Article Per Year (5 Year)
p-Index From 2020 - 2025
0.444
P-Index
This Author published in this journals
All Journal Building of Informatics, Technology and Science Jurnal Pengabdian Kepada Masyarakat Bersinergi Inovatif
Prasetyaningrum, Putri
Unknown Affiliation
Computer Science & IT Education Other

Published : 2 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search

 1 
MEMPERKUAT BISNIS LOKAL DENGAN PEMBUATAN WEBSITE UNTUK UMKM DAPUR MAMA GEA Setyaningsih, Putry; Susilawati, Indah; Prasetyaningrum, Putri
Jurnal Pengabdian Kepada Masyarakat Bersinergi Inovatif Vol. 2 No. 1 (2024): Jurnal Pengabdian Kepada Masyarakat Bersinergi Inovatif
Publisher : PT. Gelora Cipta Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Perusahaan kini memanfaatkan teknologi berbasis website sebagai strategi untuk memperkenalkan usaha dan menawarkan produk kepada konsumen tanpa batasan ruang dan waktu. Dari perusahaan besar hingga usaha kecil dan menengah, sistem penjualan online menjadi pilihan utama karena biaya promosi yang terjangkau. Dengan menggunakan website untuk promosi, toko-toko konvensional dapat memanfaatkan media ini untuk memperkenalkan produk mereka. Salah satu Usaha Mikro Kecil dan Menengah (UMKM) yang berpotensi dikembangkan adalah usaha kuliner Dapur Mama Gea di Banguntapan. Usaha kuliner ini memiliki peluang besar karena makanan merupakan kebutuhan pokok yang banyak dicari oleh konsumen dari berbagai kalangan ekonomi.
Perbandingan Metode TF-IDF dan Bag of Words dalam Analisis Sentimen Diet Kopi Americano di Media Sosial Twitter Menggunakan Naïve Bayes Suryanti, Rahmatika; Prasetyaningrum, Putri
Building of Informatics, Technology and Science (BITS) Vol 7 No 1 (2025): June (2025)
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v7i1.7244

Abstract

The popularity of diet coffee, particularly the Americano variant, has risen alongside the growing trend of healthy lifestyles in society. This phenomenon has led to various public opinions circulating on social media, which need to be analyzed to better understand consumer perceptions. This study compares two commonly used text feature representation methods, Term Frequency-Inverse Document Frequency (TF-IDF) and Bag of Words (BoW), in sentiment analysis using the Naïve Bayes algorithm. Using relevant keywords, data were collected from Twitter and underwent preprocessing stages including case folding, cleansing, tokenizing, stopword removal, and stemming. Sentiment labeling was conducted manually based on keyword indicators, and the data were classified into positive, negative, and neutral categories. The evaluation results show that the TF-IDF model achieved an accuracy of 85%, outperforming BoW which obtained 64%. This performance gap indicates that the choice of feature representation method plays a crucial role in the success of sentiment classification. This research is expected to serve as a reference for optimizing text representation techniques to analyze public opinion on social media, particularly concerning diet products and low-calorie beverages.
Co-Authors Indah Susilawati Setyaningsih, Putry Suryanti, Rahmatika