Article Per Year (5 Year)
p-Index From 2020 - 2025
3.826
P-Index
This Author published in this journals
All Journal Jurnal Informatika dan Rekayasa Perangkat Lunak ATRABIS: Jurnal Administrasi Bisnis (e-Journal) Jurnal Cahaya Mandalika Jurnal Teknologi Sistem Informasi dan Sistem Komputer TGD MANAJEMEN Bulletin of Science Education Jurnal Akuntansi dan Keuangan Prosiding Seminar Nasional Sisfotek (Sistem Informasi dan Teknologi Informasi) Journal of Artificial Intelligence and Engineering Applications (JAIEA) Jurnal Hukum Prioris Journal of Informatics, Electrical and Electronics Engineering AMMA : Jurnal Pengabdian Masyarakat Fundamental and Applied Management Journal JURNAL RUMPUN MANAJEMEN DAN EKONOMI MERDEKA: Jurnal Ilmiah Multidisiplin Ekliptika: Jurnal Inovasi Teknologi Berkelanjutan International Journal of Economics and Management Research
Rudi Kurniawan
Unknown Affiliation
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 Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Materials Science & Nanotechnology Mathematics Nursing Social Sciences Other

Published : 29 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 29 Documents
Search

 1   2   3 
Pengaruh Iklan di Media Sosial Terhadap Keputusan Peserta Didik Dalam Memilih Kampus LP3I (Survey Pada Mahasiswa Kampus LP3I Tasikmalaya, Karawang dan Cirebon) Rudi Kurniawan
ATRABIS: Jurnal Administrasi Bisnis (e-Journal) Vol 5 No 2 (2019): ATRABIS: Jurnal Administrasi Bisnis - Desember 2019
Publisher : Program Studi Administrasi Bisnis POLITEKNIK LP3I BANDUNG

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.38204/atrabis.v5i2.241

Abstract

Tujuan penelitian ini adalah untuk mengetahui dan menganalisispengaruh personal relevance, interactivity, message, brand familiarity dan testimonial terhadap keputusan peserta didik dalam memilih kampus LP3I. Metode penelitian yang digunakan adalah metode deskriptif dan verifikatif dengan jenis penelitian survey. Data diperoleh memlalui instrument penelitian berupa kuestioner, wawancara, observasi dan studi dokumen. Jumlah saampel dalam penelitian ini adalah 331 orang peserta didik kampus LP3I ( Tasikmalaya, Karawang dan Cirebon). Dengan menggunakan analisis regresi linear berganda, ditemukan bahwa secara parsial personal relevance, interactivity, message, brand familiarity dan testimonial mempunyai pengaruh signifikan terhadap keputusan peserta didik dalam memilih Kampus LP3I. Disamping itu, secara simultan personal relevance, interactivity, message, brand familiarity dan testimonial memiliki pengaruh yang signifikan terhadap keputusan peserta didik dalam memilih Kampus LP3I.
Analisis Data Stok Alat Kesehatan menggunakan Metode Regresi Linier Berdasarkan Nilai RMSE Trian Nurmansyah; Rudi Kurniawan; Yudhistira Arie Wijaya
Jurnal Informatika dan Rekayasa Perangkat Lunak Vol 6, No 1 (2024): Maret
Publisher : Universitas Wahid Hasyim

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36499/jinrpl.v6i1.10275

Abstract

Inventory of healthcare equipment, whether in hospital or clinic settings, represents a significant investment requiring substantial cost allocation. However, estimating these equipment needs often relies solely on the overall available stock, as monthly or yearly requirements tend to fluctuate. Consequently, this approach leads to an inability to meet all necessary equipment needs, resulting frequently in surplus inventory. Therefore, anticipating this issue requires predicting healthcare equipment stock at Klinik Pembina Sehat. This study aims to forecast equipment stock using the linear regression algorithm method. The selection of this algorithm is due to its suitability in handling the linear relationship between dependent and independent variables. Research findings demonstrate the developed model's ability to predict healthcare equipment stock with a reasonably high level of accuracy, with a Root Mean Square Error (RMSE) value of 93.359. This value signifies a relatively low prediction error, indicating the model's precision in estimating stock requirements. Thus, this research holds the potential to enhance operational efficiency in managing healthcare equipment stock within the clinic and serves as a foundation for further studies to improve stock planning processes in similar healthcare institutions.
Aplikasi Pembuatan Form Ekspor Pajak Berbasis Web Di PT. XYZ Zen Munawar; Dadang Sudrajat; Rudi Kurniawan; Ajudin; Novianti Indah Putri
Prosiding SISFOTEK Vol 7 No 1 (2023): SISFOTEK VII 2023
Publisher : Ikatan Ahli Informatika Indonesia

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

Abstract

PT. XYZ Indonesia is a subsidiary of M Inc which operates in the fashion doll sector which produces children's toys such as Barbie dolls and Hot Wheels toy cars. This company still uses Microsoft Excel to create Tax Export Forms, but this system still has weaknesses because it takes a long time and is less efficient. The aim of this research is to determine system constraints and the solutions needed to overcome these constraints. The research was conducted using the SDLC or Software Development Life Cycle methodology with a waterfall model. The data collection techniques used were literature studies, field studies and interviews. The result of the research carried out is the design of a Web-Based Export Tax Form application using the Asp.Net MVC framework. With this system it is hoped that it can improve the performance of Finance staff.
KEPASTIAN HUKUM WILAYAH KERJA PENGUSAHAAN PANAS BUMI EX KONTRAK OPERASI BERSAMA DIENG DAN PATUHA DALAM KERANGKA PERIZINAN PANAS BUMI DI INDONESIA: Legal Certainty Of Geothermal Concession Working Area Ex Joint Operation Contract Of Dieng And Patuha In The Framework Of Geothermal Licensing In Indonesia Defrizal, Defrizal; Zaenal Muttaqien; Rudi Kurniawan
Jurnal Hukum PRIORIS Vol. 11 No. 1 (2023): Jurnal Hukum Prioris Volume 11 Nomor 1 Tahun 2023
Publisher : Faculty of Law, Trisakti University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25105/prio.v11i1.18718

Abstract

Legal certainty in the area of geothermal energy exploitation is very important to ensure the sustainability of geothermal operations in Indonesia. One interesting case to be analyzed is the joint operation contract in the Dieng and Patuha areas. Legal certainty regarding geothermal licensing in these areas has a significant impact on investment and the development of geothermal technology in Indonesia. This study aims to find out the legal status of the Decree of the Minister of Energy and Mineral Resources No. 2789 K/30/MEM/2012 and Minister of Energy and Mineral Resources Decree No. 2192K/30/MEM/2014 reviewed with the licensing theory and legal certainty theory. This study uses a normative legal method with a juridical-normative approach. The results found that the legal product from the geothermal authority in the form of a Decree of the Minister of Energy and Mineral Resources concerning Affirmation of the Working Area for Geothermal Resources Concession has an impact on the absence of legal certainty even though the existing regulations are sufficient to regulate geothermal permits. This should be an urgency to implement laws that apply as a form of guaranteeing absolute legal certainty. Keywords: Geothermal, Licensing, Dieng-Patuha, Legal Certainty, Urgency
Bibliometrik Analisis: Teknologi Permainan Bidang Pendidikan Pada Sekolah Menengah Pertama Rudi Kurniawan; Dadang Sudrajat
Prosiding SISFOTEK Vol 8 No 1 (2024): SISFOTEK VIII 2024
Publisher : Ikatan Ahli Informatika Indonesia

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

Abstract

This study explores the use of game technology to enhance learning motivation and engagement among junior high school students in Indonesia. The background of the problem indicates low levels of learning motivation and student engagement in conventional learning processes. The root of this problem is linked to traditional teaching methods that are less engaging for students. This study aims to evaluate the effectiveness of game technology in addressing this issue. The research method employed a mixed methods approach involving a literature review, the development of educational game modules, and case studies in several junior high schools in Indonesia. The data used includes surveys on students' learning motivation, observations of student engagement, and interviews with teachers. The study also collected qualitative data from students' firsthand experiences in using game technology in learning. The results of the study demonstrate that the integration of game technology into the junior high school curriculum significantly increases students' learning motivation and engagement. Students who used educational games showed increased interest in the subject matter, were more active in class participation, and had a better understanding of the concepts taught. This study concludes that game technology is an effective tool for improving the quality of education in junior high schools and recommends a broader adoption of this technology in the Indonesian education system.
Bibliometrik Analysis: Konten Video Untuk Meningkatkan Daya Tarik Pariwisata Arif Rinaldi Dikananda; Dadang Sudrajat; Fatihanursari Dikananda; Rudi Kurniawan; Martanto
Prosiding SISFOTEK Vol 8 No 1 (2024): SISFOTEK VIII 2024
Publisher : Ikatan Ahli Informatika Indonesia

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

Abstract

The use of video content as a marketing tool in the tourism industry has seen a significant increase in recent years. This research aims to explore and develop effective video content strategies in increasing tourism appeal and influencing tourists' decisions to visit certain destinations. Research methods include bibliometric analysis of video content used in tourism marketing, as well as experiments to test the effectiveness of various video content strategies. The results of the study show that the characteristics of travel vlogs that include personal narratives, attractive visuals, and relevant information can increase user travel intentions. Additionally, audience engagement through short videos has proven to be a key factor in increasing travel interest. This research makes a new contribution in understanding the role of video content in tourism marketing and developing a video marketing strategy model that can be applied by the tourism industry to increase the attractiveness of tourist destinations. By utilizing the results of this study, the tourism industry can optimize the use of video content to reach a wider audience and increase positive perceptions of tourist destinations.
Optimalisasi Algoritma K-Means untuk Analisis pengelompokan Data Jurusan Siswa Baru Berbasis Numerical Measure Mahda, Muhammad; Rudi Kurniawan; Tati Suprapti
Jurnal Teknologi Sistem Informasi dan Sistem Komputer TGD Vol. 8 No. 1 (2025): J-SISKO TECH EDISI JANUARI
Publisher : STMIK Triguna Dharma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53513/jsk.v8i1.10599

Abstract

Dalam analisis pengelompokan data, algoritma K-Means adalah teknik yang umum digunakan. Karena memengaruhi kualitas pengelompokan, sangat penting untuk memilih jumlah cluster K yang tepat. Tujuan penelitian ini adalah untuk mengoptimalkan algoritma K-Means, yang menggunakan Davies-Bouldin Index (DBI) untuk menilai dua jenis jarak numerik, yaitu EuclideanDistance dan ManhattanDistance, untuk pengelompokan data jurusan siswa baru. KDD (Knowledge Discovery in Database) adalah pendekatan yang digunakan, yang mencakup proses Data Selection, Preprocessing, Transformasi, Data Mining, dan Evaluasi. Dataset jurusan siswa baru dengan cluster K antara 2 dan 10 digunakan untuk eksperimen. Hasil penelitian menunjukkan bahwa EuclideanDistance memiliki pemisahan cluster yang lebih baik daripada ManhattanDistance, dengan nilai DBI terendah (0.603) pada K=2. Hasil ini menunjukkan bahwa Euclidean Distance lebih efektif dalam mengoptimalkan pengelompokan data. Metode ini dapat diterapkan dalam analisis data pendidikan dan bidang lain.
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.
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%
Co-Authors ., Rusliyawati Ajudin Andika Isma Andina Dwijayanti Antoni Zulius Arif Rinaldi Dikananda Budi Santoso Dadang Sudrajat Defrizal Defrizal Dian Prawitasari Diva Aulia Nurfitriani Dwi Eko Eva Yuniarti Utami Fachry Abda El Rahman 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 Kalimatus Saroh Khadeeja Kushariyadi LATIFAH Lisnawati Mahda, Muhammad Mamlu'atul Khoiru Nisa Martanto Maulana Ridwan Mochammad Imron Awalludin Muhammad Iqbaludin Nabila Salsa Azzahra Nadia Ayu Majiid Nafhan Khairuddin Fathin Novianti Indah Putri NUR HAMIDAH Nurlaila Syarfiah Asfo Nurul Fauziah Nurwijayanti Oktoriza, Linda Ayu Prima Vandayani Riri Narasati Ryan Hamonangan Saeful Amri Saeful Anwar Salsa Filiya Santi Nurjulaiha Setiyo Adi Wibowo Shinta Virgiana Suhita Whini Setyahun Suteja Syaiful Imanudin Tati Suprapti Tirta, M Sandy Trian Nurmansyah Tuti Nuriyati Umi Hayati Windi Herlita Vidila Yudhistira Arie Wijaya Yunia Eka Putri Yuyun Taufik Zen Munawar Zul Fahnur Zakaria Zulfa Hana Aqliyah