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Department of Statistic, Faculty of Science and Mathematics , Universitas Diponegoro Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro Gedung F lt.3 Tembalang Semarang 50275
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Jurnal Gaussian
Published by Universitas Diponegoro
ISSN : -     EISSN : 23392541     DOI : -
Core Subject : Education,
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Jurnal Gaussian terbit 4 (empat) kali dalam setahun setiap kali periode wisuda. Jurnal ini memuat tulisan ilmiah tentang hasil-hasil penelitian, kajian ilmiah, analisis dan pemecahan permasalahan yang berkaitan dengan Statistika yang berasal dari skripsi mahasiswa S1 Departemen Statistika FSM UNDIP.
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Articles 15 Documents
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Search results for , issue "Vol 11, No 4 (2022): Jurnal Gaussian" : 15 Documents clear
ANALISIS SENTIMEN PADA ULASAN APLIKASI INVESTASI ONLINE AJAIB PADA GOOGLE PLAY MENGGUNAKAN METODE SUPPORT VECTOR MACHINE DAN MAXIMUM ENTROPY Fath Ezzati Kavabilla; Tatik Widiharih; Budi Warsito
Jurnal Gaussian Vol 11, No 4 (2022): 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.11.4.542-553

Abstract

Investment is money or asset to earn profits in the future. Online investment applications are already available, one of which is Ajaib. A review of Ajaib’s application is needed to find out reviews given are positive or negative. Sentiment analysis in Ajaib is used to see the user's response to Ajaib’s performance which is divided into positive and negative classes. Sentiment analysis of the Ajaib’s reviews classification can be used with the Support Vector Machine and Maximum Entropy methods. Support Vector Machine on non-linear problems inserts the kernel into a high-dimensional space, to find a hyperplane that can maximize the distance between classes. The kernel used in SVM is the Radial Basis Function (RBF) kernel with gamma parameters of 0.002 and Cost (C) of 0.1; 1; 10. Maximum Entropy is a classification technique that uses the entropy value to classify data with the evaluation model used, namely 5-fold cross-validation. The algorithm which has the highest accuracy and kappa statistics is the best algorithm for classifying the sentiments of Ajaib users. The results using the Support Vector Machine algorithm show the overall accuracy is 85.75% and the kappa accuracy is 58.07%. The results using the Maximum Entropy algorithm show an overall accuracy of 83% and kappa accuracy of 50.5%. This shows that sentiment using the Support Vector Machine has a better performance than Maximum Entropy.
MENGATASI OVERDISPERSI DENGAN REGRESI BINOMIAL NEGATIF PADA ANGKA KEMATIAN IBU DI KOTA BANDUNG Hilma Mutiara Winata
Jurnal Gaussian Vol 11, No 4 (2022): 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.11.4.616-622

Abstract

The maternal mortality rate in the city of Bandung is still a concern for the government, even though various health programs have been held to handle it. The very slight reduction in maternal mortality is a reason for further research to look for factors that have a significant effect. The data on maternal mortality cases usually contain a lot of zeros and follow the Poisson distribution so that they are solved with a Poisson regression model, however the model formed cannot be used because the model shows overdispersion with a deviation value of more than one. Therefore, to overcome this problem, negative binomial regression is used as a solution. This negative binomial regression model produces three predictor variables out of seven variables that have a significant effect on maternal mortality in the city of Bandung including pregnant women receiving FE1 (30 tablets), deliveries assisted by health personnel and postpartum service coverage. Then tested the goodness of the model from the negative binomial regression model by looking at the AIC value. The true negative binomial regression model is better because the AIC value is 109.4 which is smaller than 121.65 which is the AIC value of the Poisson regression model.
PEMODELAN TOPIK ULASAN APLIKASI NETFLIX PADA GOOGLE PLAY STORE MENGGUNAKAN LATENT DIRICHLET ALLOCATION Gina Rosalinda; Rukun Santoso; Puspita Kartikasari
Jurnal Gaussian Vol 11, No 4 (2022): 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.11.4.554-561

Abstract

The vast amount of review data available on the Google Play Store can be utilized to extract hidden essential information. These reviews have an unstructured format that requiring particular methods to automatically collect and analyze the review data. Topic modeling is an extension of text analysis that can find main themes or trends hidden in large sets of unstructured documents. This study applies topic modeling with the Latent Dirichlet Allocation (LDA) method to Netflix application review data sourced from the Google Play Store web. The Latent Dirichlet Allocation (LDA) method is a generative probabilistic model from textual data that can explain the hidden semantic themes in the review document. This research aims to analyze hidden topics that application users discuss. These hidden topics contain essential valuable information for Netflix users and the company. Users can use this information to decide before using Netflix services. Meanwhile, Netflix can use this information to improve the quality of its services. This research use data from a web scraping Netflix review on the Google Play Store from January 2021–August 2021. The results of topic modeling show that of the twelve topics generated, the most discussed topic by users is payment methods.
PENGGUNAAN SELEKSI FITUR CHI-SQUARE DAN ALGORITMA MULTINOMIAL NAÏVE BAYES UNTUK ANALISIS SENTIMEN PELANGGGAN TOKOPEDIA Tri Ernayanti; Mustafid Mustafid; Agus Rusgiyono; Arief Rachman Hakim
Jurnal Gaussian Vol 11, No 4 (2022): 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.11.4.562-571

Abstract

E-commerce is a medium for online shopping that is popular among the public. Ease of access for all internet users and the completeness of products offered by e-commerce are new alternatives in meeting the needs of the community. This causes stiff competition in the e-commerce, so e-commerce need to carry out the right marketing strategy in order to compete in obtaining, retaining, and partnering with customers, one of which is by reviewing aspects of customer satisfaction. Tokopedia is an e-commerce buying and selling online that connects sellers and buyers throughout Indonesia for free. In this study, an analysis of Tokopedia's customer sentiment was carried out with the Multinomial Naïve Bayes classification. Algorithm Multinomial Nave Bayes is a model development of the Nave Bayes. The difference lies in the selection of data, if Naïve Bayes uses a Gaussian that is suitable for continue, while Multinomial Naïve Bayes is suitable for discrete data such as the number of words in a document. Multinomial Naïve Bayes is the simplest method of probability classification but is sensitive to feature selection, so the amount of data is determined by the results of Chi-Square.Multinomial Naïve Bayes is used to classify customer opinions that are positive and negative so that they can form customer satisfaction factors Tokopedia, while the Chi-Square used to measure the level of feature dependence with class (positive and negative) so as to eliminate disturbing features in the classification process. Classification performance results using Multinomial Naïve Bayes without Chi-Square obtained accuracy and kappa statistics of 88% and 75.95%, while using Chi-Square obtained accuracy and kappa statistics of 95% and 89.99%, respectively. This means that Multinomial Naïve Bayes has quite effective performance and results in analyzing Tokopedia customer satisfaction sentiment and the use of Chi-Square for feature selection can improve the accuracy of the classification process. 
PREDIKSI TINGKAT TEMPERATUR KOTA SEMARANG MENGGUNAKAN METODE LONG SHORT-TERM MEMORY (LSTM) Rahmatul Akbar; Rukun Santoso; Budi Warsito
Jurnal Gaussian Vol 11, No 4 (2022): 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.11.4.572-579

Abstract

Temperature is one of the most important attributes of climate, temperature affects life in many different ways such as in agriculture, aviation, energy, and life in general. Temperature prediction is needed to make the right step to prevent the negative impact of climate change. Long Short-Term Memory (LSTM) is the method that can predict time series data, using the unique design of neural networks, LSTM can help to prevent vanishing gradient from happening which allows LSTM model to use more data from the past to predict the future. Hyperparameters like LSTM unit, epochs, and batch size are used to make the best model, the best model is the one with the lowest loss function. This research used climate data from 1 January 2019 until 31 December 2021 consist of 1096 data in total. The best prediction in this research is made by the model with 70% training data, 0,009 learning rate, 128 LSTM unit, 16 batch size, and 100 epochs with the lowest loss function of 0,013, this model gives MAPE value of 1,896016% and RMSE value of 0,725.

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