cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
-
Contact Email
-
Phone
-
Journal Mail Official
-
Editorial Address
-
Location
Kota surabaya,
Jawa timur
INDONESIA
Jurnal Infra
Published by Universitas Kristen Petra Surabaya
ISSN : -     EISSN : -     DOI : -
Core Subject : Science,
Computer Science & IT
Arjuna Subject : -
Articles 1,326 Documents
 129   130   131   132   133 
Penerapan Convolutional Neural Network dengan Pre-Trained Model Xception untuk Meningkatkan Akurasi dalam Mengidentifikasi Jenis Ras Kucing Abraham Imanuel; Djoni Haryadi Setiabudi
Jurnal Infra Vol 10, No 2 (2022)
Publisher : Universitas Kristen Petra

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

Abstract

Raising pets is a common thing that humans often do. Cats are one of the domestic pets that are fancied by humans. But, raising a cat is not an easy task. This is because every cat’s breed has its own characteristics that will affect its type of raising. Because of that, there is a need for a system that can identify a cat’s breed to help someone in deciding which type of cat is suitable for him/her. In the past, there was also research about cat’s breed detection using SSD Mobilenet_v1 FPN method, but the accuracy was not high enough, which was 81.74%. This thesis will be done with the implementation of transfer learning method on Pre-Trained Convolutional Neural Network Xception, which is a CNN Model that is inspired by CNN Model Inception. CNN Model Xception is an Inception Model that replaces the use of Inception modules with depth wise separable convolutions. CNN Model Xception is used in this thesis with the purpose of increasing the accuracy of cat’s breed detection. Output of the system shows that the highest accuracy that could be made in detecting cat’s breeds on The Oxford-IIIT Pet Dataset is 89.58% or 0.8958. Compared to SSD Mobilenet_v1 FPN method, which accuracy was 81.74%, implementing Xception method gives an increase in the accuracy for 7.84%. Other than that, it is also found that the dataset quality has an impact on model’s accuracy.
Prediksi Penjualan Pada Data Penjualan Perusahaan X Dengan Membandingkan Metode GRU, SVR, DAN SARIMAX Jordan Nagakusuma; Henry Novianus Palit; Hans Juwiantho
Jurnal Infra Vol 10, No 2 (2022)
Publisher : Universitas Kristen Petra

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

Abstract

Sales forecasting is an attempt to predict sales using several methods, such as statistical methods, machine learning, and others. Sales forecasts are considered important because poor forecasts can have an impact on the company's inventory so that it can cause storage of too much or too little goods, causing the company to lose. Therefore, we need a model that can predict sales so that companies can plan before filling stock. However, forecasts cannot be done directly, because a company's sales data is definitely influenced by various factors and sales last month are not always the same as in the future, so external data is needed in predicting future sales. Therefore, in this thesis a prediction will be made using 3 models, namely the GRU, SVR and SARIMAX models with the help of external data in the form of CPI data and inflation data. In addition, this thesis also conducted a correlation test to determine whether the sales data to be predicted has significance/relationship with external data so that it helps in predicting sales data. The results obtained from this study are that pot data is more suitable for using univariate data with the GRU model, with RMSE Train 3.22, RMSE Test 2.93. For hanger and sealware data, the best model for prediction is the SARIMAX model with univariate data type (RMSE 30.43) and multivariate data type (RMSE 8.07).
Sales Management Support and Analytics untuk Meningkatkan Koordinasi Pekerja dan Pelayanan Pembeli UD. XYZ Melissa Marvella; Leo Willyanto Santoso; Yulia Yulia
Jurnal Infra Vol 10, No 2 (2022)
Publisher : Universitas Kristen Petra

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

Abstract

UD. XYZ is a business that sells products with a wholesale preorder system. The products offered are handicrafts such as of invitations and souvenirs. In running a business, especially in order processing, each salesperson is tasked with interacting with the customers. This causes customers to have a different contact numbers regarding UD. XYZ. Matter of separate information, it becomes difficult to track orders. The method used in charge on designing information system is the V-Shaped Model. The VShaped Model method is one of the SDLC (System Development Life Cycle) models. Meanwhile, the testing method used is White box testing and User Experience Questionnaire (UEQ). Based on the research that has been done, white box testing the results are 98.5%. On the other hand, the User Experience Questionnaire (UEQ) based on 6 UEQ scales resulted in 5 categories above the average and 1 category with good scores.
Penerapan SVM untuk Klasifikasi Sentimen pada Review Comment Berbahasa Indonesia di Online Shop Yoshua Refo; Silvia Rostianingsih; Liliana Liliana
Jurnal Infra Vol 10, No 2 (2022)
Publisher : Universitas Kristen Petra

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

Abstract

With so many users accessing online shops, comments are an important aspect when shopping. Buyers can provide comments about the goods or thing that have been purchased, both negative comments and positive comments. By collecting various kinds of comments, the data can be used to classify comments. This research will use the Support Vector Machine (SVM) algorithm which is considered the right method for text classification. The method will be tested for its performance, seen from how good and accurate the method used in classifying comments is. In addition, this research also uses kernels, namely Linear kernels, Radial Basis Function (RBF) kernels, and Polynomial kernels as test scenarios. Based on the test results shown, SVM is a good method in classifying text. SVM classifies text that has gone through the preprocessing stage with an accuracy value of 88% on the RBF kernel, 87% on the linear kernel, and 87% on the polynomial kernel. The accuracy value in the aspect classification itself is 78% on the RBF kernel, 78% on the Linear kernel and 74% on the Polynomial kernel.
Penerapan Ensemble Learning Menggunakan Metode Support Vector Machine, Naïve Bayes Classifier, dan Valence Aware Dictionary for Sentiment Reasoning untuk Meningkatkan Akurasi Sentiment Analysis pada Review Aplikasi Google Play Tania Sunyoto; Djoni Haryadi Setiabudi; Alvin Nathaniel Tjondrowiguno
Jurnal Infra Vol 10, No 2 (2022)
Publisher : Universitas Kristen Petra

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

Abstract

In an age where almost everyone owns a smartphone, more and more mobile applications are being developed and distributed to Google Play. To decide which application to download, customers are influenced by ratings and reviews. Reviews provide more information than ratings, but there are so many that they are difficult and take a long time to obtain. The application of sentiment analysis supported by high accuracy in reviews can make it easier for customers to get sentiment information from th e application and help them make decisions to download / use the application or not. This research uses a combination of Naïve Bayes and SVM machine learning models with the VADER lexicon model, then Ensemble Learning is carried out using Majority Voting, Majority Weighted Voting, and Stacking to improve accuracy. The results of this system indicate that by using Ensemble Learning the accuracy result increases but not significantly even decreases from SVM results of 88.88% to 88.87% using Stacking.
Hybrid Recommendation System untuk Peminjaman Buku Perpustakaan dengan Collaborative dan Content-Based Filtering Adrianus Aditya Widjaja; Henry Novianus Palit
Jurnal Infra Vol 10, No 2 (2022)
Publisher : Universitas Kristen Petra

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

Abstract

The development of technology is growing rapidly and followed with the growth of digital data in the internet. Therefore, recommendation system was invented and can be used in many aspects of human life. For example, a book recommendation system can be used to help user to choose a book to be read which is suitable with their preferences. Using a recommendation system can help to reduce the required time to choose a book because of the massive choices of books. This research using hybrid recommendation system which combined collaborative filtering and content-based filtering method. The purpose of this study was to achieve a better recommendation outcome. To measure how well the result of the recommendation, mean reciprocal rank and mean average precision was used. The results showed that weighted hybrid yields a better score than the other two methods. The score was 0.2113 and 0.0988 respectively
Form Evaluasi Online Mata Kuliah Pra Skripsi Dan Skripsi Berbasis Android Stephen Cornelius Hertanto; Silvia Rostianingsih; Liliana Liliana
Jurnal Infra Vol 10, No 2 (2022)
Publisher : Universitas Kristen Petra

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

Abstract

In order to support the vision of "Green Campus" implemented by Petra Christian University Surabaya as well as to accommodate the evaluation interests of courses in the new curriculum, namely pre-thesis and thesis. At the evaluation stage of the course, it involves many parties, so to digitize all documents and facilitate coordination, an application is needed to assist the entry process and produce the required reports. So far, the evaluation process that has occurred is quite long and takes a lot of time, namely when evaluating the Pre-Thesis to Thesis. As well as seeing the increasing number of Petra students every year, the coordinating lecturers and supervisors on Pre Thesis and Thesis will be more and more difficult in handling the evaluation. Therefore, this thesis creates an "Online Evaluation Form for Pre-thesis and Thesis courses based on Android" which is an application on an Android-based smartphone that aims to facilitate the coordination of lecturers and supervisors in evaluating these courses. By making this application, it will be easier for the lecturers to fill in the scores and evaluation of the Pre-thesis and Thesis reports without taking much time, and the data created can be more accurate and faster. So that the delivery of grades to students becomes shorter.
Pencarian Rute Indoor Terpendek dalam Lingkungan Universitas Kristen Petra Surabaya menggunakan Algoritma D* Lite berbasis Android Richard Hans Krisnajana; Agustinus Noertjahyana
Jurnal Infra Vol 10, No 2 (2022)
Publisher : Universitas Kristen Petra

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

Abstract

In today's era, a large building divided into several floors often confuses visitors in looking for a place / room and spends a lot of time in vain, one example is the Petra Christian University Surabaya environment. The application that will be made can make it easier for students, lecturers, staff and visitors to Petra Christian University Surabaya in finding the shortest route to their destination. To run the application, a server is needed in the form of a database to store nodes, space data, wi-fi data, and geomagnetic data. Data retrieval is done manually and processed so that it can be used as a reference. After the data retrieval process, the user can enter input in the form of their destination and the system will calculate the shortest route and display the route to the user. Based on the results of the tests that have been carried out, the application that has been made is able to provide the shortest route and display it to the user. In addition, the application is also able to provide the shortest route calculation with a short average time, and provide the user with an estimate of the exact location and time during the navigation process
Aplikasi Penerjemah Kegiatan Seminar Menjadi Video Bahasa Isyarat BISINDO Dengan Speech To Text Marcel Slamet Sugianto; Liliana Liliana; Anita Nathania Purbowo
Jurnal Infra Vol 10, No 2 (2022)
Publisher : Universitas Kristen Petra

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

Abstract

Information at this time is very much needed to increase our knowledge. However, this delivery can be hindered by several conditions such as the inability to hear the deaf community. Based on data from the Data and Information Center of the Ministry of Health of the Republic of Indonesia in 2019, 7.09% of the Indonesian population is deaf. In addition, the delivery of information at the seminar can be hindered by noise and participants sitting far from the speaker will have difficulty hearing the speaker's voice. In this study, we will use Speech-To-Text on the Android application which aims to help translate information in the form of voice delivered as at a seminar into text and will be converted into BISINDO sign language video. The results of testing the use of the Speech-To-Text feature in the application that has been made show that it is able to accommodate approximately 100 words in 1 minute at a time when the speaker speaks without any pause. The Speech-To-Text feature used takes approximately 2 seconds to translate the received voice and the time lag required by the speaker device to the participant's device takes approximately 3-5 seconds after using 5 different internet speeds. For the accuracy of the Speech-To-Text feature that was tested using 3 narrations read by 4 different people, the accuracy of the Speech-To-Text feature has an accuracy of above 80% in general, although there is an accuracy that is below 80% due to the ambiguity of the pronunciation.
Sistem Rekomendasi Content Based Filtering Pekerjaan dan Tenaga Kerja Potensial menggunakan Cosine Similarity Philips Nogo Raharjo; Andreas Handojo; Hans Juwiantho
Jurnal Infra Vol 10, No 2 (2022)
Publisher : Universitas Kristen Petra

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

Abstract

During the pandemic, there was an economic problem that forced companies to do something to avoid any loss. One of the action is to terminate the employment with their workforces. In the conventional way, the workforce and the company will waste a lot of time looking for the right fit for them. So, the recommendation system for jobs and workforce plays an important role in these conditions. Because with the existence of recommendation system that can help from both sides, it will speed up the meeting between companies that need workers and workers who need jobs. Based on the test have been carried out, the recommendation system using the TF-IDF model can provide good recommendations based on the calculation of the Mean Reciprocal Rank getting 0.857 and Mean Average Precision of 0.833, where these results are quite good.

Page 132 of 133 | Total Record : 1326

 129   130   131   132   133 

Filter by Year

2013 2022


Reset
Filter By Issues
All Issue Vol 10, No 2 (2022) Vol 10, No 1 (2022) Vol 9, No 2 (2021) Vol 9, No 1 (2021) Vol 8, No 2 (2020) Vol 8, No 1 (2020) Vol 7, No 2 (2019) Vol 7, No 1 (2019) Vol 6, No 2 (2018) Vol 6, No 2 (2018) Vol 6, No 1 (2018) Vol 5, No 2 (2017) Vol 5, No 1 (2017) Vol 4, No 2 (2016) Vol 4, No 1 (2016) Vol 4, No 1 (2016) Vol 3, No 2 (2015) Vol 3, No 2 (2015) Vol 3, No 1 (2015) Vol 3, No 1 (2015) Vol 2, No 2 (2014) Vol 2, No 2 (2014) Vol 2, No 1 (2014) Vol 2, No 1 (2014) Vol 1, No 2 (2013) Vol 1, No 2 (2013) Vol 1, No 1 (2013) Vol 1, No 1 (2013) More Issue