cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
Hapnes Toba
Contact Email
hapnestoba@it.maranatha.edu
Phone
+6222-2012186
Journal Mail Official
hapnestoba@it.maranatha.edu
Editorial Address
Fakultas Teknologi dan Rekayasa Cerdas Universitas Kristen Maranatha Jl. Prof. Drg. Suria Sumantri No. 65 Bandung
Location
Kota bandung,
Jawa barat
INDONESIA
JuTISI (Jurnal Teknik Informatika dan Sistem Informasi)
Published by Universitas Kristen Maranatha
ISSN : 24432210     EISSN : 24432229     DOI : https://doi.org/10.28932/jutisi
Core Subject : Science,
Computer Science & IT
Paper topics that can be included in JuTISI are as follows, but are not limited to: • Artificial Intelligence • Business Intelligence • Cloud & Grid Computing • Computer Networking & Security • Data Analytics • Datawarehouse & Datamining • Decision Support System • E-Systems (E-Gov, E-Health, E-Commerce, etc.) • Enterprise System (SCM, ERP, CRM) • Human-Computer Interaction • Image Processing • Information Retrieval • Information System • Information System Audit • Enterprise Architecture • Knowledge Management • Machine Learning • Mobile Computing & Application • Multimedia System • Open Source System & Technology • Semantic Web & Web 2.0
Arjuna Subject : Ilmu Komputer (semua kategori) - Aplikasi Ilmu Komputer
Articles 13 Documents
 1   2 
Search results for , issue "Vol 4 No 2 (2018): JuTISI" : 13 Documents clear
Implementasi Convolutional Neural Network untuk Sistem Prediksi Pigmen Fotosintesis pada Tanaman Secara Real Time Kestrilia Rega P; Ivan Christianto O; Hendry Setiawan
Jurnal Teknik Informatika dan Sistem Informasi Vol 4 No 2 (2018): JuTISI
Publisher : Maranatha University Press

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

Abstract

It is common that evaluation on plant health is done by conducting measurement on photosynthetic pigments. Analysis of the presence or absence of some particular pigments could reveal any information about plant responses to the environment or climate changes. This is due to the fact that relative pigment concentrations are influenced by environmental factors such as light and nutrient availability. In this research, a non-destructive and rapid method was developed to identify the existence of photosynthetic pigments in plant leaf i.e. chlorophyll, carotenoid, and anthocyanin. The method used leaf’s RGB digital image as the color representation of the pigments contained in the plant being evaluated. The intelligence agent which is responsible to learn the data and provide information about the pigments was developed based on convolutional neural network (CNN) model. This model was chosen due to its capability to receive a digital image and automatically search for the best feature to learn it. Therefore, plant evaluation could run in real time. The result of the experiment reveals that CNN model could learn the color-pigment relationship very well. The best architecture is ShallowNet using Adam optimizer, batch size 30 and trained with 15 epoch. The MSE of the pigments prediction reaches 0.0055 (actual data range -0.2 up to 2.2) for training and 0.029 for testing.
Analisis Performa dan Pengembangan Sistem Deteksi Ras Anjing pada Gambar dengan Menggunakan Pre-Trained CNN Model Muftah Afrizal Pangestu; Hendra Bunyamin
Jurnal Teknik Informatika dan Sistem Informasi Vol 4 No 2 (2018): JuTISI
Publisher : Maranatha University Press

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

Abstract

The main objective of this research is to develop an image recognition system for distinguishing dog breeds using Keras’ pre-trained Convolutional Neural Network models and to compare the accuracy between those models. Specifically, the models utilized are ResNet50, Xception, and VGG16. The system that we develop here is a web application using Flask as its development framework. Moreover, this research also explains how the deep learning approaches, such as CNN, can distinguish an object in an image. After testing the system on a set of images manually, we learn that every model has different performance, and Xception came out as the best in term of accuracy. We also test the acceptance of the user interface we develop to the end-users.
Penerapan Metode EOQ dan ROP untuk Pengembangan Sistem Informasi Inventory Bengkel MJM berbasis Web Trian Rafliana; Bernard Renaldy Suteja
Jurnal Teknik Informatika dan Sistem Informasi Vol 4 No 2 (2018): JuTISI
Publisher : Maranatha University Press

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

Abstract

MJM is a small company that works in the automotive business especially for a two-wheeled vehicle. Nowadays MJM has already using the information system to make the listings for the items, sales, and procurement. When the stock is getting low, MJM orders the item to the supplier but sometimes it’s not optimal because the amount of the items they bought are always too much or too less than the needs, and sometimes they ordered the items on the wrong time. The goal here is to make the owner of the shop able to analyze the optimal amount of items when ordering using the EOQ (Economic Order Quantity) method and to analyze when is the right time to reorder the items using ROP (ReOrder Point), so in the future this application will be useful for the owner.

Page 2 of 2 | Total Record : 13

 1   2 

Filter by Year

2018 2018


Reset
Filter By Issues
All Issue Vol 11 No 3 (2025): JuTISI Vol 11 No 2 (2025): JuTISI Vol 11 No 1 (2025): JuTISI Vol 10 No 3 (2024): JuTISI Vol 10 No 2 (2024): JuTISI Vol 10 No 1 (2024): JuTISI Vol 9 No 3 (2023): JuTISI Vol 9 No 2 (2023): JuTISI Vol 9 No 1 (2023): JuTISI (in progress) Vol 8 No 3 (2022): JuTISI Vol 8 No 2 (2022): JuTISI Vol 8 No 1 (2022): JuTISI Vol 7 No 3 (2021): JuTISI Vol 7 No 2 (2021): JuTISI Vol 7 No 1 (2021): JuTISI Vol 6 No 3 (2020): JuTISI Vol 6 No 2 (2020): JuTISI Vol 6 No 1 (2020): JuTISI Vol 5 No 3 (2019): JuTISI Vol 5 No 2 (2019): JuTISI Vol 5 No 1 (2019): JuTISI Vol 4 No 3 (2018): JuTISI Vol 4 No 2 (2018): JuTISI Vol 4 No 1 (2018): JuTISI Vol 3 No 3 (2017): JuTISI Vol 3 No 2 (2017): JuTISI Vol 3 No 1 (2017): JuTISI Vol 2 No 3 (2016): JuTISI Vol 2 No 2 (2016): JuTISI Vol 2 No 1 (2016): JuTISI Vol 1 No 3 (2015): JuTISI Vol 1 No 2 (2015): JuTISI Vol 1 No 1 (2015): JuTISI More Issue