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Jurnal Infra
Published by Universitas Kristen Petra Surabaya
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Core Subject : Science,
Computer Science & IT
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Articles 79 Documents
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Search results for , issue "Vol 10, No 2 (2022)" : 79 Documents clear
Aplikasi Moblie Learning untuk Meningkatkan Interaksi Pembelajaran dalam Mendukung Penyerapan Materi Pembinaan UMKM oleh LPPM Universitas Kristen Petra dengan Menerapkan Model Learning Group Cynthia Wijaya; Djoni Haryadi Setiabudi; Justinus Andjarwirawan
Jurnal Infra Vol 10, No 2 (2022)
Publisher : Universitas Kristen Petra

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Abstract

 Synchronous learning provided by LPPM in learning events is often limited, and makes MSME participants have to understand and develop the material that has been delivered individually after receiving the lesson. This makes the lack of interaction in learning, and reduces the interest and attention of MSMEs to improve learning from the material obtained, especially when they find difficulties in learning. This can affect the level of absorption of material by MSMEs to be not optimal. Several previous studies using mobile learning still have weaknesses, namely learning is still more focused on individual learning so there is no interaction in the learning process through mobile learning.To overcome this problem, in this study a mobile learning application was designed with a learning group model that has collaborative features, such as the task forum feature where MSMEs can choose the question to be worked on and then give each other comments on whether the answers in each question are correct, problem box to help each other provide solutions from a lack of understanding of the material, discussion forums to discuss things that have not been discussed in the material, glossary, chat with tutors and mobile push notification support to encourage MSMEs to be more active in mobile learning.The results of this study based on questionnaires and activity calculations, it can be concluded that the mobile learning application with the learning group model helps MSMEs to understand or increase the absorption of material in training. In addition, it also makes MSMEs active in participating in the training, thus indirectly helping better absorption of material.
Sistem Otomasi Rute Order Picking Pada Gudang dengan Metode Simulated Annealing Stienley Nagata Cahyady; Andreas Handojo; Tanti Octavia
Jurnal Infra Vol 10, No 2 (2022)
Publisher : Universitas Kristen Petra

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Abstract

Order Picking is a process to make a selection from products and picking them up from the place that products are stored and then sort the products out to fulfill the customer orders. Order picking process is the most expensive activity in the warehouse. The reason is order picking needs a lot of workforce and if order picking done manually it will cost as much as 55 % of the total cost of the warehouse. That’s why order picking is the correct part to be optimize to make warehouse become more effective and efficient. In this thesis, a web based application designed to solve all the problems above, which includes showing the shortest route to be pick for orders picking with simulated annealing method. Other than that, the application will be included with a hardware named RFID reader which can detect product placement and pickup from the shelf. The result of this thesis showed that simulated annealing algorithm able to reduce the range that are needed to order picking as much as 51.57058354 % for 100 data, 35.56569879 % for 500 data and 28.18222784% for 1000 data with fixed parameters. For the RFID reader it have the accuracy of 40% for reading products on the shelf. This is because the signals from tags clashing with each other which make the reader unable to read all of them.
Aspect-Based Sentiment Analysis pada Ulasan ECommerce dengan Metode Support Vector Machine untuk Mendapatkan Informasi Sentimen dari Beberapa Aspek Hansen Gunawan Sulistio; Andreas Handojo
Jurnal Infra Vol 10, No 2 (2022)
Publisher : Universitas Kristen Petra

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Abstract

In this era of globalization, all people's activities are starting to use technology to facilitate their daily activities. One of the most impactful forms of digitization activities is online buying and selling activities such as the use of the Tokopedia and Shopee platforms. The existence of a review feature on online buying and selling places (e-commerce) is one of the factors supporting the increase in people's online transactions. The number of people who have started to implement online buying and selling activities in their daily lives has resulted in an increase in the number of reviews on e-commerce.The large number of reviews makes it difficult for potential buyers to review a product to be purchased. The factors that determine the shopping experience of each individual are different so that the reviews and ratings given by each individual on a product or store vary. This affects the average rating of a product or store so that the average rating on a product does not necessarily represent the quality of the product. To overcome this problem, the author makes a system where prospective buyers and sellers will be facilitated to assess an aspect of a product. The system created by the author shows several aspects that are crucial for buyers and sellers in reading a review, such as general aspects, accuracy, quality, service, delivery, packaging, and price using the Support Vector Machine method. In these aspects, the system created will show sentiments on reviews that have been written by buyers such as positive, negative, or neutral sentiments. In addition to showing the sentiments of aspects of a product, this system also shows which aspects affect the product rating the most, the aspects that are most frequently discussed, what aspects are most rated positively and negatively.The results of the thesis show that the aspect that is often discussed is the quality aspect. General aspects, accuracy, quality, service, delivery, and packaging affect the rating value on a product rating while the price aspect does not affect a product rating. Compared to the Shopee platform, there are more positive reviews written on Tokopedia than reviews on Shopee.
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

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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

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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

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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

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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

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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

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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

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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.

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