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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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OPTIMASI PORTOFOLIO CAPITAL ASSET PRICING MODEL (CAPM) PADA INDEKS BISNIS-27 Aditya Fadillah Aridwianto; Dhelia Artasevia Artasevia; Najwa Mayang Vianisa; Syifa Gumay; Valentin Asman Lestari; Di Asih I Maruddani
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.14.2.547-553

Abstract

Stock efficiency analysis helps investors understand the intrinsic value of a stock and serves as a foundation for identifying risk factors and potential returns associated with it. This study evaluates stocks in the Business Index 27 from 22 Mei 2023 – 22 Mei 2024 by considering criteria such as positive returns, lowest correlation, and sectoral differences. Based on this evaluation, three stocks with efficient performance were selected: MEDC, BRPT, and JSMR. An optimal portfolio was formed by weighting these three stocks using the Capital Asset Pricing Model (CAPM) method, with weight proportions of MEDC at 23.3%, BRPT at 11.7%, and JSMR at 64.9%. Risk evaluation using the Historical Simulation method to calculate Value at Risk (VaR) indicates a potential loss of 10%. This study provides insights into identifying efficient stocks and forming an optimal portfolio, which can assist investors in making investment decisions in the Business Index 27.
IMPLEMENTASI METODE SUPPORT VECTOR REGRESSION UNTUK PREDIKSI HARGA SAHAM PT. ADARO ENERGY TBK Anik Sri Mulyani; mustafid mustafid; Tatik Widiharih
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.14.2.457-468

Abstract

Stock investment is very attractive to investors because it provides many benefits. The higher the profit offered in investing, the higher the risk investors will face. Stock price analysis is needed to predict stock prices to reduce the risk of loss. Stock data has dynamic, non-linear, and unpredictable characteristics, so a method is required to overcome the limitations of time series data. The Support Vector Regression method will be applied in this study to predict PT. Adaro stock prices. The Support Vector Regression method is one method that does not require assumptions, so it can be used to overcome the limitations of regression analysis with time series data. The problem often faced when using the SVR method is determining the optimal hyperparameters. This study determines the optimal hyperparameter using the grid search algorithm. The data is divided into training and testing data with a ratio of 90:10. The best kernel to predict the share price of PT. Adaro is using a linear kernel with a value of Cost = 4 and epsilon = 0.0001. The model produces MAPE testing data of 1.608%, which means the model is very good for prediction.
OPTIMALISASI PORTOFOLIO MENGGUNAKAN METODE MEAN-SEMIVARIANCE PADA SAHAM IDX30 Tsara Firda Nabila; Sudarno sudarno; Masithoh Yessi Rochayani
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.14.2.366-377

Abstract

Investment is one way to maximize income. Through investment, investors who invest funds will benefit. However, investment is inseparable from risk. Investors can reduce investment risk by forming an optimal portfolio. Mean-semivariance is one method of portfolio formation invented by Markowitz in 1959. Mean-semivariance is a mean-variance development method, but this method is free from all assumptions and this method measures portfolio risk by using semivariance and semideviation. The investment that many investors choose is a stock investment. This research uses stocks that have consistently joined the IDX30 for five years (2018-2022). IDX30 is composed of 30 stocks with relatively large market capitalization, high liquidity, and good fundamentals. The optimal portfolio is formed by calculating the weight of each stock using a function, so as to get the smallest risk. Based on the four optimal portfolios that are formed through the process, it is known that the optimal portfolio with the best performance is Portfolio 2. The Sharpe index belonging to Portfolio 2 is 0.083507. The investment weight for each share that makes up Portfolio 2 is 16.1039% for shares of PT Adaro Energy Indonesia Tbk; 57.5554% for shares of PT Indofood CBP Sukses Makmur Tbk; and 26.3407% for shares of PT Perusahaan Gas Negara Tbk.
ANALISIS SISTEM ANTREAN DENGAN METODE BAYESIAN Sanjaya Pamungkas; Sugito Sugito; Bagus Arya Saputra
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.14.2.599-607

Abstract

Along with the times, the transportation sector has progressed quite rapidly. In connection with the transportation sector, a phenomenon that is easily found in everyday life is the queue at public transportation facilities. One of them is at the transportation facility at the airport. At the airport the queue that occurs is due to the large number of aircraft that come to get service from airport service facilities. However, the queue can be minimized with a good system. The purpose of this research is to find out changes or additional information from aircraft services, get a queue system model, and find out whether the service at the airport is good or not. The Bayesian method is used to combine prior information from previous research data (Widiawati, 2010) and current observed data (samples) to obtain updated information. The sample distribution (Weibull and inverse Gaussian) of the current observed data and the prior distribution (inverse Gaussian and Weibull) obtained from the prior information in the previous research data (Widiawati, 2010). The prior distribution and the likelihood function of the sample distribution are combined to obtain the posterior distribution. After calculating the posterior distribution, it is found that the model of the aircraft queue at Adi Soemarmo International Airport - Surakarta is (GAMMA/GAMMA/3): (GD/∞/∞) with steady state conditions already met (ρ<1) and based on the results of the performance measure of the aircraft queue system at Adi Soemarmo International Airport has a good condition.
Pengklasteran dan Penerapan Rantai Markov untuk Prediksi Produksi padi dan Lahan Panen di Kalimantan Barat Aisyah Ulfah; Sudarno sudarno; Rahmila Dapa
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.14.2.280-289

Abstract

The population growth rate in Indonesia is very high. This causes the need for rice to also increase. The increase in population growth is not matched by the growth of harvested land. This causes the area of harvested land to decrease because a lot of land is converted into settlements. Rice production and harvest land in West Kalimantan has decreased in the period 2019 to 2021. The purpose of this study is to classify and predict rice production and harvest land in West Kalimantan. In this research, the clustering method used is Cluster Time Series with Average Linkage method. Average Linkage is one of the hierarchical groupings based on the average distance between objects. The Silhouette Coefficient value is used to determine the optimal number of clusters. This research also uses the Markov Chain method to predict rice production and harvestable land. Markov chain is a stochastic process that explains future events only depend on today's events and do not depend on past circumstances. The results of this study obtained two clusters and many districts/cities are in clusters that have low rice production and harvest land. The prediction results of rice production and harvested land in Kalimantan have the greatest chance of experiencing a decline.
BOOTSTRAP AGGREGATING CLASSIFICATION AND REGRESSION TREES (BAGGING CART) UNTUK KLASIFIKASI POTENSI KARYAWAN RESIGN BERDASARKAN KENYAMANAN BEKERJA Muhammad Fajar Syabana; Tatik Widiharih; Masithoh Yessi Rochayani
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.14.2.512-523

Abstract

Phenomenon of employee resignation is a significant challenge for companies because it affect the productivity and stability of the company's operations. Every companies supposed to analyze the potential of employee resignation. This research aims to classify the potential of employee resignation based on working comfort and applies the classification modeling method from Decision Tree: Classification And Regression Trees (CART) and the ensemble Bootstrap Aggregating (Bagging) method. CART is a non-parametric method that is effective in building classification and prediction models based on decision trees, while Bagging is an ensemble method that combines several CART models to improve the accuracy and stability of predictions. The CART model provides an accuracy of 73% and f1-score of 62%, while the Bagging CART model provides an accuracy of 87% and f1-score of 88%. This research shows an increase in accuracy when using Bagging CART model of 14%. The most important variable to build the model and make predictions is the age. Age is also used as the root node in building CART model.
FUZZY POSSIBILISTIC C-MEANS (FPCM) CLUSTERING UNTUK IDENTIFIKASI KELUHAN UTAMA PELANGGAN INDIHOME PADA DATA TWEETS Taufik Aji Putra; Iut Tri Utami; Ardiana Alifatus Sa&#039;adah
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.14.2.423-432

Abstract

Customer complaints reveal product or service issues and can drive improvements to enhance satisfaction. Many companies use Twitter as a platform to interact with customers, making handling complaints through social media crucial for building a positive image and maintaining customer loyalty. IndiHome Regional 4 faces challenges in identifying main complaints due to a high volume of complaints on Twitter. Cluster analysis groups similar complaints, aiding the identification process. Text mining converts textual data into numerical format, streamlining complaint processing. Fuzzy Possibilistic C-Means Clustering, a fuzzy-based method, enables data membership across clusters with varying degrees of membership. By adopting relative (fuzzy) and absolute (possibilistic) membership, more accurate data placement is achieved. Data consists of IndiHome Regional 4 customer complaint tweets received via the Twitter channel "IndiHomeCare" from January to December 2022. The clustering process formed 4 clusters based on the smallest Extended Xie-Beni Index value, tested with different cluster numbers (3-7). Witel Yogyakarta had the highest members and complaints in each cluster, while Witel Kudus had the lowest. Word Cloud analysis revealed main complaints in each cluster, including WiFi-related subscription costs, internet disruptions, customer service issues, and slow connections.
PERBANDINGAN METODE OPTIMASI SILHOUETTE, ELBOW, DAN GAP STATISTICS DALAM MENENTUKAN NILAI K TERBAIK PADA ANALISIS K-MEANS CLUSTERING Metalia Widya Diantika; Agus Rusgiyono; Bagus Arya Saputra
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.14.2.335-344

Abstract

Stunting is a condition of malnutrition status that is chronic in growth and development from the beginning of life, malnutrition puts children at greater risk of death. One of the efforts to overcome stunting is to determine in advance the provinces that need to be prioritized in handling the factors that cause stunting by grouping 34 provinces in Indonesia. This study uses k-means clustering to partition data according to their respective characteristics into the form of two or more clusters, determining the optimal number of clusters through elbow optimization methods, gap statistics and silhouette. The method used to test the best cluster results is the Davies Bouldin Index (DBI) method. The results of the elbow method clustering test produce K = 3 with a DBI value of 0.6392677, the gap statistics method produces K = 1 without DBI testing because only 1 cluster is formed, while the silhouette method produces K = 2 with a DBI value of 0.2116945. This shows that the results of clustering k-means with the silhouette method produce better cluster quality because it has a lower DBI value than other methods.
PERBANDINGAN REGRESI NONPARAMETRIK SPLINE TRUNCATED DAN KERNEL GAUSSIAN DALAM MENGANALISIS FAKTOR-FAKTOR PENENTU INDEKS PEMBANGUNAN MANUSIA (IPM) DI INDONESIA uci nopita safitri; Idhia Sriliana; Regina Adelisa; Muhammad Hafiz; 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.14.2.554-564

Abstract

The Human Development Index (HDI) is an important indicator for measuring the quality of development in a region. This study compares two nonparametric regression approaches, namely truncated spline regression and Gaussian kernel regression, in analyzing the factors influencing HDI in Indonesia in 2024. The independent variables used include Expected Years of Schooling (HLS), Mean Years of Schooling (RRLS), and the percentage of the poor population (PPM). Nonparametric regression is chosen for its ability to capture complex relationships between variables without strict linearity assumptions. The results show that both methods effectively model the relationship between the variables and HDI. Truncated spline regression performs better in detecting structural changes, while kernel regression is more flexible in capturing smooth relationships. Model evaluation using the coefficient of determination (R²) and mean squared error (MSE) indicates that truncated spline yields an R² of 92.79% and an MSE of 1.8617, while Gaussian kernel regression results in an R² of 82.25% and an MSE of 3.6837. Therefore, truncated spline regression proves to be more accurate in modeling the relationship between determining factors and HDI, and it can serve as a more suitable alternative for analyzing complex and nonlinear patterns in human development policy research.
PERAMALAN INDEKS HARGA SAHAM GABUNGAN (IHSG) DENGAN METODE FUZZY TIME SERIES CHEN DAN CHENG Marselinus Tolhas Gratias Lumbanbatu; Puspita Kartikasari; Deby Fakhriyana
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.14.2.469-479

Abstract

The Jakarta Composite Index (JCI) is an index that measures the performance of all stocks listed on the Indonesia Stock Exchange. JCI can be used as one of the indicators used by investors to determine the movement of stocks in the Indonesian capital market. Decisions made by investors will have a stronger basis if forecasting is done. Investors can decide to exit the market or enter the market. The forecasting method used to forecast the JCI value in this study is Fuzzy Time Series (FTS). This method has advantages compared to other time series methods, where the FTS method does not require the fulfillment of classical assumptions as in ARIMA.  Both forecasting methods will apply the Sturges and Average Based formulas in determining the class. The data used in this study is the JCI closing data on March 1, 2022 - March 1, 2023. The data is divided into two categories with 239 data as training data, namely data on March 1, 2022 - February 15, 2023 and 10 data as testing data, namely data on February 16 - March 1, 2023. The forecasting accuracy measure used in this study is sMAPE. Among the four forecasting methods, the best forecasting method is Cheng's Fuzzy Time Series method by applying the Sturges formula in determining the number of classes with an sMAPE value on testing data is 0.37%.

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