Article Per Year (5 Year)
p-Index From 2020 - 2025
0.751
P-Index
This Author published in this journals
All Journal Jurnal Penelitian Sistem Informasi Jurnal Penelitian Teknologi Informasi dan Sains Polygon: Jurnal Ilmu Komputer dan Ilmu Pengetahuan Alam Neptunus: Jurnal Ilmu Komputer dan Teknologi Informasi
Nazwa Alya Faradita
Unknown Affiliation
Computer Science & IT Decision Sciences, Operations Research & Management

Published : 4 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 4 Documents
Search

 1 
Desain Sistem Pengadaan Barang Inventaris dengan Pendekatan SDLC dan Waterfall Nazwa Alya Faradita; Warda Hamidah; Armansyah Armansyah
JURNAL PENELITIAN SISTEM INFORMASI (JPSI) Vol. 2 No. 2 (2024): MEI : JURNAL PENELITIAN SISTEM INFORMASI
Publisher : Institut Teknologi dan Bisnis (ITB) Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54066/jpsi.v2i2.1791

Abstract

Procurement of goods, tools, and production materials within a company is a crucial process in maintaining smooth operations and efficiency. However, there are often challenges in managing the procurement process, such as lack of transparency, administrative complexity, and lack of integration between the procurement system and the inventory system. This research aims to develop a procurement application for goods, tools, and production materials using the System Development Life Cycle (SDLC) and Waterfall approach to ensure a structured development process. The research method involved observations and interviews to understand user needs as well as analysis of the existing system. The results of this research summarize the design of an application that improves the procurement process, increases transparency, and strengthens the integration between procurement and inventory management. This application is expected to contribute to improving the efficiency of procurement management in a corporate environment.
Deteksi Tingkat Kematangan Buah Tomat Dengan Transformasi Ruang Warna HSI Supiyandi Supiyandi; Arizka Anggraini; Warda Hamidah; Nazwa Alya Faradita; Adisty Maysandra
Jurnal Penelitian Teknologi Informasi dan Sains Vol. 2 No. 2 (2024): JUNI : JURNAL PENELITIAN TEKNOLOGI INFORMASI DAN SAINS
Publisher : Institut Teknologi dan Bisnis (ITB) Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54066/jptis.v2i2.2095

Abstract

Among the vegetables most commonly consumed by people around the world are tomatoes. One of the potential vegetable commodities to be developed is tomato plants. This plant can thrive in rice fields, dry land, and highlands. Use of Technology Digital images are images that can be processed by computers directly. A matrix with M columns and N rows can be used to describe a digital image. The smallest element in an image is called a pixel or image element, and is the intersection between columns and rows. image processing is the process of processing an image numerically; in this case, each pixel or point in the image is treated. One method of image processing is to use computer software to process each pixel in the image. It is easier for object recognition applications in image processing to identify objects based on differences in hue values when the hue values of objects are limited to a certain value. The color space system that mimics the capabilities of the human eye is called the HSI color space model. HSI incorporates the grayscale or color components of an image. The test image of Tomato fruit with a value of H = 32 S = 0.675 I = 83 can be considered ripe, according to the range of fruit reference values that have been established through the use of the HSI method.
Identifikasi Tingkat Kematangan Buah Tomat Melalui Warna dengan Penerapan Jaringan Saraf Tiruan (JST) Nazwa Alya Faradita; Lailan Sofinah Harahap
Polygon : Jurnal Ilmu Komputer dan Ilmu Pengetahuan Alam Vol. 2 No. 6 (2024): November : Polygon : Jurnal Ilmu Komputer dan Ilmu Pengetahuan Alam
Publisher : Asosiasi Riset Ilmu Matematika dan Sains Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62383/polygon.v2i6.292

Abstract

The selection of agricultural and plantation products often relies on human perception of fruit color. Manual identification through visual observation has several drawbacks, such as time consumption, fatigue, and varying perceptions of quality. Digital image processing technology enables automatic sorting of products. This study applies the Perceptron learning method to identify tomato ripeness. Tomato images are captured using a webcam, analyzed through color histograms, and identified using artificial neural networks. The identification success rate reaches 43.33%, with outputs categorized as Unripe (10%), Half-Ripe (6.66%), and Ripe (26.66%).
Klasifikasi Bobot Telur Ayam Ras menggunakan Visi Komputer dan Segmentasi Citra Supiyandi Supiyandi; Warda Hamidah; Nazwa Alya Faradita; Arizka Anggraini; Adisty Maysandra
Neptunus: Jurnal Ilmu Komputer Dan Teknologi Informasi Vol. 3 No. 1 (2025): Februari : Neptunus : Jurnal Ilmu Komputer Dan Teknologi Informasi
Publisher : Asosiasi Riset Teknik Elektro dan Informatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61132/neptunus.v3i1.586

Abstract

This study aims to classify chicken eggs based on their physical size using the concept of computer vision and image segmentation techniques. Compared to the standard methods that have been used so far, this alternative technology is expected to help standardize measurements, cost efficiency, and work effectiveness. In this study, the classification of chicken eggs was carried out using image segmentation and regression analysis. Thus, it is expected that the classification of chicken eggs will have increasingly accurate values. After the image is taken using a webcam, the image segmentation process is used to divide the image into homogeneous areas based on the RGB (true color) color intensity similarity standard. Regression analysis is used to study and measure the relationship between the number of pixels and the weight of the object. The number of pixels indicating the area of ​​the object is the result of image segmentation, which will be entered into the regression equation to calculate the weight (grams). The results showed that the color characteristics of chicken eggs have a normalization of R at least 0.41 and a normalization of G at least 0.3. In addition, the classification test has an accuracy of 100% (36/36) and a weight estimation accuracy of 42 percent (15/36).
Co-Authors Adisty Maysandra Arizka Anggraini Armansyah Armansyah Lailan Sofinah Harahap Supiyandi Supiyandi Warda Hamidah