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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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Design of Student Attendance Information System at MTS Assalafiyah, Tegal City Silvia Berliana Ghany; Muhammad Fadlullah
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 4 No. 2 (2025): February 2025
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59934/jaiea.v4i2.755

Abstract

The Attendance Information System is a modern solution for recording student attendance in schools, replacing conventional manual attendance lists. Student attendance tracking comprises two main aspects: attendance timekeeping and working timekeeping. With technological advancements, particularly in computing, educational institutions can leverage these tools to enhance operational efficiency. This study aims to design and develop a web-based Student Attendance Information System at MTs Assalafiyah Kota Tegal. The system is designed to streamline attendance management, making it faster, more structured, and resource-efficient. The research employs the waterfall development method, consisting of several stages: requirements analysis, system design, coding, testing, and implementation for end-users. These stages are carried out sequentially to ensure that the system meets the needs of all stakeholders, including students, teachers, and school administrators. Using a web-based system supported by online networks, attendance processes can be conducted in real-time and with greater transparency. The results of this study are expected to significantly contribute to administrative efficiency in schools, improve the accuracy of student attendance data, and simplify the management of attendance records in educational settings. Additionally, this system supports digitalization efforts in education, particularly in school administration.
Optimization of the K-Nearest Neighbors (KNN) Algorithm in Imbalanced Dataset Classification Using the SMOTE Technique Abi Fajar Ahmad Fauzi; Ahmad Faqih; Kaslani
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 4 No. 2 (2025): February 2025
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59934/jaiea.v4i2.756

Abstract

The naturalization of players for Indonesia's national football team has sparked diverse reactions on Twitter, ranging from support to opposition. This situation poses challenges for sentiment analysis, particularly in interpreting public opinion on the policy. A significant challenge arises from the imbalance in sentiment classes, with neutral sentiments outweighing positive and negative ones. This research investigates the effect of class imbalance on sentiment analysis accuracy by employing the KNN algorithm enhanced with the SMOTE technique. A quantitative approach is used, adopting an experimental method aligned with the KDD process stages. The findings reveal that the KNN algorithm without SMOTE achieved an accuracy of 54.77%, with a Precision of 0.65, Recall of 0.57, and F1-Score of 0.44. However, integrating SMOTE with the KNN algorithm significantly improved the outcomes, boosting accuracy to 81.49%, with a Precision of 0.87, Recall of 0.80, and F1-Score of 0.80. These results demonstrate that oversampling techniques like SMOTE are highly effective in mitigating class imbalance and enhancing classification performance, especially for underrepresented classes. This study underscores the efficacy of SMOTE as a solution for addressing class imbalance in sentiment analysis tasks.
Naive Bayes Algorithm to Enhance Sentiment Analysis of Coursera Application Reviews on Google Play Store Masdarul Rizqi; Martanto; Arif Rinaldi Dikanda; Dede Rohman
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 4 No. 2 (2025): February 2025
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59934/jaiea.v4i2.758

Abstract

Coursera is an online learning platform that provides various courses and certifications. This study aims to analyze user perceptions of the Coursera application after the reviews are translated into Indonesian, identify factors that influence positive and negative sentiment, and activate the effectiveness of the Naive Bayes algorithm in classifying review sentiment. The method used is Knowledge Discovery in Databases (KDD), with stages of data collection, preprocessing, and sentiment analysis using Naive Bayes. The results of the study show that the translation of reviews does not change the essence of user perception. Analysis of key words reveals positive experiences such as "kursus", "berguna", and "terima kasih", as well as criticism related to application performance. Factors such as price, content, and user experience play an important role in positive sentiment, while technical issues are the main cause of negative sentiment. The Naive Bayes model shows high accuracy with an accuracy value of 83.62%, precision of 83.34%, recall of 87%, and F1-score of 85.2%. These results indicate that the Naive Bayes algorithm is effective in analyzing sentiment of Coursera application user reviews. Further research is recommended to explore other algorithms or expand the analysis by considering additional factors that can influence user sentiment
FP-Growth Algorithm for Association Model Optimization in Household Sales Data Zulfa Hana Aqliyah; Rudi Kurniawan; Tati Suprapti
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 4 No. 2 (2025): February 2025
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59934/jaiea.v4i2.760

Abstract

This research aims to find the value of support and confidence parameters needed so that associations between products can be identified and get the value of support, confidence, lift for the association rules found, and identify products that have the highest support value in frequent itemsets. The method used is Knowledge Discovery in Databases (KDD) with the stages of data collection, data pre-processing, data transformation, data mining, dan interpretation and evaluation. Sales transaction data was collected from January 1 to September 30, 2024, focusing on support and confidence values. The results showed that the association was successfully found with a parameter value of support 0.02 and confidence 0.5. In the association found, the products SWEAT BRONZE PANTS MINI M5 and SWEAT BRONZE PANTS MINI L5 have a support value of 0.004, confidence of 0.073, and lift of 1.421. These values indicate that although the frequency of this association is low, its strength exceeds that of a random association, which can be used in marketing strategies like product bundling.The product “SENSI PEREKAT S20” has the highest support of 0.149 (14.9%. The findings provide insight into the use of data mining algorithms to design data-driven marketing strategies and more efficient inventory management.
Implementation of Bridge Filtering to Prevent DHCP Starvation Attack (Case Study: SD Inpres Papindung) Yosua Nggaba Patimay; Fajar Hariadi; Raynesta Mikaela Indri Malo
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 4 No. 2 (2025): February 2025
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59934/jaiea.v4i2.761

Abstract

The impact of internet cut off or disrupted at SD Inpres Papindung, the teachers will have difficulty finding teaching materials and may have to return to traditional methods that are less effective. Updating Dapodik data will also be disrupted, which could affect school administration and evaluation. The unavailability of an internet connection does occur due to technical reasons or due to intentional elements, one of which is the use of Yersinia software. Yersinia is a piece of software that is commonly used to attack available networks by sending fake MAC addresses continuously so that the IP address on the DHCP server runs out, so that the client does not get an IP address which causes the client unable to access the internet. This attack are called DHCP Starvation Attack. This can be prevented using Bridge Filtering method. Bridge Filtering can filter only recognized MAC addresses to transmit data or can request an IP address from the DHCP server, while MAC addresses that are not recognized or foreign devices will not be allowed to request from that port or will not receive an IP address from the DHCP server. The aim of implementing Bridge Filtering is to measure the creativity of the Bridge Filtering method in preventing DHCP Starvation Attack attacks so that clients who want to connect to the network can access the internet and improve network quality at SD Inpres Papindung.The results show that the Bridge Filtering method effective in preventing DHCP Starvation Attacks and improves network quality at SD Inpres Papindung which is shown by an increase of throughput, before implementation it was 794.66 Kbps and after implementation it was 1283.70 Kbps, there was a decrease of delay from 6.89 ms before implementation to 4.06 ms after implementation. There was also a decrease in jitter before implementation 10.56 ms and after implementation 6.05 ms, but caused an increase in packet loss which was 0.30% before implementation and after implementation increased to 0.79%. Of the four variables, all of them remain at the same level except for the throughput variable, where there is a change from the fair category to the good category, so that it has an impact on the quality of the internet network at SD Inpres Papindung after implementing Bridge Filtering which is stated to be better than before implementing Bridge Filtering on the internet network at SD Inpres Papindung.
Strategies for Reducing Defect Rates in Commercial Feed Production Using the RCA, FMEA, and 5W+1H Methods in the Feed Industry at PT XYZ Reina Anindita Putri; Akmal Suryadi
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 4 No. 2 (2025): February 2025
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59934/jaiea.v4i2.762

Abstract

The high level of defects in feed production, especially related to particle size (particle out of standard) is the main focus of this research because it has a significant impact on the quality of the final product and customer satisfaction. To overcome these problems, this research uses the Root Cause Analysis (RCA) method to identify the root causes of the problem, Failure Mode and Effect Analysis (FMEA) to determine risk priorities based on the Risk Priority Number (RPN) value, and the 5W+1H approach to provide implementable improvement proposals for the factors that cause defects. The results show that the dominant defect is caused by the clogged boiler duct factor with an RPN value of 200 which is the top priority in improvement efforts. The proposed improvement recommendations include conducting routine inspection and cleaning of boiler lines, as well as creating a checklist and cleaning schedule to ensure boiler lines remain clean and free from blockages. The implementation of this strategy is expected to reduce the level of defects so as to improve the efficiency of the production process and support the company's competitiveness in the animal feed industry.
Improvement of Students' Academic Achievement Classification Model Through the Analytical Hierarchy Process Algorithm in Elementary School Burujul Kulon III Candra Rahmawati Candra; Martanto; Arif Rinaldi Dikananda; Dede Rohman
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 4 No. 2 (2025): February 2025
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59934/jaiea.v4i2.764

Abstract

This study aims to improve the classification model of students' academic achievement at SD Negeri Burujul Kulon III using the Analytical Hierarchy Process (AHP) algorithm. This method is applied to analyze various student assessment criteria, including knowledge, skills, spiritual attitudes, and social attitudes, in order to create an objective and systematic evaluation system. The Knowledge Discovery in Databases (KDD) approach is used to ensure structured data management, including the stages of data collection, selection, transformation, analysis, and evaluation. The population of this study was all students of SD Negeri Burujul Kulon III with a sample size taken using the Stratified Random Sampling method, which ensures accurate representation of all grade levels. Data were collected through documentation studies, including exam result reports, skill assessments, and student behavior records observed by teachers. The analysis was carried out by applying the AHP algorithm to determine the priority weight of each assessment criterion through pairwise comparisons. The weights obtained are used to calculate the final grade which is the basis for classifying student achievement. The results of the study indicate that the AHP algorithm is able to produce a more accurate and relevant classification model to identify students with superior achievement, which is not only based on academic exam results but also includes student skills and attitudes. The resulting system provides significant benefits in academic decision-making, such as awarding outstanding students, identifying students who need special attention, and developing more effective learning strategies. This research also contributes to the development of data-based technology for educational evaluation, and can be an important reference for other educational institutions that want to improve the quality of evaluation, learning effectiveness, and student data management comprehensively, systematically, and sustainably in the future.
The Improvement of Indonesian Film Genre Clustering Model Using the K-Means Algorithm in Film Production Decision-Making Wiratriyana; Martanto; Arif Rinaldi Dikananda; Mulyawan
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 4 No. 2 (2025): February 2025
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59934/jaiea.v4i2.765

Abstract

The Indonesian film industry is expanding rapidly, but understanding audience preferences remains a significant challenge for producers. This study aims to cluster Indonesian films by genre and synopsis using the K-Means algorithm to aid in marketing strategies and content development. The dataset comprises 1,271 Indonesian film entries, including attributes like release year, genre, synopsis, and user ratings. The research follows the Knowledge Discovery in Databases (KDD) framework, which involves data selection, preprocessing, transformation, clustering with K-Means, and evaluation using the Elbow method to identify the optimal number of clusters. The results show that the K-Means algorithm successfully grouped the films into three clusters: drama, horror, and others. The analysis indicates that drama films dominate the high-rating cluster, while horror films are more commonly found in the low-rating category. The use of Principal Component Analysis (PCA) in the visualization aids in interpreting the clustering results, providing a clearer view of the data distribution. These findings highlight the potential for improving film production strategies by aligning content with audience preferences. By understanding genre patterns and ratings, producers can make more informed decisions in marketing and content development.
Web-Based Geographic Information System for Toddlers Nutritional Status in Rindi District Meychristian V. Ama Lay; Fajar Hariadi; Hawu Yogia Pradana Uly
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 4 No. 2 (2025): February 2025
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59934/jaiea.v4i2.767

Abstract

Sistem Informasi Geografis (SIG) merupakan teknologi yang memudahkan visualisasi dan analisis data spasial, berguna dalam berbagai bidang termasuk kesehatan. Salah satu penerapannya adalah pemantauan status gizi balita, dimana masalah gizi dapat berdampak serius terhadap pertumbuhan balita. Di Kecamatan Rindi, Sumba Timur terdapat 200 kasus balita dengan masalah gizi, namun pemantauan gizi masih dilakukan dengan menggunakan peta manual yang tidak dimutakhirkan secara berkala. Hal ini menghambat efektivitas program intervensi gizi karena proses pemantauan data dilakukan secara manual dan kurang optimal. Penelitian ini bertujuan untuk mengembangkan SIG berbasis web untuk memetakan status gizi balita di Kecamatan Rindi. Sistem dirancang dengan menggunakan metode waterfall, yang meliputi tahapan analisis kebutuhan, perancangan sistem, implementasi, pengujian, dan pemeliharaan. Metode ini dipilih untuk memastikan bahwa pengembangan sistem dilakukan secara terstruktur dan menyeluruh. SIG yang dihasilkan diharapkan mampu memberikan informasi yang akurat dan real time, sehingga memudahkan dalam mengidentifikasi desa-desa yang memiliki masalah gizi. Hasil pengujian Black Box menunjukkan bahwa seluruh menu pada sistem berfungsi dengan baik dan dari hasil pengujian SUS diperoleh nilai akhir sebesar 78,25 yang menunjukkan bahwa penilaian Status Gizi Balita SIG di Kecamatan Rindi adalah Cukup dengan kategori penilaian Baik.
Enhancing Election Staff Selection through Decision Tree-Based Classification Rizal Rayyan Firdaus; Nana Suarna; Irfan Ali; Ahmad Rifai
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 4 No. 2 (2025): February 2025
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59934/jaiea.v4i2.768

Abstract

The selection of competent election committee members is a critical aspect in ensuring the success of a fair and transparent election process. However, the subjective nature of this selection process necessitates a data-driven approach to optimize the selection of officials who meet the required competency criteria. This research aims to classify the competencies of prospective election committee members using the Decision Tree algorithm based on demographic data and technological attributes of the population. The study employs the Knowledge Discovery in Databases (KDD) methodology, which includes the stages of data selection, preprocessing, transformation, data mining, and evaluation. In this process, data collected through various attributes are processed to build a classification model. The Decision Tree algorithm is applied to extract patterns from the data, resulting in a decision tree that can classify individuals into different competency classes based on existing features. The research findings indicate that the Decision Tree algorithm effectively classifies respondents into several competency classes that represent varying levels of skills and interest in the election process. The model shows that Class 4 is the dominant class, indicating that most respondents have moderate competency in technological skills and interest in elections. Class 3 represents individuals with higher technological skills but moderate interest, while Classes 2 and 1 represent individuals with varying combinations of interest and skills. This study demonstrates that using the Decision Tree algorithm in the KDD process is highly effective in objectively classifying the competencies of prospective election committee members. By analyzing the interactions among relevant attributes, the model provides insights that can improve the accuracy of election official selection. This data-driven approach can be adapted to other contexts requiring competency classification, offering broader benefits for various criteria-based selection systems.

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