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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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Automatic Waste Type Detection Using YOLO for Waste Management Efficiency Alfattah Atalarais; Kana Saputra S; Hermawan Syahputra; Said Iskandar Al Idrus; Insan Taufik
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.v4i2.770

Abstract

The management of waste in Indonesia is currently suboptimal, with only 66.24% being effectively managed, leaving 33.76% unmanaged. This highlights a significant challenge in waste management, primarily due to a lack of understanding in selecting appropriate waste types. Advances in deep learning and computer vision offer promising solutions to this issue. This study employs the YOLOv8l model, a well-regarded deep learning model for object detection, to develop an automated waste type detection system integrated with trash bins. The dataset comprises 2800 images across four classes, each containing 700 images, and is split with an 80:10:5 ratio for training, validation, and testing. Evaluation on test data yields a mean Average Precision (mAP) of 96.8%, indicating robust model performance in object detection. The model's accuracy is further validated with a score of 89.98%. Real-time testing conducted at Merdeka Park, Binjai, demonstrates the system's capability to detect waste with varying confidence levels, consistently above the 0.5 threshold. The highest confidence was observed in bottle detection at 0.94, and the lowest in cans at 0.64, underscoring the system's reliability across different detection scenarios within a 30cm range.
Analysis of Beverage Sales Data Using the FP-Growth Algorithm at Sini Aja Cafe Widisa Adi Kumara; Rini Astuti; Willy Prihartono; 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.v4i2.772

Abstract

The growth of information technology and data mining techniques has greatly helped analyze consumer purchasing behavior, particularly in marketing and inventory management. This study aims to uncover association patterns between products frequently bought by customers at Sini Aja Cafe and to measure these patterns' support and confidence values. The research uses Knowledge Discovery in Databases (KDD), including stages like data selection, preprocessing, transformation, applying the FP-Growth algorithm, and interpreting results. Data from 1,083 beverage sales transactions at Sini Aja Cafe from August 1 to October 31, 2024. The findings reveal five significant association rules when applying a minimum support of 0.1 (10%) and confidence of 0.3 (30%). Notably, if customers buy Red Velvet Oreo, there is a 56% chance they will also buy Thai Tea. Thai Tea sales dominate with a support value 0.557 (55.7%). The support values of the association rules range from 0.141, categorized as medium, and the confidence values range from 0.235, categorized as low. These findings offer valuable insights for the cafe owner to optimize operations, enhance customer satisfaction, and increase profits.
Optimization of Social Assistance Recipient Determination using Gradient Boosting Algorithm Windi Herlita Vidila; Rudi Kurniawan; Saeful Anwar
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.v4i2.773

Abstract

This research aims to classify social assistance recipients to ensure the accuracy of aid distribution by utilizing the Gradient Boosting algorithm on RapidMiner. The data used is data on residents who are categorized as receiving and not receiving social assistance in Cicadas village with a total dataset consisting of 670 entries with 18 attributes that will be divided equally between eligible and ineligible recipients. This research uses KDD (Knowledge Discover in Database) analysis which includes the stages of data selection, pre-processing, transformation, modeling, and interpretation of results. This research uses a quantitative approach, focusing on the distribution of datasets in a ratio of 70:30 with a stratified sampling technique for training and testing purposes. The experimental results show that the selected method is effective in classifying recipients by obtaining an accuracy of 91.67%, this accuracy result can be relied upon to support decision-making in social assistance distribution. The findings underscore the potential of machine learning in optimizing social welfare initiatives by improving target accuracy and ensuring aid reaches the rightful recipients.
Analysis of Fatigue Levels Using Multiple Linear Regression Methods on PT XYZ Workers Audiansyah Agni Nirvana; Rizqi Novita Sari
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.v4i2.774

Abstract

PT XYZ is a company engaged in warehousing. In every process, there are various activities that can cause fatigue in workers. Currently, researchers want to analyze the effect of body condition, material, and warehouse conditions on the fatigue level of warehouse workers. To do this, companies can use multiple linear regression tests with SPSS software. In the multiple linear regression test that has been carried out, it is known that the body condition and material factors do not have a strong influence on the level of fatigue that occurs. This is because the Pvalue >0.05. While the warehouse condition factor has a strong influence on the level of fatigue, H0 is rejected because it has a Pvalue of 0.015 <0.05. From the results of the anova test conducted, it can be seen that the three existing factors simultaneously affect the fatigue level of PT XYZ workers. This is because the significance value in the anova test is 0.004; smaller than the value of 0.005. The benefit of this research is that PT XYZ can determine the right solution in order to reduce the level of fatigue so that warehouse productivity increases.
Use of K-Means Algorithm in Model Improvement Production Data Grouping for Determination Convection Production Strategy Ica Pandia Ica; 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.v4i2.775

Abstract

This research was conducted to implement the K-Means Clustering algorithm in grouping convection production data to support the improvement of efficiency and effectiveness of production strategies. The data used is PT ABC's production data, which consists of important attributes, such as Production ID, Production Date, Product Name, Product Type, Color, Size, Raw Materials, and Order. The research method follows the stages of Knowledge Discovery in Database (KDD), which includes selection, preprocessing, transformation, data mining, and evaluation, so as to ensure that the data processed is relevant and ready to be analyzed. The grouping process is carried out using the K-Means algorithm, which groups data based on attribute similarity by determining the optimal number of clusters. The evaluation of the clustering results was carried out using the Silhouette Score and Davies-Bouldin Index metrics, where the results showed values that represented good cluster quality. A high Silhouette Score indicates that the data in the cluster has good uniformity, while a low Davies-Bouldin Index indicates a clear distance between clusters. The results of the grouping produce three main clusters that illustrate different production patterns, such as clusters with high, medium, and low order quantities. This analysis of the cluster provides important insights in supporting strategic decision-making, such as prioritizing resource management in high-order clusters and evaluating production efficiency in low-order clusters. This research is in line with previous literature that shows that the K-Means algorithm can be used effectively in big data grouping to support strategic planning. The practical contribution of this research is to help convection companies in understanding production patterns, so that production strategies can be designed more efficiently, responsively, and directionally. For further research, it is recommended to add new variables, such as production costs or work duration, as well as test other clustering algorithms to obtain more comprehensive results.
Development of Educational Game for Introduction Animal Types Using the ADDIE Method Smart Apps Creator In Improving Knowledge Students Azzahra Rizky Artoti; 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.v4i2.777

Abstract

The development of technology in education opens up opportunities for innovation to create interactive learning media, especially for early childhood. This research aims to develop educational games based on Smart Apps Creator using the ADDIE method to introduce animal species to Al-Washliyah kindergarten students. The method used is ADDIE, consisting of five stages, namely: Analysis, Design, Development, Implementation, and Evaluation. in this study conducted validity, reliability, normality, homogeneity, and anova tests to measure the effectiveness of this learning media. The results showed that this animal species recognition educational game succeeded in improving student understanding with an average score before the use of learning media of 59.2% increasing to 87.73% after using learning media. Validity and reliability tests show that this learning media meets the criteria of effective, easy-to-use, and interesting learning media.
Implementation of Hazard Identification, Risk Assessment, and Determining Control (Hiradc) System for Hazard Risk Management in Mining Work Environment at PT XYZ Sadewa; Hafid Syaifullah
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.v4i2.783

Abstract

The mining industry is known as a high-risk sector, which often faces challenges in identifying the causes of work accidents. This research focuses on the implementation of the Hazard Identification, Risk Assessment, and Determining Control (HIRADC) system at PT XYZ, especially at the gold mine. The aim of this research is to improve work safety through hazard identification and effective risk management. The method used includes observation and descriptive analysis of secondary data from the Occupational Health and Safety (K3) department. The research results show that there are 9 potential dangers, with 6 of them in the extreme risk category and the other three in the high category. Through the implementation of the 50 proposed control measures, the risk level was successfully reduced to a safer category. These findings emphasize the importance of implementing HIRADC to reduce work accidents and support operational sustainability in the mining industry.
Application of the K-Means Algorithm in Enhancing the Clustering Model for Job Seekers in Cirebon City Laylatunna'imah; Martanto; Arif Rinaldi Dikananda; Ahmad Rifa’i
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.v4i2.785

Abstract

The development of information technology opens up opportunities to improve the efficiency of job search through the grouping of job seekers based on specific characteristics. This study uses the K-Means algorithm to analyze data on job seekers in Cirebon City for 2018–2022, focusing on education level and gender. The stages of the research include (1) data selection, (2) data preprocessing, (3) transformation of attributes into numerical format, (4) data grouping using RapidMiner, and (5) evaluation of clustering results using the Davies-Bouldin Index (DBI). The results showed that the optimal number of clusters was two (K=2), with a DBI value of 0.608 which indicates good cluster separation. The first cluster consists of job seekers with a higher level of education, while the second cluster has a lower level of education. Gender did not show a significant influence. These findings provide strategic insights for governments and companies in developing data-driven policies, such as more effective training or recruitment programs. The K-Means algorithm has proven its potential in supporting strategic decision-making in workforce management and being adaptable to other regions.
Analysis the Level of Passenger Satisfaction with Community Services at Terminal Type A Purabaya using Service Quality Methods Vanesha N.M Simanjuntak; Dira Ernawati
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.v4i2.787

Abstract

Terminal Tipe A Purabaya is an institution providing various types of land transportation services under the Ministry of Transportation. The main issue faced by the Type A Purabaya Terminal lies in improving the quality of public service to meet expectations and enhance passenger satisfaction. The Service Quality (Servqual) method is employed, with validity and reliability testing conducted using SPSS software. Data analysis reveals a gap between the actual performance experienced by passengers and their expectations regarding services at the Type A Purabaya Terminal. Based on the calculations, it can be concluded that there is a discrepancy between service expectations and the actual performance at the terminal. The analysis identifies nine attributes with negative gap values, indicating that the services received by passengers remain unsatisfactory. The attributes requiring improvement include bus departure and arrival schedules, staff knowledge of routes and schedules, the accuracy of information provided by staff, facilities for the elderly and disabled, as well as various other aspects related to comfort, safety, and cleanliness of the facilities. Through the Service Quality analysis, the Type A Purabaya Terminal can prioritize service attributes that need enhancement and design effective improvement strategies. This will ensure continuous service improvements aligned with passenger expectations.
Analysis of Passenger Satisfaction Levels Using the K-Means Cluster and Hierarchial Cluster Methods in Purabaya Sidoarjo Type A Terminal Services Zahra Khania 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.v4i2.788

Abstract

Purabaya Type A Terminal is one of the terminals that provides public transportation facilities in the form of bus transportation ranging from city buses, intra-provincial intercity buses, and inter-city inter-provincial buses. This study aims to analyze the level of passenger satisfaction with the service at the terminal and group each variable into a homogeneous group. The research methods used are the K-means Cluster and Hierarchial Cluster methods. The data from the questionnaire will be grouped into several groups that have relatively homogeneous properties using the help of SPSS software. From the results of the study, the output of Non-Hierarchial clusters was obtained in the form of Initial Cluster Centers, Iteration History, Final Cluster Centers, ANOVA, and Number of Cased In Each Cluster. Meanwhile, the output of the Hierarchial cluster is in the form of Case Processing Summary and Dendogram Using Average Linkage. Through analysis using the cluster method, all variables were obtained including in cluster 1 and none were included in cluster 2, the iterations carried out were 2 times and the valid data value was 100 data with a missing value of 0.

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