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JAIS (Journal of Applied Intelligent System)
Published by Universitas Dian Nuswantoro
ISSN : 25020493     EISSN : 25029401     DOI : -
Core Subject :
Journal of Applied Intelligent System (JAIS) is published by LPPM Universitas Dian Nuswantoro Semarang in collaboration with CORIS and IndoCEISS, that focuses on research in Intelligent System. Topics of interest include, but are not limited to: Biometric, image processing, computer vision, knowledge discovery in database, information retrieval, computational intelligence, fuzzy logic, signal processing, speech recognition, speech synthesis, natural language processing, data mining, adaptive game AI.
Arjuna Subject : -
Articles 6 Documents
 1 
Search results for , issue "Vol. 10 No. 1 (2025): April 2025" : 6 Documents clear
A Non-Invasive Allergy Detection using Convolutional Neural Network Model Aripin; Badia, Giulia Salzano; Safira, Intan
(JAIS) Journal of Applied Intelligent System Vol. 10 No. 1 (2025): April 2025
Publisher : LPPM Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/jais.v10i1.12783

Abstract

Skin allergy detection is critical to detect allergies that trigger serious reactions such as anaphylaxis, so people can avoid allergens and reduce the risk of complications such as anaphylactic shock. Therefore, early allergy detection screening is essential to determine the risk of allergies. This research aims to develop a system to detect skin allergies caused by food, through sensors applied to human skin using the Convolutional Neural Network (CNN) model. The research steps include literature studies, data acquisition, preprocessing, learning processes, and testing. The developed system uses a camera to capture allergic reactions on the skin. Data acquisition consists of two types of data, namely primary data and secondary data. Primary data acquisition is done by taking images of normal and allergic patient skin. Meanwhile, secondary data acquisition is obtained from Kaggle. The captured images are processed by image processing and analyzed using the CNN model. The image dataset consists of four classes, namely atopic, angioedema, normal skin, and urticaria. The CNN model consists of several layers, including convolutional layers, pooling, and fully connected layers. The results of the research showed that the prototype product can detect changes in the skin surface due to allergic reactions, such as redness or swelling, quickly and accurately. Testing the learning process with the CNN model resulted in an accuracy rate of 92%. Meanwhile, the accuracy results of testing prototype products on patients with skin allergies were 93%. It shows that the system can detect types of allergies on the skin accurately and efficiently. This system provides a practical and fast solution for the public to detect allergies, while contributing to the advancement of medical technology.Keywords - social robots, adaptive learning, reinforcement learning, human-robot interaction, sensor fusion, educational robotics
Implementation of a Water Nutrient Monitoring System for Integrated IoT-Based Hydroponics at CV Tirta Fertindo Purnomo, Ananda Pandu Candra; Assariy, Annas Faiz; Prastiawan, Galih; Arifin, Zaenal
(JAIS) Journal of Applied Intelligent System Vol. 10 No. 1 (2025): April 2025
Publisher : LPPM Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/jais.v10i1.12817

Abstract

CV Tirta Fertindo Pratama is an agribusiness company specializing in hydroponics in Semarang. The company manages hydroponic farms for various vegetables and fruits but faces challenges in manually measuring Potential of Hydrogen (pH), electrical conductivity (EC), and Total Dissolved Solids (TDS), which affect product quality. To address this issue, an Internet of Things (IoT)-based hydroponic plant monitoring system was developed using temperature, pH, and TDS sensors connected to an ESP32 microcontroller. This system enables remote automated measurement and control, displaying data via an LCD. The implementation of this IoT system enhances efficiency and consistency in plant management by enabling real-time monitoring and more precise control, reducing the risk of manual errors, and improving hydroponic crop production. Keywords - hydroponics; IoT; pH; EC; TDS; microcontroller
Adaptive Learning Model for Social Robots Using Visual and Proximity Sensors in Dynamic Educational Environments Tamamy, Aries Jehan; Pambudi, Arga Dwi; Arifin, Zaenal; Harsono, Budi
(JAIS) Journal of Applied Intelligent System Vol. 10 No. 1 (2025): April 2025
Publisher : LPPM Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/jais.v10i1.12997

Abstract

Social robots are increasingly being integrated into educational environments to support learning and engagement. However, most existing systems lack the adaptability required to respond appropriately to dynamic human behavior in real-time classroom settings. This paper presents an adaptive learning framework for social robots that utilizes visual and proximity sensor data to perceive human spatial context and adjust interaction strategies accordingly. A Deep Q-Network (DQN)-based reinforcement learning algorithm is employed to map environmental states to socially appropriate actions such as maintaining distance, initiating interaction, or retreating. The robot was trained in a simulated classroom environment consisting of dynamic student agents with randomized behaviors. Experimental results show that the robot achieved a cumulative reward improvement of over 500%, reduced its average distance error from 0.45 m to 0.18 m, and increased its interaction success rate from 50% to 88% over 100 training episodes. These results confirm the effectiveness of the proposed model in enabling real-time behavioral adaptation. The framework contributes to the development of context-aware, socially intelligent robotic systems capable of enhancing Human-Robot Interaction (HRI) in educational applications. Future work includes extending the model to incorporate emotional cues and real-world validation with physical robot platforms. Keywords - social robots, adaptive learning, reinforcement learning, human-robot interaction, sensor fusion, educational robotics
Classification of Oil Loss Levels in Palm Oil Processing Using Near-Infrared Spectroscopy with Machine Learning Muhamad Ilham Fauzan; BAskara, Jaka Adi; Putri, Wahyuningdiah Trisari Harsanti; Pranoto, Gatot Tri
(JAIS) Journal of Applied Intelligent System Vol. 10 No. 1 (2025): April 2025
Publisher : LPPM Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/jais.v10i1.13037

Abstract

Oil losses in palm oil processing materials, such as Final Effluent, Empty Fruit Bunches, Kernels, Pressed Fiber, and Decanter Solids, pose significant challenges in ensuring production efficiency. FOSS-NIRS technology has been proven capable of quickly and efficiently detecting oil content, but its detection accuracy requires further analytical support. This study aims to develop a machine learning model that can accurately classify FOSS-NIRS data to detect oil losses that are either above the standard (red category) or below the standard (green category). By utilizing FOSS-NIRS data across five material categories, the proposed model is expected to provide precise predictions and support decision-making in palm oil production processes. The results of the study indicate that applying machine learning methods to FOSS-NIRS data can enhance the accuracy of oil loss classification, making it a potential solution for broader implementation in the palm oil processing industry to optimize production efficiency.
Temperature Monitoring of Lithium Battery Using Kalman Filter: A Simulation-Based Study Arifin, Zaenal; Islahudin, Nur; Tamamy, Aries Jehan; Heryanto, M Ary
(JAIS) Journal of Applied Intelligent System Vol. 10 No. 1 (2025): April 2025
Publisher : LPPM Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/jais.v10i1.13469

Abstract

Battery temperature plays a vital role in determining the performance, safety, and lifespan of lithium-ion batteries in electric vehicle (EV) applications. This study presents a simulation-based approach for monitoring surface temperature using Kalman filter estimation, which integrates air temperature, current load, and battery characteristics. A mathematical model of thermal dynamics is developed and used for real-time temperature prediction. The results demonstrate that the Kalman filter is effective in estimating the surface temperature accurately, even with uncertain measurements. This work also discusses the integration of an actuator (fan/cooler) and PID control to maintain the temperature around the ideal level of 25°C, showcasing the potential of this system for smart thermal battery management in cost-constrained embedded systems.   Keywords - Temperature Monitoring; Kalman Filter; Thermal Modeling; Estimation Algorithm; State Estimation; Simulation;
Analysis of the Effectiveness of IC Tester Demonstrator Tools in Accelerating the Diagnosis of TTL Logic Gate Damage: A Quasi-Experimental Study at UMK Setyaningsih, Noor Yulita Dwi; Wibowo, Budi Cahyo; Rozaq, Imam Abdul
(JAIS) Journal of Applied Intelligent System Vol. 10 No. 1 (2025): April 2025
Publisher : LPPM Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/jais.v10i1.13853

Abstract

In the practicum process for testing the condition of the TTL IC, it is still done manually and requires time to complete. With class conditions where students who participate have different levels of understanding. The smooth implementation of digital electronics practicum activities will be hampered if the ICTTL (Integrated Circuit Transistor Transistor Logic) components used in the experiment do not have normal operating functions. This study analyzes the effectiveness of the IC tester teaching aid in accelerating the diagnosis of TTL logic gate damage through a quasi-experimental study at UMK. Data were taken from 9 students grouped by ability ( Good , Sufficient , Less ) by comparing the time of manual testing and testing using the teaching aid . From the results, it was found that the teaching aid provided good effectiveness in the TTL IC testing process, namely 60-72% by reducing the average from 121-159 seconds (manual testing) to 45-58 seconds/student. The consistency of the time required to check the IC for all groups of student abilities became the same after using this teaching aid Keywords - Demonstration Tools, IC Tester, Effectiveness, Quasi-Experiment

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