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INDONESIA
Jurnal Infra
Published by Universitas Kristen Petra Surabaya
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Core Subject : Science,
Computer Science & IT
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Articles 1,326 Documents
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Penerapan Algoritma TextRank dan Dice Similarity Untuk Verifikasi Berita Hoax Christian Khontoro; Justinus Andjarwirawan; Yulia Yulia
Jurnal Infra Vol 9, No 1 (2021)
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Abstract

Hoax or in Indonesian, hoax is fake news or news that has no source. Hoax are a series of information that is misguided, but sold as truth[5]. The problems above are the basis for creating a verification system for this hoax news. The TextRank and Dice Similarity algorithms will be used to help verify the inputed news is a hoax or fact. Where in this study, the TextRank algorithm is used to find the most important keywords in a news which will then be used to become keywords in search engines. Then the Dice Similarity algorithm is used to measure the level of similarity of the news entered with the news obtained from search results on search engines. The hoax verification system that has been done has been tested using several similarity weights to find which similarity weights are the most optimal. The data used were 50 hoax news and 50 fact news. From this test, the optimal similarity weight is 40% with an accuracy of 84%. With details of 50 hoax data, 47 news were declared hoax, 2 news items were declared facts, and 1 news was declared unknown. Of the 50 fact news, 37 news were declared facts, 13 were declared hoax, and no news was declared unknown.
Penerapan Random Forest dalam Email Filtering untuk Mendeteksi spam Billy Christanto; Djoni Haryadi Setiabudi
Jurnal Infra Vol 8, No 2 (2020)
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Abstract

Email became an integral part of the internet experience. As users increase, marketing via email also became more popular. These emails often annoy users, hence the name “spam”. Because of its excessive number, the need to separate important messages from unimportant ones emerges. Up until this point, there’s no optimal solution to this problem. Among the methods being used, machine learning based solutions show the most promising results.  The method being tested is Random Forest, which is often regarded as superior compared to Naïve Bayesian, a popular algorithm for email filtering. Both of the algorithms are to be subjected to tests and compared for their accuracy, recall and precision. The effects of pre-processing and stemming to the dataset will also be tested. This research shows that both models produce similar accuracy, recall and precision that reach 96% for each category. Tests also show that Random Forest needs around  80 times more time to train it’s model compared to Naive Bayesian so it became not suitable for email filtering purposes
Penerapan Convolutional Neural Network Untuk Klasifikasi Kanker Kulit Melanoma Pada Dataset Gambar Kulit Michael Kurniawan Soegeng; Liliana Liliana; Agustinus Noertjahyana
Jurnal Infra Vol 9, No 1 (2021)
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Abstract

Melanoma skin cancer is one of the most dangerous skin cancers where the ferocity and speed of metastasis has caused a high mortality rate among afflicted when the cancer is not treated. Early detection of the cancer and prevention by removing the affected skin have been shown to decrease the mortality rate on afflicted patient. Thus, development of a method to help automatically diagnose the cancer and classify between cancer and normal mole or birthmark is needed. Previous methods still show limitations in classifying melanoma skin cancer. This study proposes a classification system using convolutional neural network trained on the original ISIC 2020 dataset and hair removed dataset which is then combined using ensemble. The dataset used is first preprocessed using the hair removal algorithm convolutional neural network using EfficientNet B0 – B7 and ResNet-50-v2 will be trained using ISIC 2020 dataset and ISIC 2020 dataset processed with hair removal algorithm.The model is evaluated using test data from ISIC 2020 dataset on area under the receiver operating characteristic curve (ROC AUC). The model trained will then be combined using ensemble where the result of the model will be averaged to give a combined prediction. The result of the test shows that the model trained is capable to classify melanoma and non-melanoma images. It is also shown that by removing hair from the skin image reduces the accuracy of th e model. Using Ensembling on the different models trained into one meta-model also increases the accuracy of the prediction giving a high final accuracy of 93.108%.
Pembuatan Sistem Informasi Administrasi Pada UD. Satu Saudara Plastik Kevin Yosi Putra Utama; Yulia Yulia; Silvia Rostianingsih
Jurnal Infra Vol 8, No 2 (2020)
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Abstract

UD Satu Saudara Plastik is a home industry company that manufactures plastic goods. Examples of goods produced are wall clocks, bottle caps, motorcycle mirrors, plastic bottles, and push-steps on motorcycles. Before application development begins, an analysis and design with data flow diagrams and entity relation diagrams. This application is made with HTML, PHP, JavaScript, MySQL database, and Bootstrap. The application made includes customer order features, sales data, customer order status, raw material stock, raw material purchase. The final result of application development is that the application has a master data feature for raw materials, finished materials, positions, supporting, customers, orders, production requests, input costs, so as to produce customer order reports, income statements, raw material stock reports, reports of total orders, order payment report, revenue report. Based on testing, the system can carry out transaction activities such as sales transactions, and reporting activities for UD Satu Saudara Plastik. The results of the questionnaire show for the display application, more than 70% of respondents answered well. For ease of use of the application, 57% of respondents were satisfied. For the suitability of the application with the needs, 57% of respondents were satisfied and 12.5% very satistfied. For the result application is 87.5% of respondents were satisfied, and around 12.5% of respondents felt less satisfied.
Pemanfaatan Simplex Noise Untuk Menghasilkan Map Yang Natural dengan Unity Engine Calvin Vionaldy Tjiandra; Rudy Adipranata; Lily Puspa Dewi
Jurnal Infra Vol 9, No 1 (2021)
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Abstract

In the development of game, terrain is the first thing that theplayer see in the game. Landscape, assets on terrain, and theheight kurvator on the terrain will give an environment that canenhance the experience in playing. But the process of making suchterrain will take a long time because of the terrain setting whichdevelopers do. Moreover, if the developer needs to create manyterrain with a similar parameter. not just that, because thesimilarity from the terrain, the terrain itself will becomerepetitive. To avoid that, it is better to use the Procedural ContentGenerator or PCG to create a base terrain with every project.Simplex noise is one of many PCG method to generate a terrainheight map to be developed by developer. Terrain that had beengenerated can be explore by the player , and to diminish thechance of slump because of the repetitive play, the surface will bedifferent each time the method generate.This paper will be focus on the implementation of simplex noise togenerate a base terran for a 3D game with the usage of C# fromunity. simplex noise will generate 3 biomes which are the forest,savanna, mountain. Perlin noise will be implemented as well forthe purpose of comparison method, perlin noise will generate thesame biome as simplex noise. This program will see the result ofthe surface generated by both methods.This paper will test some terrain models by how the result ofsimplex generated terrain and perlin generated terrain can makea variety for terrain. Terrain will change each time the method isrunning in the program. In the result of the paper, it is found thatperlin have an advantage to generate that have a low to middleheight terrain and simplex have the advantage to generate aterrain that have a middle to high height.
Deteksi Alat Pelindung Diri Menggunakan Metode YOLO dan Faster R-CNN Jonathan Adiwibowo; Kartika Gunadi; Endang Setyati
Jurnal Infra Vol 8, No 2 (2020)
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Abstract

In order to ensure security and safety measures in industrial zones or any other areas that needed to use personal protective equipment are an important matter. Many workers keep disregarding and violating the rule to use personal protective equipment in the area. Therefore, a program was created that could help supervising the workers to use personal protective equipment. In this study an experiment will be conducted to help recognize the characteristics of personal protective equipment, especially in head. In recent studies that have been carried out Rifki Dita Wahyu Pradana, et. Al. using CNN to produce an overall accuracy 80%. This study will be using 2 methods, You Only Look Once and Faster Region-Convolutional Neural Network (Faster R-CNN). The YOLO method is used to find regions of worker’s head while Faster R-CNN method is used to classify personal protective equipment used by worker. The results of the Faster R-CNN classification will be calculated using a confusion matrix in order to get the accuracy of the correct prediction. The results from this study will identify workers using personal protective equipment in the video. Average accuracy that has been obtained is 93.61%.
Aplikasi Sentiment Analysis Terhadap Pelaksanaan Pembelajaran Jarak Jauh Universitas Kristen Petra Dengan Metode Naive Bayes Classifier Kezia Sekarayu Setyawati; Andreas Handojo; Henry Novianus Palit
Jurnal Infra Vol 9, No 1 (2021)
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Abstract

In maintaining student satisfaction during online learning period, Petra Christian University conducted a survey to know student’s response. The result then processed manually and takes a long time to gather an information. Therefore, a sentiment analysis application is needed which will help to collect information from survey in a shorter time by classifying sentiments and topics related to online learning. The method used to classify topics and sentiments is the Naive Bayes Classifier. Data will be prepared through preprocessing, by eliminating the same sentence, changing abbreviated words, stemming, and stop word removal.The classification model produced and used in this application can classify student survey data related to the implementation of online learning based on positive and negative sentiments, also based on the topic, material, lecturers, learning media, and supporting facilities. The accuracy of the classification model is 89% for sentiment classification and 80% for topic classification.
Evaluasi Kinerja Penggabungan Knowledge Graph Embedded-Based Question Answering dan TransP pada Data Freebase Fransisco Remon Liemena; Henry Novianus Palit; Alvin Nathaniel Tjondrowiguno
Jurnal Infra Vol 8, No 2 (2020)
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Abstract

In the past few years, data storage and analysis using graph keep increasing. One of the implementation of this is knowledge graph. There are many methods proposed on information extraction from knowledge graph, one of them is natural language question answering. However, all of the researches around question answering use direct query to find the answer. Knowledge Graph Embedding-based Question Answering (KEQA) is the latest method that implements deep learning and embedding to answer questions. Experiments demonstrate that KEQA outperforms other question answering methods. Despite having high accuracy, KEQA still uses simple and outdated embedding method.Knowledge graph embedding is one of the method for knowledge graph representation where the entities and relations are represented in vector (embedding) using deep learning. Many proposed embedding methods do not really consider the depth of a knowledge graph. TransP is a proposed method that consider the indirect relationship to represent a knowledge graph. Experimental results show that TransP outperforms other embedding methods in the task given. Based on this, KEQA will be built using TransP with the expectation that the accuracy of KEQA will increase.Based on the result of the experiment, TransP achieves Mean Rank of 5.390,25 and HIT10 of 28,5%. After that, KEQA with embedding can achieve up to 88,89% accuracy, and KEQA without embedding can achieve up to 88,89% accuracy. Experiment also shows that scoring parameters value with affect KEQA with embedding. In conclusion, TransP can increase the accuracy of KEQA.
Implementasi Website Kelas Untuk Pengerjaan Proyek Mata Kuliah Yanes Robert Yeska Kakihary; Alexander Setiawan; Lily Puspa Dewi
Jurnal Infra Vol 9, No 1 (2021)
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Abstract

Currently, the process of working on a project or group assignment from the teacher will often experience a lack of control over the process.  The whole of a group work is only seen from the results and accuracy in the collection.  In fact, in addition to results and timeliness, teachers also need to know the process of each individual to understand their level of ability.Looking at the background of the problem, an application is designed that improves individual performance control in project work or group assignments.  This application will use a special framework to solve control problems, and can manage project work management to make it better.  Applications will be created using the PHP programming language and MySQL as a database. The results obtained from this application include the creation of a working website with a framework for project control to regulate existing work processes.
Implementasi Distributed Database Pada Learning Management System Menggunakan Platform Redhat Openshift Bryant Plaudo Santoso; Agustinus Noertjahyana; Justinus Andjarwirawan
Jurnal Infra Vol 8, No 2 (2020)
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Abstract

The use of technology now has begun to spread to the world of education. One of them is the use of E-Learning. In a learning management system, instructors can give assignments, materials, tests, or quizzes to students. But if many people access it, it can cause problems on the server. If the server goes down during an exam, it will be a serious problem because students will not be able to access the server. To overcome this, we need several web servers that are ready to serve users so that computing is not only focused on a web server. This research will test the implementation of distributed database on learning management system. The application will run on Openshift. Distributed databases will use MySQL Cluster by using sharding method that can make data into multiple partitions and stored on multiple database servers. With the implementation of distributed database, it is expected to increase the availability of applications. So when there is a database server down, the application can still be run properly. In addition, this can also minimize the database server to be overloaded because it is accessed by many users.

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