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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

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.

IMPLEMENTATION OF FACE RECOGNITION USING GEOMETRIC FEATURES EXTRACTION Risanuri Hidayat; Muhammad Oka Bagus Wibowo; Brama Yoga Satria; Anggun Winursito
Jurnal Ilmiah Kursor Vol 11 No 2 (2021)
Publisher : Universitas Trunojoyo Madura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21107/kursor.v11i2.284

The face is among the biometric objects used to recognize one’s identity. There are various face recognition system methods that can be applied, one of which is geometric features-based face recognition. Geometric features are unique features extraction of one’s facial components. These features are obtained by calculating the comparison values of the distance measurement between facial components served as a reference like eyes, nose, and mouth. This research implemented a face recognition system using the geometric features method on a significantly low-spec computer system. This implementation was carried out by building a system, installing it on a computer system, and then testing it using laptops or computer devices and the camera web. The face recognition system would process the facial input images, extract their geometric features, and match the results with the data stored in the database. The research results were a low-spec computer system that could recognize its users by providing real-time feedback in the form of users’ names with an accuracy of 98%.

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

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

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/

Performance Analysis of EMG Signal Classification Methods for Hand Gesture Recognition in Stroke Rehabilitation Winursito, Anggun; Arifin, Fatchul; Muslikhin, Muslikhin; Artanto, Herjuna; Caryn, Femilia Hardina
Elinvo (Electronics, Informatics, and Vocational Education) Vol. 8 No. 2 (2023): November 2023
Publisher : Department of Electronic and Informatic Engineering Education, Faculty of Engineering, UNY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21831/elinvo.v8i2.76811

This study evaluates the performance of different classification methods in classifying healthy individuals and stroke patients. The hand gesture variations of the subjects were also analyzed based on electromyography (EMG) signals. Several classification methods were tested in this analysis to find out which method had the most suitable performance. The results showed that Decision Tree and Naive Bayes classifiers achieved the highest performance in classifying EMG signals from healthy individuals and stroke patients, with both methods showing high accuracy, precision, recall, and F1 score. Specifically, Decision Tree excelled in overall accuracy and recall, while Naive Bayes showed superior precision. For hand gesture recognition, SVM, KNN, and Random Forest classifiers showed similarly high performance, achieving accuracy, precision, recall, and F1 score above 82%. Naive Bayes also performed well, especially in precision, while Decision Tree performed poorly compared to other methods. This insight can form the basis for the development of more effective and personalized rehabilitation systems for stroke patients, by utilizing reliable and accurate EMG signal classification

Advanced Multimodal Emotion Recognition for Javanese Language Using Deep Learning Arifin, Fatchul; Nasuha, Aris; Priambodo, Ardy Seto; Winursito, Anggun; Gunawan, Teddy Surya
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 12, No 3: September 2024
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52549/ijeei.v12i3.5662

This research develops a robust emotion recognition system for the Javanese language using multimodal audio and video datasets, addressing the limited advancements in emotion recognition specific to this language. Three models were explored to enhance emotional feature extraction: the SpectrogramImage Model (Model 1), which converts audio inputs into spectrogram images and integrates them with facial images for emotion labeling; the Convolutional-MFCC Model (Model 2), which leverages convolutional techniques for image processing and Mel-frequency cepstral coefficients for audio; and the Multimodal Feature-Extraction Model (Model 3), which independently processes video and audio features before integrating them for emotion recognition. Comparative analysis shows that the Multimodal Feature-Extraction Model achieves the highest accuracy of 93%, surpassing the Convolutional-MFCC Model at 85% and the Spectrogram-Image Model at 71%. These findings demonstrate that effective multimodal integration, mainly through separate feature extraction, significantly enhances emotion recognition accuracy. This research improves communication systems and offers deeper insights into Javanese emotional expressions, with potential applications in human-computer interaction, healthcare, and cultural studies. Additionally, it contributes to the advancement of sophisticated emotion recognition technologies.

Pengembangan Sistem Monitoring Kesehatan Jantung Tahan Noise Berbasis Sinyal EKG Winursito, Anggun
Jurnal Sarjana Teknik Informatika Vol. 10 No. 2 (2022): Juni
Publisher : Program Studi Informatika, Universitas Ahmad Dahlan

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

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.

Improving Speed Performance of Select Random Query in SQL Database Utomo, Muhammad Nur Yasir; Bastian, Alvian; Winursito, Anggun
INTEK: Jurnal Penelitian Vol 7 No 1 (2020): In Press
Publisher : Politeknik Negeri Ujung Pandang

Select random is a query in a SQL database that can retrieve data randomly from a table. Select random is often used to present data in various applications such as websites, data mining and others. Unfortunately, ordinary select random query is inefficient in terms of processing time if used in large table. This paper, tries to solve this problem by proposing two optimized methods of select random query, namely the Small Percentage Order by Rand (SPO-Rand) and the Filtered Column Order by Rand (FCO-Rand). The two proposed methods are then compared in terms of processing speed with a standard Select Random query or Normal Order by Rand (NO-Rand). The scenario of the experiment is to collect five random data from several data sets, ranging from 10.000 to 200.000 data. Based on the results of experiments that have been conducted, the proposed FCO-Rand method obtained the best process speed with 0.074 seconds at 200.000 data, followed by SPO-Rand with 0.265 seconds. These result are much faster than the standard random select method (NO-Rand) which takes up to 7,035 seconds for the same task.

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