results so that they can be used to support the phoneme identification process to realize visual-to-text applications in Indonesian. The classification algorithm used in this study was neural network backpropagation. Some of the proposed efforts to optimize the performance of the classification process included using a comparison of the proportion of datasets, estimating the number of hidden layers, and reducing the dimensions of the dataset using the principal component analysis (PCA) method to reduce the amount of data that is considered less important without reducing the level of information. The dimensions of the data before reduction were 1280 × 7100 data matrices and 1280 × 50 data matrices after reduction. The accuracy results obtained in data optimization using the PCA were equal to 87.16% with a data proportion of 8 : 2 and fifty important data points were used in the data optimization process using the PCA.
Co-Authors Agustini, Dini ahmad yani Alfa Mitri Suhara, Alfa Mitri Amrullah, Muhammad Rifqi Hanif Amza, Andi Andhy Romdani, Andhy ANDI NAHRUL HAYAT, MOHAMMAD EXCEL Andryani, Dinda Syaqila Anjani, Ratna Dewi Anshary , Muhammad Adi Khairul Apriana, Erfan Ardiansyah, Ivan Nur Asep Andang, Asep Asmah, Asmah Aswari, Muslim Awaludin, Robi Azzahra, Elsa B. Kurniawan Badriah, Liah Cinantya Paramita Clara Maria Livia Suitela Dany, Rahmad Dedi Nurcipto, Dedi Dita Ayu Mayasari, Dita Ayu Erika Dina Permata Ernita Susanti Fenita Purnama Sari Indah firmansyah maualana sugiartana nursuwars Hanny Haryanto Hariyanto, Stefhant Hayani, Surma Hendrico, Hendrico Herlinda, Mega Hernawati, Diana Hidayati Hidayati I Nyoman Adi Putra Ihtifazhuddin Hawari iin gusmana iin Ilema, Resty Iliyani, Shopiya Imam Taufiqurrahman Indra Pahala Irawan, Henky Jayadi Jayadi Joko Sampurno Juhary Ali Kardiman, Kardiman Karlina Muzianti, Intan Lutfi Amin, Shella Makiyah, Yanti Sofi Marno Marno Mekata, M Menik Dwi Kurniatie Mitsudo, Mitsudo Monitasari, Anna Mubarokiyah, Tasya Azizah Muhammad Miftah Muhammad Miftah, Muhammad Muljono, - Mulyana Mulyana Mustofa, Romy Faisal Muzahar Muzalfa Nakiatun Niza Nadlirah, Tasnim Ahya Naeli Laelal Khiaroh Nasution, Kartini Ningrum, Afifah Rindhika Setya Nuraeni, Nisa Nurfadilah Siregar Nurlaili, Fitroh Nurwulan Adi Ismaya Okto Ivansyah Rachmasari, Sri Ratnasari, Yanti Rita Dwi Pratiwi Riyandi, Albert Saifullah, Firman Sari Ayu Wulandari Sasmita, Yopi Wiliyana Sawitri, Anak Agung Sagung Sayful Arif Shofa, Rahmi Nur Sofi Makiyah, Yanti Steven Adi Santoso Suhada, Karya Sumarjo, Jojo Sumarjo, Jojo Sumarjo Sutisna Sutisna T. Idehara Toto Haryadi, Toto Tulus Suryanto Usrah, Ifkar Vincent Suhartono Wahono, Puji Wilanno, Marga Rizqi Wisnu Agastya Wistama, Sri Tirto Mada Yudiana, Yudiana Yusrizal Yusrizal