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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
Muhamad Agung Suhendra
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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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.
Object Tracking Based on Camera Using Anfis and Fuzzy Classifier for RGB Color Iqbal Robiyana; Timbo Faritcan Parlaungan; Sarifudin; Suhendra, Muhamad Agung
TIME in Physics Vol. 1 No. 2 (2023): August
Publisher : Universitas Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11594/timeinphys.2023.v1i2p85-91

Abstract

Image processing technology has a wide range of applications, such as in the medical, military, surveillance, and robotics industries. Analyzing objects in images is crucial when it comes to image processing. This study focuses on image processing to track objects of red, green, and blue (RGB) colors through the utilization of a camera. There are two research schemes: image processing and data classification. The data classification method used is the fuzzy and adaptive neuro-fuzzy inference system (ANFIS). The methods of image subtracting and region properties are commonly utilized for image processing. Based on the classification data results, the fuzzy logic classification demonstrated a higher accuracy rate of 86% when compared to Anfis' 65%. This was observed when both classification models were tested using a random sample. The value of Anfis is small due to the limited size of the training data used. As a result, it is recommended to use a fuzzy classifier for object color tracking for good performance.
A Computational Study of Numerical Integration in Physics Applications Using Trapezoidal and Simpson's Methods Suhendra, Muhamad Agung; Assegaf, Sufiyah; Robiyana, Iqbal; Nurizati
TIME in Physics Vol. 2 No. 2 (2024): September
Publisher : Universitas Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11594/timeinphys.2024.v2i2p85-95

Abstract

This research conducts a comprehensive evaluation of the efficiency and accuracy of two widely-used numerical integration methods, the Trapezoidal Rule and Simpson's Rule, within the context of solving physics-related problems. The study focuses on four representative cases: the calculation of kinetic energy, the determination of electric field strength, the work done by an ideal gas, and the gravitational potential energy. The performance of these methods is analyzed through key metrics such as convergence behavior, error magnitude, and computational time. The findings reveal that Simpson's Rule consistently delivers higher accuracy compared to the Trapezoidal Rule, especially for functions exhibiting non-linear characteristics. This highlights Simpson's Rule as a preferred method for complex physical problems, while the Trapezoidal Rule remains effective for simpler cases requiring lower computational overhead.
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.
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 Anderias Eko Wijaya Assegaf, Sufiyah Iqbal Robiyana Nurizati Nurizati Permana, Roni Suhendra, Muhamad Agung Tedi Sumardi Timbo Faritcan Parlaungan Timbo Faritcan Parlaungan Siallagan