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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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Design And Development of An Automated Watering System For Curly Red Chili Plants Based on Internet Of Things Saputra, Muchammad Yoga; Nurchim, Nurchim; Maulindar, Joni
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.v5i3.7263

Abstract

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.
Implementation of Honey Encryption to Improve Resilience against Brute Force Attacks in Cloud-based Microservices Communication Purba, Gabriel Nathanael; Yudistira, Bagus Gede Krishna; Aryanto, Kadek Yota Ernanda
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.v5i3.7211

Abstract

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.
Improving the Accuracy of COCOMO II in Software Projects Using Hybrid GWO-PSO Putri, Rahmi Rizkiana; Novitasari, Desy Candra
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.v5i3.7603

Abstract

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.
Geographic Information System (GIS) For Road Repair Planning Prioritization Using Naive Bayes Alamsyah, Bintang; Hasanah, Herliyani; Oktaviani, Intan
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.v5i3.7396

Abstract

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.
Sentiment Analysis Of Reading Difficulties In Grade 7 Secondary School Students Using The Support Vector Machine (SVM) Algorithm Algipari, Rasyid Zanuar; 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.v5i3.7289

Abstract

The reading ability of junior high school students in Indonesia is still relatively low, as evidenced by the results of the National Assessment and the viral case of 29 grade VII junior high school students in Pangandaran who are not yet fluent in reading. This study aims to analyze public perception of this phenomenon through a sentiment analysis approach based on YouTube comment text and using the Support Vector Machine (SVM) algorithm. The steps of the SEMMA method are applied, starting with collecting comment data, preprocessing the text using dictionary-based methods and TF-IDF, and finally classification using SVM. The dataset used includes 1,055 comments. The results of the study show that the SVM algorithm is able to classify sentiment into three categories (positive, negative, and neutral) with an accuracy of 87%. These results indicate that the majority of people care about the quality of basic education. This study contributes to the computational understanding of public perception and can be used as a reference for data-based literacy guidelines.
Implementation of a Food Menu Recommendation System at Ndalem Uti Restaurant Using Collaborative Filtering Based on User Preferences Rhemadanu, Andreas; Susanto, Rudi; Asri, Anindhiasti Ayu Kusuma
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.v5i3.7119

Abstract

This study aims to implement a menu recommendation system at Ndalem Uti Restaurant using a Collaborative Filtering approach based on user preferences. The main problem faced is that customers have difficulty choosing the best-selling menu because of the many choices and minimal information regarding the popularity of each menu. To overcome this, the Collaborative Filtering method is used with the User-Based Cosine Similarity calculation to measure the similarity of preferences between users. The test results show that the recommendation accuracy level reaches 96%, including for new users. This system is able to provide more personalized menu recommendations based on customer order history and ratings. This implementation is expected to improve user experience, optimize sales, and be a solution for the development of culinary businesses in the future.
Sentiment Analysis of Netizen Opinions on TikTok Towards iPhone Using Naïve Bayes Algorithm and Support Vector Machine (SVM) Pebriana, Sela; Sugianto, Castaka Agus
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.v5i3.7011

Abstract

This study aims to analyze TikTok users’ sentiment toward the iPhone by utilizing TikTok comments as the primary data source. TikTok was chosen due to its high user engagement and ease of access to spontaneous public opinions. A total of 964 comments were collected and processed through a data cleaning stage. The sentiments were classified into positive and negative categories using two popular machine learning algorithms: Naïve Bayes and Support Vector Machine (SVM). This comparison was conducted to evaluate the effectiveness of each algorithm in handling local social media data, which is typically brief and unstructured. The results show that Naïve Bayes achieved an accuracy of 74%, while SVM reached 71%. These findings indicate that Naïve Bayes performs better in fast sentiment analysis of short-text public opinions and has practical potential for monitoring consumer perception and supporting efficient digital marketing strategies.
Analysis Of Customer Understanding Level Of The E-Policy System In The Sinar Mas Online Insurance Application In The Lhokseumawe Branch Work Area Muliana, Syarifah; Nurdin, Nurdin; Alqhifari, Azka; Khaidar, Al; Jessika, Jessika
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.v5i3.7824

Abstract

Digital transformation in the insurance industry is driving companies to adopt electronic systems, including the implementation of e-policies as a replacement for physical policy documents. This study aims to analyze the level of customer understanding of the e-policy system on the Sinar Mas Online Insurance application in Lhokseumawe branch. The research method used is a quantitative approach with data collection techniques through distributing questionnaires to 100 active customers. The results show that most customers are aware of the existence of e-policies, but still face obstacles in understanding their functions, legality, and how to access documents through the Sinar Mas Online application. Factors such as age, education level, and experience using digital services have been shown to influence the level of customer understanding. This study recommends the need for continuous education and the development of a more intuitive application interface to improve digital literacy and user convenience in accessing e-policies. These findings are expected to provide evaluation material for companies in improving their information systems and digital communication strategies for customers.
Enhancement of Rivest Shamir Adleman (RSA) Key Generation Utilizing the Diffie-Hellman Algorithm for PDF File Security Murti, Maliyah; Rahmadani, Rahmadani; Puspadini, Ratih
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.v5i3.7629

Abstract

The development of digital technology poses challenges to data security, especially in frequently used PDF files. Cryptography can be used for data security. The conventional RSA algorithm with a static public key is vulnerable to attacks. This study integrates Diffie-Hellman to generate a shared key as a public exponent (e) in RSA, making key generation more dynamic and secure. The method includes system analysis, a combined implementation of Diffie-Hellman and RSA, and testing on PDF files. Data is converted to decimal numeric format before encryption and decryption. The encryption is stored in (*.txt), decryption returns the file to the original PDF. The results show that the use of a shared key improves security even though the encrypted file size increases by about 4-5 times from the original file. This system is expected to be a better solution for PDF security and modern cryptography.
Air Temperature and Humidity Monitoring System for Oyster Mushroom Cultivation Ardani, Hasby Arif; Maulindar, Joni; Indah, Ratna Puspita
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.v5i3.7348

Abstract

Oyster mushroom cultivation demands the stability of the microenvironment, especially in the aspects of temperature and humidity in the barn space. Manual monitoring methods that are still commonly used are often less responsive and inefficient in capturing real-time changes in conditions. To answer these problems, this research designs and builds an Internet of Things (IoT)-based monitoring system integrated with the Ubidots platform as a control center and data visualization. This system is able to read and send precise temperature and humidity data using the Hypertext Transfer Protocol (HTTP) protocol. This system uses the DHT22 sensor which is more accurate than previous studies that generally use the DHT11 sensor. The test results show that the system works responsively, stably, and accurately in presenting real-time environmental data, with a temperature reading error rate of about ±0.5°C and ±5% humidity. The implementation of the system at the oyster mushroom cultivation site in Jonggrangan Village, Klaten Utara, proved its effectiveness in helping farmers monitor and maintain mushroom growth conditions remotely in an efficient and sustainable manner.Oyster mushroom cultivation demands the stability of the microenvironment, especially in the aspects of temperature and humidity in the barn space. Manual monitoring methods that are still commonly used are often less responsive and inefficient in capturing real-time changes in conditions. To answer these problems, this research designs and builds an Internet of Things (IoT)-based monitoring system integrated with the Ubidots platform as a control center and data visualization. This system is able to read and send precise temperature and humidity data using the Hypertext Transfer Protocol (HTTP) protocol. This system uses the DHT22 sensor which is more accurate than previous studies that generally use the DHT11 sensor. The test results show that the system works responsively, stably, and accurately in presenting real-time environmental data, with a temperature reading error rate of about ±0.5°C and ±5% humidity. The implementation of the system at the oyster mushroom cultivation site in Jonggrangan Village, Klaten Utara, proved its effectiveness in helping farmers monitor and maintain mushroom growth conditions remotely in an efficient and sustainable manner.

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