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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.
Arjuna Subject : -
Articles 733 Documents
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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.
PEMODELAN PRODUK DOMESTIK BRUTO DI INDONESIA DENGAN PENDEKATAN SEMIPARAMETRIK POLINOMIAL LOKAL DILENGKAPI GUI-R Muftia Lutfi Cahyani; Suparti Suparti; Budi Warsito
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.12.2.189-198

Abstract

Gross domestic product is a measuring tool for a country's economy that needs to be known so that the country is able to consider decisions taken regarding future economic policies. The local polynomial semiparametric method that combines parametric regression and local polynomial nonparametric can be one way of predicting a country's GDP. This method is used because in GDP modeling there is one independent variable that has a linear relationship while the other variables have a pattern that tends to cluster. The modeling aims to obtain a semiparametric local polynomial model on GDP in Indonesia with the influence of coal export volume as a parametric independent variable and world oil prices as a nonparametric independent variable from the first quarter of 2005 to the second quarter of 2021 which is equipped with a GUI to simplify calculations. Based on experiments on several types of kernels, bandwidth and model degrees, the best model is local polynomial semiparametric model with Gaussian kernel weighting at degree 2 which has the smallest GCV. This model also has an R-Square value of 89.2% where the value of GDP is strongly influenced by world oil prices and coal export volumes together. The forecasting ability of this best model is said to be good because it has a MAPE of 17.127%.
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.
CLUSTERING KARAKTERISTIK INDUSTRI KECIL DAN MENENGAH DI KOTA KENDARI MENGGUNAKAN ALGORITMA k-PROTOTYPES Reihanah, Khalifah Nadya; I Maruddani, Di Asih; Widiharih, Tatik
Jurnal Gaussian Vol 12, No 3 (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.12.3.340-351

Abstract

Industri Kecil Menengah (IKM) have important roles in economic development. The large number of IKM cannot be separated from various problems. The basic problems faced by IKM in Kendari are limited capital, inadequate human resources, difficulty in obtaining raw materials, and the Indonesian economy which has slumped due to the impact of the COVID-19 pandemic. This research was conducted with the aim of classifying the characteristics of the IKM with the optimal number of clusters. The method used is k-Prototypes Clustering with values of k = 2, 3, 4, ..., and 10. The k-Prototypes method is a clustering method that maintains the efficiency of the k-Means algorithm in handling large data when compared to the hierarchical clustering method. This method can group mixed type data (consisting of numeric type data and categorical type data). Based on the analysis, the optimal number of clusters is five clusters, with a Silhouette Index value of 0.461. Cluster 5 is the best IKM cluster with the highest average number of workers and the highest average investment value, while cluster 2 has the lowest average investment value and IKM in this cluster is relatively new compared to IKM in other clusters.
PERBANDINGAN METODE DOUBLE EXPONENTIAL SMOOTHING HOLT DAN FUZZY TIME SERIES CHENG PADA PERAMALAN HARGA EMAS DI INDONESIA DILENGKAPI GUI R Fauziyyah, Fida; Sugito, Sugito; Santoso, Rukun
Jurnal Gaussian Vol 12, No 4 (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.12.4.509-519

Abstract

Investment is placing a certain amount of money at this time to get some profit in the future. Investments are divided into three based on the period, namely short-term investments, medium-term investments, and long-term investments. Gold is an example of a good long-term investment. Gold price forecasting is an important thing to know when investing in gold. In this study, gold price data is divided into two parts, namely training data consisting of 674 data from 1 September 2020 to 6 July 2022 and testing data consisting of 75 data from 7 July 2022 to 19 September 2022. The data indicates that there is a trend element so it is suitable for analysis using the Double Exponential Smoothing Holt and Fuzzy Time Series Cheng. Data processing using the Double Exponential Smoothing Holt and Fuzzy Time Series Cheng methods is complemented by the creation of a Graphical User Interface (GUI) which can facilitate the process of selecting the best method. The analysis's findings indicate that Double Exponential Smoothing Holt (0.5427603%), which has a reduced MAPE value than Fuzzy Time Series Cheng (0.6053103%), is the best method.
PERBANDINGAN MODEL REGRESI STRATIFIED COX DAN EXTENDED COX PADA ANALISIS SURVIVAL PENDERITA KANKER PAYUDARA Samosir, Jessika Aurora; sudarno, sudarno; Maruddani, Di Asih I
Jurnal Gaussian Vol 13, No 1 (2024): 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.13.1.59-69

Abstract

Breast cancer is a tumor that develops in the breast where cells in the mammary gland divide and develop uncontrollably. Breast cancer is the most common cancer that causes death in women among other cancers. This study aims to determine the factors that affect the survival of breast cancer patients from the METABRIC database. This study was analyzed using survival analysis method. The method that is often used is the Cox proportional hazard model where the proportional hazard assumption must be met. There is variable that do not meet the assumption so that the methods used are stratified Cox and extended Cox. The stratified Cox model overcomes variables that do not meet the assumption by stratifying variables that do not meet the assumption. The extended Cox model overcomes variables that do not meet the assumption by interacting the variables with a time function. The time functions used in this study are linear time functions and logarithmic time functions. Based on the smallest AIC value, the best model is the stratified Cox regression model without interaction. Factors that affect the survival of breast cancer patients from the METABRIC database are tumor size, chemotherapy, stage 1, stage 2, and type of surgery.
PERBANDINGAN ANALISIS SURVIVAL MENGGUNAKAN REGRESI COX PROPORTIONAL HAZARD DAN REGRESI WEIBULL PADA PASIEN COVID-19 DI RSUD TAMAN HUSADA BONTANG Damayanti, Sindi; Wuryandari, Triastuti; Sudarno, Sudarno
Jurnal Gaussian Vol 12, No 3 (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.12.3.453-464

Abstract

COVID-19 is brought on by the Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2) and transmitted to humans through animal. SARS-CoV-2 infection affects patient's metabolism and causes hyperinflammatory. This condition affects individuals with risk factors such as age, gender, diabetes, heart disease, hypertension, Chronic Obstructive Pulmonary Disease (COPD), obesity, and Acute Respiratory Distress Syndrome (ARDS). One approach to figuring out the association between the time of an occurrence and the independent factors is the Cox Proportional Hazard Regression. The Cox PH regression is a semiparametric model because it doesn’t require a specific distribution test. There is a parametric model used in modeling and analyzing failure time data, namely Weibull regression. The case study is patients with COVID-19 at Taman Husada Bontang Regional Public Hospital who underwent hospitalization from August 2021 to September 2021 data. Based on the Cox PH Regression and Weibull Regression models, variables that affect the survival time of COVID-19 patients are heart disease and ARDS. The AIC value obtained using the Cox Proportional Hazard regression is 635.6149, this value is smaller than the Weibull regression which is 745.5509 so the use of survival analysis with the Cox Proportional Hazard regression is better than the Weibull regression in this case.
ANALISIS REGRESI COX UNTUK MENENTUKAN FAKTOR-FAKTOR YANG MEMPENGARUHI LAMA STUDI MAHASISWA S1 FMIPA UNIVERSITAS LAMBUNG MANGKURAT Tanjung, Winda Adinda; Anggraini, Dewi; Annisa, Selvi
Jurnal Gaussian Vol 13, No 1 (2024): 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.13.1.1-12

Abstract

The length of study is the time it takes a student to complete his education. One of the tertiary institutions that is trying to improve the quality of its student degrees in Indonesia is the Faculty of Mathematics and Natural Sciences, University of Lambung Mangkurat (ULM). This study aims to determine the factors that influence the length of study of ULM FMIPA undergraduate students. The analysis used to determine the effect of these factors is survival analysis through the Cox Proportional Hazard Regression method. The results of this study indicate that the Chemistry Study Program has the greatest chance of being able to complete studies ≤4 years or graduate on time compared to other Study Programs. From the process of the Cox Proportional Hazard Regression method, it was found that the factors that significantly affected the length of study of FMIPA ULM undergraduate students were Gender (Female), GPA (> 3.50) and Status of Residence (Bos/Dormitory/Lodge).
ANALISIS SENTIMEN VAKSIN COVID-19 PADA TWITTER MENGGUNAKAN RECURRENT NEURAL NETWORK (RNN) DENGAN ALGORITMA LONG SHORT-TERM MEMORY (LSTM) Maharani, Chintya Ayu; Warsito, Budi; Santoso, Rukun
Jurnal Gaussian Vol 12, No 3 (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.12.3.403-413

Abstract

The Coronavirus, also known as the Covid-19 pandemic, has reached every country worldwide, including Indonesia. Covid-19 is still prevalent and has killed many people in Indonesia. This makes it impossible to stop Covid-19 from spreading. The government's attempt to stop the Covid-19 pandemic is acquiring the vaccine. The administration of the Covid-19 vaccine has generated much discussion on social media, particularly Twitter. Tweets displaying public opinion on Twitter can be used for sentiment analysis and categorizing public opinion on the Covid-19 vaccine. 20,000 tweets were collected by Twitter crawling between January 10 and January 15, 2022. 3.290 tweets were left after pre-processing and meaningless tweets were eliminated. The data were processed using the Recurrent Neural Network method with the Long Short-Term Memory algorithm to determine its accuracy and identify topics often discussed by the public on Twitter. The LSTM method is capable of storing old information/data. A model with 70% training data, a learning rate of 0.01, 100 LSTM units, 32 batch sizes, 100 epochs, a cross-entropy loss function, and Adam optimizers was used to build the classification in this study. The accuracy value obtained from the performance evaluation of the Long Short-Term Memory model research was 80.34%.
PREDIKSI HARGA DAGING SAPI DI KABUPATEN BREBES MENGGUNAKAN PEMODELAN ARFIMA DENGAN EFEK GARCH Imani, Nanda Diva Lingkar; Tarno, Tarno; Saputra, Bagus Arya
Jurnal Gaussian Vol 12, No 4 (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.12.4.570-580

Abstract

Beef is a source of animal protein which is rich in nutrients and much-loved by the people of Indonesia. Brebes Regency is an area in Indonesia that has local livestock assets, namely Java Brebes cattle or also known as Jabres cattle. The existence of this jabres cattle is one of the guardians of beef price stability in Brebes in particular and in Central Java in general. The price of beef often fluctuates, to minimize losses, it is necessary to predict the market price. The model for predicting research data is the ARFIMA-GARCH model which is a model that can explain long memory patterns in time series data and experience heteroscedasticity problems. This study aims to obtain the best model with time series analysis and predict the selling price of beef in Brebes Regency for the next 52 weeks using ARFIMA modeling which is enhanced using the addition of the GARCH model. The results of the analysis that has been carried out on beef price data in Brebes Regency can be concluded that the best model obtained is the ARFIMA model ([9], 0.5461747, 0) – GARCH (1, 1). Based on the predictions that have been made using the best model, the resulting MAPE value is 1.56375%, so the model is very good for predicting beef prices in Brebes Regency in the next several periods.

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