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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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PENGGUNAAN MIXTURE MODEL KERNEL-GENERALIZED PARETO DISTRIBUTION DAN D-VINE COPULA DALAM MENGANALISIS UKURAN PELANGGARAN DATA Dzikra, Fathiyyah Yolianda; Wilandari, Yuciana; Hakim, Arief Rachman
Jurnal Gaussian Vol 12, No 3 (2023): 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.12.3.392-402

Abstract

The research conducted on the 2015-2021 Data Breach Report in the U.S. Department of Health and Human Services is a study related to the estimation and modeling of the breach sizes each type of entity using the Kernel-Generalized Pareto Distribution Mixture Model method, as well as the estimation of the dependence of breach sizes between years with the D-Vine Copula. The D-Vine Copula can accommodate the complex dependencies demonstrated by data breach reports across all enterprise categories. Before researching with D-Vine Copula, we will first model and estimate breach size parameters for each type of entity using the Mixture Model Kernel-Generalized Pareto Distribution (GPD). The Mixture Model can accommodate large data breach sizes via GPD and also allows the use of non-parametric kernel distributions to model smaller data breach sizes. The data resulting from the logarithmic transformation of entity data in the Business Associate and Healthcare Provider types has a right short-tail with Weibull distribution, while the Health Plan category has a right heavy-tail with Frechet distribution. The three types of entity were estimated using the maximum likelihood Cross-Validation method. Dependency estimation with D-Vine Copula shows that the breach sizes between years measure has a positive dependency.
ANALISIS SENTIMEN KEBIJAKAN PENYELENGGARA SISTEM ELEKTRONIK LINGKUP PRIVAT MENGGUNAKAN PENALIZED LOGISTIC REGRESSION DAN SUPPORT VECTOR MACHINE Amalia, Nur Afnita; Utami, Iut Tri; Wilandari, Yuciana
Jurnal Gaussian Vol 12, No 4 (2023): 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.12.4.560-569

Abstract

The implementation of the Electronic System Operator (ESO) regulation, which imposes blocking sanctions on several ESOs that do not register, has caused a variety of opinions from the public, especially on social media Twitter to raise the hashtag #BlokirKominfo. In this research, sentiment analysis was carry outed to determine the response of Twitter users to the implementation of ESO regulations by MoCI. Sentiment analysis is a textual information extraction process that classifies sentiment into positive and negative categories. The steps that are used including crawling data, text preprocessing, labeling, feature selection, term weighting with TF-IDF and classification using the Penalized Logistic Regression (PLR) with the L1 regularization and Support Vector Machine (SVM) with the RBF kernel. Sentiment classification in PLR is basically finding the optimal weight parameter. The idea of SVM sentiment classification is to find the best hyperplane to separate the data points. Evaluation of classification performance uses the accuracy value calculated through the confusion matrix. The highest percentage of accuracy in sentiment classification results using the PLR is 84,12% and SVM is 83,53%. It means that the PLR algorithm works better than the SVM algorithm in classifying public sentiment towards the implementation of ESO regulations on Twitter.
Clustering Kabupaten/Kota di Provinsi Papua Berdasarkan Produk Domestik Regional Bruto Menurut Lapangan Usaha Menggunakan Single Linkage dan K-Medoids Allo, Caecilia Bintang Girik
Jurnal Gaussian Vol 13, No 1 (2024): 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.13.1.111-120

Abstract

Gross Regional Domestic Product (GRDP) using the production approach represents the total value added from goods and services produced by different sectors within a specific region over a defined timeframe. There are 17 business sectors used to obtain the GRDP. The growth rate of GRDP in Papua is decrease in 2023. The growth rate is only 3,44%,  whereas the previous year it reached 4,11%. An analysis is needed to assist the government to enhance the GRDP in Papua. Clustering method can group districts/cities that have similar characteristics. The aims of this article is to determine the best method for clustering districts/cities in Papua using GRDP data. Single Method and K-Medoids is used in this article. Based on silhouette coefficients, Single Methods is better than K-Medoids to clustering districts/cities in Papua. Based on criteria of silhouette coefficients, number of clusters formed is three.
PERBANDINGAN PERFORMA DISTANCE MEASURES PADA ALGORITMA NEAREST CENTROID NEIGHBOR DAN K-NEAREST NEIGHBOR DALAM PROSES KLASIFIKASI BINTANG Dewi Sri Susanti; Nurul Azizah; Selvi Annisa
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.15.1.89-97

Abstract

Stars are celestial bodies that can be classified based on several characteristics, including temperature, luminosity, radius, magnitude, stellar color, and spectral class. Stars are generally grouped into six categories: brown dwarfs, red dwarfs, white dwarfs, main sequence stars, supergiants, and hypergiants. Stellar classification is important to astronomers because it can help identify new types of stars and improve our understanding of their composition, temperature, and evolutionary stages. This classification process can be done using the Nearest Centroid Neighbor (NCN) and k-Nearest Neighbor (k-NN) algorithms, by applying various distance measures such as Euclidean, Manhattan, Minkowski, Chebyshev, Cosine, Jaccard, and Hamming. This study aims to compare the performance between NCN and k-NN using these seven distance measures. The results show that Euclidean, Manhattan, and Minkowski distances produce a perfect performance of 100% in both algorithms. Chebyshev distance yielded perfect performance in k-NN but slightly lower in NCN with a performance of 92%. Thus, the k-NN algorithm provides superior performance compared to the NCN algorithm in the stellar classification process.
KLASIFIKASI SENTIMEN KASUS ONLINE TRADING BINOMO PADA TWITTER MENGGUNAKAN ALGORITMA CONVOLUTIONAL NEURAL NETWORK (CNN) Elva Nadia Nugroho; Tatik Widiharih; Arief Rachman Hakim
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.15.1.46-56

Abstract

Investment in futures trading and technology is growing in Indonesia, making many domain sites of online trading companies appear that are easy for everyone to access. The Binomo app is so viral among the public because of an ad that displays professional traders who can earn 1000 USD a day without leaving their homes. Binary options trading ultimately causes losses for some people, allegedly caused by affiliates. The Binomo case is widely discussed on social media, especially Twitter. Tweets displaying public opinion on Twitter can be used for sentiment analysis and categorizing public opinion on the Binomo case. 2.686 tweets were collected by Twitter scraping between January 1 and April 1, 2022. 1.519 tweets were left after pre-processing. The data were processed using the Convolutional Neural Network algorithm with the Word2Vec method to determine their accuracy and identify topics often discussed by the public on Twitter. A CNN model with 70% training data, 3, 4, 5, kernel sizes, 4 batch sizes, 30 epochs, and Adam optimizers was used to build the classification in this research. The accuracy value obtained from the performance evaluation of the Convolutional Neural Networ model research was 89%.
PREDIKSI KEKERINGAN DENGAN MENGGUNAKAN RANDOM FOREST DAN XGBOOST SEBAGAI STRATEGI MITIGASI BENCANA DI KABUPATEN MAJENE Muh. Hijrah; Putri Indi Rahayu; Wahyudi Wahyudi
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.15.1.98-109

Abstract

The growing incidence of drought attributable to climate change poses serious challenges to food systems and agricultural livelihoods in Majene Regency, West Sulawesi. To address this, the present study compares the predictive accuracy of Random Forest and XGBoost in classifying drought occurrences from meteorological variables, using 300 monthly observations recorded by BMKG between 2000 and 2024. Exploratory data analysis revealed that 60% of drought events are concentrated in August and the target variable is severely imbalanced (16.3% drought vs. 83.7% non-drought). At the default threshold of 0.50, Random Forest outperforms XGBoost in all metrics. However, because imbalanced data causes both models to underdetect drought at the default threshold (sensitivity = 0.267), threshold optimization was applied by lowering the decision threshold to τ = 0.15. The result of threshold optimalization was significantly improved Random Forest sensitivity from 0.267 to 0.867, detecting 13 out of 15 drought events in the test set while maintaining an AUC-ROC of 0.916. Feature importance analysis consistently identifies X3 as the dominant predictor in both models. Random Forest with threshold τ = 0.15 is recommended as the primary model for drought early warning systems in Majene.
OPTIMASI XGBOOST DALAM PREDIKSI KECEPATAN KENDARAAN SECARA REAL-TIME : PERBANDINGAN METODE TUNING HYPERPARAMETER Panji Lokajaya Arifa; Kusman Sadik; Agus M Soleh; Cici Suhaeni
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.15.1.01-11

Abstract

Real-time vehicle speed prediction plays a vital role in the development of intelligent transportation systems aimed at improving traffic flow and safety. This study investigates the performance of the XGBoost algorithm enhanced with three hyperparameter tuning techniques: Grid Search, Bayesian Optimization, and Genetic Algorithm. A simulated dataset was constructed reflect diverse urban traffic scenarios, incorporating environmental variables such as weather, road conditions, and traffic density. The models were assessed using 5 and 10-fold cross-validation based on prediction metrics (MSE, RMSE, MAE and R²) as well as computational efficiency in terms of training and inference time. The findings reveal that Bayesian Optimization achieves the highest prediction accuracy, while Grid Search offers the fastest training time. Genetic Algorithm demonstrates a balanced trade-off between accuracy and computational efficiency, making it a competitive and practical choice. These results highlight the importance of selecting hyperparameter tuning strategies based on specific system needs in real-time traffic prediction using XGBoost. 
LATENT DIRICHLET ALLOCATION DALAM IDENTIFIKASI RESPON MASYARAKAT INDONESIA TERHADAP PROFESI PEGAWAI NEGERI SIPIL Nurul Fajrin Aghentika; Sugito Sugito; Budi Warsito
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.15.1.57-66

Abstract

Data on social media comments can be extracted to produce hidden information that is useful as a guide for evaluation and decision making. YouTube has a comments feature as a forum for expressing opinions, experiences and questions. Civil Servants are known as one of the job choices of Indonesian people, the government announced that there were resignations of Civil Servant Candidates in 2022. Responses written in the comment column are difficult to understand, topic modeling can be applied as a text analysis process to find descriptions from unstructured data. Latent Dirichlet Allocation method is able to find out hidden topics in a document as well as the words that make up a topic so that the application of this method will help in identifying responses discussed by the audience. The data used is textual data in the form of comments from YouTube scrapping during 2022. The results of topic modeling form eight topics, namely retirement life, parents hopes, dream jobs, civil servants, job differences, characteristics of generation Z, salary and benefits, and reasons for resignation. The RStudio GUI program can make it easier for users to analyze topic modeling with similar methods.
PENGENDALIAN KUALITAS PUPUK NITROGEN, PHOSPAT, KALIUM (NPK) PELANGI FUSION DI PT PUPUK KALIMANTAN TIMUR MENGGUNAKAN PETA KENDALI MEWMA & MEWMV Bungan Tcania Paulina Grace; Puspita Kartikasari; Deby Fakhriyana
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.15.1.110-120

Abstract

The NPK Pelangi Fusion fertilizer is one of the main products manufactured by PT Pupuk Kalimantan Timur. NPK fertilizer is a blend of various plant nutrients, primarily Nitrogen, Phospat, and Kalium, aimed at enhancing crop yields. However, deviations from the NPK fertilizer specifications can lead to inconsistent plant growth and low harvest yields. Statistical Process Control (SPC) is a method used to process data and monitor production processes using statistical techniques, with the goal of detecting changes in process performance through the use of control charts. In this study, the Multivariate Exponentially Weighted Moving Variance (MEWMV) control chart is used to monitor the variance of the production process due to its optimal performance in detecting small variance shifts. Additionally, the Multivariate Exponentially Weighted Moving Average (MEWMA) control chart is used to monitor the mean of the NPK Pelangi Fusion production process, as it quickly detects subtle shifts in variance. The analysis results indicate that the optimal weighting for the MEWMV control chart is ω=0.2 and λ=0.4, resulting in an Average Run Length (ARL) of 370. Similarly, the optimal weighting for the MEWMA control chart is λ=0.06, with an upper control limit of H=9.80 and an ARL of 200. This study concludes that the mean and variance of the multivariate production process have been effectively controlled.
MODELING CHILD MALNUTRITION IN EAST JAVA USING THE INTEGRATION OF PARTIAL LEAST SQUARE WITH IMPORTANCE-PERFORMANCE ANALYSIS Zulfani Alfasanah; Bambang Widjanarko Otok; Muhammad Ahsan
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.15.1.12-23

Abstract

Child malnutrition remains a persistent public health concern in Indonesia, particularly in East Java, where stunting and undernutrition rates remain high despite national progress. Malnutrition in children is a multidimensional issue, influenced not only by dietary intake and disease but also by socio-economic conditions, caregiving practices, and environmental health factors such as access to clean water, sanitation, and health services. Addressing this complexity requires robust analytical methods. This study applies Partial Least Squares–Importance Performance Analysis (PLS-IPA) to model the pathways influencing child malnutrition and identify priority interventions. Results indicate that socio-economic factors act as primary predictors influencing food security, healthcare access, and parenting practices. Among direct determinants, environmental health services, including sanitation, clean water access, and maternal healthcare, show the strongest direct effect in reducing malnutrition. IPA results highlight food security as the most critical intervention priority due to its high importance but moderate field performance. Strengthening socio-economic development, improving food security, and sustaining environmental health interventions are recommended to support long-term reductions in child malnutrition in East Java.

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