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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 790 Documents
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PENGKLASIFIKASIAN PENYAKIT HIPERTENSI MENGGUNAKAN METODE CHI-SQUARE AUTOMATIC INTERACTION DETECTION Kharisma Dwi Wahyuni; Darnah Darnah; M Fathurahman
Jurnal Gaussian Vol 14, No 2 (2025): 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.14.2.489-499

Abstract

Data mining is a technology used as an automated tool in the decision-making with classification being one of its techniques. The Chi-square Automatic Interaction Detection method classifies data by dividing samples into groups based on certain criteria, displaying results in a tree diagram. This study aims to obtain related factors, classification results, and accuracy of hypertension status classification of visitors to the East Kalimantan Provincial Health Office stand at the 2023 Kaltim Expo in Samarinda City using the CHAD method. The study found that age and family history are factors related to hypertension status. Classification results showed 10 elderly visitors without a family history of hypertension (2 with hypertension, 8 without), 216 non-elderly visitors without a family history of hypertension (9 with hypertension, 207 without), 10 elderly visitors with a family history of hypertension (9 with hypertension, 1 without), and 117 non-elderly visitors with a family history of hypertension (36 with hypertension, 81 without). The accuracy of hypertension status classification using the CHAID method was 86%.
ANALISIS TINGKAT PENGANGGURAN TERBUKA BERDASARKAN INDIKATOR SOSIAL DI PROVINSI KEPULAUAN BANGKA BELITUNG MENGGUNAKAN PENDEKATAN REGRESI B-SPLINE Khoirun Laili Nur Amaliah; Diska Amalya; Nila Selviana; Muthia Afrilita; Dea Saskia Amanda Putri; Ineu Sulistiana
Jurnal Gaussian Vol 14, No 2 (2025): 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.14.2.401-410

Abstract

The Open Unemployment Rate (TPT) is usually an important indicator in assessing the level of effectiveness of regional development in providing employment. In the Bangka Belitung Islands Province, the dynamics of the TPT have shown a fluctuating pattern over the past few years. The fluctuation of the TPT can be caused by several social indicators such as the Labor Force Participation Rate (TPAK), the Gross Participation Rate (APK) of upper education, and the Poverty Depth Index (P1). Judging from the data obtained, the form of data that has an irregular pattern, the method used in this study is b-spline regression which can capture fluctuating patterns in the data. The results show that the b-spline model is very good at capturing fluctuating patterns in the Open Unemployment Rate with several other social indicators seen from the MAE value of 0.05879, RMSE of 0.081630 and Adjusted R-squared of 0.7206 indicating that the b-spline regression model is able to explain around 72.06% of the variation in the response variable. Based on the results of the simultaneous test, it shows that variable have a significant effect on TPT, while the partial test it shows APK SMA and P1 have a significant effect on TPT.
Estimasi Probit Biner Semiparametrik Spline Truncated pada Status Persentase Penduduk Miskin Rizki Adisetya Tahwin; Vita Ratnasari; I Nyoman Budiantara
Jurnal Gaussian Vol 14, No 2 (2025): 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.14.2.631-641

Abstract

Regression analysis is a statistical method used to model the relationship between predictor variables and response variables. To analyse categorical response variables, probit regression can be used. There are three probit models based on the type of response variable, namely binary probit, multinomial probit, and ordinal probit. Binary probit is a method used to analyse response variables with two categories. Probit models are often used because they produce more stable probabilities in small samples because it uses the normal distribution. There are three types of binary probit models based on curve approaches, namely parametric, non-parametric, and semi-parametric. Semi-parametric regression was chosen because it combines parametric and non-parametric components. Conventional semi-parametric regression often cannot provide accurate estimates. Therefore, the use of truncated splines is relevant because they can handle the flexibility of unknown functions. This study aims to estimate a semi-parametric binary probit model with truncated splines using maximum likelihood estimation. The resulting likelihood function is not in closed form, requiring Newton-Raphson numerical iteration. The results show that the best model is obtained with one knot point, which has an accuracy of 84.21% and an AUC of 0.84, indicating that the model's prediction classification is verry good.
PEMODELAN REGRESI GAMMA MENGGUNAKAN METODE OPTIMASI BROYDEN-FLETCHER-GOLDFARB-SHANNO (BFGS) (Studi Kasus : Pencemaran Sungai di Kota Semarang) Efifah Nur Safitri; Arief Rachman Hakim; Suparti Suparti
Jurnal Gaussian Vol 14, No 2 (2025): 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.14.2.247-256

Abstract

Semarang City is one of the major industrial areas in Central Java and is located not far from residential areas. These large industries utilize a variety of chemicals to meet their needs, producing various wastes that are the main cause of high concentrations of Chemical Oxygen Demand (COD) in waters. Measurements on the COD value obtained are continuous data with Gamma distribution. In this study, Gamma regression is used to model the relationship between one or more predictor variables and a positive continuous response variable following a Gamma distribution. Parameter estimation in the Gamma regression model uses the Maximum Likelihood Estimation (MLE) method because it does not produce an analytical solution, an optimization method will be carried out with the Broyden-Fletcher-Goldfarb-Shanno (BFGS) algorithm. This study aims to determine the Gamma regression model in the case of river pollution in Semarang City, determine the form of parameter estimation in the Gamma regression model and get what factors affect river pollution in Semarang City. Based on the  value of 0.4431498 where the ability of the predictor variables to explain the response variable is 44.32%, the remaining 55.68% of the response variable is explained by other factors not contained in the model.
PENERAPAN MODEL AUTOREGRESSIVE FRACTIONALLY INTEGRATED MOVING AVERAGE DALAM MERAMALKAN NILAI TUKAR RUPIAH TERHADAP US DOLLAR (USD/IDR) Ayu Fajar Rusadi; Yeni Rahkmawati; Fitri Handayani
Jurnal Gaussian Vol 14, No 2 (2025): 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.14.2.535-546

Abstract

The Autoregressive Fractionally Integrated Moving Average (ARFIMA) model is employed to analyze data exhibiting long memory characteristics using a fractional differencing coefficient. This study differs from previous research, as no existing studies have been found that discuss the forecasting of the rupiah exchange rate against the United States Dollar (USD) using recent data and the ARFIMA model. This study examines the daily exchange rate of the Rupiah against the United States Dollar (USD/IDR) from January 2023 to March 2025, totalling 535 observations. The results indicate a general weakening trend of the Rupiah during this period. The Hurst exponent (H = 0.8372489), which falls within the range 0.5 < H < 1, confirms the presence of long memory properties. The best fitting ARFIMA model is identified as ARFIMA(2,0.3372489,1), with a BIC value of 4387.69. The model equation is: . Forecasting results show a downward trend, suggesting potential appreciation of the rupiah against the USD in the future. These findings provide valuable insights into exchange rate dynamics and have important implications for economic planning and policy in Indonesia.
COMPARATIVE ANALYSIS OF K-MEANS, K-MEDOIDS, AND FUZZY C-MEANS FOR CLUSTERING PROVINCES IN INDONESIA BASED ON RICE PRODUCTION IN 2024 Ilham Mujahidin; Siti Hadijah Hasanah
Jurnal Gaussian Vol 14, No 2 (2025): 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.14.2.356-365

Abstract

Rice is one of the main commodities in Indonesia's agricultural sector that plays a crucial role in maintaining national food security. This study aims to cluster 38 provinces in Indonesia based on the similarity of rice production in order to understand the spatial variation of agricultural performance in different provinces. In this analysis, three clustering methods were used, namely K-Means, K-Medoids, and Fuzzy C-Means, by considering two main variables: the area of harvest and the amount of rice production in 2024, whose data were sourced from the Central Bureau of Statistics. Evaluation of cluster quality was conducted using the Davies-Bouldin Index (DBI). The results showed that the K-Means method produced the most optimal clustering with the lowest DBI value of 0.276 at the number of clusters , compared to K-Medoids (0.279 at ) and Fuzzy C-Means (0.285 at ). The clusters formed show a clear separation between provinces with high and low production levels. Provinces with high agricultural intensification and a large contribution to production belong to the main cluster, while areas with limited resources and low production form a separate cluster. Several other clusters reflect medium to high production characteristics with varying development potential. This finding reflects the diversity of agricultural conditions influenced by infrastructure, intensification, and geographical and climatic factors.
Estimator Campuran Spline Truncated dan Deret Fourier dalam Regresi Nonparametrik Untuk Data Kategori Kadek Adi Surya Negara; I Nyoman Budiantara; Vita Ratnasari
Jurnal Gaussian Vol 14, No 2 (2025): 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.14.2.588-598

Abstract

Nonparametric regression analysis is ideal when data patterns are uncertain because the approach is highly flexible. In some nonparametric cases, each predictor variable exhibits a different form of association with the response variable. Using only one estimator may result in estimates that do not align with the actual data patterns. Therefore, a mixed estimator approach is needed to overcome this problem. This research introduces a truncated spline and Fourier series mixed estimator, in which the relational structure between the response and predictor variables changes across certain intervals, while others demonstrate a recurring pattern. However, most studies on nonparametric regression still use response variables expressed as quantitative data, even though there are conditions where the response variables are categorical data. Therefore, this study will develop a mixed estimator for categorical data. This study aims to obtain truncated spline and Fourier series mixed estimators in nonparametric regression for categorical data using the Maximum Likelihood Estimation method followed by Newton Raphson iteration. This study produces parameter estimators in mixed models by combining truncated spline functions and Fourier series functions in binary categorical data using the Newton Raphson iteration approach.
Penerapan Metode Fuzzy Time Series Markov Chain Untuk Meramalkan Nilai Transaksi Belanja Menggunakan Uang Elektronik di Indonesia Muhammad Irsadul Ibaad; Meiliyani Siringoringo; Ika Purnamasari; Desi Yuniarti; Suyitno Suyitno
Jurnal Gaussian Vol 14, No 2 (2025): 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.14.2.290-301

Abstract

Forecasting is an analytical process used to predict future conditions based on historical and current data to minimize errors. The Fuzzy Time Series (FTS) Markov Chain method is effective for handling uncertain, nonlinear, and fluctuating data, making it suitable for forecasting electronic payment transactions in Indonesia. These transactions often show gradual trends, seasonality, and external influences such as policy changes and consumer behavior, leading to data uncertainty that traditional models struggle to capture. A key factor in the FTS method is the interval length, which affects the accuracy of fuzzy set formation. This study compared two interval determination methods: Sturges formula and automatic clustering, to forecast Indonesia’s electronic payment transaction value for September 2024. Results showed that Sturges produced a forecast of Rp52,836.07 billion with a MAPE of 8.51%, while automatic clustering yielded a forecast of Rp55,369.31 billion with a lower MAPE of 3.90%. The findings indicate that the hybrid FTS-Markov Chain approach, especially when combined with automatic clustering, offers better accuracy. It adapts more effectively to the natural structure of the data, making it a more reliable method for forecasting complex and uncertain transaction patterns.
The Effectiveness of ECM - MIDAS Based on Principal Component Analysis (PCA) in Predicting GDP in Indonesia Fajar Fithra Ramadhan; Dea Malaika; Ni Kadek Dwi Utami; Fitri Kartiasih
Jurnal Gaussian Vol 14, No 2 (2025): 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.14.2.411-422

Abstract

GDP is closely related to monetary policy, because changes in GDP often affect decisions taken by the central bank in formulating policies to maintain economic stability. This study aims to predict the value of Gross Domestic Product (GDP) by developing a more accurate and efficient model. The variables analyzed include primary money, net domestic assets, net foreign position, and foreign exchange reserves as independent variables, and gross domestic product (GDP) as the dependent variable. The method used combines the Error Correction Model (ECM) into the Mixed Data Sampling (MIDAS) and Principal Component Analysis (PCA) models, this approach provides a more comprehensive analytical framework to capture complex interactions between variables with different frequencies, while taking into account long-term and short-term dynamics that influence each other. The results of the study indicate that the combination approach of PCA and MIDAS with the Almon distribution is more effective in capturing data patterns than other approaches that only use PCA with the average or median of economic indicators. The ECM-MIDAS-PCA model with the Almon weight function showed the best results, marked by an Adjusted R-Square value of 22.33% and low prediction error. The Error Correction Term (ECT) coefficient of -0.1579 indicates a correction towards long-term equilibrium of 15.79% per quarter, so that the process towards equilibrium can be achieved in 6.33 quarters.
IMPLEMENTASI METODE CONVOLUTIONAL NEURAL NETWORK UNTUK KLASIFIKASI SENTIMEN ULASAN PENGGUNA APLIKASI MYPERTAMINA Arta Marisa Pardede; Mustafid Mustafid; Sugito Sugito
Jurnal Gaussian Vol 14, No 2 (2025): 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.14.2.345-355

Abstract

Sentiment analysis has gained significant attention in recent years, with researchers employing various machine learning and deep learning techniques. While Convolutional Neural Networks (CNN) were initially designed for image processing, their effectiveness in text classification tasks has been established. This study focuses on enhancing sentiment classification performance for customer reviews of MyPertamina by utilizing a CNN model with Word2Vec embeddings. The research involves analyzing user reviews obtained from the Google Play Store for MyPertamina's mobile application. The CNN model, incorporating Word2Vec embeddings, is trained to classify these reviews into positive and negative sentiment categories. Experimental evaluations reveal that the optimal hyperparameters for the Word2Vec model are a window size of 5 and a word embedding dimension of 300. Regarding the CNN model, both the dropout rate and learning rate significantly impact classification performance. The best results are achieved with a learning rate of 0.001 and a dropout rate of 0.3. The findings demonstrate that the CNN model with Word2Vec embeddings achieves an impressive accuracy of 96.14% in classifying customer reviews within the MyPertamina application. This underscores the efficacy of employing this approach to improve sentiment classification for customer feedback. 

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