Jurnal Gaussian
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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<![CDATA[PERBANDINGAN METODE LVQ DAN BACKPROPAGATION UNTUK KLASIFIKASI STATUS GIZI ANAK DI KECAMATAN SANGKUP ]]>
<![CDATA[Alamri, Fahima]]>;
<![CDATA[Ningsih, Setia]]>;
<![CDATA[Djakaria, Ismail]]>;
<![CDATA[Wungguli, Djihad]]>;
<![CDATA[K. Hasan, Isran]]>
<![CDATA[ Jurnal Gaussian Vol 12, No 3 (2023): Jurnal Gaussian ]]>
Publisher : <![CDATA[Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro ]]>
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DOI: 10.14710/j.gauss.12.3.314-321
<![CDATA[The problem of children nutrition isi still a problem in various regions in Indonesia. Poor or poor nutrition of children is influenced by several factors, namely insufficient food intake and infectious diseases. Undernutrition or poor nutrition can be known from the nutritional status assessment obtained from classifying the nutrional status of children. Classification is a part of data mining that is often used to classify data based on certain data or variables. This study aims to compare the classification of the nutritional status of children using data mining with the learning vector quantization (LVQ) and backpropagation methods. Test were carried out using a comparasion ratio of training and testing data, namely 75% and 25%. From the research results, LVQ is superior with an accuracy of 95.12% and backpropagation of 80.49%.]]>
<![CDATA[CLUSTERING KARAKTERISTIK INDUSTRI KECIL DAN MENENGAH DI KOTA KENDARI MENGGUNAKAN ALGORITMA k-PROTOTYPES ]]>
<![CDATA[Reihanah, Khalifah Nadya]]>;
<![CDATA[I Maruddani, Di Asih]]>;
<![CDATA[Widiharih, Tatik]]>
<![CDATA[ Jurnal Gaussian Vol 12, No 3 (2023): Jurnal Gaussian ]]>
Publisher : <![CDATA[Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro ]]>
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DOI: 10.14710/j.gauss.12.3.340-351
<![CDATA[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.]]>
<![CDATA[PERBANDINGAN ANALISIS SURVIVAL MENGGUNAKAN REGRESI COX PROPORTIONAL HAZARD DAN REGRESI WEIBULL PADA PASIEN COVID-19 DI RSUD TAMAN HUSADA BONTANG ]]>
<![CDATA[Damayanti, Sindi]]>;
<![CDATA[Wuryandari, Triastuti]]>;
<![CDATA[Sudarno, Sudarno]]>
<![CDATA[ Jurnal Gaussian Vol 12, No 3 (2023): Jurnal Gaussian ]]>
Publisher : <![CDATA[Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro ]]>
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DOI: 10.14710/j.gauss.12.3.453-464
<![CDATA[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.]]>
<![CDATA[ANALISIS SENTIMEN VAKSIN COVID-19 PADA TWITTER MENGGUNAKAN RECURRENT NEURAL NETWORK (RNN) DENGAN ALGORITMA LONG SHORT-TERM MEMORY (LSTM) ]]>
<![CDATA[Maharani, Chintya Ayu]]>;
<![CDATA[Warsito, Budi]]>;
<![CDATA[Santoso, Rukun]]>
<![CDATA[ Jurnal Gaussian Vol 12, No 3 (2023): Jurnal Gaussian ]]>
Publisher : <![CDATA[Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro ]]>
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DOI: 10.14710/j.gauss.12.3.403-413
<![CDATA[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%.]]>
<![CDATA[PEMODELAN DATA LONGITUDINAL MENGGUNAKAN REGRESI POLINOMIAL LOKAL PADA KELOMPOK SAHAM PERUSAHAAN PENYEDIA JASA TELEKOMUNIKASI DENGAN GUI R ]]>
<![CDATA[Noer Rachma, Gustyas Zella]]>;
<![CDATA[Suparti, Suparti]]>;
<![CDATA[Santoso, Rukun]]>
<![CDATA[ Jurnal Gaussian Vol 12, No 3 (2023): Jurnal Gaussian ]]>
Publisher : <![CDATA[Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro ]]>
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DOI: 10.14710/j.gauss.12.3.352-361
<![CDATA[The economic development of a country can be seen based on the capital market that was growing and developing. One of the most popular capital market instruments is stocks. Stocks based on market capitalization groups include longitudinal data. One of the statistical methods for longitudinal data modelling is nonparametric regression which has no modelling assumptions requirement. This research models monthly stock prices using a nonparametric local polynomial method with the selection of the best model which has minimum value of Mean Square Error (MSE). The data was divided into 2 parts, namely in sample data from November, 2018 to June, 2021 to form a model and out sample data from July, 2021 to February, 2022 used for evaluation of model performance by Mean Absolute Percentage Error (MAPE) values. The best model is the local polynomial model with Biweight kernel function of degree 5, local point of 4, bandwidth of 37, and MSE value of 0.03481085. MAPE out sample of data value is 31.13%, which indicating that the model has sufficient forecasting. In this research arrange Graphical User Interface (GUI) by using R software with shiny package is built to make display output data analyzing more easy and more interactive.]]>
<![CDATA[PEMODELAN ANTAR VARIABEL EKONOMI SECARA SIMULTAN MENGGUNAKAN PENDEKATAN VECTOR ERROR CORRECTION MODEL (VECM) ]]>
<![CDATA[Halim, Rossa Fitria]]>;
<![CDATA[Sudarno, Sudarno]]>;
<![CDATA[Tarno, Tarno]]>
<![CDATA[ Jurnal Gaussian Vol 12, No 3 (2023): Jurnal Gaussian ]]>
Publisher : <![CDATA[Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro ]]>
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DOI: 10.14710/j.gauss.12.3.414-424
<![CDATA[The movement of the Jakarta Composite Index (IHSG) is influenced by internal factors such as inflation, the BI Rate, exchange rate, and external factors consisting of world gold prices and world crude oil prices. The six economic variables have a relationship simultaneously. Vector Error Correction Model (VECM) is a Vector Autoregressive (VAR) which has non-stationary but has a long-term cointegration. The purpose of this study is to analyze the cointegration among economic variables and determine the model of economic variables. Data for the variables is monthly data for the period January 2012 to December 2021 which has fulfilled stationarity at first level of difference. The optimum lag chosen is lag 1 so that the model to be used is VECM(1) and the resulting VAR system has less than one modulus for the VAR to be stable. Johansen's cointegration test yielded 5 cointegrations, so each short-term period adjusts simultaneously and tends to adjust with each other to achieve long-term equilibrium. The Mean Absolute Percentage Error (MAPE) value in the evaluation of model accuracy ranges below 10%, so the model’s performance is very good.]]>
<![CDATA[IMPLEMENTASI METODE NAIVE BAYES CLASSIFIER UNTUK KLASIFIKASI SENTIMEN ULASAN PENGGUNA APLIKASI NETFLIX PADA GOOGLE PLAY ]]>
<![CDATA[Rieuwpassa, Jessica Athalia]]>;
<![CDATA[Sugito, Sugito]]>;
<![CDATA[Widiharih, Tatik]]>
<![CDATA[ Jurnal Gaussian Vol 12, No 3 (2023): Jurnal Gaussian ]]>
Publisher : <![CDATA[Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro ]]>
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DOI: 10.14710/j.gauss.12.3.362-371
<![CDATA[The COVID-19 pandemic has led to restrictions on activities in public places or facilities, such as cinemas. This has resulted in increased users of streaming service applications such as Netflix where users can access videos or movies online. Netflix users continue to increase from year to year, but its users began to decrease along with other streaming applications. Related to this, sentiment analysis was carried out on the classification of positive and negative reviews given by users on the Google Play website. The classification is expected to produce good accuracy and be analyzed so that it can be useful information for Netflix and potential users of streaming applications. The Naive Bayes Classifier method is a classification algorithm that is easy to apply and has high effectiveness for classifying text. This method utilizes the concept of conditional probability and has a strong assumption of independence. This study uses 2.850 Netflix application review data on Google Play which is then processed and divided into training data and test data with a ratio of 80:20. Classification with the Naive Bayes Classifier produces an accuracy value of 87,33%, a precision value of 87,6%, a recall value of 93,53%, and an F-measure value of 90,47% so it can be concluded that the performance of the Naive Bayes method is good for classifying user reviews of the Netflix.]]>
<![CDATA[KAJIAN SISTEM ANTRIAN PADA COUNTER KASIR DOMINO’S PIZZA MENGGUNAKAN MEAN VALUE ANALYSIS (STUDI KASUS: DOMINO’S PIZZA GAJAH MADA PEKALONGAN) ]]>
<![CDATA[Putri Milenia, Erin Novela]]>;
<![CDATA[Sugito, Sugito]]>;
<![CDATA[Widiharih, Tatik]]>
<![CDATA[ Jurnal Gaussian Vol 12, No 3 (2023): Jurnal Gaussian ]]>
Publisher : <![CDATA[Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro ]]>
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DOI: 10.14710/j.gauss.12.3.425-433
<![CDATA[Queuing is the phenomenon that occurs when a service needs more than it can handle. This phenomenon is common in many places, such as restaurants. Attempts to analyze the behavior of queuing systems are called queuing system studies, one of which is the use of mean analysis (MVA). MVA can be used when arrival and service times do not follow an exponential distribution. The case study is the queuing system of Domino's Pizza Gajah Mada Pekalongan, which has two counters and took seven days to observe. This study aims to apply MVA and determine performance measures for queuing systems. In this study, MVA can be used because the arrival-to-service time does not follow an exponential distribution. The resulting cue model is (Gamma/GEV/2). (GD/∞/∞) and utilization is 0.43045. The average customer queuing and in the system are at most one customer. The average time to queue is 31.80336 seconds, the average time to complete a service is 321.0971 seconds, and the probability that the system isn’t busy 0.39816 or 39.8%.]]>
<![CDATA[PERBANDINGAN KINERJA METODE KLASIFIKASI K-NEAREST NEIGHBOR DAN SUPPORT VECTOR MACHINES PADA DATASET PARKINSON ]]>
<![CDATA[Ridho, Wahyu Anwar]]>;
<![CDATA[Wuryandari, Triastuti]]>;
<![CDATA[Hakim, Arief Rachman]]>
<![CDATA[ Jurnal Gaussian Vol 12, No 3 (2023): Jurnal Gaussian ]]>
Publisher : <![CDATA[Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro ]]>
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DOI: 10.14710/j.gauss.12.3.372-381
<![CDATA[The government program in the form of social assistance (bansos) is part of the effort to improve the welfare of the community and ensure basic needs and improve the standard of living of the recipients. However, there are often cases of mistargeting of social assistance programs by the government. Improper data management and Data Terpadu Kesejahteraan Sosial (DTKS) which are not used as the cause of the distribution of social assistance are not well targeted. The data can be analyzed using the classification method to determine whether or not the family accepts the ban from the government. This study classifies the SUSENAS data by comparing K-Nearest Neighbor (KNN) and Support Vector Machines (SVM). The advantage of the KNN method lies in the level of accuracy to solve problems with large data while the SVM method has better performance in various fields of application such as bioinformacs, handwriting recognition, text classification and so on. Based on training data and testing data comparison 85%:15% showed that KNN method had a better classification performance than the SVM method. The accuracy value of KNN method is 80,95% higher than the accuracy value of SVM method is 78,79%.]]>
<![CDATA[PERAMALAN HARGA BERAS PREMIUM BULANAN DI TINGKAT PENGGILINGAN MENGGUNAKAN FUZZY TIME SERIES MARKOV CHAIN ]]>
<![CDATA[Sari, Virgania]]>;
<![CDATA[Hariyanto, Sylvia Ayu]]>
<![CDATA[ Jurnal Gaussian Vol 12, No 3 (2023): Jurnal Gaussian ]]>
Publisher : <![CDATA[Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro ]]>
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DOI: 10.14710/j.gauss.12.3.322-329
<![CDATA[Rice is one of the crucial food commodities in Indonesia whose price fluctuates every year. Forecasting is the science of predicting an event in the future and predicting future conditions using historical data. One of the forecasting methods is the Fuzzy Time Series which is used to predict time series data that can be widely used on any real time data. This research used forecasting with the Fuzzy Time Series Markov Chain method because this method provides a good accuracy value. The historical data used is monthly data on the average price of premium rice at the Indonesian mill level for the period January 2014-July 2022 then divided into training data and testing data. The error rate used is MAPE and the results of calculations with Fuzzy Time Series Markov Chain on data testing the period November 2020-July 2022 obtained a very good MAPE value of 0.81%. Forecasting results for the period August 2022 obtained the results of Rp. 9.627,99]]>
