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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 745 Documents
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PEMODELAN CLUSTERWISE LINEAR REGRESSION UNTUK IDENTIFIKASI FAKTOR YANG MEMENGARUHI PREVALENSI STUNTING DI JAWA TENGAH Pratiwi, Berliana Ercha; Saputro, Anton; Mila, Afifa Nur; Mukid, Moch. Abdul; Rochayani, Masithoh Yessi
Jurnal Gaussian Vol 15, No 1 (2026): 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.15.1.67-76

Abstract

Sustainable development in the Sustainable Development Goals (SDGs) emphasizes health as a key pillar, including in overcoming malnutrition that causes stunting. Central Java Province recorded a stunting prevalence rate of 20.7% in 2023, so it is necessary to analyze the factors that influence this condition. This study uses the Clusterwise Linear Regression (CLR) method to identify factors that contribute to the prevalence of stunting based on regional characteristics. The variables analyzed include the percentage of low birth weight babies (LBW), mothers who exclusively breastfeed less than six months, women who marry at an early age, households with proper sanitation, households with clean water sources, and households that have a Prosperous Family Card (KKS). The results showed that there were 3 optimal clusters. The coefficient of determination for each cluster was 99.52% for cluster 1, 99.76% for cluster 2, and 98.26% for cluster 3.
PENERAPAN KLASIFIKASI REGRESI LOGISTIK BINER DAN ADAPTIVE BOOSTING MENGGUNAKAN CLASSIFICATION AND REGRESSION TREES PADA PREDIKSI PENYAKIT HEPATITIS C Oktaviani, Ellina Dhiya Ulhaq; Santoso, Rukun; Widiharih, Tatik
Jurnal Gaussian Vol 15, No 1 (2026): 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.15.1.120-130

Abstract

Chronic liver disease is primarily attributed to the hepatitis C virus. Disorders of liver function can inhibit metabolism and threaten health. Hepatitis C disease must be detected earlier to reduce the risk of spreading it. Data processing using the Binary Logistic Regression and Adaptive Boosting classification methods to predict the category of patients with positive or negative hepatitis C status. Problems with unbalanced data are found in the classification process. Data imbalance can be overcome with the Synthetic Minority Over-Sampling Technique (SMOTE). Data retrieval was obtained from the 2020 UCI (University of California Irvine) Machine Learning Repository regarding data on predictions of hepatitis C patients which were downloaded on October 25, 2022. The results for the accuracy of the classification show that the Binary Logistic Regression method produces an accuracy value of 97,44%, the value sensitivity of 100%, and specificity of 97,17%. The accuracy of the classification produced by the Adaptive Boosting method with an accuracy value of 92,31%, a sensitivity value of 63,64%, and specificity of 100%. Binary Logistic Regression is the best method that can classify hepatitis C status of patients with the highest sensitivity of 100%.
ANALISIS KINERJA PORTOFOLIO SAHAM PADA INDEKS IDX30 DENGAN MEAN-SEMIVARIANCE MODEL Syuraihi, Syafi’us; Putri, Nemat Mukti; Feryansyah, Nanda Rahma; Syaefihardiansyah, Luwi; Hutama, Faris Reza; Ariq, Muhammad Hauzan; Maruddani, Di Asih I
Jurnal Gaussian Vol 15, No 1 (2026): 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.15.1.24-35

Abstract

Investment involves allocating funds to gain future profits, one way being the purchase of stocks representing company ownership. Investors seek high returns with low risk, but stock price fluctuations introduce risk. Diversification through a stock portfolio helps minimize this risk. The Mean Variance method by Markowitz in 1952 optimizes portfolios based on risk and return, but it assumes data must be normally distributed, often misaligned with financial data. This study adopts the Mean-Semivariance optimization method, which does not require normality assumptions and is more suitable for non-normal data. The study uses 6 stocks from the IDX30 index, to form 2 portfolios with 3 stocks each. The results show an optimal portfolio composed of BMRI stocks with a weight of 48,69%, PGEO stocks with a weight of 17,01%, and INKP stocks with a weight of 34,31%. This portfolio has a Sharpe index of 0,03985, indicating better risk optimization using the Mean-Semivariance method.
ESTIMATOR RIDGE-DERET FOURIER PADA REGRESI NONPARAMETRIK Muthahharah, Sidratul; Budiantara, I Nyoman; Ratnasari, Vita
Jurnal Gaussian Vol 15, No 1 (2026): 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.15.1.77-88

Abstract

Nonparametric regression is frequently applied to describe the connection between variables when the functional form is not defined. The Fourier series estimator is especially effective in capturing periodic patterns in regression curves. In multivariable cases, however, strong correlations among predictor variables often lead to multicollinearity, which results in unstable parameter estimation due to the near-singularity of the design matrix. While the Fourier approach has been widely developed for curve estimation, its theoretical framework has not explicitly accommodated this issue. This research introduces a ridge–Fourier series estimator for nonparametric regression to achieve stable parameter estimation in the presence of multicollinearity. The estimator is derived under a penalized likelihood framework by incorporating a ridge penalty into the Fourier series model and optimizing it using Maximum Likelihood Estimation (MLE). This approach yields a closed-form estimator with reduced variance and improved numerical stability while retaining the flexibility of the nonparametric structure. The oscillation parameter and ridge penalty parameter are determined through the Generalized Cross Validation (GCV) criterion to achieve an optimal smoothing level. This research's major contribution involves the theoretical formulation and derivation of the ridge–Fourier series estimator within the additive nonparametric regression model.
PENERAPAN METODE RANDOM FOREST UNTUK ANALISIS SENTIMEN PENGGUNA APLIKASI BANK DIGITAL SEABANK Bangun, Fenansia Clara Hana; Mustafid, Mustafid; Fakhriyana, Deby
Jurnal Gaussian Vol 15, No 1 (2026): 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.15.1.36-45

Abstract

Sentiment analysis on reviews of an application becomes an option to see users responses to the service of that particular application. Random Forest is one of the classification modeling techniques that originate from a combination of Decision Trees, providing the final result based on majority voting. This research aims to improve the performance of sentiment classification on customer reviews of Seabank, one of the most widely used digital banking services in Indonesia, by utilizing the Random Forest algorithm. The study involves sentiment analysis of user reviews on the Seabank application, collected from 15,000 reviews on Google Playstore. The review features available on Google Playstore are used as a means to convey opinions as user feedback for an application. Random Forest is trained to classify reviews into 3 sentiment classes: positive, neutral, and negative. Based on the research conducted with model evaluation using Confusion Matrix, an accuracy value of 94.1% was obtained, indicating that Random Forest's accuracy in classifying Seabank customer reviews is 94.1%. This demonstrates the effectiveness of using Random Forest in text review classification due to its high accuracy value.

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