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All Journal Jurnal Penelitian Pendidikan IPA (JPPIPA) Jurnal Teknologi Informasi dan Komunikasi Time in Physics: Journal for Theoretical, Instrumentation, Material-Molecular, and Education Physics Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics
Tedi Sumardi
Program Studi Fisika, Fakultas Sains, Universitas Mandiri
Agriculture, Biological Sciences & Forestry Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Health Professions Materials Science & Nanotechnology Mathematics Physics

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RANCANG BANGUN SISTEM KENDALI LAMPU RUMAH BERBASIS ANDROID MENGGUNAKAN ARDUINO Tedi Sumardi Tedi; Siti Nurasiah
Jurnal Teknologi Informasi dan Komunikasi Vol 14 No 2 (2021): Oktober
Publisher : STMIK SUBANG

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47561/a.v14i2.214

Abstract

Home light control system is made to make it easier for residents in controlling the home light practically and efficiently while it on/off. The system is designed using Bluetooth that connect to ATmega328 microcontroller as a main component. The design steps consist of identification and analysis of demands, hardware dan software design, device manufacturing, and testing. The controller system worked when the module bluetooth is connected between smartphone and ATmega328. The results showed that the testing of this home light controller system can work as well as working principle that have been designed.
Pengenalan Suara sebagai Pengendali Mobile Robot dengan Metode Adaptive Neuro-Fuzzy Inference System Muhamad Agung Suhendra; Timbo Faritcan Parlaungan; Tedi Sumardi
TIME in Physics Vol. 1 No. 1 (2023): February
Publisher : Universitas Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11594/timeinphys.2023.v1i1p43-49

Abstract

Voice recognition or speech recognition is a biometric technology that has very wide applications, one of which is for simple robot motion control. There are three stages in this research, namely data acquisition, feature extraction, and data classification. For feature extraction, the wavelet transform method is used which can analyze non-stationary and non-linear signals, while for data classification, the Adaptive Neuro-Fuzzy Inference System (Anfis) method is used. The result of data classification is 92.25% and 7.75% error. So, based on the results of the classification accuracy, the robot can be moved via voice commands and to anticipate the error value, the ultrasonic sensor feature is added to the robot as an alternative control.
Penerapan Simulasi AI Sistem Drone Ganda untuk Optimasi Lintasan pada Pemantauan Perkebunan Sumardi, Tedi; Suhendra, M. Agung; Robiyana, Iqbal; Wijaya, Anderias Eko
Jurnal Teknologi Informasi dan Komunikasi Vol 18 No 1 (2025): April
Publisher : STMIK Subang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47561/jtik.v18i1.286

Abstract

Drone-based monitoring systems have emerged as an effective solution to improve the efficiency of large-scale agricultural land surveillance, particularly in oil palm plantations. This study proposes an artificial intelligence (AI)-based simulation using dual drones to map optimal and distributed flight paths. The simulation considers the random wind effect on trajectory accuracy using a grid-based waypoint approach across the plantation area. The results show that both drones successfully completed the land inspection mission with an average wind-induced deviation of ±0.14 meters, indicating system stability under dynamic environmental conditions. Drone 1 covered a total distance of 9244.10 meters, while Drone 2 covered 10602.47 meters. A 3D trajectory visualization illustrates that the path deviations remained controlled. This research provides a foundation for developing more adaptive and efficient autonomous drone systems in the context of smart farming.
EEG-Based Emotion Classification in Response to Humorous, Sad, and Fearful Video Stimuli Using LSTM Networks: A Comparative Study with Classical Machine Learning Models Muhamad Agung Suhendra; Tedi Sumardi; Iqbal Robiyana; Nurizati, Nurizati
Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol. 7 No. 2 (2025): May
Publisher : Jurusan Teknik Elektromedik, Politeknik Kesehatan Kemenkes Surabaya, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/ijeeemi.v7i2.100

Abstract

Emotion recognition based on EEG signals is a critical area within affective computing, with applications in mental health monitoring, human-computer interaction, and neuroadaptive systems. However, accurately classifying emotional states from inherently non-stationary and noisy EEG data remains a major challenge. This study explores the classification of three discrete emotions, Humorous, Sad, and Fearful, elicited through video stimuli, using EEG recordings from six participants acquired via a 19-channel Mitsar amplifier at a 500 Hz sampling rate. Preprocessing steps included bandpass filtering (1–40 Hz), epoch segmentation, and multi-domain feature extraction encompassing statistical measures, spectral features, differential entropy, Hjorth parameters, and hemispheric asymmetry indicators. Data augmentation was applied to balance class distributions, particularly for the underrepresented fear category. The resulting features were normalized and structured to support temporal deep learning and classical machine learning models. The classification performance of Long Short-Term Memory (LSTM) networks was evaluated alongside Support Vector Machine (SVM), k-Nearest Neighbors (k-NN), and Random Forest (RF) classifiers. While LSTM demonstrated competency in capturing temporal dependencies, especially in fear recognition, SVM achieved the highest overall accuracy, 94.12%, outperforming LSTM at 85.16%, RF at 90.00%, and k-NN at 78.01%. These results suggest that when robust and discriminative features are employed, traditional models like SVM can surpass deep learning methods, particularly in small-scale EEG datasets with limited temporal complexity. This study underscores the importance of aligning model architecture with feature representation and contributes a comparative evaluation framework for EEG-based emotion recognition systems.
Canonical Correlation Analysis and Its Extension for SSVEP-based BCI Detection: A Systematic Review Suhendra, Muhamad Agung; Robiyana, Iqbal; Sumardi, Tedi; Sulaeman, Ahmad Sofyan; Putro, Permono Adi; Nurizati, Nurizati; Suryadi, Usep Tatang; Wijaya, Anderias Eko; Ajidarmo, Sunanto; Budiman, Arief; Amri, M. Faizal
Jurnal Penelitian Pendidikan IPA Vol 10 No 12 (2024): December
Publisher : Postgraduate, University of Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/jppipa.v10i12.9844

Abstract

SSVEP-based Brain-Computer Interfaces (BCIs) utilize steady-state visual evoked potentials, which are brain responses triggered by visual stimuli flickering at specific frequencies. Users can focus on these stimuli, allowing the system to interpret their intent based on the brain's electrical activity. This technology has applications in communication for individuals with disabilities, gaming, and neuro-feedback, offering an ultimate means of interaction through thought alone. In this study, systematic literature review was conducted to identify analytical methods for SSVEP spellers with PRISMA method from the eligibility criteria. CCA and its extension become gold-standar method that give excellent performances for SSVEP recognition and signal classification. Some uniques features also found such as MsetCCA, FB-CCA, MF-CCA, TW-CCA, CP-CCA, IIS-CCA, TT-CCA and RLS-CCA. Therefore, we have various options for choosing the best method for recognizing SSVEP from EEG signals based BCI.
Thermal Image-Based Classification of Okra Maturity: A Comparative Study of CNN, SVM, and LSTM Sumardi, Tedi; Robiyana, Iqbal; Permana, Roni; Suhendra, Muhamad Agung
Jurnal Penelitian Pendidikan IPA Vol 11 No 11 (2025): November: In Progress
Publisher : Postgraduate, University of Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/jppipa.v11i11.12748

Abstract

Post-harvest quality assessment remains a major challenge in agriculture, particularly for okra (Abelmoschus esculentus), which deteriorates rapidly due to high moisture content. Traditional grading based on manual inspection often results in inconsistency and product damage. This study explores thermal imaging as a non-destructive alternative for okra maturity classification. A dataset of 501 thermal images was acquired under controlled conditions and analyzed using three machine learning models: Convolutional Neural Network (CNN), Support Vector Machine (SVM) with Histogram of Oriented Gradients (HOG) features, and Long Short-Term Memory (LSTM) network. Experimental results show that CNN achieved the highest accuracy (99.01%), outperforming SVM (95.05%) and LSTM (91.09%). Confusion matrix and ROC analyses confirmed CNN’s superiority in capturing spatial thermal patterns related to maturity stages. Compared with RGB or hyperspectral imaging reported in prior studies, thermal imaging integrated with AI provides a more robust, illumination-independent, and non-destructive solution. The findings demonstrate the potential of CNN-based thermal imaging systems for automated sorting of okra in agricultural supply chains. Future work will focus on larger datasets, multi-class maturity levels, and real-time implementation to enhance practical deployment in post-harvest management.
Co-Authors Amri, M. Faizal Anderias Eko Wijaya Arief Budiman Iqbal Robiyana Muhamad Agung Suhendra Nurizati Nurizati Permana, Roni Permono Adi Putro Siti Nurasiah Suhendra, Muhamad Agung Sulaeman, Ahmad Sofyan Sumardi, Tedi Timbo Faritcan Parlaungan Siallagan Usep Tatang Suryadi, Usep Tatang