cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
Ardiansyah
Contact Email
ardiansyah@fmipa.unila.ac.id
Phone
-
Journal Mail Official
jurnalpepadun@fmipa.unila.ac.id
Editorial Address
Gedung Ilmu Komputer Fakultas Matematika dan Ilmu Pengetahuan Alam - Universitas Lampung Jalan Soemantri Brojonegoro No.1 Bandarlampung
Location
Kota bandar lampung,
Lampung
INDONESIA
Jurnal Pepadun
Published by Universitas Lampung
ISSN : -     EISSN : 27743403     DOI : https://doi.org/10.23960/pepadun
Core Subject : Science,
Computer Science & IT
Pepadun Journal is a journal to publish research in the fields of computer science, information systems, and informatics to researchers, scientists, and professionals. For every edition published by the Pepadun Journal, we put our effort: Using standard procedures and times for submitted manuscripts, Provide a good editorial service for every submitted manuscript, Attract national and international writers to contribute to submitting quality manuscripts, Managing journals with good quality standards Pepadun is published three times a year by Computer Science Department, University of Lampung. Contributed papers must be original and offer a state-of-the-art contribution. Each manuscript will be peer-reviewed by reviewers in the relevant field ensuring the quality of the publication. Pepadun offers an open-access license (CC-BY), authors retain the copyright.
Arjuna Subject : Ilmu Komputer (semua kategori) - Aplikasi Ilmu Komputer
Articles 113 Documents
 8   9   10   11   12 
Desain Modul Data Konsumen dan Pemasok untuk Sistem Informasi E-Commerce Ultra Mikro Usaha Mikro Kecil dan Menengah Berbasis Website Tasya Aprilla Almuqaramah Yuton; Irwan Adi Pribadi; Yohana Tri Utami
Jurnal Pepadun Vol. 4 No. 1 (2023): April
Publisher : Department of Computer Science, Faculty of Mathematics and Natural Sciences, University of Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23960/pepadun.v4i1.148

Abstract

Given the rapid development of technology, it makes competition in the business world and requires UMKMs (Micro, Small and Medium Enterprises) to keep up with the times, so that they can achieve the desired targets in marketing and so that information is more quickly received by consumers.  During this pandemic, many people lost their jobs and switched to running their own businesses to meet their daily needs so it is possible for people to open Ultra Micro UMKM Businesses with a capital of between 2 to 5 million rupiah. With the number of UMKMs developing in Bandar Lampung City, competition is created in the business world, a strategy for sales is needed that can make it easier for sellers and consumers to buy products. Web-based information systems are one way to compete creatively in the business world. This E-commerce Information System has a database of product sales, product suppliers, product data, and sales reports. The database of product data is products purchased through suppliers, then the product sales database is generated from products that have been sold through online sales carried out locally within the scope of Bandar Lampung City. This research method uses Black-box Testing and has involved 3 examiners and showed a good response to the suitability of UMKM E-Commerce needs.
Penerapan Pengkodean Data Base-64 dan Kode QR Citra Foto Wajah untuk Autentikasi Tanda Tangan Dokumen Digital dengan Library Javascript Hana Afriliza; Febi Eka Febriansyah
Jurnal Pepadun Vol. 4 No. 1 (2023): April
Publisher : Department of Computer Science, Faculty of Mathematics and Natural Sciences, University of Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23960/pepadun.v4i1.149

Abstract

The application of technology has an impact on replacing conventional methods with modern ones, one of which is providing document authentication. Signatures can be used as a solution in authenticating documents because they are considered capable of being used as mathematical proof that the document is not modified illegally. The difference that lies between the signature on digital documents and conventional documents is in terms of security. Signatures on conventional documents are prone to forgery. This is due to the absence of evidence to ensure that the document has been signed by the party concerned. Thus, the use of signatures on digital documents is more secure than on conventional documents. The security that is carried out to ensure its authenticity is by utilizing a QR code which contains an id, photo of the signatory which is encoded first using base-64 coding, and a link which is used to verify the authenticity of the document. The verification process carried out to ensure its authenticity is by scanning the QR code embedded in the document. The library developed using Javascript and Waterfall is used as a development method, and the testing method used is black-box testing.
Penerapan Metode Support Vector Machine (SVM) dalam Klasifikasi Penderita Diabetes Mellitus Fanni Lufiana; Favorisen Rosyking Lumbanraja; Yunda Heningtyas; Kurnia Muludi
Jurnal Pepadun Vol. 4 No. 1 (2023): April
Publisher : Department of Computer Science, Faculty of Mathematics and Natural Sciences, University of Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23960/pepadun.v4i1.150

Abstract

Diabetes Mellitus (DM) is a chronic disease characterized by the body's inability to metabolize carbohydrates, fats, and proteins, resulting in increased blood sugar (hyperglycemia) due to low insulin levels. Diabetes is due to a combination of heredity (genetics) and unhealthy lifestyles. Hemoglobin A1c is a blood test used to diagnose and manage diabetes patients when measuring blood sugar levels. This study aims to analyze predictive models for the classification of people with diabetes using R Shiny and evaluate the results of the support vector machine method's classification performance. There are many ways to diagnose diabetes, and the support vector machine is one of the machine learning algorithms used in this study's classification case (SVM). This study uses data from Diabetes 130-US Hospital For Years 1999-2008, which was sourced from the UCI Machine Learning Repository and consists of 34 variables and 84900 records, with dataset distribution and testing techniques using the 10-fold cross-validation method and three kernels in modeling using SVM, namely linear, Gaussian, and polynomial. The results obtained are a simple predictive model analysis system for classifying people with diabetes with shiny, making it easier for users to find out the prediction results and obtain the highest accuracy result, which is 82.76 percent of the gaussian kernel.

Page 12 of 12 | Total Record : 113

 8   9   10   11   12 

Filter by Year

2020 2023


Reset
Filter By Issues
All Issue Vol. 4 No. 1 (2023): April Vol. 3 No. 3 (2022): December Vol. 3 No. 2 (2022): August Vol. 3 No. 1 (2022): April Vol. 2 No. 3 (2021): December Vol. 2 No. 2 (2021): August Vol. 2 No. 1 (2021): April Vol. 1 No. 1 (2020): December