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All Journal Jurnal Informatika dan Rekayasa Perangkat Lunak ATRABIS: Jurnal Administrasi Bisnis (e-Journal) Jurnal Teknologi Sistem Informasi dan Sistem Komputer TGD MANAJEMEN Bulletin of Science Education Prosiding Seminar Nasional Sisfotek (Sistem Informasi dan Teknologi Informasi) Journal of Artificial Intelligence and Engineering Applications (JAIEA) Jurnal Hukum Prioris Jurnal Ilmiah MEA (Manajemen, Ekonomi, dan Akuntansi) Journal of Informatics, Electrical and Electronics Engineering AMMA : Jurnal Pengabdian Masyarakat JURNAL RUMPUN MANAJEMEN DAN EKONOMI MERDEKA: Jurnal Ilmiah Multidisiplin Journal of Economic and Bussiness Retail Ekliptika: Jurnal Inovasi Teknologi Berkelanjutan
Rudi Kurniawan
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Religion Aerospace Engineering Agriculture, Biological Sciences & Forestry Humanities Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Chemistry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Environmental Science Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice Materials Science & Nanotechnology Mathematics Nursing Social Sciences Other

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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.
Optimizing Naïve Bayes Algorithm Through Principal Component Analysis To Improve Dengue Fever Patient Classification Model Santi Nurjulaiha; Rudi Kurniawan; Arif Rinaldi Dikananda; 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.v4i2.798

Abstract

Dengue fever is an infectious disease that has a significant impact on public health in tropical regions, including Indonesia. Early detection and proper classification of DHF patients is essential to reduce severity and mortality. For this reason, a method that can improve the accuracy in diagnosing this disease is needed. Principal Component Analysis (PCA) and Naïve Bayes (NB) are two commonly used techniques in medical data analysis. PCA is used to reduce the dimensionality of data to reduce complexity, while Naïve Bayes is used for classification of data based on probability. This study aims to optimize the use of PCA and Naïve Bayes in improving the accuracy of the dengue patient classification model. The method used in this study involves processing a medical dataset of dengue patients containing various clinically relevant attributes. The dataset was then processed using PCA to reduce dimensionality and identify key features that affect classification. Next, Naïve Bayes was applied to classify the data based on the selected features. This study compares the performance of classification models that use a combination of PCA and Naïve Bayes with models that only use Naïve Bayes without dimensionality reduction. The results show that the use of PCA in data processing significantly improves the accuracy of the classification model compared to the model that only uses Naïve Bayes. The combination of PCA and Naïve Bayes produces a more efficient model and has a higher accuracy rate in identifying patients with DHF risk. Thus, the application of PCA and Naïve Bayes in the classification of DHF patients can be an effective tool in assisting the medical diagnosis process, which in turn can reduce misdiagnosis and improve patient recovery rates. This research contributes to the development of artificial intelligence technology in the medical field, especially to improve the accuracy of dengue disease diagnosis, and serves as a basis for further research in the use of machine learning techniques in healthcare. This study analyzes the performance of the Naïve Bayes algorithm in classifying dengue fever patient data, by comparing models that use Principal Component Analysis (PCA) as a dimension reduction method and models that do not use it. The results show that the Naïve Bayes model without PCA has an accuracy of 49.96%, which is close to the random guess rate. This finding indicates that the model is less effective in recognizing patterns in the data. In contrast, the application of PCA successfully increased the model's accuracy to 50.03%
Optimizing Email Spam Classification Using Naïve Bayes and Principal Component Analysis Shinta Virgiana; 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.v4i2.803

Abstract

In the ever-evolving digital era, email spam filtering is an important challenge to maintain the security and comfort of email services. The Naïve Bayes algorithm is widely used for spam email classification because of its ability to manage large data, although there are still limitations in terms of accuracy, precision and recall. This research aims to improve spam email classification performance by combining Naïve Bayes and Principal Component Analysis (PCA) to optimize model accuracy and explore optimal parameters in the reduction dimension. The research methodology goes through the Knowledge Discovery in Database (KDD) stages which include selection, preprocessing, transformation using PCA, development of a classification model using Naïve Bayes, and evaluation of model performance. The dataset used consists of emails categorized as spam and non-spam. The experimental results show that the combination of Naïve Bayes and PCA achieves the highest accuracy of 99.24% with 7 principal components. The fixed number of components approach shows better performance compared to preserving variance, emphasizing the importance of selecting appropriate PCA parameters in improving the effectiveness of model classification. This research shows that PCA not only reduces the complexity of the dataset but also increases the efficiency of the classification algorithm.
Usability Scale System Method on Convogenius Platform for MSME Business Optimization Syaiful Imanudin; Rudi Kurniawan; Umi Hayati
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.v4i2.810

Abstract

Usability is a critical factor in the successful adoption of technology, particularly for AI-based platforms designed to support micro, small, and medium enterprises (MSMEs). Convogenius AI is developed to assist MSME operations, yet its effectiveness and user-friendliness must be evaluated. This study aims to assess the usability of the Convogenius AI platform using the System Usability Scale (SUS) method and identify areas requiring improvement. The research employs a SUS survey to measure aspects such as ease of use, functional integration, and the need for technical support. The findings reveal favorable SUS scores for ease of use (average 3.04) and user intention to repeatedly use the platform (average 3.05). However, deficiencies are noted in system complexity (average 2.96) and technical support requirements (average 2.95). Overall, Convogenius AI is accepted by MSME users but requires enhancements in interface design and consistency to improve user experience. These improvements can potentially increase user satisfaction and support the operational efficiency of MSMEs.
Optimizing the Classification Model for Plant Medicine Supplies Using the Decision Tree Algorithm at the Anugrah Tani Shop, Brebes Regency: Inggris Saeful Amri; 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.v4i2.825

Abstract

Retail businesses in the agricultural industry often face difficulties in estimating inventory needs, especially plant medicines which are important for protecting plants from pests and diseases. The lack of an accurate inventory prediction system can cause stock discrepancies, as happened at the Anugrah Tani Store, Brebes Regency, thereby disrupting operations and customer satisfaction. This research uses the Decision Tree classification technique to increase the accuracy of predicting the need for plant medicine supplies, with a clustering approach using the K-Means algorithm to determine the optimal K value through the Davies-Bouldin Index (DBI) calculation. A DBI value of -0.065 indicates good cluster quality with an optimal K of 2, where Cluster 0 has high inventory needs (1138 data) and Cluster 1 has low needs (4 data). The analysis results show that the accuracy level of the Decision Tree model is 98.25%, which is quite high. This model is not only able to predict inventory patterns accurately but also provides in-depth insights to support stock decision making. This research proves that the Decision Tree algorithm can help inventory management with a faster response to customer needs, while contributing to the development of machine learning-based classification models for the agricultural and retail sectors.
Association Analysis of Printing and Photocopying Sales Data in Adzmi Art Shop Cirebon Uses the FP-Growth Algorithm Suteja; Rudi Kurniawan; Yudhistira Arie Wijaya
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.v4i2.830

Abstract

In the digital era, transaction data analysis plays a crucial role in strategic decision-making, especially for SMEs such as Toko Adzmi Art in Cirebon Regency. This study aims to develop a sales data association model using the FP-Growth algorithm to identify product association patterns. Daily transaction data over a year were collected, processed through data cleaning, standardization, and transformation, and analyzed using RapidMiner software. Minimum support and confidence parameters were applied to evaluate the frequency and strength of product relationships. The results show that the combination of "Photocopy" and "Passport Photo" services has a confidence of 0.491 and a support of 0.061, with "Photocopy" as the most in-demand product (support 0.497). These findings open opportunities for bundling strategies and inventory optimization to enhance operational efficiency. This model provides an empirical foundation for SMEs to leverage data mining technology to improve competitiveness and customer satisfaction.
Optimizing Grocery Sales Data Grouping Using the Fuzzy C-Means Algorithm: Case Study of Nafhan Mart Store Nafhan Khairuddin Fathin; 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.v4i2.842

Abstract

The sale of staple food products at Nafhanmart Store, Cirebon Regency, includes essential household items such as rice, cooking oil, sugar, and flour, which maintain stable demand as basic necessities. This study focuses on improving sales clustering models at Nafhanmart using the Fuzzy C-Means (FCM) algorithm, a prominent method in data mining. Key factors influencing sales include price, sales volume, demand, and remaining stock. Accurate clustering analysis is vital for strategic inventory management and profit maximization. The research applies the Knowledge Discovery in Database (KDD) methodology, encompassing data selection, preprocessing, transformation, FCM implementation, and evaluation using the Davies-Bouldin Index (DBI). Attributes analyzed include price, sales volume, demand, and remaining stock. The FCM algorithm clusters data based on patterns, with DBI evaluating clustering quality and determining optimal clusters. Data analysis and visualization were conducted using RapidMiner. Results show that the FCM algorithm achieves optimal clustering quality with a DBI score of 0.452 for two clusters, outperforming three clusters (DBI 0.474) and four clusters (DBI 0.536). Price and demand are identified as critical factors influencing clustering outcomes. These findings enhance the clustering model, offering actionable insights for inventory management and sales strategy, while showcasing the FCM algorithm's adaptability for other SMEs to support data-driven decision-making.
DETERMINASI FAKTOR-FAKTOR KINERJA PERUSAHAAN TERHADAP STRUKTUR MODAL PADA SUB SEKTOR TELEKOMUNIKASI Julia Arum Mustikasari; Rudi Kurniawan; Dian Prawitasari; Fakhmi Zakaria
Jurnal Ilmiah Manajemen, Ekonomi, & Akuntansi (MEA) Vol 9 No 1 (2025): Edisi Januari - April 2025
Publisher : LPPM STIE Muhammadiah Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31955/mea.v9i1.5470

Abstract

Modal merupakan komponen penting dan diperlukan untuk membangun dan menjaga keberlangsungan bisnis dengan mempertimbangkan cara terbaik untuk mengalokasikan dananya agar dapat meningkatkan atau memaksimalkan keuntungan. Maka dari itu penelitian berikut bertujuan untuk memahami determinasi faktor-faktor kinerja perusahaan pada struktur modal pada perusahaan sub sektor telekomunikasi termuat di BEI tahun 2020-2023. Desain pada penelitian yang digunakan yaitu kuantitatif dengan teknik analisis yang dioprasikan melalui aplikasi SPSS 26 dengan metode purposive sampling sejumlah 10 perusahaan sesuai kriteria. Metode analisis yang dipakai adalah analisis deskriptif, uji asumsi klasik, uji koefisienn determinan, uji f, dan uji t. Hasil penelitian ini mengungkapkan secara parsial aktiva struktur berdampak positif pada modal struktur. Sedangkan volatilitas laba, dan TATO secara parsial berdampak negatif. Fleksibilitas keuangan berdampak positif tetapi tidak signifikan pada struktur modal. Secara keseluruhan struktur aktivitas, volatilitas laba, TATO, dan Fleksibilitas keuangan berdampak pada struktur modal. Riset ini memakai data triwulan pada perusahaan telekomunikasi masa Covid-19, sehingga data yang diperoleh lebih optimal karena selama 3 bulan data akan terupdate
Pelaksanaan Promosi Tiktok Ditoko Online Devaira Nabila Salsa Azzahra; Rudi Kurniawan
JURNAL RUMPUN MANAJEMEN DAN EKONOMI Vol. 2 No. 3 (2025): Mei
Publisher : CV. KAMPUS AKADEMIK PUBLISHING

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61722/jrme.v2i3.4714

Abstract

The development of digital technology has revolutionized how small and medium enterprises (SMEs) implement marketing strategies, particularly through social media platforms such as TikTok. This study aims to analyze the TikTok-based content promotion strategy carried out by Toko Devaira, a local fashion brand that has successfully built brand awareness and increased sales through a creative and structured approach. Using a qualitative descriptive method, this research explores the creative process behind promotional content creation, from concept development and production to content distribution using TikTok features such as try-on hauls, dance challenges, and live streaming. The findings indicate that Toko Devaira's promotional strategy reflects the application of Integrated Marketing Communication (IMC) and the AIDA model, successfully generating high engagement through direct interaction with the audience. This study provides practical insights for MSME players in developing relevant, effective, and consumer-experience-based digital marketing strategies.  
MEMBANGUN KARAKTER REMAJA DALAM PERSPEKTIF PENDIDIKAN ISLAM Ferdi Saputra; Tuti Nuriyati; Rudi Kurniawan; Maulana Ridwan
MERDEKA : Jurnal Ilmiah Multidisiplin Vol. 2 No. 5 (2025): Juni
Publisher : PT PUBLIKASI INSPIRASI INDONESIA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62017/merdeka.v2i5.4720

Abstract

The development of adolescent character from the perspective of Islamic education serves as a response to the various moral and social challenges faced by the younger generation in the modern era. Adolescent character, especially religious character, is a crucial aspect in shaping a whole, balanced, and virtuous personality. Using a descriptive qualitative method through literature review, the authors examine Islamic sources such as the Qur’an, Hadith, and scholarly perspectives to formulate effective character-building methods, including role modeling, habituation, advice, and storytelling. Islamic education is regarded as a strong spiritual and moral foundation, and it serves as an integral solution for guiding adolescents to become faithful, responsible, and positively contributing members of society. The authors hope this work provides both theoretical and practical contributions for educators, parents, and educational institutions in implementing Islamic values in the lives of adolescents.
Co-Authors Ajudin Antoni Zulius Arif Rinaldi Dikananda Budi Santoso Dadang Sudrajat Defrizal Defrizal Dian Prawitasari Diva Aulia Nurfitriani Fakhmi Zakaria Fatihanursari Dikananda Ferdi Saputra fikri firmansyah Fitriyana M. Rukin Herry Subagyo Hilda Novia Ramadani Ibnu Fajri Ida Farida iis Riyana Irham Zaini Mubarok Joko Supriyanto Julia Arum Mustikasari Kalimatus Saroh Kekieta Gustie Berizky LATIFAH Lisnawati Mahda, Muhammad Mamlu'atul Khoiru Nisa Martanto Maulana Ridwan Mochammad Imron Awalludin Muhammad Iqbaludin Muttaqien, Zaenal Nabila Salsa Azzahra Nafhan Khairuddin Fathin Novianti Indah Putri NUR HAMIDAH Nurul Fauziah Oktoriza, Linda Ayu Riri Narasati Ryan Hamonangan Saeful Amri Saeful Anwar Santi Nurjulaiha Setiyo Adi Wibowo Shinta Virgiana Suteja Syaiful Imanudin Tati Suprapti Tirta, M Sandy Trian Nurmansyah Tuti Nuriyati Umi Hayati Windi Herlita Vidila Yudhistira Arie Wijaya Yunia Eka Putri Zen Munawar Zulfa Hana Aqliyah