Article Per Year (5 Year)
p-Index From 2020 - 2025
0.444
P-Index
This Author published in this journals
All Journal JuTISI (Jurnal Teknik Informatika dan Sistem Informasi)
Kristiawan Kristiawan
Universitas Kristen Maranatha
Computer Science & IT

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

Found 2 Documents
Search

 1 
Deteksi Buah Menggunakan Supervised Learning dan Ekstraksi Fitur untuk Pemeriksa Harga Kristiawan Kristiawan; Deon Diamanta Somali; Try Atmaja Linggan jaya; Andreas Widjaja
Jurnal Teknik Informatika dan Sistem Informasi Vol 6 No 3 (2020): JuTISI
Publisher : Maranatha University Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28932/jutisi.v6i3.3029

Abstract

The role of technology in the business world is growing over time. The development of technology, making machines step by step is able to replace the work done by humans. The industrial revolution is a clear example of such technologial development and its use in our daily life. in the fourth industrial revolution that we face today, IOT technology provides the ability of the five senses and think like humans to machines. Over time, human work will be replaced by such technology which provides efficiency like never before. One technology that can provide efficiency is computer vision. In retail context, computer vision can help humans to recognize fruits in supermarkets so that it will help customers do self-service, without having to ask the clerk in the fresh section of the supermarket so that supermarkets can be more efficient and customers can be served better and faster. Computer Vision and machine learning can help retail companies provide self service price checkers for fruit products in supermarkets.
Perbandingan Algoritma Machine Learning dalam Menilai Sebuah Lokasi Toko Ritel Kristiawan Kristiawan; Andreas Widjaja
Jurnal Teknik Informatika dan Sistem Informasi Vol 7 No 1 (2021): JuTISI
Publisher : Maranatha University Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28932/jutisi.v7i1.3182

Abstract

Abstract — The application of machine learning technology in various industrial fields is currently developing rapidly, including in the retail industry. This study aims to find the most accurate algorithmic model so that it can be used to help retailers choose a store location more precisely. By using several methods such as Pearson Correlation, Chi-Square Features, Recursive Feature Elimination and Tree-based to select features (predictive variables). These features are then used to train and build models using 6 different classification algorithms such as Logistic Regression, K Nearest Neighbor (KNN), Decision Tree, Random Forest, Support Vector Machine (SVM) and Neural Network to classify whether a location is recommended or not as a new store location. Keywords— Application of Machine Learning, Pearson Correlation, Random Forest, Neural Network, Logistic Regression.
Co-Authors Andreas Widjaja Andreas Widjaja Deon Diamanta Somali Try Atmaja Linggan Jaya