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All Journal JURIKOM (Jurnal Riset Komputer) Jurnal Sistem Informasi dan Ilmu Komputer Jurnal Ilmiah Research Student
Dwi Prapita Sari
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Religion Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Electrical & Electronics Engineering Mathematics Medicine & Pharmacology

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Analisis Performa Algoritma Pendeteksian Tepi pada Citra Multispektral Supiyandi Supiyandi; Dinah Makhroza Silalahi; Dwi Prapita Sari; Rosa Prahasti; Donny Dwi Putra
Jurnal Sistem Informasi dan Ilmu Komputer Vol. 2 No. 3 (2024): Agustus : Jurnal Sistem Informasi dan Ilmu Komputer
Publisher : Universitas Katolik Widya Karya Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59581/jusiik-widyakarya.v2i3.3472

Abstract

Multispectral image is a type of digital image that captures spectral information in several channels or bands. Edge detection is one of the basic techniques in image processing which is used to identify the boundaries of objects in an image. This research aims to analyze the performance of several edge detection algorithms on multispectral images. The algorithms tested include the Sobel, Prewitt, Roberts, Canny, and Laplacian of Gaussian (LoG) algorithms. Tests were carried out on high resolution multispectral images from the Landsat-8 satellite. The evaluation metrics used are accuracy, precision, recall, and F1-score. The research results show that the Canny algorithm has the best performance with the highest F1-score compared to other algorithms. Apart from that, this research also analyzes the effect of the number of channels in multispectral images on the performance of edge detection algorithms.
IMPLEMENTASI APLIKASI STOK BARANG PERANGKAT JARINGAN BERBASIS WEB DI PT ZATHCO Inneke putri; Dwi prapita sari; Mhd ikhsan rifki
JURNAL ILMIAH RESEARCH STUDENT Vol. 1 No. 3 (2024): Januari
Publisher : CV. KAMPUS AKADEMIK PUBLISING

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61722/jirs.v1i3.835

Abstract

The system currently used experiences problems in handling recipient and delivery data, customer data, and inventory data which are recorded on paper and only copied by the admin to the company computer. These problems can result in product calculation errors, problems in recording and reporting product recipients and deliveries, and in several months the product in and out can reach the target. There are often differences in inventory. This is caused by a helper or admin error. The warehouse department is recording, receiving and sending products. In addition, the accumulation of large numbers of files can make it difficult to find the product data you need, and searching files can take time and interfere with other tasks. The aim of this research is to develop an inventory management application that can manage recipient or delivery data, inventory data, and delivery data using visual modeling used in building object-oriented systems and waterfall system development methods. This is about developing a website that is created to simplify incoming goods data and outgoing goods data so that it can help business processes in the company.
Perbandingan Kinerja Identifikasi Model VGG-19 Dengan Inception V3 Dalam Klasifikasi Penyakit Appendicitis Dwi Prapita Sari; Ilka Zufria
JURNAL RISET KOMPUTER (JURIKOM) Vol. 13 No. 1 (2026): Februari 2026
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v13i1.9540

Abstract

Appendicitis is a surgical emergency that requires rapid and accurate diagnosis. However, limitations in ultrasound (USG) image interpretation often pose a risk of misdiagnosis, particularly in scenarios with limited medical data. This study aims to determine the most effective classification model for a clinical decision support system by comparing two transfer learning-based Convolutional Neural Network (CNN) architectures: VGG-19 and InceptionV3. Utilizing a dataset of 2,168 images split into 70% training, 10% validation, and 20% testing data, the models were evaluated using metrics such as accuracy, precision, recall, F1-score, and Area Under Curve (AUC). The results demonstrate that InceptionV3 delivered significantly superior performance, achieving an accuracy of 0.9033%, an F1-score of 0.8946% for the appendicitis class, and an AUC of 0.9502%. In contrast, VGG-19 only reached an accuracy of 0.8255%, with a recall for the appendicitis class as low as 0.8019%. The poor recall performance of VGG-19 indicates a high risk of missed diagnosis. This research contributes by recommending a more reliable and effective model to support AI-based appendicitis identification, specifically in limited data scenarios.
Co-Authors Dinah Makhroza Silalahi Donny Dwi Putra Ilka Zufria Inneke Putri Mhd Ikhsan Rifki Rosa Prahasti Supiyandi Supiyandi