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All Journal Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) Jurnal Simantec Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Best : Journal of Applied Electrical, Science and Technology Journal of Technology and Informatics (JoTI) Jurnal Polimesin
Sudianto, Achmad Imam
Universitas Trunojoyo Madura
Automotive Engineering Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Energy Engineering Materials Science & Nanotechnology Mechanical Engineering Physics

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Sensor Fusion using Model Predictive Control for Differential Dual Wheeled Robot Sudianto, Achmad Imam; Muslim, Muhammad Aziz; Rusli, Moch
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Vol. 8, No. 1, February 2023
Publisher : Universitas Muhammadiyah Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22219/kinetik.v8i1.1614

Abstract

Every mobile robot mission starts with the robot being moved to the task site. From there, the robot executes its tasks. A control system is required to move the mobile robot's actuator (which may be in the shape of wheels or legs) and comprehend the environment around the robot to perform these movements (perception). This research aims to develop a technique to control a robot’s movement while detecting obstacles and distances toward an object. The robot is equipped with LIDAR and a camera to perform these tasks. The control is divided into two major parts, low-level and high-level controller. As part of a low-level controller robot, the Model Predictive Control (MPC) method is proposed to help with the control of the wheel while the Artificial Neural Network (ANN) approach to use in this study to identify obstacles and the Convolutional Neural Network (CNN) method for detecting objects, both ANN and CNN as a control for high-level part of the robot. The results of this study can prove that CNN can help detect existing objects with a value of 45% for detecting some objects. The obtained result from the MPC method, which has been combined with an ANN as an obstacle detector, is that the smaller the horizon value, the shorter the time needed to reach the desired coordinates with the result being 45 seconds.
Optimizing K-Nearest Neighbors with Particle Swarm Optimization for Improved Classification Accuracy Dafid, Ach.; Sudianto, Achmad Imam; Thinakaran, Rajermani; Umam, Faikul; Adiputra, Firmansyah; Izzuddin; Sitepu Debora , Ribka
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol. 11 No. 2 (2025): June
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v11i2.30775

Abstract

This study aims to improve the performance of the K-Nearest Neighbors (KNN) algorithm in classifying public reviews of Batik Madura through optimizing the K value using the Particle Swarm Optimization (PSO) algorithm. Public reviews collected from the Google Maps platform are used as a dataset, with positive, negative, and neutral sentiment categories. Optimization of the K value is carried out to overcome the constraints of KNN performance, which is highly dependent on the K parameter, with PSO providing a more efficient approach than the grid search method. However, PSO also presents challenges such as sensitivity to parameter tuning and potential computational overhead. This study has succeeded in developing a web-based system using the Python Streamlit framework, which makes it easy for users to access sentiment analysis results. Testing shows that optimizing the K value with PSO increases the accuracy of KNN to 88.5% with an optimal K value of 19. However, this accuracy is not compared to other optimization techniques, leaving its relative advantage unverified. The results are expected to help Batik Madura entrepreneurs in evaluating public perception and guiding strategic innovations. Research outputs include a prototype, intellectual property registration, and journal publication, although the role of deep learning models is only briefly noted without further development.
Application of digital image processing to the measurement of Leaf Area Index (LAI) of rice plants (Oryza Sativa L.) Sudianto, Achmad Imam; Husna, Arifah
Jurnal Simantec Vol 13, No 2 (2025): Jurnal Simantec Juni 2025
Publisher : Universitas Trunojoyo Madura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21107/simantec.v13i2.30151

Abstract

Indonesia's main staple food, rice, is by far the largest commodity. In improving food security through the productivity of this local staple crop, care is needed from planting to harvesting. One of the physiological parameters that can determine biomass production and photosynthesis in rice is the leaf. We can measure this part of the plant through various methods ranging from conventional techniques to computer image processing techniques such as canny edge detection and ImageJ software. Through the comparison of these two methods, it is found that canny edge detection has a smaller average error value when compared to ImageJ, which is 3.76% and 4.53% respectively. With this final value, it is proven that canny edge detection can be an alternative technique to measure the value of LAI (Leaf Area Index) in rice plants.Keywords: Canny, ImageJ, Image Processing, Leaf Width, Rice
Integration of Concatenated Deep Learning Models with ResNet Backbone for Automated Corn Leaf Disease Identification imam sudianto, Achmad; Sigit Susanto Putro; Eka Mala Sari; Ika Oktavia Suzanti; Aeri Rachmad; Wildan Surya Wijaya
BEST Vol 7 No 2 (2025): BEST
Publisher : Universitas PGRI Adi Buana Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36456/3kct9e57

Abstract

Corn is one of Indonesia's food commodities, which is an alternative food that supports food diversification in Indonesia. However, leaf infections in corn plants often cause significant yield losses and threaten food security. Early detection of this disease is very important, especially for small farmers, because conventional diagnostic methods that rely on agronomists are expensive and time-consuming. Recent advances in Agricultural Artificial Intelligence (AI) and image processing have facilitated automatic plant disease recognition through Convolutional Neural Networks (CNN), with ResNet as the main backbone combined through concatenation with MobileNetV3, DenseNet161, and GoogleNet. The dataset consists of 4,000 images divided into 2,560 training data, 640 validation data, and 800 test data, with image sizes adjusted to 224×224 pixels. The dataset consists of 4,000 images distributed across four categories: gray leaf spot, common rust, northern leaf blight, and healthy leaf. The testing was conducted using three different optimizers, namely Adam, RMSprop, and SGD, with a learning rate of 0.01. The experimental results showed that the SGD optimizer provided the best performance with a loss value of 0.2275, accuracy of 0.9513, precision of 0.9536, recall of 0.9513, and F1-score of 0.9512. These findings confirm that the combination of ResNet, MobileNetV3, DenseNet161, and GoogleNet architectures with the SGD optimizer can significantly improve the accuracy of corn leaf disease detection, making it a potential application for automatic detection systems in support of smart farming practices.
Fluid pressure optimization of a PID-controlled hydraulic jack for enhanced lifting efficiency and stability Hairil Budiarto; Ibnu Irawan; Achmad Imam Sudianto; Ahmad Sahru Romadhon
Jurnal Polimesin Vol 23, No 3 (2025): June
Publisher : Politeknik Negeri Lhokseumawe

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30811/jpl.v23i3.6650

Abstract

Hydraulic and mechanical jacks are widely used for lifting applications but face limitations in efficiency and load handling. Despite being powerful, hydraulic jacks are prone to pressure loss and fluid leakage under static load, while mechanical systems lack automation and practicality. This research presents a hybrid hydraulic jack system integrating a DC motor-driven screw actuator and proportional–integral–derivative (PID) control for adaptive fluid pressure regulation. The purpose of this research is to develop an automatic hydraulic jack that integrates mechanical and hydraulic systems to improve the efficiency of load lifting time and reduce the risk of fluid leakage due to prolonged static pressure. The system was tested under three different loads: 90 kg, 110 kg, and 130 kg, with corresponding pressure setpoints of 170, 195, and 223 psi. Using the Ziegler–Nichols tuning method, the PID controller achieved high accuracy with error deviations of 1.1 psi, 0.1 psi, and 1.5 psi, respectively. These results represent a 95–99% precision rate in pressure regulation, compared to uncontrolled systems. The findings demonstrate the ability of the system to maintain pressure stability under varying load conditions, therefore reducing the risk of leakage and mechanical fatigue. This PID-based jack offers a cost-effective and efficient alternative to conventional power-pack hydraulic systems, particularly in mobile or resource-constrained applications.
A Real-Time Human-Drone Interaction System for Cornfield Perimeter Monitoring Using Hand Gesture Control Fadzillah Akbar Subkhi; Muhammad Fuad; Sri Wahyuni; Achmad Imam Sudianto; Vivi Tri Widyaningrum; Ach. Dafid
Journal of Technology and Informatics (JoTI) Vol. 8 No. 1 (2026): Vol. 8 N. 1 (2026)
Publisher : Universitas Dinamika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37802/joti.v8i1.1314

Abstract

Perimeter monitoring in agricultural fields is essential for maintaining security and ensuring continuous observation of field conditions. This study develops a real-time human–drone interaction system using hand-gesture recognition based on MediaPipe Hands and a Support Vector Machine (SVM) classifier. A custom dataset of 24,000 images across 12 gesture classes was collected and converted into 42 hand landmarks (x, y, z), normalized relative to the wrist point. The SVM model with an RBF kernel was trained using an 80:20 split and achieved a testing accuracy of 99.18%. The system operates at 109 FPS with an average latency of 9.16 ms, enabling rapid and reliable drone responses to gesture commands. Field testing in a cornfield with FPV camera visualization demonstrated that the system consistently recognized gestures in varying outdoor lighting, allowing drones to execute precise perimeter checks and maneuvers. These results highlight the significant potential of integrating gesture recognition with drone control, providing a practical, real-world solution that advances smart farming, increases agricultural efficiency, and supports technological progress toward Sustainable Development Goals. The proposed system thus offers a lightweight, responsive, and impactful tool for modern agricultural perimeter monitoring.
Brush-shaped Motion Gesture of UGV Using Hand Gesture Recognition Agus Murdiono Agus; Muhammad Fuad; Hairil Budiarto; Faikul Umam; Vivi Tri Widyaningrum; Achmad Imam Sudianto
Journal of Technology and Informatics (JoTI) Vol. 8 No. 1 (2026): Vol. 8 N. 1 (2026)
Publisher : Universitas Dinamika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37802/joti.v8i1.1315

Abstract

Manual observation of corn leaf diseases in agricultural fields often faces challenges related to time, effort, and accuracy. To address these challenges, brush-shaped motion patterns, such as zig-zag and boustrophedon trajectories, provide an effective solution by enabling uniform area coverage while reducing redundant traversal, energy consumption, and sensing gaps, making them well-suited for precision agriculture applications. Building on this approach, the system utilizes the MediaPipe framework for hand landmark tracking and the K-Nearest Neighbors (KNN) algorithm to recognize six navigation commands: forward, backward, stop, turn_right, turn_left, and capture. These commands are transmitted via Wi-Fi with an average latency of 0.001964 s. To ensure navigation accuracy during pattern execution, corrections are made using rotary encoders. Gesture classification experiments on 6,000 samples achieved a maximum accuracy of 99.46% across two participants, with stable KNN performance under both indoor and outdoor lighting variations, as well as hand distances ranging from 50 cm. Furthermore, the capture gesture produced an average image acquisition latency of 0.3037 s at various UGV observation positions. In summary, these results demonstrate that integrating real-time gesture control with UGV maneuvers enables systematic field surveys for maize leaf disease monitoring and supports Sustainable Development Goal (SDG) 2 through precision agriculture technology.
Co-Authors Ach. Dafid Aeri Rachmad Agus Murdiono Agus Ahmad Sahru Romadhon Arifah Husna Baehaqi Dafid, Ach Eka Mala Sari Fadzillah Akbar Subkhi Faikul Umam Firmansyah Adiputra Hairil Budiarto Ibnu Irawan Ika Oktavia Suzanti Izzuddin Muhammad Aziz Muslim Muhammad Fuad Sigit Susanto Putro Sitepu Debora , Ribka Sri Wahyuni Thinakaran, Rajermani Vivi Tri Widyaningrum Wildan Surya Wijaya