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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 524 Documents
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Classification of Purple Passion Fruit Ripeness Levels Using Convolutional Neural Network (CNN) Mochammad Gani Alfa Alkhoiri Siregar; Said Iskandar Al Idrus; Hermawan Syahputra; Insan Taufik; Kana Saputra S
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.v5i2.1787

Abstract

Passiflora edulis Sims (purple passion fruit) is a fruit that offers numerous health benefits and possesses high economic value. However, the manual assessment of ripeness by traders tends to be subjective and inconsistent, leading to post-harvest losses of up to 50%. This study developed a classification model for determining the ripeness level of purple passion fruit using a Convolutional Neural Network (CNN) and implemented it in a web-based application. The CNN model was designed to classify four ripeness stages (unripe, half-ripe, ripe, and rotten) with the addition of a non-passion-fruit class to enhance the system’s robustness. The dataset consisted of 2,000 images divided into five classes: four ripeness levels of purple passion fruit (unripe, half-ripe, ripe, and rotten) and one non-passion-fruit class as a comparator. All images were in JPG and PNG formats. The CNN architecture comprised four convolutional layers with 16, 32, 64, and 128 filters, respectively. Evaluation of various data-splitting ratios (80:20, 70:30, 60:40) and learning rates (0.001, 0.0001, 0.01) showed that the optimal configuration was achieved at a ratio of 80:20 with a learning rate of 0.001, resulting in a training accuracy of 96.72% and a testing accuracy of 95.76%, with a loss value of 0.1811. Validation using 5-Fold Cross Validation produced an average accuracy of 95.40%. The model was integrated into a web application developed using Flask and JavaScript, deployed on the PythonAnywhere cloud platform, enabling users to upload images and automatically obtain ripeness predictions to assist traders in sorting fruits more quickly and accurately.
Optimization of Financial Management at Idhotun Nasyi’in Islamic Boarding School using a Website Application Arinil Haqqoh; Kemal Farouq Mauladi; M. Hasan Wahyudi
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.v5i2.1800

Abstract

Idhotun Nasyi'in Islamic Boarding School faces challenges in financial management due to its manual reliance on Microsoft Excel, which is prone to data loss, input errors, and tracking difficulties. This research aims to design and build a web-based financial management application to address these issues. Developed using the System Development Life Cycle (SDLC) waterfall model with PHP and MySQL, the application features core functionalities such as income and expense management, transaction categorization, and real-time financial report generation in PDF format. Black box testing results indicate that the application functions effectively, simplifying the tasks of administrators and treasurers in monitoring cash flow. The implementation of this system is expected to enhance the accuracy, efficiency, and transparency of the boarding school's financial management.
Design of a Modern Web-Based Mail Management Information System Using the Prototype Model at PT Kalimantan Teknologi Indonesia Reza Maulana; Aditya Mukti; Yoki Firmansyah; Deasy Purwaningtias
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.v5i2.1802

Abstract

PT Kalimantan Teknologi Indonesia manages its incoming and outgoing mail using a conventional manual approach, which often results in inefficiency, loss of documents, and difficulties in information retrieval. This study aims to design a web-based mail management information system that enhances the efficiency, accuracy, and traceability of administrative correspondence. The research adopts the Prototype development methodology, consisting of requirement analysis, system design, prototype creation, and evaluation. Data were collected through interviews, observations, and document analysis to identify user needs and administrative workflows. The system was developed using Figma employed for interface prototyping. The resulting system provides features for digital recording, classification, searching, and reporting of both incoming and outgoing mail. Testing results indicate that the prototype effectively reduces administrative workload, improves data accuracy, and accelerates document tracking. This study demonstrates that a well-structured digital correspondence system can serve as a foundation for administrative transformation in small and medium-sized enterprises.
Implementation of an Executive Information System for Thesis Document Submission with the Addition of AES-256-CBC Cryptography Algorithm Taqiyuddin Ahmad Al Aufa; Adriano Femaz Rivaldy; Amalia Anjani Arifiyanti; Agung Brastama Putra
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.v5i2.1803

Abstract

The rapid digitalization of higher education demands secure and efficient management of academic documents such as thesis submissions. This study aims to develop an Executive Information System (EIS) for Thesis Document Submission integrated with AES-256-CBC cryptographic security to ensure data confidentiality, integrity, and controlled access. The system is implemented as a web-based platform using the Laravel framework and MySQL database, where each uploaded thesis document is automatically encrypted, and only authorized users with a valid Master Key can decrypt it. The AES-256-CBC algorithm generates unique ciphertexts for every encryption process, supported by randomized Initialization Vectors and separate key management to prevent unauthorized access or data leakage. Furthermore, the EIS dashboard implements the drill-down method, presenting real-time analytical information. This allows academic leaders to navigate hierarchically from high-level summaries to specific, detailed data, enhancing their ability to monitor thesis submissions and make informed decisions effectively. The results indicate that the integration of cryptography and executive information management enhances both document security and administrative efficiency, providing a reliable and transparent solution for safeguarding academic data within higher education institutions.
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; Trisman Harefa; Kristof Martin. E Telaumbanua; Adieli Laoli
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; Abdul Aziiz; 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 Marwan Suhendra; Badariatul Lailiah; Yanto Yanto; Lady Agustin Fitriana
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.

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