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All Journal Jurnal technoscientia Infotech Journal Jurnal Informasi dan Komputer Jurnal Informatika dan Rekayasa Perangkat Lunak INTERNAL (Information System Journal) JURNAL TEKNOLOGI TECHNOSCIENTIA Journal of Artificial Intelligence and Engineering Applications (JAIEA) Journal of Scientech Research and Development Jurnal Teknik Mesin, Industri, Elektro dan Informatika AMMA : Jurnal Pengabdian Masyarakat NERO (Networking Engineering Research Operation) INTERNAL (Information System Journal)
Nana Suarna
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Humanities Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Chemistry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice Materials Science & Nanotechnology Mathematics Mechanical Engineering Nursing Social Sciences Other

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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.
K-Means Clustering Method to Make Credit Payment Groupinhg Efficient Siti Nur Illah; Nana Suarna; Irfan Ali; Dodi Solihudin
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.v4i2.815

Abstract

Credit payment management is one of the main challenges in the financial sector, especially in grouping customers based on risk and payment patterns. This study aims to evaluate the K-Means Clustering method in improving the efficiency of credit payment data clustering. The dataset used includes information on payment history, loan amount, tenor, and credit status from financial institutions. The research approach involves data processing stages, application of the K-Means algorithm, and evaluation of results using the Davies-Bouldin Index and Silhouette Score metrics. The results of the analysis show that the K-Means method is effective in identifying customer payment patterns and dividing them into three main clusters: high, medium, and low risk. In addition, this study found that determining the optimal number of clusters using the Elbow Method can improve the accuracy of the clustering results. The resulting model makes a significant contribution to credit risk management, helping financial institutions make strategic decisions related to credit policies and risk mitigation. This study offers practical implications, including increased operational efficiency and predictive ability against potential bad debts. Further studies are recommended to integrate this method with other algorithms to improve the performance of large-scale data analysis.
Pengembangan Aplikasi Informasi Posyandu dalam Meningkatkan Layanan Kesehatan Ibu dan Anak Nana Suarna; Nining Rahaningsih; Euis Fadilah; Farah Nur Farida
AMMA : Jurnal Pengabdian Masyarakat Vol. 1 No. 04 (2022): AMMA : Jurnal Pengabdian Masyarakat
Publisher : CV. Multi Kreasi Media

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

This Community Partnership Program aims to develop a Posyandu information system application to improve the efficiency and effectiveness of maternal and child health services. This application is designed to facilitate Posyandu officers in managing patient data, recording health histories, monitoring child development, and providing relevant health information. The application development includes needs analysis, user interface (UI) design, implementation of key features, and application usage training for Posyandu officers. It is expected that with this application, the quality of health services at Posyandu can be improved, and health information access for mothers and children can be facilitated.
Optimizing the Social Assistance Recipient Model in CangkringVillage Using the Naïve Bayes Algorithm Rotika; Nana Suarna; Irfan Ali; Dendy Indriya Efendi
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.v4i2.849

Abstract

Social assistance is one of the methods used by the government to help the underprivileged. Cangkring Village is a village in Cirebon Regency that has inaccurate data on recipients of social assistance or underprivileged people. The Naive Bayes algorithm is one of the most effective techniques in machine learning for classifying data, in determining the eligibility of recipients of social assistance. The method works with a probabilistic approach to analyze data efficiently and accurately, can group data based on attributes and produce high accuracy. The problem in Cangkring Village, namely the accuracy of data on recipients of social assistance, is still a problem that requires special attention. This inaccuracy not only reduces the effectiveness of social assistance programs but also creates injustice for people in need. Invalid and inappropriate data causes the distribution of social assistance to be suboptimal. The purpose of this study is to optimize the accuracy model of social security recipients using the Naive Bayes algorithm, which can help improve the accuracy in determining eligible recipients.The method used in the study is secondary data processing taken from social assistance recipient data in Cangkring Village. This process includes data preprocessing stages, training and testing data distribution, and implementation or application of the Naive Bayes algorithm to perform classification. The results of the study show that the Naive Bayes algorithm is able to increase the accuracy of the classification of social assistance recipients with an accuracy rate of 90%, compared to the conventional method used previously. This study contributes to providing a more efficient and targeted method in selecting social assistance recipients, so that it can improve the social assistance distribution system in the future. Thus, the Naive Bayes algorithm can be an effective method for data-based decision making in the context of social policy.
Identify Rattan Sales Patterns Using the FP-Growth Algorithm on CV. Busaeri Rattan Robi; Nana Suarna; Irfan Ali; Dendy Indriya Efendi
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.v4i2.877

Abstract

This research was conducted to recognize the pattern of purchasing rattan products at CV. Busaeri Rattan by utilizing the FP-Growth algorithm. The rattan industry is faced with the challenge of understanding consumer habits in order to improve marketing strategies. The FP-Growth algorithm was chosen for its ability to efficiently identify frequent itemset patterns without requiring a lot of memory. This research includes collecting rattan sales transaction data for one year, data preprocessing, FP-Tree structure formation, and frequent itemset analysis. The analysis was conducted using RapidMiner software with a minimum support setting of 0.005 and confidence of 0.1. The processed data was then used to find combinations of products that are often purchased together. The results revealed some significant patterns, such as the products “Mandola 3/4” and “Jawit 8/11,” which are often purchased together with a confidence level of 100%. These findings provide important insights for CV. Busaeri Rattan in increasing sales through promotional strategies such as bundling or discount offers. In addition, the FP-Growth algorithm proved to be faster and more resource-efficient than traditional methods such as Apriori. The discussion shows that the discovered purchasing patterns can help CV. Busaeri Rattan better manage stock, minimize the risk of running out of goods, and design data-driven marketing strategies. The combination of products that are often purchased together can be utilized to improve customer satisfaction as well as operational efficiency. The conclusion of this research is that the FP-Growth algorithm is an effective tool for analyzing large-scale transaction data. Further research is recommended to explore the application of this algorithm to other types of products or compare it with other data mining algorithms.
Co-Authors Aby Febrian Ahmad Rifai Anita Anita Arif Rinaldi Dikananda Ayu Zulhijah Dendy Indriya Efendi Dodi Solihudin Doni Anggara Euis Fadilah Fahmi Sulaiman Farah Nur Farida Gifthera Dwilestari Iin Irfan Ali Musofi Musofi Nining Rahaningsih Puji Hartini Rizal Rayyan Firdaus Robi Rotika Siti Nur Illah Teguh Adrian Willy Prihartono Yudhistira Arie Wijaya