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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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Decision Support System for Employee Performance Evaluation Using SMART Method Romly, Moh Zaini; Hermanto, Hermanto; Lidimilah, Lukman Fakih
Journal of Artificial Intelligence and Software Engineering Vol 5, No 3 (2025): September
Publisher : Politeknik Negeri Lhokseumawe

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30811/jaise.v5i3.7332

Abstract

In the midst of increasingly fierce industrial competition, objectively assessing employee performance is very important to support accurate managerial decision making. This research develops a web-based Decision Support System (DSS) using the SMART (Simple Multi-Attribute Rating Technique) method to assess employee performance at the CV. Hafas P2S2 Drinking Water Factory. The main innovation of this research is the integration of the SMART method with a web interface specifically designed for small industries, the application of dynamic weights according to management priorities, and the validation of assessment results through the comparison of manual and automatic calculations, which is rarely done in previous studies. The system built with PHP and MySQL through a prototyping approach is proven to be able to assess employee performance objectively, systematically, and efficiently, and reduce subjective bias by 40% compared to manual methods.
Comparison of Container Orchestration (Local Kubernetes) and Virtualization Environment (Local Docker) in Node.js Application Management Satria, Chiko Gita; Krishna Yudistira, Bagus Gede
Journal of Artificial Intelligence and Software Engineering Vol 5, No 3 (2025): September
Publisher : Politeknik Negeri Lhokseumawe

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30811/jaise.v5i3.7266

Abstract

Penelitian ini membandingkan efisiensi orkestrasi container (Kubernetes lokal) dan lingkungan virtualisasi (container lokal Docker) dalam manajemen aplikasi Node.js. Dengan memanfaatkan Minikube untuk Kubernetes dan Docker langsung untuk virtualisasi berbasis container, penelitian ini mereplikasi dan menganalisis perilaku fundamental teknologi cloud-native di lingkungan lokal. Tujuan utama adalah menganalisis efisiensi orkestrasi container dibandingkan dengan implementasi virtualisasi berbasis container langsung, dengan fokus pada latensi dan throughput. Aplikasi Node.js diuji dengan tiga endpoint yang merepresentasikan beban ringan (/hello), beban CPU intensif (/load), dan latensi I/O (/sleep). Pengujian beban dilakukan menggunakan Apache JMeter dengan 1000–1500 request per menit selama 10 menit untuk setiap endpoint dan diulang lima kali. Hasil menunjukkan bahwa Docker secara umum memberikan latensi yang lebih rendah dan throughput yang lebih tinggi dibandingkan Minikube, terutama pada endpoint /hello dan /load. Hal ini mengindikasikan bahwa tanpa overhead tambahan dari lapisan orkestrasi, Docker lebih efisien untuk skenario beban ringan hingga sedang. Meskipun Minikube menyediakan fitur orkestrasi yang lengkap, ia memiliki dampak pada efisiensi. Penelitian ini menegaskan bahwa untuk pengujian lokal atau pengelolaan aplikasi skala kecil-menengah tanpa kebutuhan orkestrasi kompleks, Docker dapat menjadi pilihan yang lebih efisien.
Evaluating LMS Usability by Integrating Nielsen and Budd Principles Maulidati, Zuli; Meilani, Budanis Dwi; Sodik, Anwar
Journal of Artificial Intelligence and Software Engineering Vol 5, No 3 (2025): September
Publisher : Politeknik Negeri Lhokseumawe

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30811/jaise.v5i3.7401

Abstract

ABSTRACTLearning Management Systems (LMS) are integral to modern education, supporting course delivery, student engagement, and administrative functions. Usability challenges often divert users’ focus from learning content to navigating system complexities. Heuristic evaluation (HE) has revealed persistent design and usability issues in LMS interfaces, such as poor error prevention and inadequate documentation. To address the usability issues in the LMS, this study aims to evaluate the usability of the classroom ITATS using a dual-framework approach: Jakob Nielsen’s Heuristics and Andy Budd’s Heuristics. This combined approach aims to identify interface flaws and enhance usability. Nielsen’s heuristics address universal usability principles, while Budd’s guidelines emphasize modern web design elements like responsiveness and visual hierarchy. The study is evaluated by novice evaluators who are the end users of LMS. Involving novice evaluators in this study reveals a fresh perspective which cannot be shown by the experts. The study revealed about 158 issues found by 23 novice evaluators. Those were found according to Nielsen’s and Budd’s HE within the average of severity rating about 2.52 and 2.51, respectively. Nielsen’s heuristics highlight core principles such as feedback, visibility, and error prevention, while Budd’s heuristics emphasize simplicity, consistency, and user enjoyment.
Internet of Things-Based Automation System for Watering Cayenne Pepper Plants Akbar, Reza Maulana; Maulindar, Joni; Muhammad, Nibras Faiq
Journal of Artificial Intelligence and Software Engineering Vol 5, No 3 (2025): September
Publisher : Politeknik Negeri Lhokseumawe

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30811/jaise.v5i3.7359

Abstract

This study aims to build an Internet of Things (IoT)-based automatic watering system intended for cayenne pepper farmers in rural areas. The system is designed to irrigate plants automatically based on environmental parameters obtained in real-time. The hardware components consist of an ESP32 microcontroller, a DHT22 temperature and humidity sensor, a soil moisture sensor, a relay module, and a DC water pump. Sensor data are processed by the ESP32 and transmitted to a Supabase database using the HTTP protocol, then visualized through a local web-based interface. Testing results show that the system functions automatically and responsively when the soil moisture value falls below the predetermined threshold. The monitoring interface displays real-time temperature, soil moisture, and a history of recent watering activities. This system is considered effective in reducing labor and optimizing water usage, while also providing a digital solution that aligns with small-scale precision agriculture practices. Based on the results, the system is deemed feasible for implementation and further development.
Sentiment Analysis of Quizizz Application User Reviews Using Logistic Regression Algorithm Aditya, Ari; Tresnawati, Shandy
Journal of Artificial Intelligence and Software Engineering Vol 5, No 3 (2025): September
Publisher : Politeknik Negeri Lhokseumawe

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30811/jaise.v5i3.7292

Abstract

Digital learning applications like Quizizz are increasingly popular for offering interactive learning experiences. As user numbers grow, so do the reviews on platforms like Google Play Store, reflecting user perceptions of app quality. This study aims to analyze user review sentiment toward the Quizizz application using the Logistic Regression algorithm. The data consists of Indonesian-language reviews collected from March to December 2024. The analysis process includes text preprocessing using the Sastrawi library, lexicon-based sentiment labeling, TF-IDF weighting, and classification using Logistic Regression. The model is evaluated using accuracy, precision, recall, and f1-score. The results show that most reviews are positive, and the model performs well in sentiment classification. These findings offer insights for developers to improve the app’s quality and user experience.
Predicting Indonesian Inflation Rate Using Long Short-Term Memory (LSTM) Wijaya, Muhammad Krisna; Nastiti, Faulinda Ely; Farida, Anisatul
Journal of Artificial Intelligence and Software Engineering Vol 5, No 3 (2025): September
Publisher : Politeknik Negeri Lhokseumawe

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30811/jaise.v5i3.7178

Abstract

Inflation is a crucial economic indicator that requires an accurate prediction model. This research aims to develop a prediction system for the monthly inflation rate in Indonesia using the Long Short-Term Memory (LSTM) architecture. The method includes historical data acquisition from Bank Indonesia, preprocessing with Min-Max Scaler normalization, and training a univariate LSTM model. Evaluation results show excellent performance with an MAE of 0.2999, an RMSE of 0.3903, and an R² of 0.8796, indicating the model explains 88% of the data's variability. It is concluded that LSTM is effective for inflation forecasting in Indonesia and serves as a solid baseline for future research.
Development of a Web-Based Reservation System for Cakra Skin Beauty Using the Waterfall Method Devyanti, Kharisma Nur; Yuanda, Raihan; Rudiansah, Cahya; Lubis, Muhammad Anggi; Wicaksono, Aditya; Nasir, Muhammad
Journal of Artificial Intelligence and Software Engineering Vol 5, No 3 (2025): September
Publisher : Politeknik Negeri Lhokseumawe

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30811/jaise.v5i3.7032

Abstract

This study focuses on the development of an online beauty service reservation system at Cakra Skin Beauty Clinic using the Waterfall methodology. The project begins with requirement analysis, followed by interface design, system coding, and testing. Functional testing results show that 85.84% of the 113 test scenarios were successful, while security testing using OWASP ZAP identified 23 potential vulnerabilities. The system features automated service booking, admin scheduling management, and email notification. This research contributes to the development of an online clinic reservation system with integrated security measures and extensibility for payment integration and automated reminders. The system is expected to serve as a reference for building information systems in beauty service clinics and to improve the efficiency of existing clinical services.
Sentiment Analysis Of Instagram Comments On The BPS Province X Account Using The Naive Bayes Algorithm Based On Machine Learning Jessika, Jessika; Khaidar, Al; Nurdin, Nurdin; Muliana, Syarifah
Journal of Artificial Intelligence and Software Engineering Vol 5, No 3 (2025): September
Publisher : Politeknik Negeri Lhokseumawe

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30811/jaise.v5i3.7815

Abstract

Sentiment analysis is an approach in natural language processing that aims to identify and categorize user opinions or attitudes towards an entity based on text data. The data used consists of the last 500 uploaded captions obtained through the Phantombuster tool. The analysis stages include data crawling, preprocessing (removal of duplicate and empty data, tokenization, stopword removal, and case folding), printing using the Naïve Bayes algorithm, and visualization of the classification results. Based on the processing results, it was found that the majority of the data was classified as neutral (97.65%), while the rest was divided into positive (1.57%) and negative (0.78%) categories, with a model accuracy of 94%. Although the model accuracy is relatively high, the dominance of the neutral class indicates an imbalance in data distribution (imbalanced data) which can affect the quality of the generalization model.
Clustering Culinary Locations Using the DBSCAN Algorithm Halawa, Anestin; Lubis, Andre Hasudungan
Journal of Artificial Intelligence and Software Engineering Vol 5, No 3 (2025): September
Publisher : Politeknik Negeri Lhokseumawe

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30811/jaise.v5i3.7512

Abstract

The culinary industry plays a vital role in driving creative economic growth and local tourism while also being an integral part of urban lifestyle. Given the high number and diversity of culinary locations, clustering techniques are needed to group them based on marketing characteristics, enabling more efficient decision-making for both consumers and businesses. This study aims to cluster culinary locations based on marketing-related attributes using the DBSCAN (Density-Based Spatial Clustering of Applications with Noise) algorithm. Secondary data was obtained from Kaggle, consisting of restaurant information in Semarang City, with attributes such as rating, number of reviews, and operating hours. After preprocessing and exploratory analysis, DBSCAN was applied with adjusted parameters to generate optimal clusters. The results produced 41 clusters with diverse characteristics, including several outliers detected as noise. Performance evaluation using Silhouette Score and Davies-Bouldin Index showed that DBSCAN achieved more compact and well-separated clusters compared to K-Means. These findings demonstrate that DBSCAN is more adaptive for non-uniform culinary data with varying densities and is suitable for segmentation and strategic decision-making in the culinary industry.
Design and construction of an IoT-based solar panel batteray monitoring system for household needs Ardiyanto, Kevin; Maulindar, Joni; Dwiirawan, Ridwan
Journal of Artificial Intelligence and Software Engineering Vol 5, No 3 (2025): September
Publisher : Politeknik Negeri Lhokseumawe

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30811/jaise.v5i3.7206

Abstract

This study aims to design and build a solar panel battery monitoring system based on the Internet of Things (IoT) for household needs. Given the increasing demand for energy and reliance on fossil fuels, this system addresses the challenges of monitoring the performance and status of solar panel batteries. The methods employed include measuring electrical current using the ACS712 sensor, controlling a 5V water pump, and programming the ESP32 module. The research results indicate a reduction in water usage by up to 30%, with the watering frequency decreasing from three times to two times per day, thereby enhancing resource efficiency. Data visualization through a PCB dot matrix allows users to understand the system status in real-time. Despite limitations related to internet connectivity, this study makes a significant contribution to the development of IoT technology in the field of renewable energy and opens avenues for further research.

Page 18 of 22 | Total Record : 215

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