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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,
Other
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 15 Documents
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Search results for , issue "Vol 12, No 4 (2023): Jurnal Gaussian" : 15 Documents clear
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.
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%.
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.
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.
ANALISIS SENTIMEN KEBIJAKAN PENYELENGGARA SISTEM ELEKTRONIK LINGKUP PRIVAT MENGGUNAKAN PENALIZED LOGISTIC REGRESSION DAN SUPPORT VECTOR MACHINE Amalia, Nur Afnita; Utami, Iut Tri; Wilandari, Yuciana
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.12.4.560-569

Abstract

The implementation of the Electronic System Operator (ESO) regulation, which imposes blocking sanctions on several ESOs that do not register, has caused a variety of opinions from the public, especially on social media Twitter to raise the hashtag #BlokirKominfo. In this research, sentiment analysis was carry outed to determine the response of Twitter users to the implementation of ESO regulations by MoCI. Sentiment analysis is a textual information extraction process that classifies sentiment into positive and negative categories. The steps that are used including crawling data, text preprocessing, labeling, feature selection, term weighting with TF-IDF and classification using the Penalized Logistic Regression (PLR) with the L1 regularization and Support Vector Machine (SVM) with the RBF kernel. Sentiment classification in PLR is basically finding the optimal weight parameter. The idea of SVM sentiment classification is to find the best hyperplane to separate the data points. Evaluation of classification performance uses the accuracy value calculated through the confusion matrix. The highest percentage of accuracy in sentiment classification results using the PLR is 84,12% and SVM is 83,53%. It means that the PLR algorithm works better than the SVM algorithm in classifying public sentiment towards the implementation of ESO regulations on Twitter.

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