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All Journal Jurnal Pengabdian UntukMu NegeRI Journal of Technology Informatics and Engineering
Prasetya Prasetya
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Religion Computer Science & IT Economics, Econometrics & Finance Education Law, Crime, Criminology & Criminal Justice Social Sciences

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PELATIHAN PEMBUATAN PUBLIKASI ILMIAH GURU-GURU SMA N 2 TAMBANG KABUPATEN KAMPAR Jufrizal Syahri; Rahmiwati Hilma; Hasmalina Nasution; Prasetya Prasetya; Rahmadini Syafri; Sri Hilma Siregar; Nurlaili Nurlaili
Jurnal Pengabdian UntukMu NegeRI Vol 2 No 1 (2018): Pengabdian Untuk Mu negeRI
Publisher : LPPM UMRI

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1821.179 KB) | DOI: 10.37859/jpumri.v2i1.689

Abstract

The writing of scientific writing is one of the efforts to develop the profession of teachers of SMA N 2 Tambang in Kampar regency. This training aims to equip teachers in terms of strategies to develop scientific papers properly in accordance with the guidelines of writing, and equip teachers in the publication procedures of scientific papers in the journal. The training of scientific writing is done by varied lecture method and practice. The lecture method is needed to explain the procedure for the preparation of scientific papers, including how to arrange classroom action research into a scientific paper. Methods of practice are needed to allow the participants to write scientific papers in the form of articles for journals. The training activities of the writing of scientific papers as a whole can be said to be well seen from the achievement of the target of the trainee, the achievement of the training objectives, the achievement of the planned material targets, and the ability of the participants in the mastery of the material. The supporting factors for the implementation of Community Service activities are the availability of experts in SMA N 2 Tambang, the enthusiasm of the participants, the support of the principal of the place of administration, and the supporting funds of the faculty.
A DIGITAL PRINTING APPLICATION AS AN EXPRESSION IDENTIFICATION SYSTEM. Arman Arman; Prasetya Prasetya; Feny Nurvita Arifany; Fertilia Budi Pradnyaparamita; Joni Laksito
Journal of Technology Informatics and Engineering Vol 1 No 2 (2022): August: Journal of Technology Informatics and Engineering
Publisher : University of Science and Computer Technology

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51903/jtie.v1i2.135

Abstract

Human Computer Interaction (HCI), a growing research field in science and engineering, aims to provide a natural way for humans to use computers as tools. Humans prefer to interact with each other mainly through speech, but also through facial expressions and gestures, for certain parts of the speech and displays of emotions. The identity, age, gender, and emotional state of a person can be obtained from his face. The impression we receive from the expression reflected on the face affects our interpretation of the spoken word and even our attitude towards the speaker himself. Although emotion recognition is an easy task for humans, it still proves to be a difficult task for computers to recognize user`s emotional state. Advances in this area promise to arm our technological environment by means for more effective interactions with humans, and hopefully the impact of facial expressions on cognition will increase rapidly in the future. Will do. In recent years, the adoption of digital has increased rapidly, and the quality has improved significantly. Digital printing has resulted in fast delivery and needs-based costs. This article describes a sophisticated combination classifier approach, an empirical study of ensembles, stacking, and voting. These three approaches were tested on Nave Bayes (NB), Kernel Naive Bayes (kNB), Neural Network (NN), Auto MultiLayer Perceptron (Auto MLP), and Decision Tree (DT), respectively. The main contribution of this paper is the improvement of the classification accuracy of facial expression recognition tasks. In both persondependent and nonpersondependent experiments we showed that using a combination of these classifier combinations gave significantly better results than using individual classifiers. It has been observed from experiments that the overall voting technique by voting achieves the best classification accuracy.
A DIGITAL PRINTING APPLICATION AS AN EXPRESSION IDENTIFICATION SYSTEM. Arman Arman; Prasetya Prasetya; Feny Nurvita Arifany; Fertilia Budi Pradnyaparamita; Joni Laksito
Journal of Technology Informatics and Engineering Vol. 1 No. 2 (2022): August: Journal of Technology Informatics and Engineering
Publisher : University of Science and Computer Technology

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51903/jtie.v1i2.135

Abstract

Human Computer Interaction (HCI), a growing research field in science and engineering, aims to provide a natural way for humans to use computers as tools. Humans prefer to interact with each other mainly through speech, but also through facial expressions and gestures, for certain parts of the speech and displays of emotions. The identity, age, gender, and emotional state of a person can be obtained from his face. The impression we receive from the expression reflected on the face affects our interpretation of the spoken word and even our attitude towards the speaker himself. Although emotion recognition is an easy task for humans, it still proves to be a difficult task for computers to recognize user`s emotional state. Advances in this area promise to arm our technological environment by means for more effective interactions with humans, and hopefully the impact of facial expressions on cognition will increase rapidly in the future. Will do. In recent years, the adoption of digital has increased rapidly, and the quality has improved significantly. Digital printing has resulted in fast delivery and needs-based costs. This article describes a sophisticated combination classifier approach, an empirical study of ensembles, stacking, and voting. These three approaches were tested on Nave Bayes (NB), Kernel Naive Bayes (kNB), Neural Network (NN), Auto MultiLayer Perceptron (Auto MLP), and Decision Tree (DT), respectively. The main contribution of this paper is the improvement of the classification accuracy of facial expression recognition tasks. In both persondependent and nonpersondependent experiments we showed that using a combination of these classifier combinations gave significantly better results than using individual classifiers. It has been observed from experiments that the overall voting technique by voting achieves the best classification accuracy.
Co-Authors Arman Arman Feny Nurvita Arifany Fertilia Budi Pradnyaparamita Hasmalina Nasution Joni Laksito Jufrizal Syahri Nurlaili Nurlaili Rahmadini Syafri Rahmiwati Hilma Sri Hilma Siregar