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Journal of Artificial Intelligence and Engineering Applications (JAIEA)
Published by Yayasan Kita Menulis
ISSN : -     EISSN : 28084519     DOI : https://doi.org/10.53842/jaiea.v1i1
Core Subject : Science, Engineering,
Automotive Engineering Computer Science & IT Control & Systems Engineering
The Journal of Artificial Intelligence and Engineering Applications (JAIEA) is a peer-reviewed journal. The JAIEA welcomes papers on broad aspects of Artificial Intelligence and Engineering which is an always hot topic to study, but not limited to, cognition and AI applications, engineering applications, mechatronic engineering, medical engineering, chemical engineering, civil engineering, industrial engineering, energy engineering, manufacturing engineering, mechanical engineering, applied sciences, AI and Human Sciences, AI and education, AI and robotics, automated reasoning and inference, case-based reasoning, computer vision, constraint processing, heuristic search, machine learning, multi-agent systems, and natural language processing. Publications in this journal produce reports that can solve problems based on intelligence, which can be proven to be more effective.
Arjuna Subject : Ilmu Komputer (semua kategori) - Kecerdasan Buatan
Articles 430 Documents
 33   34   35   36   37 
Brute-Force Attack Detection on Computer Networks Using Artificial Neural Network Ikhtiar Adli Wicaksono; Muhammad Iqbal Maulana; Bagus Nurrahman; Syifa Nur Rakhmah; Findi Ayu Sariasih; Imam Sutoyo
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 5 No. 2 (2026): February 2026
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59934/jaiea.v5i2.1804

Abstract

This research aims to develop a brute-force attack detection system on computer networks using the Artificial Neural Network (ANN) algorithm. This security problem is crucial, especially in the banking sector because it can threaten login systems and sensitive customer data. The research methods include data cleansing, feature selection using the Wrapper method, ANN model training, and performance evaluation using datasets from Kaggle which include four classes of network traffic, namely Normal, Brute-force FTP, Brute-force SSH, and Web Attack Brute-force. The test results showed that the ANN model achieved an accuracy of 95%, precision of 91%, and the best performance in the Brute-force FTP class with an accuracy of 98.3%. This system has proven to be effective in detecting brute-force attack patterns and can improve the security of banking networks adaptively. This research broadens the insights of the application of ANN in network security and provides a basis for the development of systems that are more responsive to cyber threats.
Analysis of the Use of Learning Media in English Learning in the Kurikulum Merdeka at SMA Negeri 1 Idanogawo Angelin Marpaung; Harefa, Trisman; Telaumbanua , Kristof Martin. E; Laoli, Adieli
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 5 No. 2 (2026): February 2026
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59934/jaiea.v5i2.1805

Abstract

This study investigates the use of learning media in English learning media under the Kurikulum Merdeka at SMA Negeri 1 Idanogawo using a descriptive qualitative method. Data were collected through observations and interviews with English teachers, alongside observations of 11th grade students.The findings reveal that the employment of both digital and conventionalmedia effectively boosts student engagement, motivation, and comprehension. This media utilization is crucial for adapting to the diverse characteristics and needs of the students. However, teachers face notable challenges, including limited technological facilities, inadequate school infrastructur, and insufficient time for designing and implementing innovative media. The choice of learning media is influenced by factors such as facility availability, ease of use, relevance to the material, and suitability for students characteristics. The study underscores the critical need for support from both the school and the government, specifically in the form of training, infrastructure provision, and profesional development, to optimize media usage. Furthermore, it highlights the essential role of teacher creativity in selecting and developing media appropriate for the Kurikulum Merdeka, making learning more effective and adaptive to 21st century demands. Ultimately, the appropriate and innovative use of learnig media can significantly improve the quality of English learning and enhance student competencies at SMA Negeri 1 Idanogawo.
Spam Message Classification Using the Naïve Bayes Algorithm Based on RapidMiner Muhamad Yusup; Mochamad Isham Fadillah; Rifky Adinanta Fauzanie; Risca Lusiana Pratiwi; Rani Irma Handayani; Euis Widanengsih
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 5 No. 2 (2026): February 2026
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59934/jaiea.v5i2.1811

Abstract

This study implements the Naïve Bayes algorithm for classifying spam and non-spam (ham) messages using the RapidMiner Studio platform. The dataset used was obtained from the SMS Spam Collection Dataset on the Kaggle platform, which consists of 5,759 messages with a distribution of 4,075 ham messages and 1,291 spam messages. The research stages included text pre-processing, model training, and performance evaluation using accuracy, precision, recall, and F1-score metrics. The experimental results showed that the Naïve Bayes model achieved an accuracy of 89.64% with a precision of 56.93%, a recall of 100%, and an F1-score of 72.56%. The research findings indicate that the Naïve Bayes algorithm is effective in detecting spam messages with adequate accuracy, and prove that RapidMiner is an efficient tool for implementing machine learning methods in text classification.
Clustering Provinces in Indonesia Based on Economic Indicators Using the K-Means Algorithm Ilham Ilyasa; Muhamad Fazri Sugara; Aziiz, Abdul; Rani Irma Handayani; Risca Lusiana Pratiwi; Euis Wida Nengsih
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 5 No. 2 (2026): February 2026
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59934/jaiea.v5i2.1812

Abstract

This study aims to analyze and classify the level of economic development in provinces in Indonesia using the K-Means algorithm. The data used includes three main indicators, namely Gross Regional Domestic Product (GRDP) per capita, percentage of poor population, and Human Development Index (HDI) in 2024 obtained from the Central Statistics Agency (BPS). The data was processed through normalization and analysis using the Elbow method to determine the optimal number of clusters. The results were evaluated using the Davies–Bouldin Index (DBI) to assess the level of separation and compactness between clusters. The results show that the most effective division consists of three groups representing high, medium, and low levels of development. Provinces such as DKI Jakarta and Riau are included in the high development cluster, Central Java and South Sulawesi are in the medium cluster, while Papua and East Nusa Tenggara are in the low cluster. These results show that machine learning methods, particularly K-Means, are capable of identifying patterns of regional economic inequality and provide a useful basis for the government in formulating more targeted and equitable development policies.
Automated Diagnosis Assistant with Random Forest Medical Image and Algorithm Feature Extraction Muhammad Nosa Rezq Maulana
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 5 No. 2 (2026): February 2026
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59934/jaiea.v5i2.1815

Abstract

Medical image-based disease diagnosis is a complex process and requires a high level of expertise. This study aims to develop an Automatic Diagnosis Assistant using a combination of image feature extraction techniques and Random Forest (RF) classification algorithms. Medical images are processed to extract meaningful textural features, such as using the Gray Level Co-occurrence Matrix (GLCM), which is then used to train the RF model. To address the problem of data imbalance that is common in medical datasets, the SMOTE technique is applied. The performance of the model is evaluated and optimized using Randomized Search to find the best hyperparameters. The results showed that the optimized RF model was able to achieve high accuracy, with significant improvements in the Recall and F1-Score metrics compared to the baseline model. This automated diagnostic assistant is expected to be an effective tool for medical personnel in speeding up and improving diagnostic accuracy, especially in cases with high image volumes.
Evaluation of Machine Learning Algorithms in Sentiment Analysis of the Satu Sehat Application Suhendra, Marwan; Lailiah, Badariatul; Yanto, Yanto; Fitriana, Lady Agustin
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 5 No. 2 (2026): February 2026
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59934/jaiea.v5i2.1816

Abstract

This study aims to analyze and compare the performance of three sentiment classification algorithms—Support Vector Machine (SVM), Naïve Bayes (NB), and K-Nearest Neighbor (K-NN)—in classifying user reviews of the Satu Sehat application. The data preprocessing stage involves several steps, including text cleaning through normalization, removal of punctuation, numbers, and irrelevant characters, as well as the elimination of stopwords. Subsequently, stemming is performed to reduce words to their root forms. Feature extraction is conducted using the CountVectorizer method with a bag-of-words approach, which converts textual data into numerical representations. The dataset is then divided into training and testing subsets using an 80:20 train-test split ratio. Model performance is evaluated through a confusion matrix, producing key evaluation metrics such as accuracy, precision, recall, and F1-score. Based on the results of testing 9,192 user reviews, the SVM algorithm with a linear kernel demonstrated the best overall performance compared to NB and K-NN, as indicated by the highest accuracy score. These findings suggest that SVM is more effective in handling high-dimensional textual features, making it a highly suitable algorithm for sentiment analysis of digital health application reviews, particularly those related to Satu Sehat.
Geospatial Analysis of Global Temperature and Humidity Variations Using Integrated Meteorological Data Zhafira, Alya; Purwadi
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 5 No. 2 (2026): February 2026
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59934/jaiea.v5i2.1817

Abstract

Global climate monitoring is crucial for understanding variations in temperature and humidity, which directly influence ecosystems, human health, and socio-economic activities. This study presents a Geographic Information System (GIS)-based analysis and visualization of global temperature and humidity patterns using historical hourly weather data from 2012 to 2017. The dataset, obtained from open-access sources, was processed and analyzed in Google Colab using Python libraries such as pandas, geopandas, folium, and plotly. Data preprocessing involved merging city-level observations, cleaning missing values, and calculating mean temperature and humidity per location. The resulting dataset was then visualized through an interactive global map and a scatter plot to identify spatial relationships between the two climatic variables.To quantify these spatial relationships, a statistical correlation analysis was conducted, revealing a weak negative relationship between temperature and humidity (r = -0.25) across global regions.The findings reveal that regions near the equator exhibit consistently high temperatures and humidity, while higher-latitude cities show lower temperatures and more variable moisture levels. This GIS-based approach demonstrates the potential of open meteorological data for climate pattern recognition and supports reproducible workflows for environmental analysis. The results highlight the importance of integrating data science tools with GIS for accessible and scalable global climate visualization.
Baby Supplies Sales Prediction System using the Single Exponential Smoothing Method at Little Queen Baby Shop Silvia Agustin; Miftahus Sholihin; Agus Setia Budi
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 5 No. 2 (2026): February 2026
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59934/jaiea.v5i2.1824

Abstract

The increasing demand for baby equipment in Indonesia in recent years has created significant business opportunities for the retail sector, including Little Queen Baby Shop. However, seasonal fluctuations in demand often lead to stock management problems such as overstock and out of stock, which affect storage costs and customer satisfaction. This research aims to design and develop a sales prediction system for baby products using the Single Exponential Smoothing (SES) method as a solution to minimize forecasting errors and support data-driven decision-making. The research method involved collecting secondary sales data from January to November 2024, which was then processed using the SES algorithm with a smoothing parameter (α) to determine the optimal prediction values with the lowest error rate. The system was developed as a web-based application using PHP programming language and MySQL database, equipped with features such as transaction recording, stock management, sales analysis, and prediction reports for upcoming periods. The implementation results show that the SES-based prediction system provides sufficiently accurate forecasts, as indicated by low values of Mean Absolute Percentage Error (MAPE), Mean Absolute Error (MAE), and Mean Squared Error (MSE). This system enables Little Queen Baby Shop to optimize stock management, reduce the risk of losses due to excessive or insufficient inventory, and improve both operational efficiency and customer satisfaction.
Sentiment Analysis of Indonesian National Team Failure in the 2026 World Cup Qualifications Using Support Vector Machine Algorithm Nouval, Muhammad; Habibi, Fanza Maulana; Rahmi, Anisya; Amru Bittaqwa, Muhammad Dawam; Agustianto, Rizki
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 5 No. 2 (2026): February 2026
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59934/jaiea.v5i2.1830

Abstract

The Indonesian National Team's failure in the 2026 World Cup qualifiers has generated diverse responses on social media, particularly on Ferry Irwandi's YouTube channel. This study aims to analyze public sentiment towards the national team's performance based on YouTube user comments. The method used is a Support Vector Machine (SVM) with stages of data scraping, pre-processing (cleaning, case folding, normalization, tokenization, stopword removal, stemming), lexicon-based automatic labeling, and model evaluation using a confusion matrix. The data consists of 8,353 comments divided with a ratio of 80:20 for training and testing. The results show that the SVM algorithm is able to classify comments into two classes, positive and negative, with an accuracy of 81%, a precision of 82%, a recall of 83%, and an F1-score of 82%. These results demonstrate the effectiveness of SVM in accurately and stably identifying public opinion towards the Indonesian National Team's failure.
Development of Environmental Cleanliness Education Game for Grade 5 Students at SD Inpres Kalu Makaborang, Umbu Rona; Hariadi, Fajar; Sari Dewi Novyanti Bertha Mira, Tri
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 5 No. 2 (2026): February 2026
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59934/jaiea.v5i2.1834

Abstract

Technological advances have brought major changes in various aspects of life, including the world of education. One form of use of technology in education is the development of interactive learning media such as educational games. This research aims to develop an Android-based educational game that raises the theme of environmental cleanliness and is intended for 5th grade students of SD Inpres Kalu, East Sumba. The background of this research is based on the low understanding of students on the importance of maintaining environmental cleanliness, which is caused by conventional learning methods that are less interesting and less interactive. The educational game developed will contain materials such as types of waste, how to sort and dispose of waste, and dirty environmental impacts. The research method used is research and development (R&D) with a waterfall model that includes the stages of needs analysis, design, implementation, verification, and maintenance. Supporting data were obtained through interviews, observations, and literature studies. The results of the trial showed an increase in students' understanding of environmental hygiene materials, which was evidenced by an increase in the average score from 78.0 in the pre-test to 87.2 in the post-test, with a difference of 9.2 points or an increase of 11.79%. Testing using the Black Box Testing method showed that all in-game features performed as intended, while the System Usability Scale (SUS) test results obtained an average score of 83.5, which is in the excellent category.

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