Article Per Year (5 Year)
p-Index From 2020 - 2025
0.562
P-Index
This Author published in this journals
All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Jurnal Sarjana Teknik Informatika Indonesian Journal of Electrical Engineering and Informatics (IJEEI)
Anggun Winursito
Universitas Negeri Yogyakarta
Computer Science & IT Electrical & Electronics Engineering Engineering

Published : 3 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 3 Documents
Search

 1 
Troop camouflage detection based on deep action learning Muslikhin Muslikhin; Aris Nasuha; Fatchul Arifin; Suprapto Suprapto; Anggun Winursito
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 11, No 3: September 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v11.i3.pp859-871

Abstract

Detecting troop camouflage on the battlefield is crucial to beat or decide in critical situations to survive. This paper proposed a hybrid model based on deep action learning for camouflage recognition and detection. To involve deep action learning in this proposed system, deep learning based on you only look once (YOLOv3) with SquezeeNet and the fourth steps on action learning were engaged. Following the successful formulation of the learning cycle, an instrument examines the environment and performance in action learning with qualitative weightings; specific target detection experiments with view angle, target localization, and the firing point procedure were performed. For each deep action learning cycle, the complete process is divided into planning, acting, observing, and reflecting. If the results do not meet the minimal passing grade after the first cycle, the cycle will be repeated until the system succeeds in the firing point. Furthermore, this study found that deep action learning could enhance intelligence over earlier camouflage detection methods, while maintaining acceptable error rates. As a result, deep action learning could be used in armament systems if the environment is properly identified.
Pengembangan Sistem Monitoring Kesehatan Jantung Tahan Noise Berbasis Sinyal EKG Anggun Winursito
Jurnal Sarjana Teknik Informatika Vol 10, No 2 (2022): Juni
Publisher : Teknik Informatika, Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/jstie.v10i2.24153

Abstract

Penelitian mengenai sistem monitoring kesehatan jantung secara otomatis banyak dilakukan, namun masih belum menghasilkan output yang maksimal. Permasalahan utama dari penelitian yang sudah ada adalah akurasi sistem monitoring yang masih rendah terutama pada kondisi sinyal EKG yang mengandung noise. Pada penelitian ini dirancang sistem deteksi yang tahan noise melalui pengembangan algoritma kombinasi, serta dirancang prototipe hardware dan software sistem pelayanan bagi pasien dalam memonitoring kesehatan jantung. Algortima kombinasi menggunakan Wavelet dan Artificial Neural Network (ANN). Output sinyal hasil proses denoising dimasukkan dalam proses klasifikasi menggunakan ANN dan output deteksi berupa kondisi sinyal EKG yang menggambarkan keadaan jantung normal atau abnormal. Proses denoising dirancang menggunakan Wavelet dengan mengujicobaan beberapa tipe Wavelet Daubechies, Symlet, serta Coiflet pada sinyal EKG yang mengandung noise. Hasil penelitian menunjukkan bahwa algoritma kombinasi mampu memperbaiki performa sistem deteksi konvensional pada proses monitoring kesehatan jantung. Software monitoring serta prosedur pelayanan pasien juga dirancang berbasis website dan menggunakan teknologi internet of thngs.
Development of Javanese Speech Emotion Database (Java-SED) Fatchul Arifin; Ardy Seto Priambodo; Aris Nasuha; Anggun Winursito; Teddy Surya Gunawan
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 10, No 3: September 2022
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52549/ijeei.v10i3.3888

Abstract

Javanese is one of the most widely spoken regional languages in Indonesia, alongside other regional languages. Emotions can be recognized in a variety of ways, including facial expression, behavior, and speech. The recognition of emotions through speech is a straightforward process, but the outcomes are quite significant. Currently, there is no database for identifying emotions in Javanese speech. This paper aims to describe the creation of a Javanese emotional speech database. Actors from the Kamasetra UNY community who are accustomed to performing in dramatic roles participated in the recording. The location where recordings are made is free of interference and noise. The actors of Kamasetra have simulated six types of emotions, including happy, sad, fear, angry, neutral, and surprised. The cast consists of ten people between the ages of 20 and 30, including five men and five women. Both humans (30 Javanese-speaking verifiers ranging in age from 17 to 50) and a machine learning system (30 Javanese-speaking verifiers with ages between 17 and 50) verify the database that has been created. The verification results indicate that the database can be used for Javanese emotion recognition. The developed database is offered as open-source and is freely available to the research community at this link https://beais-uny.id/dataset/
Co-Authors Ardy Seto Priambodo Aris Nasuha Fatchul Arifin Muslikhin Suprapto Suprapto Teddy Surya Gunawan