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Journal Of Artificial Intelligence And Software Engineering
Published by Politeknik Negeri Lhokseumawe
ISSN : 2797054X     EISSN : 2777001X     DOI : http://dx.doi.org/10.30811/jaise
Core Subject : Science,
Computer Science & IT
Artificial Intelligence Natural Language Processing Computer Vision Robotics and Navigation Systems Decision Support System Implementation of Algorithms Expert System Data Mining Enterprise Architecture Design & Management Software & Networking Engineering IoT
Arjuna Subject : Ilmu Komputer (semua kategori) - Aplikasi Ilmu Komputer
Articles 16 Documents
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Search results for , issue "Vol 5, No 2 (2025): Juni On-Progress" : 16 Documents clear
Applying the Microservices Architecture in the Development of an Online Fish Auction System Irawan, Bei Harira; Prihadi, Deddy; Simarangkir, Manase Sahat; Miswadi, Miswadi; Sofyan, Ali
Journal of Artificial Intelligence and Software Engineering Vol 5, No 2 (2025): Juni On-Progress
Publisher : Politeknik Negeri Lhokseumawe

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30811/jaise.v5i2.6891

Abstract

Online fish auction systems offer an innovative solution to improve efficiency and transparency in fisheries transactions. Using microservices architecture enhances scalability, flexibility, andperformance. Core services like user authentication, auction management, bidding, payment, and delivery are built independently with structured APIs. Performance testing with JMeter simulates active users to measure response time, throughput, and resource usage. Resultsshow the authentication endpoint averages 120ms response time, while auction management reaches 150ms. The system’s throughput increases by 45% over monolithic architecture, handling up to 5,000 requests per minute. Simulations reveal improved resource efficiency, with CPU usage reduced by 30% and memory by 25%. The study concludes that microservices offer an efficient and reliable digital fish auction solution. Another advantage of the microservices approach is the ease of maintenance and the development of new features without disrupting existing services. Each service can be horizontally scaled according to workload demands, allowing the system to remain responsive under high traffic conditions. Therefore, this system is suitable for national or regional implementation in supporting the digital fisheries ecosystem.
Classification of Human Age Groups Based on Facial Image Using the Gabor Filter and Artificial Neural Network (ANN) Method Munawir, Munawir; Ramadhana, Nopita; Muttaqin, Khairul
Journal of Artificial Intelligence and Software Engineering Vol 5, No 2 (2025): Juni On-Progress
Publisher : Politeknik Negeri Lhokseumawe

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30811/jaise.v5i2.6935

Abstract

Facial image processing technology is developing rapidly and is used in various fields, one of which is for human age group classification. As we age, the face experiences changes such as wrinkles, bone structure, and facial proportions. This recognition process faces challenges, such as variations in texture, lighting, expression, and fine wrinkles that are difficult to detect automatically. An optimal feature extraction method is needed to improve the accuracy of age group classification. This study aims to classify age groups based on facial images using a computer system, as well as to determine the accuracy in real time and photo input. The methods used are Gabor Filter and Histogram of Oriented Gradients (HOG) as feature extraction and Artificial Neural Network (ANN) as a classification algorithm. The system is designed to operate in real time and photo input, with fast and efficient classification results. The dataset consists of 2,500 facial images, divided into five age groups, each consisting of 500 images. A total of 50 images from each age group are used as test data. The system classifies images into five age groups, namely toddlers, children, adolescents, adults, and the elderly. The research results showed an accuracy of 74% for the real-time system and 76% for the photo input system.
An IoT-Enabled Low Latency Automatic Identification System Using Round-robin Scheduling Algorithm Septian, Belen; Adrianda, Aidil; Misbahuddin, Md.; Ridho, M. Fauzan
Journal of Artificial Intelligence and Software Engineering Vol 5, No 2 (2025): Juni On-Progress
Publisher : Politeknik Negeri Lhokseumawe

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30811/jaise.v5i2.6512

Abstract

The Automatic Identification System (AIS) is a vital maritime technology that enhances navigation safety and vessel tracking. This system is integrated with the Internet of Things (IoT) for real-time data transmission. However, due to multiple tasks being employed, the system latency increases. To address this problem, this study proposes an optimized task-scheduling approach using a Round-robin algorithm with an additional task-reshuffling mechanism. The proposed method is implemented on an ESP32 microcontroller, enabling real-time processing of AIS messages while minimizing latency and energy consumption. Experimental results demonstrate that the hybrid Round-robin and Shuffling method achieves the lowest average transmission time of 28.736 seconds, outperforming traditional Priority and standard Round-robin scheduling approaches. The findings of this study contribute to enhancing real-time processing capabilities in embedded vessel tracking systems. Following this, future research should focus on addressing transient fluctuations using adaptive scheduling techniques. 
Enhancing Hoax News Detection Performance through Modified Convolutional Neural Network Architecture Mujilahwati, Siti; Reknadi, Danang Bagus
Journal of Artificial Intelligence and Software Engineering Vol 5, No 2 (2025): Juni On-Progress
Publisher : Politeknik Negeri Lhokseumawe

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30811/jaise.v5i2.6953

Abstract

The spread of fake news (hoaxes) on the internet is increasingly widespread, along with the increasing use of the internet in Indonesia. To detect hoaxes automatically, this study proposes a Convolutional Neural Network (CNN) model based on data from the Detik.com and TurnBackHoax.id sites. The data includes various categories such as politics, religion, and technology. The model was developed through several optimization stages: tokenization and text padding, utilization of pre-trained Word2Vec as embedding weights, stratified data division, handling class imbalance with class weights, and implementing a multilevel CNN architecture with dropout. Training was carried out using the Adam optimizer with a small learning rate and an early stopping technique. The evaluation results showed that the proposed model achieved an accuracy, precision, recall, and F1-score of 96%, with the fastest training time of 100.42 seconds. The model was also evaluated with the ROC_AUC model, with a score of 98.91%. This performance outperformed the CNN-1D and Augmentasi-CNN models. This approach has proven to be effective and efficient in detecting Indonesian hoax news.
Machine Learning Approaches For Classification Of Infectious Diseases Using Smote Shofwan, Ari; Sulistianingsih, Neny; Rismayati, Ria
Journal of Artificial Intelligence and Software Engineering Vol 5, No 2 (2025): Juni On-Progress
Publisher : Politeknik Negeri Lhokseumawe

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30811/jaise.v%vi%i.6960

Abstract

Infectious diseases such as acute nasopharyngitis, acute pharyngitis, and acute tonsillitis remain major public health issues, especially in primary healthcare facilities with limited resources like Puskesmas Gunungsari. This study aims to develop a machine learning-based classification model to detect infectious diseases using patient medical data. The evaluated models include Random Forest, Decision Tree, Support Vector Machine (SVM), and Neural Network, with performance assessed using k-fold cross-validation ranging from 5 to 10 folds. Evaluation results show that the Decision Tree consistently achieved the best performance, with an accuracy of approximately 91.7% to 91.9% and an F1-score ranging from 91.9% to 92.3% on cross-validation data, as well as a test accuracy of 94.7% and an F1-score of 95.0%. The Random Forest model also demonstrated good and stable performance, with accuracy between 90.5% and 90.7%. Meanwhile, SVM and Neural Network produced lower results, with maximum accuracy of around 77.0% and 71.7%, respectively. Overall, the findings demonstrate that the Decision Tree model is the most effective for supporting early diagnosis of infectious diseases at Puskesmas Gunungsari, providing superior classification capabilities compared to other models.
Application of Naive Bayes Algorithm in Analyzing Public Sentiment towards Coretax on Platform X Wijaya, Egi Putu; Rifqo, Muhammad Husni
Journal of Artificial Intelligence and Software Engineering Vol 5, No 2 (2025): Juni On-Progress
Publisher : Politeknik Negeri Lhokseumawe

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30811/jaise.v5i2.6949

Abstract

Taxation is the main state data, to make it easier for people to pay taxes, an integrated application called coretax is used. However, there are many differences because it has many problems in its use. Therefore, the study was conducted to find out public sentiment towards the coretax application using a naïve Bayes algorithm. The methods used range from data collection, data cleaning, data pre-processing, classification with textblob to classification and evaluation with naïve Bayes algorithms. Of the total 2858 total data used, the results were 782 positive sentiment data, 479 negative sentiments and 1597 neutral sentiments. The results showed that the accuracy of the model could reach 81% with an f1-score value of 80%, as well as a precision and recall value of 81%. This shows that the naïve bayes algorithm is quite good at classifying public sentiment towards the coretax application.

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