Article Per Year (5 Year)
p-Index From 2021 - 2026
0.659
P-Index
This Author published in this journals
All Journal International Journal Science and Technology (IJST) Jurnal Teknik dan Science (JTS)
Kuwat Setiyanto
Unknown Affiliation
Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Industrial & Manufacturing Engineering

Published : 3 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 3 Documents
Search

 1 
PENERAPAN ANALYTICAL HIERARCHY PROCESS DALAM WEBSITE PEMILIHAN SMARTPHONE XIAOMI Kuwat Setiyanto; Dimas Rizki Dwiyanto
Jurnal Teknik dan Science Vol. 2 No. 3 (2023): Oktober : Jurnal Teknik dan Science
Publisher : Asosiasi Dosen Muda Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56127/jts.v2i3.1201

Abstract

A smartphone is a communication tool that has a multitude of features such as communicating, playing games, photography and so on, this tool has become one of human needs in this digital era. Because of this, many people want to have their dream smartphone. But day by day there are more and more variations, especially many brands competing to be the cheapest with complete features, one of which is the Xiaomi brand. This makes potential buyers confused about choosing a smartphone that suits their needs. A Website-based Decision Support System in Smartphone Selection using the Analytical Hierarchy Process method was created to help find a cellphone that suits the buyer's wishes. In order to choose the right recommendation according to smartphone criteria that suits the buyer's needs,
PEMBUATAN WEB MARTABAK BANGKA QUM MENGGUNAKAN PHP DAN MYSQL Kuwat Setiyanto; Yoga Aprianto
Jurnal Teknik dan Science Vol. 3 No. 2 (2024): Juni : Jurnal Teknik dan Science
Publisher : Asosiasi Dosen Muda Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56127/jts.v3i2.1552

Abstract

Creating the Bangka QUM Martabak Web using PHP and MySQL as an effort to utilize technology to increase efficiency and convenience in the online martabak ordering process. This research includes developing a website that is integrated with a MySQL database to store and manage ordering information. Customers can explore the martabak menu, select the desired variant, and complete the ordering process easily. In the development process, the PHP programming method is used to design a responsive and dynamic user interface. A MySQL database is used to store order information, including customer details, type of martabak selected, and order status, enabling efficient management and tracking of order history. The Martabak Bangka QUM website aims to provide a pleasant and practical shopping experience for customers. With the online ordering system, it is hoped that it can increase business competitiveness and provide added value in providing better service to customers. 
Comparison of Machine Learning and Deep Learning in Shopee Review Sentiment Analysis Kuwat Setiyanto; Azzahra Dania Indriyani
International Journal Science and Technology Vol. 4 No. 2 (2025): July: International Journal Science and Technology
Publisher : Asosiasi Dosen Muda Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56127/ijst.v4i2.2225

Abstract

This study aims to compare the performance of Machine Learning algorithms (Random Forest and Support Vector Machine) and a Deep Learning model (Long Short-Term Memory) in analyzing user review sentiment of the Shopee application. A total of 50,000 Indonesian-language reviews were collected through web scraping from the Google Play Store. After preprocessing and feature extraction, the three models were developed and evaluated using accuracy, precision, recall, and F1-score metrics. The results indicate that the LSTM model achieved the best performance in classifying sentiment into three categories: positive, negative, and neutral. Furthermore, the model was implemented into an interactive sentiment analysis dashboard using Streamlit, enabling users to explore and test sentiment in real time. This research demonstrates that the application of Machine Learning and Deep Learning technologies is effective in analyzing public opinion and can support strategic decision-making in the context of e-commerce.
Co-Authors Azzahra Dania Indriyani Dimas Rizki Dwiyanto Yoga Aprianto