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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
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Comparative Performance Analysis of BERT and RoBERTa for Email Spam Classification Purwadi; Hafizh Dzaky Ahya Gemilang
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.v5i2.1968

Abstract

The rapid advancement of information technology has increased the use of email as a primary digital communication medium, while also contributing to the growing volume of spam emails that threaten productivity and information security through phishing and malware. An accurate and adaptive email spam classification system is therefore required. This study aims to analyze and compare the performance of BERT and RoBERTa transformer models for email spam classification. An experimental research approach was employed using an email dataset consisting of spam and non-spam (ham) classes. The research process includes data collection, text preprocessing, model fine-tuning, and performance evaluation using accuracy, precision, recall, F1-score, and confusion matrix metrics. The results show that both BERT and RoBERTa achieve high classification performance. However, RoBERTa demonstrates superior results, particularly in terms of spam recall and overall accuracy, indicating a stronger ability to detect spam emails. This advantage is attributed to RoBERTa’s optimized pre-training strategy, which improves contextual semantic understanding of email content. In conclusion, RoBERTa is more effective than BERT for email spam classification and can serve as a reliable model for developing robust transformer-based spam detection systems.
Public Opinion Sentiment Analysis of Government Fuel Purchasing Policy by the Private Sector Using Support Vector Machine (SVM) Methods Muhammad Rossi Satria Fitrah; Afif Al Qifary; Ahmad Maulana Wahyudi; Dea Deswina Sumarna; Muhammad Nabiel Alfarizi; Fuad Nur Hasan
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.v5i2.1970

Abstract

Government policies that provide opportunities for the private sector to participate in the purchasing and distribution of fuel oil (BBM) have triggered various reactions within society. The diversity of opinions expressed on social media reflects public perceptions of the effectiveness and potential impacts of these policies. This study aims to examine public sentiment toward the government policy by applying the Support Vector Machine (SVM) method. Data were collected from various social media platforms containing public responses to the issue of private sector involvement in fuel purchasing. The analysis process consisted of several stages, including data collection, data preprocessing (comprising cleansing, tokenizing, stopword removal, and stemming), feature extraction using the Term Frequency Inverse Document Frequency (TF-IDF) approach, and sentiment classification using the SVM algorithm. The results show that the SVM algorithm performs well in classifying public opinions into two sentiment categories, positive and negative, with a relatively high level of accuracy. The analysis indicates that the majority of public opinions tend to be negative, driven by concerns over potential price disparities, weakened government oversight, and possible socio-economic impacts. The findings of this study are expected to provide constructive input for the government in evaluating and developing energy policies that are more transparent and oriented toward public interest.
Implementation of MTTR and MTBF for Determining the Average Maintenance Interval of Press Machines at PT X Rubayatun, Renno Ananda Saputra; Iriani
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.v5i2.1973

Abstract

The independent internship program at PT X aims to apply Industrial Engineering concepts in a real industrial environment, particularly in the field of machine maintenance. This study focuses on analyzing the reliability of the press machine at Lane Kiln 2, which plays a crucial role in the ceramic production process. The methods used in this analysis are Mean Time Between Failures (MTBF) and Mean Time To Repair (MTTR) to evaluate the frequency of machine failures and the efficiency of repair time. Based on the data processing results presented in Chapter III, the MTBF value obtained is 703 minutes approximately 12 hours, and the availability of the press machine at Lane Kiln 2 is 93.64%. These results indicate that the machine availability level is relatively high and capable of supporting smooth production operations. However, the findings also suggest that although the repair process has been carried out effectively.
Designing an Online To-Do-List System to Support Student Productivity in Managing Lecture Assignments and Islamic Boarding School Activities Made Asri Syaiba Almihda Hasan; Faradiba Isqi Al-Azizih; Ahmad Hamdani
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.v5i2.1974

Abstract

This study on designing an online To-Do-List system intended to assist mahasiswa santri (Islamic boarding school students attending university) in increasing productivity in managing academic tasks and pesantren actvities. Mahasiswa santri pften struggle to balance academic responbilities with demanding religious routines. To address this issue, the system is developed in the from of a web-based application that can be accessed flexibly. The main features provided include task recording, schedule reminders, and activity progress monitoring. The system design is developed using the prototype method, allowing users to provide direct feedback throughout the design process. The resulting interface and system flow align with user needs and show that the proposed system has the potencial to help users manage their time more efficiently and foster discipline toward their daily schedules. Thus, this system can serve as a digital solution that supports the balance between academic and religious activities for mahasiswa santri. Keyword : To-Do-List System, Productivity, Mahasiswa Santri, Web-Based Application, Prototype Method
Risk Analysis and Occupational Safety Control Strategies at Each Workstation in the Production Division of PT XYZ Widyatama Pratama; Iriani
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.v5i2.1978

Abstract

In facing increasingly intense business competition, companies are required to have human resources with optimal performance in order to achieve organizational goals. Employee performance is influenced by various factors, including work discipline and the implementation of Occupational Safety and Health (OSH). Work discipline reflects employees’ compliance with company rules and procedures, while the implementation of OSH plays an important role in creating a safe, healthy, and comfortable working environment, thereby increasing productivity and reducing the risk of workplace accidents. PT XYZ, a manufacturing company engaged in fertilizer production, has implemented OSH programs in accordance with applicable Indonesian regulations, including Law Number 1 of 1970 concerning Occupational Safety and Government Regulation Number 50 of 2012 concerning the Implementation of the Occupational Safety and Health Management System. Effective OSH implementation is carried out through hazard identification and risk assessment to minimize the potential for occupational accidents and work-related diseases. Through practical work activities, students are given the opportunity to apply academic knowledge directly in an industrial environment and to understand work processes, safety culture, and professional work ethics. The synergy between higher education institutions and the industrial sector is expected to create a productive, safe, and sustainable working environment, while also preparing graduates to face the challenges of the professional world.
Design of a Local Server-Based Student Data Collection System at Salafiyah Syafi’iyah Sukorejo Islamic Boarding School Sariratul Aisyah; Fitri, Nurdiana; A. Hamdani
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.v5i2.1980

Abstract

Management of dormitory room data in Islamic boarding schools is still carried out manually by recording information in notebooks. This approach has the potential to cause data duplication, inaccuracies in record-keeping, loss of students' room placement history, and slow data retrieval processes. This study focuses on designing a dormitory room data management information system based on a local server, without including the implementation stage. The research methods include requirements analysis, system modeling using Unified Modeling Language (UML), and user interface design. The result of this study is a complete and structured system design that can be used as a reference for system development and implementation at a later stage.
Sentiment Analysis of Public Opinion on Rupiah Redenomination Policy Using Support Vector Machine and SMOTE Haidar Aslam; Haikal Nurul Barki; Adhi Prasetyo Wibowo; Faqih Al Araf; Abdul Hamid Musawir; Fuad Nur Hasan
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.v5i2.1982

Abstract

The government’s planned rupiah redenomination has generated a substantial wave of public opinion across social media platforms. This study aims to analyze public sentiment by examining comments on YouTube and classifying them into two categories: positive and negative. The data are collected through web scraping conducted on December 21, 2025, using the keyword “rupiah redenomination.”Given the pronounced imbalance between negative and positive opinions, this study applies the Synthetic Minority Over-sampling Technique (SMOTE) to balance the class distribution within the training data. The research pipeline consists of text preprocessing, feature extraction using Term Frequency–Inverse Document Frequency (TF-IDF), and classification using a linear-kernel Support Vector Machine (SVM). Experimental results indicate that the SVM model achieves an accuracy of 88.28%. The application of SMOTE is shown to effectively enhance the model’s ability to identify the minority class, with the recall for positive sentiment reaching 0.71. Furthermore, the analysis reveals that public opinion is predominantly negative (83.93%), reflecting widespread concern regarding the potential economic implications of the policy.
Design of a Web-Based Letter Disposition Information System in the Population Control and Family Planning Sector of the Banyuwangi Regency Social Service Imroatin Nur Arifah; Navita Inka Ristiani; A.Hamdani
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.v5i2.1987

Abstract

This research aims to design a web-based letter disposition information system for the Population Control and Family Planning Sector of the Banyuwangi Regency Social Service. Mail management which is still done manually causes various problems, such as delays in the disposition process, risk of data duplication, and difficulties in searching mail archives. To overcome this problem, this research uses field research methods supported by literature study as a theoretical basis. System development was carried out using the Software Development Life Cycle (SDLC) approach using a prototype model which includes communication stages, rapid planning, system modeling, prototype development, and system implementation. The system designed includes features for managing incoming mail, outgoing mail, digital disposition, and electronic archiving of letters. The design results show that this system is able to increase efficiency, accuracy and transparency in the letter administration process, and is expected to become the basis for system development in the next implementation stage.
Design and Build an Internship Information System at PT. Perkebunan Nusantara IV Regional I Medan Web Based Herman, Mutia; Muhammad Richie Hadiansah
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.v5i2.1995

Abstract

The administrative process for the internship program at PT Perkebunan Nusantara IV Regional I Medan is still carried out conventionally, starting from the registration stage to recording attendance and daily journals. This situation causes various problems such as input errors, delays in the verification process, and difficulties in monitoring the attendance and activities of interns. This study aims to design a web-based internship information system as a solution to improve data management efficiency and reduce existing administrative problems. The method used in this study follows the SDLC approach with the Waterfall model, which includes needs analysis, system design, implementation with Laravel and Tailwind CSS, and testing using the Black Box Testing method. The findings of this study indicate that the developed system can support online registration, location-based attendance, daily journal filling, and participant management by the admin more quickly, accurately, and integrated. Testing shows that all main functions operate according to the specified scenario. This system makes a significant contribution to supporting more efficient and up-to-date internship administration and has the potential for further development to improve the quality of internship services at related institutions.
Comparison of Machine Learning Classification Algorithm Performance for Depressive Symptom Recognition in College Students Arinda Aulia; Falah Affandi; Puan Syaharani Sitorus; Chairil Umri; Ferizal Fadli Tanjung; Mhd. Furqan
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.v5i2.1998

Abstract

College students are vulnerable to depressive symptoms due to academic, social, and personal pressures, which can impact mental health and academic achievement. Early detection is necessary to prevent this condition from developing into a more serious condition, but conventional methods often lack objectivity. With the development of artificial intelligence, machine learning classification algorithms offer a more accurate approach to recognizing patterns of depressive symptoms. This study compared the performance of several classification algorithms, namely Random Forest, K-Nearest Neighbor, Logistic Regression, Decision Tree, Naive Bayes, and Support Vector Machine, using a dataset of depressive symptoms in college students. Evaluation was carried out based on accuracy, precision, recall, and F1-score. The results showed that Logistic Regression achieved the best performance with an accuracy of 95.62%. This suggests that selecting the right algorithm can improve the effectiveness of early depression detection systems in college students and support data-driven mental health efforts.

Page 42 of 43 | Total Record : 430

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