Article Per Year (5 Year)
p-Index From 2020 - 2025
7.806
P-Index
This Author published in this journals
All Journal International Journal of Electrical and Computer Engineering Indonesian Journal of Electrical Engineering and Informatics (IJEEI) JITK (Jurnal Ilmu Pengetahuan dan Komputer) International Journal of Computing Science and Applied Mathematics INTECOMS: Journal of Information Technology and Computer Science Jurnal Nasional Pendidikan Teknik Informatika (JANAPATI) JSAI (Journal Scientific and Applied Informatics) Jurnal Ilmu Teknik dan Komputer Journal of Computer Science and Engineering (JCSE) Jurnal Informatika Teknologi dan Sains (Jinteks) Jurnal Sistem Informasi Dan E-Bisnis Jurnal Ilmu Komputer Dan Informatika Sinergi: Jurnal Pengabdian Kepada Masyarakat JPMTT (Jurnal Pengabdian Masyarakat Teknologi Terbarukan) JCOSIS (Journal Computer Science and Information Systems)
Ayumi, Vina
Universitas Mercu Buana
Agriculture, Biological Sciences & Forestry Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice Mathematics Social Sciences

Published : 61 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search
Journal : JCOSIS (Journal Computer Science and Information Systems)

 1 
Implementasi Metode Color Blob Detection Pada Objek Daun Sawi Wahyu Adianto; Dwika Putra, Erwin; Handrie Noprison; Vina Ayumi; Marissa utami; Mariana Purba
JCOSIS (Journal Computer Science and Information Systems) Vol. 1 No. 1 (2024): Mei
Publisher : Institute for Research and Community Service

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61567/jcosis.v1i1.176

Abstract

Tujuan : Sistem aplikasi Mendeteksi Objek Daun Sawi dengan menerapakan Metode Color Blob Detection menggunakan Bahasa pemrograman matlab. Mekanisme pembuatan aplikasi dimulai dari pembuatan rancangan aplikasi, GUI (Grafik User Interface) sampai dengan pembuatan coding. Metode/Design/Pendekatan: model deteksi Objek Daun Sawi dengan menerapakan Metode Color Blob Detection menggunakan Bahasa pemrograman matlab Hasil/Temuan: Hasil pengujian yang dilakukan dengan memiliki tingkat keakuratan paling tinggi yaitu dengan jarak pengambilan sampel objek dengan jarak 50 cm, dan tingkat keakuratan paling rendah dengan jarak pengambilan sampel objek dengan jarak 20 cm. Kebaharuan/Originalitas/Nilai: Tingkat akurasi deteksi daun sawi maka dapat disimpulkan berhasil dengan tingkat keberhasil akurasi 67.7% Keywords: Color Blob Detection, Image Processing, Matlab
Best Selling Product Sales Prediction Using K-Nearest Neighbors (KNN) Algorithm Marissa Utami; Vina Ayumi
JCOSIS (Journal Computer Science and Information Systems) Vol. 1 No. 2 (2024): Oktober
Publisher : Institute for Research and Community Service

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61567/jcosis.v1i2.214

Abstract

Purpose: This research aims to evaluate the performance of the K-Nearest Neighbor (KNN) algorithm in predicting product sales based on historical data. The data includes product price information, sales amount in the previous month, product category, promotion, and seasonal factors. This research involves several experiments with various K values and the application of data normalization to optimize prediction accuracy. Methods/Study design/approach: The KNN algorithm was chosen for its simplicity and ability to handle multivariate data. Results/Findings: The results show that the value of K=7 is the optimal parameter for this dataset, with a Root Mean Squared Error (RMSE) value of 110. Normalizing the data is proven to improve the model's accuracy, reducing the RMSE value by about 10% compared to the unnormalized data. Product price, previous sales amount, and promotion features significantly contribute to sales prediction. Novelty/Originality/Value: This research is expected to provide insight for companies that want to use machine learning to predict product sales and support business decision-making.
Co-Authors Abd Rahman Abrar, Muhammad Fauzan Angga, Tyas Dwi Anita Ratnasari Anzalna, Akbarakah Bagus Priambodo Cahyo Pambudi Darmo Desi Ramayanti Desmon, Johanes Devi Fitrianah Dhamanti, Inge Dianing Asri, Sri ferdi Fitri Yakub Hadiguna Setiawan Handrie Noprison Hong Wei Ida Farida Ida Nurhaida Ida Nurhaida Ida Nurhaida Inge Handriani, Inge Iryani, Lemi Jumaryadi, Yuwan Laksono Trisnantoro Mailana, Siska Mariana Purba Mariana Purba Naufal, Muhamad Fahri Nengsiana, Nengsiana Noprisson, Handrie Nur Ani PAISAL PAISAL Purba, Mariana Putra, Erwin Dwika Remmy A. M. Zen Salamah, Umniy Sarwati Rahayu Sri Dianing Asri Tjahjono, Benny Uus Rusmawan, Uus Wachyu Hari Haji Wachyu Hari Haji Wahyu Adianto Wina Firly, Tiara Yogga Herlambang, Helwy