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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 790 Documents
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PERBANDINGAN METODE HAZARD MULTIPLIKATIF DAN ADITIF PADA LAJU PERBAIKAN KONDISI KLINIS PASIEN STROKE DI RS MH THAMRIN CILEUNGSI TAHUN 2021 Zulfa Luthfiyyah Ayunda; Triastuti Wuryandari; 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.14.2.378-389

Abstract

Stroke is a condition that can result in permanent brain damage or even death. In Indonesia, the prevalence of stroke increased from 7% in 2013 to 10.9% in 2018. Numerous factors can affect a stroke patient's ability to recover. Survival analysis is a method that can be used to identify the variables that influence stroke patient’s ability to recover. Cox proportional hazard and Lin-ying additive hazard approaches were utilized in this study to analyze stroke patient data from MH Thamrin Cileungsi Hospital. The most widely used regression model for survival data is the Cox proportional hazard which makes the assumption that the ratio between the hazard functions of various people is constant. In contrast, in additive hazard regression there is no assumption of proportionality. Age and cardiac history are the factors that have an impact on how well stroke patients recover, according to the findings. The Lin-Ying additive hazard approach yields the best results since its RMSE value is lower (0.3808777) than that of the cox proportional hazard model (0.9248512).
PENERAPAN METODE ADAPTIVE BOOSTING (ADABOOST) PADA DECISION TREE UNTUK ANALISIS SENTIMEN PELANGGAN MAXIM Erni Triana; Mustafid Mustafid; Rukun Santoso
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.14.2.608-618

Abstract

Information technology is currently growing rapidly, one form of technology beneficiary is using the internet, namely online transportation services based on mobile applications. Maxim is one of the online transportation services in Indonesia that offers relatively cheaper prices compared to other online transportation services. This study aims to apply the Adaptive Boosting (Adaboost) method with Decision Tree to classify Maxim's customer review data so that it can establish customer satisfaction factors. Review data was obtained from June – December 2022 with a total of 1500 reviews. Classification was carried out using the Adaptive Boosting method with a Decision Tree and Tuning Hyperparameter Grid Search. Adaptive Boosting is used to improve the performance of the Decision Tree so it can work better. The Grid Search algorithm is used to determine the best hyperparameter combination in Adaptive Boosting so that the classification process can be more optimal. Classification using the Adaptive Boosting model with Decision Tree yields accuracy, precision and recall values of 83,69%, 86,75% and 85,71% with the best parameter combination based on Grid Search is n_estimator (number of trees) 300 and learning rate 0,001. Based on this accuracy value, it can be concluded that the Adaptive Boosting model is quite good at classifying Maxim's customer review data.
PENERAPAN MODEL REGRESI RANDOM FOREST UNTUK PREDIKSI HARGA LAPTOP BERDASARKAN FITUR LAPTOP Iftahli Nurol Ilmi; Mustafid Mustafid; 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.14.2.269-279

Abstract

The increasing use of computer science systems in various fields of work is driving the growth of laptop. There are many choices of laptop products with different specifications. Laptop price predictions can help consumers to find out the price range of laptops according to the laptop features they want. This study aims to apply the random forest regression method to predict laptop price based on laptop features. The data is splitted into 2 parts, 1130 data as training set and 283 data as testing set. Hyperparameters tuning was performed using random search CV with 10 fold cross-validation to find the combination of hyperparameters that produced the optimal model. The best hyperparameters obtained to build a random forest regression model are ntree = 400, mtry = 2, and nodesize = 2. The MAPE value of the testing set for the random forest regression model is 14,3% which indicates the performance of the model has good forecasting ability. The results of the analysis show that the random forest regression method can be applied to predict laptop prices based on laptop features. Based on the variable importance, the variable that has the greatest contribution to the laptop price prediction results is RAM with VI = 0,359.
PERFORMA PREDIKSI: KOINTEGRASI GARCH-SVR VS. GARCH UNTUK VOLATILITAS HARGA KOMODITAS ENERGI GLOBAL Prajna Pramita Izati; Fariz Budi Arafat
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.14.2.500-511

Abstract

Forecasting provides benefits in decision-making, one of which is forecasting the volatility of global energy commodity prices. However, there are challenges in forecasting volatility due to the presence of heteroskedasticity and long-memory effects in the data. Therefore, a combination of the GARCH and SVR methods is needed as a cointegration-based machine learning approach. The aim of this study is to compare the forecasting performance of GARCH and GARCH-SVR for global energy commodity price volatility. The findings indicate that the GARCH-SVR model performs well when volatility data exhibits non-stationary long-memory characteristics, whereas the GARCH model is more suitable when the volatility data shows stationary long-memory characteristics.
PERBANDINGAN HUKUM MORTALITAS GOMPERTZ DAN MAKEHAM DALAM KONSTRUKSI TABEL MORTALITAS INDONESIA IV Alya Nurhaliza; Dewi Sri Susanti; Aprida Siska Lestia
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.14.2.445-456

Abstract

Developing accurate mortality tables that reflect real conditions is a major challenge. Some researchers have proposed mortality laws using relatively simple equations that consider only age. Gompertz’s law accounts for the increasing risk of death with age, while Makeham’s law adds age-independent risk factors. This study aims to estimate the parameters of the probability of death function in both Gompertz and Makeham laws using the least squares method. The estimation process involves minimizing the squared error function of the mortality probability to form a normal equation. In this study, the resulting equation cannot be solved explicitly, a numerical approach using the Secant method is applied. The estimated parameters are then used to construct a mortality odds table, which is compared with data from the Indonesian Mortality Table IV. Model evaluation using the Mean Squared Error (MSE). The results of the analysis show that the Makeham model with the least squares method provides the best performance, indicated by the lowest MSE value of 0.0002002492 for men and 0.0003069904 for women. These findings indicate that the Makeham model outperforms the Gompertz model in constructing mortality tables using the least squares method.
PEMODELAN ANGKA HARAPAN HIDUP DI INDONESIA DENGAN PENDEKATAN BAYESIAN: BAYESIAN ADAPTIVE SAMPLING DAN BAYESIAN MODEL AVERAGING DALAM SELEKSI VARIABEL Fariz Budi Arafat; Prajna Pramita Izati
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.14.2.325-334

Abstract

This study applies Bayesian Adaptive Sampling (BAS) and Bayesian Model Averaging (BMA) to model life expectancy in Indonesia, addressing model uncertainty and improving predictive accuracy. The analysis incorporates Zellner’s g-prior, which enhances variable selection by balancing prior information and data-driven learning, ensuring more stable and reliable parameter estimation. Bayesian methods provide greater flexibility compared to classical regression, particularly in managing heterogeneous demographic data. The results identify poverty rate, healthcare professional ratio per 1,000 residents, percentage of infants receiving exclusive breastfeeding, and regional health expenditure as key determinants of life expectancy. The poverty rate negatively impacts life expectancy, whereas the other factors contribute positively, highlighting the importance of healthcare access, infant nutrition, and government investment in public health. The final model achieves an R² of 78.1%, indicating that these variables collectively explain a substantial proportion of life expectancy variability. By integrating Zellner’s g-prior, Bayesian inference facilitates a robust selection of influential predictors, leading to more precise policy recommendations. The study suggests that enhancing healthcare distribution, promoting breastfeeding awareness, and optimizing health budget allocation can significantly improve life expectancy outcomes. Bayesian methods provide a powerful framework for demographic modeling by incorporating uncertainty and refining estimation accuracy.
EVALUASI MODEL KLASIFIKASI DALAM DETEKSI PENIPUAN TRANSAKSI: STUDI KASUS PADA DATA TIDAK SEIMBANG Jefita Resti Sari; Kusman Sadik; Agus M. Soleh; Cici Suhaeni
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.14.2.565-576

Abstract

The rise in digital transactions increases the risk of credit card fraud, highlighting the need for a smart and accurate detection system. This study aims to develop a classification model that can effectively detect fraudulent transactions, despite data imbalance challenges. Data processing involves the use of the SMOTE technique to improve the representation of minority classes and Optuna for hyperparameter tuning. Three machine learning models are applied: Logistic Regression, Random Forest, and XGBoost. Model performance is evaluated using precision, recall, f1-score, and ROC-AUC. The results show that Random Forest achieves the best performance, with a precision of 0.91, recall of 0.74, and f1-score of 0.82. Logistic Regression achieves high recall but very low precision, while XGBoost produces a competitive AUC but a lower f1-score than Random Forest. This research highlights the importance of algorithm selection, data balancing with SMOTE, and parameter tuning to build an effective and adaptive fraud detection system for imbalanced data
PERBANDINGAN PERFORMA MODEL ARIMA-GARCH DAN LSTM DALAM MERAMALKAN JUMLAH KUNJUNGAN WISATAWAN DANAU KASTOBA Laily Nissa Atul Mualifah; Dalilah Husna; Jasmita Yasmin; Avrel Chesia Berbina; Fadhilah Yumna; Muhammad Ali Uraidly; Adelia Putri Pangestika
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.14.2.314-324

Abstract

Kastoba Lake, located on Bawean Island, East Java, is a unique natural tourist destination with significant potential for further development. To enhance strategic tourism management, predicting tourist visit numbers is necessary. This study aims to assess the performance of the ARIMA-GARCH and Long Short-Term Memory (LSTM) models in predicting daily tourist arrivals to Kastoba Lake, based on data collected between March 2023 and July 2024. These two methods were specifically selected because the dataset exhibits nonlinear patterns and heterogeneous variance. The ARIMA-GARCH model was employed to handle heteroscedasticity within the data, while LSTM was chosen for its ability to effectively learn and represent long-term patterns. The findings indicate that both models deliver comparable performance and are highly capable of identifying the underlying data trends. Moreover, each model is effective in forecasting short-term tourist visits, particularly over a 7-day horizon (one week). Consequently, these models are reliable tools for predicting and analyzing tourism trends at Kastoba Lake.
ANALISIS KEMISKINAN, KETIMPANGAN, DAN PENGANGGURAN TERHADAP IPM DI 3 PROVINSI SUMATERA MENGGUNAKAN SPLINE TRUNCATED Lutfiah Firlian; Idhia Sriliana; Della Nur Afni; Ukasyah Aflah; Pepi Novianti
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.14.2.480-488

Abstract

This study aims to analyze the effect of the open unemployment rate (TPT), economic inequality (Gini ratio), and the percentage of poor people on the Human Development Index (HDI) in three provinces with the highest poverty rates on Sumatra Island, namely Aceh, Bengkulu, and South Sumatra. The truncated spline nonparametric regression method was used in this study to accommodate the complex relationship between variables without assuming a particular distribution. The results of the analysis show that all three independent variables have an effect. The results of the analysis show that the best model is obtained with four knot points, producing a minimum Generalized Cross Validation (GCV) value of 7,9580594, a minimum Mean Squared Error (MSE) value of 3.899, and a coefficient of determination (R²) of 77,96%. With a fairly high level of accuracy, this model can be used as a basis for further analysis in understanding the relationship between economic variables and the HDI. In addition, this model can be a reference in formulating more effective policies to reduce economic inequality and improve the welfare of people in the poorest areas on Sumatra Island.
ANALISIS KUALITAS STANDARD STRENGTH PADA PORTLAND COMPOSITE CEMENT DENGAN METODE CONTROL CHART DAN EVALUASI KAPABILITAS PROSES PRODUKSI Riza Sasmita; Yenni Kurniawati; Dina Fitria
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.14.2.390-400

Abstract

The quality of cement products can be determined based on the results of standard strength tests, which are carried out using cube-shaped test samples through a compression testing machine. In the context of a continuously developing manufacturing industry, the demand for high-quality cement in Indonesia has also increased significantly. Therefore, it is necessary to implement production process control to ensure consistent quality that meets market needs.  The main focus of the research is on evaluating process stability and the process's ability to meet standard strength specifications using the X-bar and R control chart methods and process capability calculations. The results show that process variability is under statistical control when viewed from the R control chart, but the process average is not under statistical control, as indicated by 8 out of 21 points outside the control limits on the X-bar control chart. These points were identified as variations due to common causes inherent in the process. Nevertheless, process capability analysis showed that the process was technically capable of meeting specifications, with a Cp value of 4.35. Therefore, it was concluded that the Standard Strength production system at the company was capable despite statistical variation in the process mean.

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