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All Journal Jurnal Gaussian
Rukun Santoso
Departemen Statistika, Fakultas Sains dan Matematika, Undip
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Analisis Sentimen Pada Perusahaan Penyedia Jasa Logistik J&T Menggunakan Algoritma Multinomial Naive Bayes dan Support Vector Machine Helmi Aulia Rahman; Rukun Santoso; Tatik Widiharih
Jurnal Gaussian Vol 12, No 2 (2023): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/j.gauss.12.2.242-253

Abstract

Online shopping is a way to a faster and easier process of buying things or needs for people these days. Logistic services are essential in the process of buying things online, for they will be the one who ship the package to the buyer. PT. Global Jet Express or J&T is one of many logistics service provider company that are available in Indonesia. J&T has a Twitter account which is used for communicating with their customers. Opinions that were posted by J&T consumers on Twitter could be used as a data to do sentiment analysis which the purpose is to extract information that are told by people in Twitter about J&T. Data crawling was done for 15.000 tweets that were posted during the period of 4th to 10th of July 2022, duplicated tweets and those who has the exact same contents were removed resulting the data reduced to 2500 tweets. Tweets will be divided into two class; positive class and negative class Some classification methods are commonly used in text classification, such as Random Forest, Decision Tree, Naïve Bayes Classifier, Support Vector Machine etc. Data in this research will be classified using Multinomial Naïve Bayes and Support Vector Machine to compare their accuracy, the reason for the comparison is these methods have significant difference in their concept complexity. Multinomial Naïve Bayes classify data by finding the greatest conditional probability value, whilst Support Vector Machine classify data by finding the best hyperplane to divide into two class. Multinomial Naïve Bayes has the accuracy of 72,80% and Support Vector Machine has the accuracy of 82,40%. Based on their accuracy, Support Vector Machine has the best performance in classifying public opinions about J&T on Twitter.
PREDIKSI HARGA EMAS DUNIA MENGGUNAKAN METODE LONG-SHORT TERM MEMORY Tania Giovani Lasijan; Rukun Santoso; Arief Rachman Hakim
Jurnal Gaussian Vol 12, No 2 (2023): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/j.gauss.12.2.287-295

Abstract

Gold investment is one of the investments that is quite lot of interest by the public and also is considered safer because it has relatively low risk and tends to be stable compared to other investment instruments, especially amid the uncertainty of global economic conditions caused by the COVID-19 pandemic. Awareness about gold price predictions can provide information to people who want to invest in gold so they have higher opportunity to earn profits and minimize the risks obtained. The gold prices prediction method used in this study is Long-Short Term Memory (LSTM) using RStudio. LSTM is one of the method that is widely used to predict time series data. LSTM is a variation of the Recurrent Neural Network (RNN) that is used as a solution to overcome the occurrence of exploding gradient or vanishing gradient in RNN when processing long sequential data. The best LSTM model in this study for predicting gold prices is  the model with MAPE value 2,70601, which is a model with a training data and testing data comparison 70% : 30% and hyperparameters batch size 1, units 1, AdaGrad optimizer, and learning rate 0,1 with 500 epochs.
Co-Authors Arief Rachman Hakim Helmi Aulia Rahman Tania Giovani Lasijan Tatik Widiharih