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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 215 Documents
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Butterfly Feature Extraction Using HSV, Lacunarity, and CNN Rahayu, Putri Nur; Sukarno, Friska Intan; Augustino, Immanuel Freddy; Yuniati, R. A. Norromadani; Rakhmadi, Ardhon
Journal of Artificial Intelligence and Software Engineering Vol 5, No 2 (2025): June
Publisher : Politeknik Negeri Lhokseumawe

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30811/jaise.v5i2.6876

Abstract

This study aims to extract the morphological features of butterflies using the HSV (Hue, Saturation, Value) and lacunarity. The HSV method is used to obtain color information from butterfly images. lacunarity is used to extract texture characteristic to enhance the visual representation of the object. These extracted features are used as input for the processing of classification using algorithm of Convolution Neural Network (CNN). Based on the experimental result, the classification has accuracy 70%. This accuracy indicates that the combination of HSV and lacunarity methods is sufficiently effective in describing of the visual butterflies features for automatic classification.
Implementation Of Web-based Digital Library To Improve Learning Resource Access Services With RAD Method Approach Risyda, Fitria; Gardenia, Yulisa; Awaludin, Muryan
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.v5i2.6993

Abstract

The advancement of digital technology opens up opportunities for libraries to improve their services, including at the Universitas Dirgantara Marsekal Suryadarma (UNSURYA) Library. Fast, flexible, and integrated access are the main needs of the academic community. However, the system at the UNSURYA library currently has limitations such as limited operating hours and physical constraints in managing digital collections such as e-books, digital books, theses, and dissertations. This study aims to develop a digital library that can improve accessibility and user experience in accessing learning resources at the UNSURYA campus library. Application development using the Rapid Application Development (RAD) method, functional testing of the application using blackbox testing and usability testing to see user responses to the application that has been developed. The results of the usability testing questionnaire using the System Usability Scale (SUS) Score calculation showed a value of 80, the conversion of this value shows the "Good" category which indicates that the digital library application that has been built is considered good by users and has met the usability criteria to improve learning resource access services and workflows at the UNSURYA Library
Comparison of the Performance of Fuzzy Tsukamoto and Fuzzy Mamdani in an Internet of Things Based Grape Greenhouse Control System Rusadi, Athirah; Ula, Munirul; Daud, Muhammad; Nurdin, Nurdin; Hasibuan, Arnawan
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.v5i2.6936

Abstract

The application of Internet of Things in agriculture, particularly in grape greenhouses, enables automated environmental control to enhance efficiency and crop yield. This study compares the performance of two fuzzy logic methods, Fuzzy Mamdani and Fuzzy Tsukamoto, in a temperature and humidity control system based on IoT using the DHT22 sensor. The system is designed to automate irrigation via actuators based on sensor data. Performance evaluation was conducted using RMSE, MAE, and standard deviation metrics. The results show that the Tsukamoto method achieved lower RMSE 2.6928, MAE 2.2625, and standard deviation 1.1080 compared to the Mamdani method, which recorded RMSE of 2.9039, MAE of 2.3947, and standard deviation of 1.9268. However, a paired t-test yielded a p-value of 0.0690 0.05, indicating no statistically significant performance difference. Thus, while Fuzzy Tsukamoto appears superior in metrics, both methods are considered equally effective for controlling environmental conditions in grape greenhouses.The application of Internet of Things in agriculture, particularly in grape greenhouses, enables automated environmental control to enhance efficiency and crop yield. This study compares the performance of two fuzzy logic methods, Fuzzy Mamdani and Fuzzy Tsukamoto, in a temperature and humidity control system based on IoT using the DHT22 sensor. The system is designed to automate irrigation via actuators based on sensor data. Performance evaluation was conducted using RMSE, MAE, and standard deviation metrics. The results show that the Tsukamoto method achieved lower RMSE 2.6928, MAE 2.2625, and standard deviation 1.1080 compared to the Mamdani method, which recorded RMSE of 2.9039, MAE of 2.3947, and standard deviation of 1.9268. However, a paired t-test yielded a p-value of 0.0690 0.05, indicating no statistically significant performance difference. Thus, while Fuzzy Tsukamoto appears superior in metrics, both methods are considered equally effective for controlling environmental conditions in grape greenhouses.
Implementation of Random Forest Algorithm in Lecturer Performance Evaluation System Sufina, Sufina; Wati, Lidya
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.v5i2.6975

Abstract

Lecturer performance evaluation is a crucial process in improving the quality of education in higher education institutions. However, one of the main challenges in evaluating lecturer performance is analyzing large amounts of data objectively and efficiently. This study applies the Random Forest algorithm to automatically evaluate lecturer performance data based on the Employee Performance Target (SKP). The method used is Rapid Application Development (RAD), which includes requirements planning, design, construction, and system implementation. The results of the study show that the implementation of the algorithm in the developed system is capable of classifying lecturer performance with an accuracy rate of 72.73% for main performance assessment and 81.82% for behavioral performance. These results indicate that the Random Forest algorithm can be used as a supporting tool in data-driven lecturer performance evaluation.Lecturer performance evaluation is a crucial process in improving the quality of education in higher education institutions. However, one of the main challenges in evaluating lecturer performance is analyzing large amounts of data objectively and efficiently. This study applies the Random Forest algorithm to automatically evaluate lecturer performance data based on the Employee Performance Target (SKP). The method used is Rapid Application Development (RAD), which includes requirements planning, design, construction, and system implementation. The results of the study show that the implementation of the algorithm in the developed system is capable of classifying lecturer performance with an accuracy rate of 72.73% for main performance assessment and 81.82% for behavioral performance. These results indicate that the Random Forest algorithm can be used as a supporting tool in data-driven lecturer performance evaluation.
Implementation and Evaluation of a Barcode-Based Motorcycle Spare Parts Stock Opname Application Using the Spiral Model Indrayana, Didik; Prajoko, Prajoko
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.v5i2.6469

Abstract

Using a barcode-based application, this study aims to make the stock-taking process for motorcycle spare parts at PT XYZ faster and more accurate. The traditional method currently used, which involves manual recording on paper and re-entering data into spreadsheets, takes up to two weeks and is prone to human error. This research proposes a solution by developing an application that enables real-time data input, inter-branch data synchronization, and automatic report generation. The system development employs the Spiral model with a quantitative approach to measure improvements in efficiency and accuracy. The research was conducted over six months (September 2024-February 2025) at PT XYZ, covering the head office and 14 branches in Sukabumi City. Data were collected through observation, interviews, and analysis of previous stok opname documents. The results show that the application implementation reduced the processing time from two weeks to one day and significantly decreased error rates. These findings demonstrate that the barcode-based application can enhance the accuracy and efficiency of the stok opname process, providing an important contribution to optimizing inventory management of spare parts in the automotive industry.
Applying Local Interpretable Model-agnostic Explanations (LIME) for Interpretable Deep Learning in Lung Disease Detection Ananda, Sherly; Negara, Benny Sukma; Irsyad, Muhammad; Jasril, Jasril; Iskandar, Iwan
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.v5i2.7042

Abstract

Artificial Intelligence (AI) semakin banyak diterapkan dalam bidang kesehatan melalui model Machine Learning (ML) dan Deep Learning (DL). Namun, kompleksitas model modern yang bersifat black-box menimbulkan kebutuhan akan metode interpretasi yang transparan. Explainable AI (XAI) hadir untuk menjembatani hal tersebut, dengan memberikan pemahaman yang lebih baik terhadap kinerja model. Penelitian ini mengimplementasikan metode Local Interpretable Model-agnostic Explanations (LIME) untuk memvisualisasikan hasil klasifikasi model DL berbasis arsitektur ResNet18 terhadap citra Chest X-ray (CXR) pada tiga kelas: normal, COVID-19, dan pneumonia. Model mencapai precision, recall, dan F1-score rata-rata sebesar 97%, serta Accuracy sebesar 98%. Visualisasi LIME menunjukkan area citra yang berkontribusi signifikan terhadap klasifikasi, serta mampu membedakan ketiga kelas dengan baik. Hasil ini mendukung penggunaan XAI untuk meningkatkan interpretabilitas model DL dalam diagnosis medis.
Multi-Label Emotion Detection for Mental Health Monitoring Using Deep CNN and Visual Attention Hadi, Muhammad Nur; Sari, Ratri Wulan
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.v5i2.6961

Abstract

Mental health is a crucial aspect of modern human life that requires continuous monitoring. This research aims to develop a multi-label classification system to automatically detect various human emotional expressions through facial images, serving as a supportive approach for AI-based mental health monitoring. The proposed system leverages a Convolutional Neural Network (CNN) architecture integrated with a visual attention mechanism using the Convolutional Block Attention Module (CBAM). The AffectNet dataset is used as the primary data source, providing multi-label annotations for various emotional states. The model is designed using sigmoid activation and binary cross-entropy loss to handle multiple emotions simultaneously in a single image. Evaluation is conducted using the confusion matrix and metrics such as Precision, Recall, and F1-score. Experimental results demonstrate that the model achieves a Mean Average Precision (mAP) of 89.7%, indicating good performance in multi-label emotion classification. Specifically, the model achieves an F1-score of 100% for the emotions Happy, Fear, Surprise, Disgust, and Neutral, but faces challenges in distinguishing Sad (F1-score 67%) and Angry (F1-score 80%) expressions from others. Incorporating the attention mechanism proves beneficial in enhancing the overall performance of the model. This study contributes to the development of adaptive emotion recognition technologies, potentially applicable in real-time, non-invasive psychological monitoring systems.
Implementation of a Chatbot for Customer Service Integrated into an MSME Website Panca Mukti, M. Thoriq
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.v5i2.7000

Abstract

Penelitian ini berfokus pada implementasi chatbot menggunakan algoritma Artificial Neural Network (ANN) yang terintegrasi ke dalam website UMKM untuk meningkatkan layanan pelanggan secara otomatis. Metodologi yang digunakan adalah CRISP-DM (Cross-Industry Standard Process for Data Mining), yang mencakup tahapan pemahaman bisnis, pemahaman data, persiapan data, pemodelan, evaluasi, dan deployment. Data diproses menggunakan teknik Natural Language Processing (NLP) seperti case folding, tokenizing, dan stemming, kemudian direpresentasikan dengan pendekatan Bag of Words (BoW). Model ANN terdiri dari satu input layer, dua hidden layer dengan fungsi aktivasi ReLU, dan satu output layer dengan fungsi aktivasi softmax. Hasil pengujian menunjukkan akurasi sebesar 98,33%, dengan nilai precision, recall, dan F1-score masing-masing sebesar 98% tanpa indikasi overfitting. Chatbot yang dikembangkan berhasil diintegrasikan ke dalam website berbasis Laravel dan mampu merespons pertanyaan pelanggan secara otomatis.
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.

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