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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 191 Documents
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Improving Heart Disease Severity Prediction Using SMOTE for Imbalanced Data Rahayu, Ayu Hendrati; Sudrajat, Ari
(JAIS) Journal of Applied Intelligent System Vol. 9 No. 2 (2024): Journal of Applied Intelligent System
Publisher : LPPM Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/jais.v9i2.11180

Abstract

The heart disease is a prevalent and potentially fatal condition affecting individuals worldwide. In this study, we address the challenge of predicting the severity of heart disease using supervised learning techniques. Leveraging a dataset comprising various demographic and clinical attributes, we propose a solution that employs machine learning models to accurately predict the severity level of heart disease. Among the evaluated models, Random Forest emerges as the top performer, showcasing exceptional precision, recall, accuracy, and F1-score across all severity levels, with an overall accuracy of 98.8%. This highlights the robustness of the Random Forest model in accurately classifying instances across different severity levels. Following closely behind, the KNN algorithm demonstrates commendable performance, achieving an accuracy of 92% and showcasing competitive precision, recall, and F1-score values, particularly for higher severity levels. Despite its notable aspects, XGBoost ranks third among the evaluated models, with an accuracy of 90.4%. While XGBoost excels in certain aspects, such as recall for Level 3 severity, it falls short in overall performance compared to Random Forest and KNN. For future research, exploring ensemble methods that combine the strengths of different algorithms could yield even better classification results, providing avenues for further improvement in predicting the severity of heart disease
Analysis of K-Nearest Neighbor (KNN), Naive Bayes ands Decision Tree C4.5 Algorithm With Classification Method In Breast Cancer Using RapidMiner Iqbal, Muhammad; Donny, Maulana; Wahyu, Hadikristanto; Tedi, Kurniadi Nanang; Amali; Ismasari, Nawangsih
(JAIS) Journal of Applied Intelligent System Vol. 9 No. 2 (2024): Journal of Applied Intelligent System
Publisher : LPPM Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/jais.v9i2.11242

Abstract

Breast cancer is cancer that forms in the cells of the breast. It is the most common cancer in women and the leading cause of cancer deaths in women worldwide. Breast cancer is usually divided into two types: benign, or usually called benign and malignant, or usually called malignant. Benign cancers are usually characterized by small, round, tender lumps. In the fields of medicine, finance, marketing, and social science, data mining is a popular tool for performing proven analysis. This study will compare K-Nearest Neighbor (KNN), Naive Bayes, and Decision Tree C4.5 approaches for classifying breast cancer. The problem of this research is which algorithm has a high level of accuracy that can be used with breast cancer datasets and can provide information about patterns or models for early detection of breast cancer. The results of the research conducted using CRISP-DM show that K-Nearest Neighbor (KNN) has the highest accuracy value with 97.14% and its AUC value is 0.976. The AUC value also showed excellent classification, with an AUC value between 0.90 and 1.00.
English Class Scheduling Information System at Indonesian-American Educational Institutions Bajsair, Faik; Baisyir, Fauzi; Pranoto, Gatot Tri
(JAIS) Journal of Applied Intelligent System Vol. 9 No. 2 (2024): Journal of Applied Intelligent System
Publisher : LPPM Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/jais.v9i2.11304

Abstract

The purpose of the research is to create and implement a simple class scheduling application that is useful to minimize the occurrence of clashes of time, classes, levels, teachers and students at the same time. The research method used is the Descriptive Method with the type of case study research. The descriptive method is a method of researching the status of a group of people, an object, a set of conditions, a system of thought or an event in the present. From this Thesis or Final Project, the author can draw the conclusion that the English Class Scheduling Information System in Indonesian-American Educational Institutions is more effective, fast, conceptual, and up to date in data processing
Comparison of Shallot Price Prediction In Pati City With LSTM, GRU and Linear Regression Asyari, Fajar Husain; Proborini, Ellen; Safitri, Melina Dwi; Rachmawanto, Eko Hari
(JAIS) Journal of Applied Intelligent System Vol. 9 No. 2 (2024): Journal of Applied Intelligent System
Publisher : LPPM Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/jais.v9i2.11373

Abstract

Shallots are superior vegetable plant and contribute quite significantly to the development of the national economy. The price of shallots fluctuates almost every year. At certain times the price of shallots soars due to high demand while the supply in the market is insufficient. Therefore, an analysis is needed to see what phenomena significantly affect the increase in the price of shallots. The methods used in the study were LSTM, GRU and LR. The results of the analysis show that the LSTM algorithm gets a MAE value of 0.011072172783, MAPE 3.93678% and RMSE 0.03139695060, this error is the lowest compared to GRU getting MAE value is 0.01185741, MAPE 4.2282357% and RMSE 0.03122299395 and LR with MAE 0.0134737280395416, MAPE 5.45081% and RMSE is 0.0313332635305961, so LSTM is a suitable algorithm for predicting shallot data in Pati district.
Broad Learning System for Investigating Corrosion Inhibition Efficiency of Heterocyclic Compounds Akrom, Muhamad; Prabowo, Wahyu Aji Eko
(JAIS) Journal of Applied Intelligent System Vol. 4 No. 2 (2019): Journal of Applied Intelligent System
Publisher : LPPM Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/jais.v4i2.12487

Abstract

This study explores the use of Broad Learning Systems (BLS) to predict the corrosion inhibition efficiency (CIE) of heterocyclic compounds, addressing limitations of deep neural networks (DNNs) such as vanishing gradients and computational inefficiency. BLS prioritizes network width over depth, enabling faster learning and improved generalization. Trained on quantum chemical properties (QCPs) of 192 heterocyclic compounds, BLS outperformed multilayer perceptron neural networks (MLPNN) and random forest (RF) models, achieving lower mean absolute error (MAE: 1.41), root mean square error (RMSE: 1.79), and higher R² (0.993). Predicted CIE values for quinoxaline derivatives (95.39% and 94.05%) aligned closely with experimental data. This study demonstrates the potential of BLS as an efficient, accurate, and scalable approach for predicting corrosion inhibition capabilities, contributing to advancements in corrosion science and environmentally friendly solutions. Keywords - machine learning, broad learning system, neural network, corrosion.
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;

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