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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 33 Documents
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Search results for , issue "Vol 5, No 2 (2025): June" : 33 Documents clear
Analysis and Optimization of a Buffet Pricing Strategy in the Telecommunication Industry Using the Particle Swarm Optimization (PSO) Algorithm Merdikawati, Silvia; Oktaviani, Revina Dwi; Salahuddin, Salahuddin; Khadijah, Afni
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.v5i2.6954

Abstract

Flat-rate pricing (buffet pricing) is a common strategy in the global telecommunications industry, yet its adoption in Indonesia remains limited due to regulatory challenges, network capacity constraints, and diverse customer preferences. This study aims to optimize buffet pricing by considering user segmentation and varied service consumption patterns. A metaheuristic approach, specifically Particle Swarm Optimization (PSO), is employed to determine the optimal pricing that maximizes operator profit while maintaining customer satisfaction. A customer demand model is developed using a triangular distribution to reflect the asymmetric variability of usage. Results indicate that heavy users benefit significantly from flat-rate plans, whereas light users are better served by a hybrid pricing scheme. PSO demonstrates superior adaptability and efficiency compared to conventional methods, particularly when parameter tuning accelerates convergence. The study also highlights the importance of pricing flexibility to address heterogeneous customer needs. This study offers practical contributions to the development of data-driven, competitive pricing strategies in the evolving telecommunications market.
Smart Fisheries: Real-Time Water Quality Management and Automated Feeding System Design for Tilapia Farming using ESP32 Micro Controller Yudistira, Bagus Gede Krishna; Hapsari, Cindy; Adnyana, Gede Defry Widhi; Nath, Wiswa; Putra, I Putu Romyadhy Maha
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.v5i2.7288

Abstract

The fisheries sector in Jinengdalem Village, Buleleng, Bali holds considerable potential but continues to face challenges related to operational efficiency and unstable production outcomes. This study proposes an innovative solution through Smart Fisheries: The AI-Powered IoT in Smart Fisheries, an intelligent aquaculture system powered by Artificial Intelligence (AI) and the Internet of Things (IoT). The system is designed to perform real-time monitoring of water parameters, automate feeding processes, and analyze fish growth in order to enhance aquaculture productivity and sustainability. The research methodology follows a Research and Development (RD) framework, utilizing the ADDIE model (Analysis, Design, Development, Implementation, Evaluation). Preliminary results indicate that the system provides accurate environmental data and supports data-driven decision-making in fishery management. This project is expected to serve as a replicable model for implementing digital aquaculture technologies in similar regions.
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.
Optimization of Stunting Risk Prediction Using a Hybrid Genetic-Machine Learning Model Heryati, Agustina; Terttiaaivini, Terttiaavini; Marcelina, Dona; Romli, Harsi
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.v5i2.6988

Abstract

Stunting is a chronic nutritional problem that remains a national priority issue in Indonesia. According to the 2022 Indonesian Nutrition Status Survey (SSGI), the national stunting prevalence reached 21.6%, with a target reduction to 14% by 2024. Accurate prediction of stunting risk remains a challenge, particularly in regions like Palembang City, which exhibit diverse socio-economic conditions and complex anthropometric characteristics. This study develops a hybrid machine learning model for stunting risk prediction by integrating classification algorithms with a Genetic Algorithm (GA) for feature selection. The hybrid approach aims to enhance predictive accuracy and efficiency based on numerical and socio-economic data. A total of 6,000 samples were used, and after preprocessing (trimming, winsorization, normalization, and SMOTE), 5,366 clean data samples were obtained. Four classification algorithms were tested: Decision Tree, K-Nearest Neighbor, Random Forest, and XGBoost. The best performance was achieved by the XGBoost model, with an accuracy of 84.08%, recall of 93%, and F1-score of 0.91 for the majority class. By integrating the Genetic Algorithm, optimal accuracy reached 95.34% in the third generation of feature selection. This study contributes a hybrid machine learning-based predictive framework that can be adopted by local health institutions for more targeted early detection of stunting risk.
Design and Construction of an Internet of Things-Based Landslide Early Detection System in Landslide-Prone Areas Prasetyo, Sidik; Susanto, Rudi; Pramono, Pramono
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.v5i2.6847

Abstract

Landslides are one of the natural disasters that often occur in Indonesia due to the geographical conditions dominated by mountainous and hilly areas, coupled with high rainfall. Landslides can cause huge losses and casualties due to the absence of a system that can provide real-time warnings as a preventive measure. This research aims to design and build an Internet of Things (IoT)-based landslide early detection system that is able to detect environmental conditions that have the potential for landslides in real-time. The system uses an ESP32 microcontroller as the control center connected with a rain sensor (YL-83), a tilt sensor (MPU6050), and a soil moisture sensor (Capacitive Soil Moisture). Data from the sensors is sent via RESTful API and WebSocket with WiFi connection to the monitoring website. The system is also equipped with a buzzer and RGB LED as a warning indicator if environmental conditions are detected that have the potential for landslides. For a power source, a rechargeable 18650 battery is used and combined with a Step-Up and Charger Module J5019 to maintain voltage stability. The test results conducted in the test environment obtained 40 experimental data with stable sensor readings, and all components can function properly and can display data on the monitoring website. This system offers a practical solution to support disaster mitigation in landslide-prone areas.
Interpreting Lung Disease Detection from Chest X-rays Using Layer-wise Relevance Propagation (LRP) Fauziyyah, Laila Nurul; Negara, Benny Sukma; Irsyad, Muhammad; Iskandar, Iwan; Yanto, Febi
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.v5i2.7043

Abstract

Penelitian ini mengusulkan pendekatan klasifikasi penyakit paru berbasis citra X-ray menggunakan arsitektur VGG16 yang dilengkapi metode interpretabilitas Layer-wise Relevance Propagation (LRP). Dataset terdiri dari tiga kelas: COVID-19, pneumonia, dan normal, yang diproses melalui augmentasi dan normalisasi. Model dilatih dengan rasio data 70:30, learning rate 0.001, batch size 32, dan optimizer Adam. Hasil pelatihan menunjukkan akurasi tinggi sebesar 96,78% dengan nilai precision, recall, dan F1-score yang seimbang. Metode LRP digunakan untuk menyoroti area penting pada citra yang berkontribusi terhadap prediksi model, sehingga meningkatkan transparansi keputusan. Kontribusi utama penelitian ini adalah integrasi VGG16 dengan LRP dalam klasifikasi multi-kelas citra X-ray, yang memberikan hasil akurat sekaligus interpretasi visual yang mendukung kepercayaan dalam aplikasi medis.
Development of the Shortest Path Navigation Feature in a 360° Virtual Campus Tour Using Dijkstra's Algorithm Alifi, Muhammad Riza; Hodijah, Ade; Setijohatmo, Urip Teguh; Wulan, Sri Ratna; Hayati, Hashri
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.v5i2.6839

Abstract

A 360° virtual campus tour allows users to independently explore all available scenes in the form of 360° panoramic photos through a self-guided navigation feature. However, not all navigation tools provided are capable of generating route recommendations for users to follow. This presents a challenge, as users may feel overwhelmed when deciding where to begin and end the tour—particularly when the number of scenes reaches into the hundreds. In certain scenarios, prolonged interaction within a virtual reality environment may lead to discomfort due to motion sickness. Implementing a shortest path algorithm offers a potential solution by guiding users through recommended routes, thereby improving exploration efficiency and reducing interaction time. This study integrates a shortest path-based navigation feature into a virtual campus tour using Dijkstra’s algorithm, consisting of: (1) a front-end navigation component for the user interface of route searching, and (2) a back-end routing component that processes pathfinding using a graph-based structure. The implemented navigation feature demonstrates high efficiency, with an average execution time of only 4.94 ms and low memory consumption, as measured by a resident set size of 710.47 KB and used heap memory of 668.61 KB.
Usability Testing Analysis of Bank Aceh Mobile Action Applications Using Human Centered Design and Heuristic Evaluation Methods Amalia, Fatihah Indah; Fitria, Rahma; Ulva, Ananda Faridhatul
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

Abstract

One of the efforts to improve regional digital banking services is through the development of Bank Aceh Syariah's Action Mobile application. This application is designed to facilitate customers in conducting various transactions online, such as transfers, payments, and mutation checks. However, despite being available on the Google Play Store and receiving favorable ratings, the usage of this application is still relatively low compared to other regional banks' mobile banking applications. Some of the problems found are related to user experience, including limited e-commerce payment features and digital wallet top-up services that only support certain platforms such as OVO, GoPay, and LinkAja. In addition, the information displayed in the transaction mutation section is also considered incomplete, resulting in complaints from users. This problem has the potential to hinder comfort in using the application and affect the level of customer satisfaction. To overcome these challenges, an evaluation of the application's usability was conducted using the Human Centered Design approach and the Heuristic Evaluation method. The evaluation involved users and experts who provided feedback based on Nielsen's ten heuristic principles. The results showed some weaknesses in the application design, such as navigation that was not fully intuitive, information delivery that was not clear, and inadequate service features. Based on these findings, a solution was designed in the form of a new application prototype that was more responsive and user-friendly. The design was carried out using the Figma application, focusing on improving the interface and adding the required features. With these improvements, it is expected that Action Mobile Bank Aceh Syariah can be more competitive in the midst of the rapid development of digital banking services. This technology is also part of a strategic step in supporting digital banking transformation in the region, as well as increasing customer satisfaction and loyalty.
Optimisation of Employee Attendance System Using Face Recognition and Geotagging Based on Mobile Android Fitria, Rahma; Sahputra, Ilham; Maulana, Riki
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.v5i2.6892

Abstract

The growth of technology is developing very rapidly in various fields, ranging from industry, offices, government, to education. One interesting innovation is the application of facial recognition to an Android-based attendance system. This system allows attendance to be carried out by scanning employee faces using Android devices in certain areas such as offices or companies. By using this technology, the attendance process which is usually done manually or using fingerprints can be optimized, thereby reducing the risk of long queues when employees are present together. In some offices, attendance is still done manually by filling in attendance books or using fingerprints. This method often causes problems, especially when many employees come at the same time. The queues that form will of course interfere with their productive time. Therefore, to overcome this problem, an Android-based attendance application is needed that integrates facial recognition technology. This application is designed so that it can only be accessed in an office environment, with certain area coverage settings. This study uses the Convolutional Neural Network (CNN) algorithm which is effective for image processing in facial recognition. In addition, researchers also apply the GPS Locking or Geotagging method to ensure that attendance can only be carried out in predetermined areas, thereby increasing the security and accuracy of attendance data. The dataset used in this study consists of facial images, where each individual is photographed in five different angles to improve the accuracy of the system. The results of this study are expected to create a more efficient and effective attendance system. By simplifying the attendance process, this technology not only saves time but also increases employee satisfaction, because they no longer have to face long queues. This is a step forward in utilizing technology to improve human resource management in the digital era.
Optimization of Employee Reward Schemes Using Genetic Algorithm: A Multi Criteria Performance Based Approach Urva, Gellysa; Desriyati, Welly
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.v5i2.7105

Abstract

Employee reward distribution plays an important role in increasing motivation and retention. Conventional employee reward models often contain elements of subjectivity and do not reflect the overall contribution of employees. This can lead to unfairness and reduce work motivation. Traditional models in reward allocation often fail to incorporate a comprehensive evaluation of employee performance based on various criteria. This study develops a multi-criteria performance-based reward allocation model using Genetic Algorithm (GA) as an optimization approach. The model is designed to consider various performance indicators such as performance, attendance, tenure, and innovation in the process of fair and proportional bonus distribution. The optimization results show a very strong positive correlation (r = 0.99) between the employee's composite score and the amount of bonus allocated. In addition, the simulation of the evolution of fitness values shows a constant increase in both the average and the best values of the solution population, confirming the effectiveness of the genetic algorithm exploration and convergence process. This model produces a bonus distribution that is proportional to employee contributions, reflecting the principles of fairness, meritocracy, and transparency in the reward system. In addition, this model is flexible to budget changes and can be replicated for real implementation. The scientific contribution of this research lies in the application of a heuristic approach to multi-criteria optimization in the context of human resource management, complementing the literature that has so far been dominated by linear models.

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