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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 1,326 Documents
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Klasifikasi Kisaran Harga Tarif Endorsement Influencer Instagram dengan Metode Decision Tree Jeshen Oktavian Nathanel; Alexander Setiawan
Jurnal Infra Vol 10, No 2 (2022)
Publisher : Universitas Kristen Petra

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Abstract

Digital marketing in this era of globalization is mandatory for small business owners or businesses that have developed. Marketing using Instagram Influencers is one way. Oftentimes, influencers set their prices according to the followers they have, while not necessarily posting can engage their followers who are active and not fake. The problem that researchers want to solve is the classification of endorsement prices according to the data they have by looking at other than followers such as engagement rate, average liker, and average comment so that the prices set by influencers are more in line with the data they have. The method used for classification is Decision Tree with CART algorithm. The resulting model will be used to classify influencer endorsement prices. The results from testing the model used are only able to achieve 50% accuracy with a RMSE value of 1.12 to classify influencer endorsement prices.
Aplikasi Sistem Pendukung Keputusan Metode TOPSIS dan AHP-TOPSIS Untuk Pemilihan Proyek Pada Perusahaan Kontraktor CV.X Aldo Kurnia Christianto; Alexander Setiawan; Andreas Handojo
Jurnal Infra Vol 10, No 2 (2022)
Publisher : Universitas Kristen Petra

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Abstract

A contractor company is an example of a company engaged in the construction sector. CV.X contractor companies need to consider project bids by considering all aspects of the company and appropriately. Inefficient project selection can be fatal. Project selection errors can occur because decision makers only rely on feelings and experiences. Therefore, a decision support system can help the decision-making process at the CV.X contractor company. The TOPSIS and AHP-TOPSIS methods are used to find out which method is better to be applied to a decision support system and make weight improvements after knowing the best method for each project category that CV.X is working on. In the construction/rehabilitation project category, AHP-TOPSIS is better than TOPSIS with a correlation coefficient of 0.8 for Kendall and 0.9 for Spearman. In the TOPSIS and AHP-TOPSIS road improvement project categories, the Kendall 1 and Spearman correlation coefficient values are 1. For the licensing project category, the TOPSIS method excels with the Kendall correlation coefficient value of 0.33 and Spearman 0.464286. TOPSIS weight improvement is done by brute force to find the combination of weights with the best correlation value. Improvements to the weight of the criteria were not carried out in the category of development/rehabilitation projects because the resulting ranking results were slightly different and were used as a reference for decision making. Improvements to the weighting criteria for the road improvement project category were not carried out. Improvements to the weighting criteria for the TOPSIS method permit project category were carried out and increased the resulting Kendall and Spearman correlation values to 0.714286 and 0.821429.
Penerapan Long-Short Term Memory dengan Word2Vec Model untuk Mendeteksi Hoax dan Clickbait News pada Berita Online di Indonesia Soni Marko Nathanniel Tannady; Djoni Haryadi Setiabudi; Alvin Nathaniel Tjondrowiguno
Jurnal Infra Vol 10, No 2 (2022)
Publisher : Universitas Kristen Petra

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Abstract

News has become information that is routinely consumed every day and can be accessed easily as technology develops. However, the easy access of readers to news also opens up space for some people to spread clickbait or hoax news to attract readers' attention for personal gain. To overcome this, one of the efforts that can be realized is with a detection model for clickbait and hoax news with machine learning methods. The method used is Long-Short Term Memory. However, with several additional applications such as adding a dropout layer, implementing a callback function and using k-fold cross validation to overcome the problem of overfitting the model which often occurs in related studies. The built model will be tested in a webpage application where users can detect news labels. On the best testing result, testing accuracy for the clickbait detection model are 72.93% and the hoax detection model are 79.17%.
Penerapan Metode KNN-Regresi dan Multiplicative Decomposition untuk Prediksi Data Penjualan pada Supermarket X Calvin Christopher Kurniawan; Silvia Rostianingsih; Leo Willyanto Santoso
Jurnal Infra Vol 10, No 2 (2022)
Publisher : Universitas Kristen Petra

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Abstract

Supermarket X is one of the supermarkets in West Nusa Tenggara that needs a way to predict sales in the future. This prediction is needed by Supermarket X to estimate the purchase plan because so far there have been frequent stockouts or oversupply which have caused losses to the company. Based on the problems that occur, this study applies the KNN Regression and Multiplicative Decomposition methods in predicting Supermarket X sales so that supermarket managers can design a strategy to make sales in the future. The results show that predictions based on divisions, departments, categories, sub categories, and products have a smaller average error rate when using the Multiplicative Decomposition method with RMSE = 492.89 and MAPE = 0.29, while the KNN Regression method has RMSE= 757.77 and MAPE= 0.36
Prototype Website Monitoring Parameter Energi Listrik pada Gedung A, B, C, D, & I di UK PETRA Gavriel Emmanuel Victorious; Lily Puspa Dewi; Resmana Lim
Jurnal Infra Vol 10, No 2 (2022)
Publisher : Universitas Kristen Petra

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Abstract

In this modern era, technology is developing very quickly which can facilitate human needs. The fast developed technology need more energy resources such as electricity. This thesis is intended to display electrical energy parameter in the Petra Christian Building via website to show the energy consumption patterns. The data processing method is carried out using PHP on the website. Predictive data analysis using data from Petra Christian University Building which is sent via Gateway to WiFi/LAN. The result of the thesis shows that, the result of the data pattern can help provide an understanding of the pattern of electricity consumption in each building. The regression prediction result has a slope of about -0.75% from the original data. The survey result has an overall rating of 4,375 out of 5 which indicates that users are satisfied with this website.
Pengaruh Feature Selection terhadap Kinerja C5.0, XGBoost, dan Random Forest dalam Mengklasifikasikan Website Phishing Michael Jonathan; Silvia Rostianingsih; Henry Novianus Palit
Jurnal Infra Vol 10, No 2 (2022)
Publisher : Universitas Kristen Petra

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Abstract

With the increase in internet users, especially websites, it provides an opportunity for phishing actors to obtain or steal personal information from users. On each website there will be a lot of information that will be used as a feature, this feature will be used to classify phishing websites. Features will be divided into 3, namely feature url, content feature, and external feature. In this study, three methods will be used, namely C5.0, XGBoost, and Random Forest. The three methods will be tested for their performance to find the best method for classifying phishing websites. In addition, this research will also utilize feature selection with the aim of removing features that have no effect so that training time can be shortened. Based on the test results obtained, it shows that C5.0 is able to provide accuracy, precision, recall, & f1-score values with an average of 93.5%, XGBoost with an average of 96.6%, and Random Forest with an average of 95.7 %. The use of feature selection in the three algorithms also shows that training time can be shortened by an average of about 3.53 times faster by using only 15 feature importance. However, with the use of feature selection, the performance on accuracy, precision, recall, & f1- score values decreased slightly even though the given decrease was not significant or had no major impact on the classification process.
Prediksi Harga Saham Yang Bersifat Siklikal Di Indonesia Menggunakan Metode LSTM dan SVM Gabriel Adisurya Harsono; Alexander Setiawan; Hans Juwiantho
Jurnal Infra Vol 10, No 2 (2022)
Publisher : Universitas Kristen Petra

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Abstract

Stock Market prediction is not an easy task, eventhou using technical analysis. Technical analysis is a technique that can predict stock market price. But using this technique is not an easy task, hence there is a change of mistake in analyzing the stoks that create losses for investors and traders. This is where machine learning and deep learning are able to predict a stock that have cyclical behavior. Cyclical stocks have patterns that other stocks do not have, where machine learning and deep learning model are able to catch and learn the pattern on cyclical stocks and predict the stock prices. The prediction are used to give recommandation for the users when to but or when to sell stocks, but it also able to give recommandation and show stock prices a year in the future. In this Undergraduatae Thesis will be used five methode and two type of data, this include LSTM, Bi-LSTM, GRU, SARIMAX, and SVR, the data contains multivariate and univariate type of data. Multivariate data will contain the stock prices, Jakarta composite index, and the support data close prices. The result of prediction will be compared with the existing data using RMSE then compare the result with all existing model. In the proses of the undergraduate thesis, every parameters in the model will be searched using GridsearchCV and the best parameter for each models. From this research found 1 best metode for multivariate prediction and one for univariate. for multivariate the best parameter for the prediction is LSTM where the RMSE value is 63.67 training and 74.82 for testing. For univariate prediction the best metode is SVR where the RMSE value is 58.04 for training and 75.29 for testing.
Prediksi Peringkat Mingguan Lagu Pada Spotify Amerika Serikat Menggunakan Multiple Charts Dataset Dengan Berbagai Metode Christianto Imanuel Aryanto; Henry Novianus Palit; Andre Gunawan
Jurnal Infra Vol 10, No 2 (2022)
Publisher : Universitas Kristen Petra

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Abstract

In 2020, the majority of the music industry's revenue, 62.1%, came from streaming music. As a result, many music business parties are striving for a hit song, particularly on Spotify US chart. However, this is difficult to achieve because nowadays, a song's performance is determined by its performance on various music charts, not by its quality. Due to that, a study in the field of hit song science will be conducted to forecast weekly song ranking on Spotify US using data from Spotify, Shazam, Airplay, and TikTok charts. Multipler linear regression, polynomial regression, gradient boosting tree, and random forest are the methods used in this study to create models, and each model will be compared using adjusted r-squared and mean absolute error (MAE) as evaluation metrics. Random forest produced the best model, with adjusted r-squared and MAE values of 93.133% and 11.687, respectively. The usage of music attribute had a negative impact on model performance. Shazam chart, on the other hand, has been shown to have a positive impact on model performance. Meanwhile, neither the Airplay nor the TikTok charts have a definite positive or negative impact. However, both have been shown to have a very weak relation with model performance. Overall, the dataset combination of Spotify, Shazam, Airplay, and TikTok chart produced the best model in this study.
Deteksi Plagiarisme pada Kode Bahasa Pemrograman Java menggunakan XGBoost Tomy Widjaja; Andre Gunawan; Liliana Liliana
Jurnal Infra Vol 10, No 2 (2022)
Publisher : Universitas Kristen Petra

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Abstract

With the ease of access to information and cloud server technology, it makes it easier for anyone to access the code data. Coupled with the industry 4.0 era, the number of informatics students is also increasing rapidly. This makes code plagiarism easier to do, especially in academic environment Manual checking of plagiarism is repetitive, difficult, and time-consuming task. Therefore, automation for high quality source code plagiarism detection is needed. The dataset used in this research was collected from “Dasar Pemrograman” class at Petra Christian University. After that the code will continue to tokenization preprocessing using java grammar stage. Then, the algorithm will calculate pairwise features using 3 main algorithms, namely levenshtein distance, greedy string tiling, and bigram which will produce 12 features and a collection of statistic features. Finally, the features will be used for the training and inference process on the XGBoost model. The test result shows that the proposed features have better performance metrics than previous research, it has f1-score of 99%. Implementation of preprocessing can also improve performance metrics on the features proposed in this study and in previous research.
Aplikasi Sistem Informasi Praktikum pada Program Studi Informatika Universitas Kristen Petra Berbasis Website Cynthia Budiono; Lily Puspa Dewi; Alexander Setiawan
Jurnal Infra Vol 10, No 2 (2022)
Publisher : Universitas Kristen Petra

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Abstract

Presently, Petra Christian University informatics study program use SAOCP for laboratory activities problems. But there are drawbacks that can be minimized. Recruitment, assessments, attendance using Microsoft Excel for every data collection carried out. The person in charge of the practicum schedule often experiences confusion in setting the schedule. The current system also reads the NRP by using the old NRP format which is start with M alphabet. Based on these problems, it is necessary to have an application that can answer problems in the informatics study program. This system is created using PHP with the CodeIgniter 3 framework as the main basis, and SQL for the database. In this information system, the user has six different types of access right, namely admin, lecturer, head of the laboratory, permanent assistant, lecturer assistant, and student. The final result of this application is the integration of information such as vacancies, ratings, attendance, and reports. Based on the results of the existing questionnaire, 100% of the correspondents considered that the features made in this application were sufficient in accordance with the needs of the company.

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