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<![CDATA[Sentiment Analysis of Quizizz Application User Reviews Using Logistic Regression Algorithm ]]>
<![CDATA[Aditya, Ari]]>;
<![CDATA[Tresnawati, Shandy]]>
<![CDATA[ Journal of Artificial Intelligence and Software Engineering Vol 5, No 3 (2025): September ]]>
Publisher : <![CDATA[Politeknik Negeri Lhokseumawe ]]>
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DOI: 10.30811/jaise.v5i3.7292
<![CDATA[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.]]>
<![CDATA[Predicting Indonesian Inflation Rate Using Long Short-Term Memory (LSTM) ]]>
<![CDATA[Wijaya, Muhammad Krisna]]>;
<![CDATA[Nastiti, Faulinda Ely]]>;
<![CDATA[Farida, Anisatul]]>
<![CDATA[ Journal of Artificial Intelligence and Software Engineering Vol 5, No 3 (2025): September ]]>
Publisher : <![CDATA[Politeknik Negeri Lhokseumawe ]]>
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DOI: 10.30811/jaise.v5i3.7178
<![CDATA[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.]]>
<![CDATA[Development of a Web-Based Reservation System for Cakra Skin Beauty Using the Waterfall Method ]]>
<![CDATA[Devyanti, Kharisma Nur]]>;
<![CDATA[Yuanda, Raihan]]>;
<![CDATA[Rudiansah, Cahya]]>;
<![CDATA[Lubis, Muhammad Anggi]]>;
<![CDATA[Wicaksono, Aditya]]>;
<![CDATA[Nasir, Muhammad]]>
<![CDATA[ Journal of Artificial Intelligence and Software Engineering Vol 5, No 3 (2025): September ]]>
Publisher : <![CDATA[Politeknik Negeri Lhokseumawe ]]>
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DOI: 10.30811/jaise.v5i3.7032
<![CDATA[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.]]>
<![CDATA[Sentiment Analysis Of Instagram Comments On The BPS Province X Account Using The Naive Bayes Algorithm Based On Machine Learning ]]>
<![CDATA[Jessika, Jessika]]>;
<![CDATA[Khaidar, Al]]>;
<![CDATA[Nurdin, Nurdin]]>;
<![CDATA[Muliana, Syarifah]]>
<![CDATA[ Journal of Artificial Intelligence and Software Engineering Vol 5, No 3 (2025): September ]]>
Publisher : <![CDATA[Politeknik Negeri Lhokseumawe ]]>
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DOI: 10.30811/jaise.v5i3.7815
<![CDATA[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.]]>
<![CDATA[Clustering Culinary Locations Using the DBSCAN Algorithm ]]>
<![CDATA[Halawa, Anestin]]>;
<![CDATA[Lubis, Andre Hasudungan]]>
<![CDATA[ Journal of Artificial Intelligence and Software Engineering Vol 5, No 3 (2025): September ]]>
Publisher : <![CDATA[Politeknik Negeri Lhokseumawe ]]>
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DOI: 10.30811/jaise.v5i3.7512
<![CDATA[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.]]>
<![CDATA[Design and construction of an IoT-based solar panel batteray monitoring system for household needs ]]>
<![CDATA[Ardiyanto, Kevin]]>;
<![CDATA[Maulindar, Joni]]>;
<![CDATA[Dwiirawan, Ridwan]]>
<![CDATA[ Journal of Artificial Intelligence and Software Engineering Vol 5, No 3 (2025): September ]]>
Publisher : <![CDATA[Politeknik Negeri Lhokseumawe ]]>
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DOI: 10.30811/jaise.v5i3.7206
<![CDATA[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.]]>
<![CDATA[Design And Development of An Automated Watering System For Curly Red Chili Plants Based on Internet Of Things ]]>
<![CDATA[Saputra, Muchammad Yoga]]>;
<![CDATA[Nurchim, Nurchim]]>;
<![CDATA[Maulindar, Joni]]>
<![CDATA[ Journal of Artificial Intelligence and Software Engineering Vol 5, No 3 (2025): September ]]>
Publisher : <![CDATA[Politeknik Negeri Lhokseumawe ]]>
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DOI: 10.30811/jaise.v5i3.7263
<![CDATA[An ideal soil moisture level between 60% and 80% is crucial for the optimal growth of curly chili plants. However, manual watering methods by farmers are often inefficient and fail to maintain consistent soil moisture. This issue forms the basis of the current research, which aims to develop an automated Internet of Things (IoT)-based watering system for curly chili plants, integrated with a monitoring website. The research method involves using an ESP32 microcontroller connected to a soil moisture sensor and a DHT22 temperature sensor. The data collected by the sensors is sent in real-time to the Firebase Realtime Database as a cloud platform and then visually displayed on a monitoring website. System testing confirmed that the setup successfully monitored soil conditions and air temperature while also controlling a DC mini water pump via a relay for automated watering based on the received data. In conclusion, the implementation of this IoT technology is expected to assist farmers in Kadokan Village in conserving water usage, improving time efficiency, and enhancing the quality and quantity of curly chili production.]]>
<![CDATA[Implementation of Honey Encryption to Improve Resilience against Brute Force Attacks in Cloud-based Microservices Communication ]]>
<![CDATA[Purba, Gabriel Nathanael]]>;
<![CDATA[Yudistira, Bagus Gede Krishna]]>;
<![CDATA[Aryanto, Kadek Yota Ernanda]]>
<![CDATA[ Journal of Artificial Intelligence and Software Engineering Vol 5, No 3 (2025): September ]]>
Publisher : <![CDATA[Politeknik Negeri Lhokseumawe ]]>
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DOI: 10.30811/jaise.v5i3.7211
<![CDATA[Arsitektur microservices berbasis cloud meningkatkan skalabilitas dan fleksibilitas sistem namun memperbesar risiko keamanan komunikasi antar layanan. Penelitian ini menerapkan Honey Encryption pada API Gateway di infrastruktur Amazon EC2, mengombinasikan Distribution Transforming Encoder dengan AES-CBC 256-bit untuk menjaga kerahasiaan dan integritas data. Hasil validasi menunjukkan bahwa dekripsi dengan kunci benar memulihkan data asli, sedangkan kunci salah menghasilkan decoy message yang plausible dan bervariasi per field, secara efektif menghambat upaya brute force. Evaluasi performa menegaskan bahwa waktu proses enkripsi dan dekripsi berbanding lurus dengan beban data namun tetap berada dalam batas latensi yang dapat diterima. Simulasi serangan memperlihatkan keunggulan decoy message dalam menyamarkan pesan asli, meningkatkan keamanan komunikasi data antar microservices.]]>
<![CDATA[Improving the Accuracy of COCOMO II in Software Projects Using Hybrid GWO-PSO ]]>
<![CDATA[Putri, Rahmi Rizkiana]]>;
<![CDATA[Novitasari, Desy Candra]]>
<![CDATA[ Journal of Artificial Intelligence and Software Engineering Vol 5, No 3 (2025): September ]]>
Publisher : <![CDATA[Politeknik Negeri Lhokseumawe ]]>
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DOI: 10.30811/jaise.v5i3.7603
<![CDATA[Accurate business forecasting provides an important foundation for managing software projects effectively. If the business estimate is not accurate, it can have an impact on the quality of project management to become less efficient. It can be risky such as an excess budget, to failing to meet the set schedule. This research includes the hybrid Grey Wolf Optimization (GWO)-Particle Swarm Optimization (PSO) method to optimize the results of business estimation, thereby resulting in more valid and reliable business estimates of software projects. The implementation of the proposed method showed a Mean Magnitude Relative Error (MMRE) value of 321.16%, which is 1243.23% lower than the results of conventional COCOMO II. The results of the trial prove that the accuracy of business estimates has increased, thus making a significant contribution to improving the effectiveness of software project management. Thus, this study provides a more reliable COCOMO II business estimation framework that can be adopted by practitioners and researchers to improve the planning, control, and evaluation process of software projects.]]>
<![CDATA[Geographic Information System (GIS) For Road Repair Planning Prioritization Using Naive Bayes ]]>
<![CDATA[Alamsyah, Bintang]]>;
<![CDATA[Hasanah, Herliyani]]>;
<![CDATA[Oktaviani, Intan]]>
<![CDATA[ Journal of Artificial Intelligence and Software Engineering Vol 5, No 3 (2025): September ]]>
Publisher : <![CDATA[Politeknik Negeri Lhokseumawe ]]>
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DOI: 10.30811/jaise.v5i3.7396
<![CDATA[Prioritizing road infrastructure repair in rural areas often faces challenges, particularly due to budget limitations and subjective evaluation processes. This study aims to design and develop a web-based Geographic Information System (GIS) integrated with the Naive Bayes classification algorithm to support objective road repair prioritization. The system was developed using the Rapid Application Development (RAD) approach, involving active user participation in iterative development cycles. The application was built using Laravel as the backend framework, Leaflet.js for interactive map visualization, and PostgreSQL with the PostGIS extension for spatial data management. The system is capable of managing regional and road data, receiving road damage reports, and classifying repair priorities into high, medium, or low categories based on parameters such as damage level, traffic volume, and road length. The classification results are visualized on an interactive map to assist village officials in monitoring infrastructure and making informed decisions. System evaluation using black box testing confirmed that all functionalities operate validly in accordance with user requirements. This system offers an accurate and transparent data-driven solution for managing road infrastructure in Jelobo Village and has the potential to be replicated in other regions with similar conditions.]]>
