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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) ComEngApp : Computer Engineering and Applications Journal Jurnal Ilmu Informasi Perpustakaan dan Kearsipan
Sari, Tri Kurnia
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Computer Science & IT Engineering Library & Information Science

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Pembuatan Direktori Objek Wisata Kabupaten Solok Provinsi Sumatera Barat Sari, Tri Kurnia; Nurizzati, Nurizzati
Ilmu Informasi Perpustakaan dan Kearsipan Vol 6, No 1 (2017): Seri E
Publisher : Program Studi Informasi, Perpustakaan, dan Kearsipan , Jurusan Bahasa dan Sastra Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (833.924 KB) | DOI: 10.24036/8409-0934

Abstract

The aims of this research are (1) making Solok tourist object directory in West Sumatera amounted to 203 tourist objects and that author can get about 21 tourist objects consisting of some districtsthat located in Solok, this matter is caused by the limited time, a long distant location and cost a lot of money, (2) the way in generating the directory is collecting data, record data, focus on ideas, create a book framework, determine subject, classification or grouping and directory preparation, (3) the constraints in making location plan. The efforts of writing to overcome obstacles in making directories is making the best possible location plan so that the users ofinformation are simplified to find the tourist object they want to visit.Keywords: Tourist object, directory
Hyperparameter optimization of convolutional neural network using particle swarm optimization for emotion recognition Rini, Dian Palupi; Sari, Tri Kurnia; Sari, Winda Kurnia; Yusliani, Novi
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 1: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i1.pp547-560

Abstract

Emotion identification has been widely researched based on facial expressions, voice, and body movements. Several studies on emotion recognition have also been performed using electroencephalography (EEG) signals and the results also show that the technique has a high level of accuracy. EEG signals that detected by standart method using exclusive representations of time and frequency domains presented unefficient results. Some researchers using the convolutional neural network (CNN) method performed EEG signal for emotional recognition and obtained the best results in almost all benchmarks. Although CNN has shown fairly high accuracy, there is still a lot of room for improvement. CNN is sensitive to its hyperparameter value because it has considerable effect on the behavior and efficiency of the CNN architecture. So that the use of optimization algorithms is expected to provide an alternative selection of appropriate hyper parameter values on CNN. Particle swarm optimization (PSO) algorithm is a metaheuristic-based optimization algorithm with many advantages. This PSO algorithm was chosen to optimize the hyperparameter values on CNN. Based on the evaluation results in each model, hybrid CNN-PSO showed better results and achieved the best value in 80:20 split data which is 99.30% accuracy.
Classification of Epilepsy Diagnostic Results through EEG Signals Using the Convolutional Neural Network Method Sari, Tri Kurnia; Rini, Dian Palupi; Samsuryadi
Computer Engineering and Applications Journal (ComEngApp) Vol. 12 No. 2 (2023)
Publisher : Universitas Sriwijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

The brain is one of the most important organs in the human body as a central nervous system which functions as a controlling center, intelligence, creativity, emotions, memories, and body movements. Epileptic seizure is one of the disorder of the brain central nervous system which has many symptoms, such as loss of awareness, unusual behavior and confusion. These symptoms lead in many cases to injuries due to falls, biting one’s tongue. Detecting a possible seizure beforehand is not an easy task. Most of the seizures occur unexpectedly, and finding ways to detect a possible seizure before it happens has been a challenging task for many researchers. Analyzing EEG signals can help us obtain information that can be used to diagnose normal brain activity or epilepsy. CNN has been demonstrated high performance on detection and classification epileptic seizure. This research uses CNN to classify the epilepsy EEG signal dataset. AlexNet and LeNet-5 are applied in CNN architecture. The result of this research is that the AlexNet architecture provides better precision, recall, and f1- score values on the epilepsy signal EEG data than the LeNet-5 architecture.
Co-Authors Dian Palupi Rini Novi Yusliani Nurizzati Nurizzati Samsuryadi Winda Kurnia Sari