Putu Desiana Wulaning Ayu
Jurusan Teknik Elektro, Universitas Udayana

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Pendampingan Pengelolaan Stok Barang dan Web Profile Pada PT. Dwi Singatama Putra Pertama, Pande Putu Gede Putra; Pradipta, Gede Angga; Liandana, Made; Ayu, Putu Desiana Wulaning
Journal of Community Development Vol. 3 No. 3 (2023): April
Publisher : Indonesian Journal Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47134/comdev.v3i3.86

Abstract

PT. Dwi Singatama Putra is a company engaged in the mechanical, electrical and engineering sector, which is located in the village of Sambangan, Buleleng Regency, Bali Province. This company was founded in 2020 which is a change from UD. Triple Engineering and CV. Triple Teknik, which was previously established in 2014. This company has problems, namely: the process of recording goods in warehouses related to certain projects is still not properly recorded and even only based on goods purchase notes so that both consumables and business property are not known. exact amount. In addition, marketing is still carried out door to door to customers so that market coverage is still limited. Considering the problem, the solution that is agreed upon and offered to partners is to provide a web-based stock/warehouse system and a web application that contains information on partner profiles. In addition, partners are also given Instagram social media accounts to help market the services offered. The activities that have been carried out are training and introduction to the use of social media marketing through Instagram, managing company profiles through the website, and using the stock system. Evaluation is carried out by means of direct interviews with partners. The results of the evaluation show that service activities can provide benefits for partners.
Pemberdayaan UMKM Tahu Goreng Kremes Di Br. Anyar-Kediri, Tabanan Mahendra, Tubagus; Ayu, Putu Desiana Wulaning; Purnama, I Gusti Agung Vony; Huizen, Roy Rudolf; Pradipta, Gede Angga; Liandana, Made
Sevanam: Jurnal Pengabdian Masyarakat Vol 3 No 2 (2024): September
Publisher : Universitas Hindu Negeri I Gusti Bagus Sugriwa Denpasar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25078/sevanam.v3i2.3840

Abstract

Tofu is rich in protein, making it good for daily consumption. Its delicious taste and affordable price make tofu a favorite food for people from various backgrounds. The increase in soybean prices does not prevent tofu entrepreneurs from continuing their production. Similarly, the reason why community service partners have started their own small independent businesses that produce various flavored fried tofu is that the raw material in the form of tofu is easily obtained and the price is still affordable, even though it has increased. The partners in this community service started their business in 2020. The crispy fried tofu produced by the partners is named Tahu OPPA, employing four employees, and the partners need a medium that can introduce their products and businesses to more people. This can help increase consumer awareness about the existence of the partner's business. Since starting the crispy fried tofu business, the partners have faced obstacles in terms of knowledge and skills to create and manage a website, as well as obstacles in digital marketing strategies to attract customers to make purchases of their products. The activity began with an analysis of the partner's situation, followed by providing assistance to the partners in creating a business profile website. The second activity continued with providing an understanding of digital marketing strategies utilizing social media to encourage consumers to make purchases
Usability and Performance Comparison: Implementation of Tibero and Oracle Databases in the Context of CAMS Software Development Komang Yuli Santika; Hostiadi, Dandy Pramana; Ayu, Putu Desiana Wulaning
Jurnal Nasional Pendidikan Teknik Informatika : JANAPATI Vol. 13 No. 3 (2024)
Publisher : Prodi Pendidikan Teknik Informatika Universitas Pendidikan Ganesha

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23887/janapati.v13i3.82519

Abstract

In the world of software development, the role of database systems is very vital. Enterprise software, designed to handle the complex challenges of large organizations, requires reliable and efficient databases. Oracle, one of the top choices in the industry, stands out with its performance and flexibility. On the other hand, Tibero, a relational DBMS from TmaxSoft, offers the high performance, reliability and scalability required in business environments that require big data management. This research was conducted to analyze the technical side of the Oracle and Tibero databases in the context of the CAMS (Customer Asset Management System) application, with a focus on usability and performance aspects. This research uses the Performance Testing method to evaluate CPU, Memory, Storage resource usage and TPS (Transaction Per Second) of the two databases as well as the System Usability Scale (SUS) to measure user experience. The results provide information to software developers in selecting databases that suit business needs, while contributing to the development of the information technology industry
Comparative Analysis of Augmentation and Filtering Methods in VGG19 and DenseNet121 for Breast Cancer Classification Seneng, I Kadek; Ayu, Putu Desiana Wulaning; Huizen, Roy Rudolf
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 3 (2025): JUTIF Volume 6, Number 3, Juni 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2025.6.3.4397

Abstract

Breast cancer is one of the most prevalent malignancies and a leading cause of mortality among women worldwide. Mammography plays a crucial role in early detection, yet challenges in manual interpretation have led to the adoption of Convolutional Neural Networks (CNNs) to improve classification accuracy. This study evaluates the performance of Visual Geometry Group (VGG19) and Densely Connected Convolutional Networks (DenseNet121) in mammogram classification. It examines the impact of data augmentation and image enhancement techniques, including Contrast-Limited Adaptive Histogram Equalization (CLAHE), Median Filtering, and Discrete Wavelet Transform (DWT), as well as the influence of varying epochs and learning rates. A novel approach is introduced by assessing data augmentation effectiveness and exploring model adaptations, such as layer incorporation and freezing during training. Classification performance is enhanced through fine-tuning strategies combined with image enhancement techniques, reducing reliance on data augmentation. These findings contribute to medical imaging and computer science by demonstrating how CNN modifications and enhancement methods improve mammogram classification, providing insights for developing robust deep learning-based diagnostic models. The highest performance was achieved using VGG19 with DWT, a learning rate of 0.0001, and 20 epochs, yielding 98.04% accuracy, 98.11% precision, 98% recall, and a 97.99% F1-score. Data augmentation did not consistently enhance results, particularly in clean datasets. Increasing epochs from 10 to 20 improved accuracy, but performance declined at 30 epochs. The confusion matrix showed high accuracy for Benign (100%) and Cancer (99.5%), with more misclassifications in the Normal class (94.5%).