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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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METODE TRIPLE EXPONENTIAL SMOOTHING HOLT-WINTER’S MULTIPLICATIVE DAN DEKOMPOSISI KLASIK MULTIPLIKATIF UNTUK PERAMALAN RATA-RATA KENAIKAN KONSENTRASI KARBON DIOKSIDA (CO2) GLOBAL Ersita, Vika; Wilandari, Yuciana; Sugito, Sugito
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.12.3.434-444

Abstract

Global warming occurs due the high concentration of Greenhouse Gases (GHG) in the atmosphere, which is called the greenhouse effect. The highest greenhouse gas that causes global warming that is being piled up in the atmosphere due human activity is carbon dioxide. Data on the average increase in global carbon dioxide (C02) concentrations are assumed contain elements of trend and seasonality. Holt-Winter's Multiplicative Triple Exponential Smoothing Method and Multiplicative Classical Decomposition the best choices in predicting data that contains trend and seasonality elements. Forecasting data on the global average increase CO2 has the objective of predicting data for the next 12 periods. The data used is data on the global average increase  for the period January 2013 to December 2022. The prediction error measure used is MAPE (Mean Absolute Percentage Error). The results of the analysis on the Triple Exponential Smoothing Holt-Winter's Multiplicative method obtained a MAPE value of 0.09395%, indicating very good prediction category, while the results of the analysis of the Multiplicative Classical Decomposition method had a MAPE value of 0.07021%, which means that it has very good category in do forecasting. Based on the MAPE value obtained, the best method is the Multiplicative Classical Decomposition method.
ANALISIS KEPUASAN TERHADAP LAYANAN APLIKASI DOLTINUKU DENGAN MENGGUNAKAN METODE STRUCTURAL EQUATION MODELING-PARTIAL LEAST SQUARE (SEM-PLS) Ul Haq, Hasna Faridah Dhiya; Hakim, Arief Rachman; Suparti, Suparti
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.12.4.605-615

Abstract

Doltinuku is an application that is used to buy and sell products and services online from MSME in Gedawang, Banyumanik Sub-District, Semarang City which was just launched in June 2021. Because this application is still new, this application needs to be developed by looking at users' satisfaction. This study wants to determine Doltinuku customer satisfaction using Structural Equation Modeling-Partial Least Square (SEM-PLS) method. PLS is an alternative method of SEM that is able to handle variance-based problems. In this study, customer satisfaction is measured through variables such as Quality, Information, Reputation, and Trust. Based on the results of the analysis, variables that have a significant effect on customer satisfaction are variable Quality and Reputation and have influence of 70.9% on satisfaction whose value is obtained from the R2 value. Then the variables that have no significant effect are variable Information and variable Trust.
PERAMALAN INDEKS HARGA SAHAM GABUNGAN (IHSG) MENGGUNAKAN MODEL INTERVENSI FUNGSI PULSE Rosilawati, Elsa Dwi; Tarno, Tarno; Wuryandari, Triastuti
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.12.3.382-391

Abstract

The intervention model is one model that is frequently used to explain how interventions from both internal and external sources can lead to dramatic fluctuations in a time series of data. The Composite Stock Price Index, known as the IDX Composite, is an index that tracks all stock price performance. For the Composite Stock Price Index from 2 October 2020 to 6 June 2022, daily close price data are used in this study. The data showed a sharp reduction starting on 9 May 2020 (T=386) and lasting for the following 4 days, which made the pulse function the likely intervention model. Rising interest rates and high inflation figures from the United States are to blame for the drop in IDX Composite close price. In addition, a lot of profit-taking was done because of the Eid holidays and the expectation of a substantial increase in COVID-19. The best intervention model created is ARIMA ([3],1,0) with an intervention order of b=0, r=0, and s=11, which can then be used to forecast Composite Stock Price Index for the following period. This is based on the outcomes and analyses. The sMAPE value in the research utilizing this model was 0.98%, suggesting very strong forecasting capabilities.
ANALISIS FAKTOR RISIKO GAGAL JANTUNG DENGAN REGRESI LOGISTIK BERBASIS IoMT Arisandi, Rizwan; Dewi, Adhe Lingga
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.12.4.549-559

Abstract

Technology in the era of revolution 4.0, which is currently developing so rapidly, has given birth to Internet of Things technology and can be implemented in the health sector or called the Internet of Medical Things (IoMT). IoMT technology can be applied to monitor heart disease patients and obtain medical record data that is useful for further decision making, such as predicting the potential for heart disease using logistic regression. This study uses medical record data for heart disease with the variable heart failure as the dependent variable and the variables age, gender, diabetes, anemia, hypertension, smoking habits as independent variables. In this research, machine learning was applied with a logistic regression algorithm on clinical data collected via IoMT devices to detect heart disease. Classification. The accuracy of the model was obtained at 75%, so it can be said that the model score is on the average model scale, which means the model is quite good. The average gender of patients who suffer a heart attack is male with an age range of 60-70 years. Furthermore, in patients who have a history of hypertension, a person's risk of developing heart failure increases by 4,2%. Meanwhile, in patients who have a history of diabetes, a person's risk of developing heart failure increases by 4%.
PENERAPAN MODEL ASYMMETRIC POWER AUTOREGRESSIVE CONDITIONAL HETEROSCEDASTICITY (APARCH) TERHADAP HARGA MINYAK MENTAH DUNIA Famuji, Ahmad; Sriliana, Idhia; Agwil, Winalia
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.13.1.99-109

Abstract

Heteroscedasticity poses a challenge in ARIMA modeling by causing residual variance to be non-constant, leading to less efficient estimates. This issue often arises in time series data due to volatility, which measures data fluctuation over time. To address heteroscedasticity, models like ARCH and GARCH incorporate variance changes into forecasting. However, they lack the ability to capture asymmetry, the difference in impact between good and bad news on volatility. The APARCH model, on the other hand, addresses this by modeling volatility with asymmetry elements. Daily world crude oil prices, known for high volatility, serve as a case study for this research. By employing the APARCH model, the study aims to forecast these prices accurately. Results indicate that the APARCH(1,1) model outperforms the best GARCH model, ARCH(2), as it yields a smaller Mean Absolute Percentage Error (MAPE) of 6.033487. This highlights the superior accuracy of APARCH in forecasting data with heteroscedasticity issues, particularly in the context of daily crude oil prices.
KAJIAN SIMULASI PERBANDINGAN METODE RIDGE REGRESSION DAN ADJUSTED RIDGE REGRESSION UNTUK PENANGANAN MULTIKOLINEARITAS Nisa, Choirun; Hastuti, Siti Hariati
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.12.3.330-339

Abstract

Regression analysis is widely used in research. However, often in using this analysis the assumption of non-multicollinearity is not fulfilled. Handling of these problems can be done using Ridge Regression (RR) and Adjusted Ridge Regression (AR) methods. This study aims to compare the performance of RR and AR in handling multicollinearity among explanatory variables in multiple regression analysis using data simulation. The simulated data contain various multicollinearity level (ρ = 0.6, 0.8, 0.9) with of each different sample size (n = 20, 50, 100). The performance of the two methods are compared using Mean Square Errors (MSE). The result shows that the AR method and the RR method produce a smaller MSE value when the sample size used is larger. The MSE value generated by the AR method tends to be smaller than the RR method which can be seen from each data repetition used. It shows that the AR method is relatively more effective than the RR method for dealing with multicollinearity problems.
IDENTIFIKASI POLA PERILAKU REMAJA DENGAN PATH ANALYSIS Saadah, Ardiana Alifatus; Fakhriyana, Deby; Hersugondo, Hersugondo
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.12.4.499-508

Abstract

Globalization has an impact on cultural changes in Indonesia. Apart from positive impacts, globalization also has several negative impacts. The decreasing level of politeness in today's teenagers is part of a cultural change that we cannot ignore. Teenagers in this era are reportedly paying less attention to how to act and behave politely. Politeness is the practical application of good manners and etiquette. To improve polite behavior in teenagers, it is important to know factors that might influence polite behavior. This study used psychological theory developed by Ajzen and Fishbein, the Theory of Reasoned Action. Model in this theory consists of four variables, namely attitude, subjective norm, behavioral intention and behavior. Analytical method used in this research is path analysis. Based on the test results, the attitude variable has an effective influence on increasing the polite behavior variable in teenagers. This is because attitude variable not only influence behavioral variable directly, but also indirectly through behavioral intention variable. Furthermore, the increase in polite behavior is significantly influenced by behavioral intentions. Model combination is able to explain 63.06% of the data diversity, while the rest is explained by other variables and error.
PEMODELAN TINGKAT PENGANGGURAN TERBUKA TERHADAP FAKTOR – FAKTOR YANG MEMPENGARUHINYA DI PULAU KALIMANTAN MENGGUNAKAN REGRESI NONPARAMETRIK SPLINE TRUNCATED Ma’rifa, Aulia; 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.13.1.48-58

Abstract

The Open Unemployment Rate is an indicator used to measure the unemployment rate in the labor force. Kalimantan Island is one of the largest islands in Indonesia with a population of around 16.8 million people and is still experiencing problems in overcoming unemployment. Efforts are needed to overcome the problem of unemployment so that it can be resolved and does not have an impact on many things. The purpose of this study was to determine the factors that influence unemployment in Kalimantan using truncated spline nonparametric regression. The nonparametric spline truncated regression analysis approach is used because the pattern of relationship between the open unemployment rate and the factors that are thought to influence it does not form a specific pattern. The results of this study obtained the best model using one knot, with the average length of schooling  and labor force participation rate  variables able to explain the variability of the open unemployment rate in Kalimantan of 56,05 percent.
METODE ENSEMBLE ROBUST CLUSTERING USING LINKS (ROCK) UNTUK PENGELOMPOKAN PERGURUAN TINGGI SWASTA (PTS) DI KOTA SEMARANG Jannah, Berliana; Utami, Iut Tri; Hakim, Arief Rachman
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.12.3.445-452

Abstract

The purpose of this research is to group PTS that have performance achievements in five years, through the quality of Human Resources and Students (Input), the quality of Institutional Management (process), the quality of Short-Term Performance Achievements (Output) and the quality of Long-Term Performance Achievements (Outcome). In addition, it can also be seen from the form of PTS, PTS Accreditation and PTS Research Performance. This PTS grouping uses mixed data, namely numerical data and categorical data. The method used for grouping mixed data is the ROCK ensemble method (Robust Clustering Using Links). The results of clustering numerical data obtained the optimum number of groups 3, on categorical data obtained the optimum group 4. After clustering each type of data and merging and clustering obtained the optimum group 3 with a threshold (θ) is 0.2. The results of each group are: low quality consist of 29 PTS, medium quality consist of 7 PTS, and high quality there is 1 PTS. The results of this research can be used to cluster private universities in Semarang City, so that it can be used as a reference for prospective students in choosing private universities in Semarang, and can be referenced to the Central Java LLDIKTI in determining the quality of private universities in Semarang City.
ANALISIS SENTIMEN PENGGUNA ONLINE TRAVEL AGENT (OTA) PADA PERUSAHAAN PEGIPEGI.COM MENGGUNAKAN RANDOM FOREST Lestari, Ayu; Santoso, Rukun; Suparti, Suparti
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.12.4.616-624

Abstract

The presence of the internet makes online applications increasingly attractive to the public in supporting their daily activities. Online applications have developed rapidly, including online travel agent (OTA) companies such as Pegipegi. Pegipegi is a platform designed to meet the community's tertiary needs, such as providing accommodations for vacations. Pegipegi has an application that can be downloaded through the Google Playstore. Google Playstore provides a review feature as a medium for communication between application owners and consumers to express opinions that felt when using the application. The reviews submitted can be used as data to carry out sentiment analysis. Data collection was carried out on 11 December 2021 – 11 December 2022. A total of 2926 reviews obtained. Sentiment analysis was able to proceed by a classification method. This research used Random Forest to classify opinions on positive and negative sentiments. Random Forest is a classification model based on the majority vote of all decision trees. Classification using Random Forest produces an accuracy of 92.27% and AUC-ROC of 82.35%. Based on this accuracy and AUC-ROC value, the Random Forest algorithm has a good model performance in classifying the opinions of Pegipegi application users because it has a good accuracy and AUC-ROC value.

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