cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
Tiani Wahyu Utami
Contact Email
jurnalstatistik@unimus.ac.id
Phone
+6285235004282
Journal Mail Official
jurnalstatistik@unimus.ac.id
Editorial Address
Sekretariat Jurnal Statistika Universitas Muhammadiyah Semarang Program Studi Statistika FMIPA Universitas Muhammadiyah Semarang
Location
Kota semarang,
Jawa tengah
INDONESIA
Jurnal Statistika Universitas Muhammadiyah Semarang
Published by Universitas Muhammadiyah Semarang
ISSN : 23383216     EISSN : 25281070     DOI : -
Core Subject : Science,
Decision Sciences, Operations Research & Management
Focus and Scope a. Statistika Teori, Statistika Komputasi, Statistika terapan b. Matematika Teori dan Aplikasi c. Design of Experiment
Arjuna Subject : Matematika - Pemodelan dan Simulasi
Articles 7 Documents
 1 
Search results for , issue "Vol 13, No 1 (2025): Jurnal Statistika Universitas Muhammadiyah Semarang" : 7 Documents clear
TRENDS IN THE FACTORS CAUSING DIVORCE IN SEMARANG RAYA IN 2023: A CORRESPONDENCE ANALYSIS APPROACH Riefky, Muhammad; Azizah, Firli; Indra Jaya, Andi
Jurnal Statistika Universitas Muhammadiyah Semarang Vol 13, No 1 (2025): Jurnal Statistika Universitas Muhammadiyah Semarang
Publisher : Department Statistics, Faculty Mathematics and Natural Science, UNIMUS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26714/jsunimus.13.1.2025.23-34

Abstract

An ideal marriage should be the foundation for a harmonious family relationship, yet many marriages end in divorce. This study aims to identify the trend patterns of divorce causes in Semarang Raya in 2023 using correspondence analysis. The research results show that the factors causing divorce vary by region. In Semarang Raya, economic factors dominate Grobogan Regency, while forced marriage factors are more frequently found in Kendal Regency. The analysis used Euclidean distance to reinforce the findings on the relationship between the factors causing divorce in each region. These findings can be used as a basis for designing more focused divorce prevention policies in accordance with the social and economic characteristics of each region.
THE APPLICATION OF THE PATH ANALYSIS MODEL IN DETERMINING THE EFFECT OF IQ AND LANGUAGE SKILLS ON MATHEMATICS LEARNING OUTCOME Wulandari, Dewi; Setyawati, Dewi; Endahwuri, Dhian
Jurnal Statistika Universitas Muhammadiyah Semarang Vol 13, No 1 (2025): Jurnal Statistika Universitas Muhammadiyah Semarang
Publisher : Department Statistics, Faculty Mathematics and Natural Science, UNIMUS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26714/jsunimus.13.1.2025.35-44

Abstract

Mathematics learning outcome is inseparable from many factors that influence it. Intelligence Quotient (IQ) and language skills are several factors that are thought to influence learning outcomes in mathematics. This study aims to determine the path analysis model of the mathematics learning outcome with IQ and language skill as the factors. The population of this research was all the students of Kesatrian I Junior High School, Semarang (SMP Kesatrian I Semarang), and by using a random sampling technique, we took 221 students as the sample. The result shows a significant influence of IQ and language skills on mathematics learning outcomes.
ORDINAL XGBOOST FOR MULTICLASS NUTRITIONAL STATUS CLASSIFICATION WITH IMBALANCED DATA Arini, Luthfia Hanun Yuli; Siniwi, Lutfiah Maharani
Jurnal Statistika Universitas Muhammadiyah Semarang Vol 13, No 1 (2025): Jurnal Statistika Universitas Muhammadiyah Semarang
Publisher : Department Statistics, Faculty Mathematics and Natural Science, UNIMUS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26714/jsunimus.13.1.2025.45-60

Abstract

Stunting is a major global health problem that affects children’s physical growth and cognitive development, particularly in developing countries. The classification of toddlers’ nutritional status to detect stunting risk often faces two primary challenges: the ordinal nature of the labels and the imbalance in class distribution, where minority classes such as stunted and tall are much smaller than the majority class (normal). This study aims to develop an Ordinal Extreme Gradient Boosting (Ordinal XGBoost) method using a Binary Decomposition approach to classify toddlers’ nutritional status in imbalanced ordinal data. Secondary data from 100 respondents were analyzed, with 80% allocatedfor training and 20% for testing. The Binary Decomposition approach transforms the three-class ordinal classification problem into two binary subproblems, each trained using XGBoost with weighted logistic loss to address class imbalance. Model performance was evaluated using four key metrics: accuracy, ordinal Mean Absolute Error (MAE), Quadratic Weighted Kappa (QWK), and Macro-F1. Results showed an accuracy of 70%, ordinal MAE of 0.30, QWK of 0.45, and Macro-F1 of 0.53. The MAE and QWK values indicate the model’s ability to preserve class order while reducing large prediction jumps, although performance on minority classes remains limited. These findings suggest that the proposed approach is effective for imbalanced ordinal data and has potential applications in toddler nutritional status monitoring systems.
COMPARISON OF K-NEAREST NEIGHBOR AND NAÏVE BAYES CLASSIFICATION METHODS FOR STATUS OF TODDLER NUTRITION DATA AT BAQA SAMARINDA SEBERANG COMMUNITY HEALTH CENTER Annabaa Aulia, Muzizah; Goejantoro, Rito; Hayati, Memi Nor
Jurnal Statistika Universitas Muhammadiyah Semarang Vol 13, No 1 (2025): Jurnal Statistika Universitas Muhammadiyah Semarang
Publisher : Department Statistics, Faculty Mathematics and Natural Science, UNIMUS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26714/jsunimus.13.1.2025.1-13

Abstract

Classification is a job of assessing data objects to put them into a certain class from a number of available classes. The naïve Bayes method is a statistical classification that can be used to estimate the probability of membership in a class. Meanwhile, the K-Nearest Neighbor (K-NN) method is a supervised method used for classification. The aim of this research is to obtain classification results of the nutritional status of toddlers at the Baqa Samarinda Seberang Community Health Center in 2022 using the naïve Bayes algorithm and the K-NN algorithm. Based on the calculation results for classification of the nutritional status of toddlers at the Baqa Samarinda Seberang Community Health Center using accuracy calculations and confusion matrices, the highest accuracy was obtained using the naïve Bayes method of 82.15% and a Press's Q value of 168 with a training data proportion of 90%: testing data of 10%. Meanwhile, the results of accuracy calculations and the confusion matrix obtained the highest accuracy in the K-NN method of 90.57% at values 3-NN, 5-NN, 7-NN, 9-NN and Press's Q value of 187.65 with a training data proportion of 90% and testing data 10%. From the results of this analysis, it was concluded that the K-NN method worked better than the naïve Bayes method in classifying the nutritional status of toddlers at the Baqa Samarinda Seberang Community Health Center.
DETERMINANTS OF OUTPATIENT CARE BEHAVIOR OF THE ELDERLY POPULATION IN WEST SULAWESI IN 2022: BACKWARD ELIMINATION LOGISTIC REGRESSION Natasya Auzea Fahyumi, Tengku; Istiana, Nofita
Jurnal Statistika Universitas Muhammadiyah Semarang Vol 13, No 1 (2025): Jurnal Statistika Universitas Muhammadiyah Semarang
Publisher : Department Statistics, Faculty Mathematics and Natural Science, UNIMUS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26714/jsunimus.13.1.2025.61-73

Abstract

Ageing population is a result of the successful development. It is characterized by an increase in the number and proportion of the elderly population. The elderly population is described as a vulnerable group. As a result of degenerative process in physical, psychological, and social activity aspects, the elderly population has a high risk of experiencing health problems. West Sulawesi is the province with the eighth highest morbidity rate for the elderly. However, this province has the lowest percentage of outpatient care, at 36,39%. Therefore, it is necessary to conduct research on the outpatient care behavior of the elderly in West Sulawesi in 2022. This research uses a binary logistic regression method. The results show that the variables marital status, education level, disability status, and activity impairment have a significant effect on outpatient care behavior in West Sulawesi in 2022. Efforts from the government and the society are needed to increase the awareness about the importance of health checks among the elderly.
APPLICATION OF BINARY LOGISTICS REGRESSION AND RANDOM FOREST TO CIGARETTE CONSUMPTION EXPENDITURE IN GORONTALO REGENCY 2022 Hamani, Mohamad Taufik; Isa, Dewi Rahmawaty; Nasib, Salmun K.; Panigoro, Hasan S.; Hasan, Isran K.; Yahya, Nisky Imansyah
Jurnal Statistika Universitas Muhammadiyah Semarang Vol 13, No 1 (2025): Jurnal Statistika Universitas Muhammadiyah Semarang
Publisher : Department Statistics, Faculty Mathematics and Natural Science, UNIMUS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26714/jsunimus.13.1.2025.14-22

Abstract

The goal of this research is to predict or identify an object's class using its available attributes through classification. The aim of this research is to use the random forest method to develop a classification model and the binary logistic regression method to discover significant determinants in cigarette consumption expenditure in Gorontalo Regency. The findings indicated that the size of the home, the number of family members, and the head of the household's educational attainment all had a considerable impact. Only the household head's educational attainment, however, consistently influences the model and satisfies the goodness of fit requirements. In contrast, the random forest model outperformed binary logistic regression in the classification analysis when classification characteristics including accuracy, precision, recall, and f1-score were assessed. Consequently, random forest was found to be the most effective classification model in this investigation.
COMPARISON OF FEEDFORWARD NEURAL NETWORK AND LONG SHORT TERM MEMORY IN SENTIMENT ANALYSIS OF SHOPEE APPLICATION REVIEWS Lusia, Dwi Ayu; Simanjuntak, Yessica Maretha
Jurnal Statistika Universitas Muhammadiyah Semarang Vol 13, No 1 (2025): Jurnal Statistika Universitas Muhammadiyah Semarang
Publisher : Department Statistics, Faculty Mathematics and Natural Science, UNIMUS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26714/jsunimus.13.1.2025.74-84

Abstract

Sentiment analysis is a method for generating types of views or opinions that express positive, neutral or negative sentiments. The application of sentiment analysis was carried out to determine the sentiment of Shopee application users. This research uses an artificial neural network algorithm to learn patterns from training data to predict the sentiment of the test data class. The aim of the research is to determine sentiment classification, identify the optimal Feedforward Neural Network and Long Short Term Memory architectural models in classifying user reviews of the Shopee application and compare the performance of the models based on the level of accuracy. The data set is divided into training data and test data respectively by 80% and 20%. The research results showed that there were 91.865 reviews with positive sentiment, 63.038 negative reviews and 26.662 neutral reviews based on Valanced Aware Dictionary Sentiment lexicon dictionary. The network architecture used is one hidden layer, with 137 hidden neurons and a two hidden layer model, with 491 units of first hidden neurons and 38 units of second hidden layer neurons. Evaluation of sentiment classification of Shopee application users resulted in the highest accuracy rate on the single-layer LSTM model, at 68,93%, with precision of 61,29%, and recall of 56,10%.

Page 1 of 1 | Total Record : 7

 1 

Filter by Year

2025 2025


Reset
Filter By Issues
All Issue Vol 13, No 1 (2025): Jurnal Statistika Universitas Muhammadiyah Semarang Vol 12, No 2 (2024): Jurnal Statistika Universitas Muhammadiyah Semarang Vol 12, No 1 (2024): Jurnal Statistika Universitass Muhammadiyah Semarang Vol 11, No 2 (2023): Jurnal Statistika Universitas Muhammadiyah Semarang Vol 11, No 1 (2023): Jurnal Statistika Universitas Muhammadiyah Semarang Vol 10, No 2 (2022): Jurnal Statistika Universitas Muhammadiyah Semarang Vol 10, No 1 (2022): Jurnal Statistika Universitas Muhammadiyah Semarang Vol 9, No 2 (2021): Jurnal Statistika Vol 9, No 2 (2021): Jurnal Statistika Universitas Muhammadiyah Semarang Vol 9, No 1 (2021): Jurnal Statistika Vol 9, No 1 (2021): Jurnal Statistika Universitas Muhammadiyah Semarang Vol 8, No 2 (2020): Jurnal Statistika Vol 8, No 2 (2020): Jurnal Statistika Universitas Muhammadiyah Semarang Vol 8, No 1 (2020): Jurnal Statistika Vol 8, No 1 (2020): Jurnal Statistika Universitas Muhammadiyah Semarang Vol 7, No 2 (2019): Jurnal Statistika Vol 7, No 2 (2019): Jurnal Statistika Universitas Muhammadiyah Semarang Vol 7, No 1 (2019): Jurnal Statistika Universitas Muhammadiyah Semarang Vol 7, No 1 (2019): Jurnal Statistika Vol 6, No 2 (2018): Jurnal Statistika Universitas Muhammadiyah Semarang Vol 6, No 2 (2018): Jurnal Statistika Vol 6, No 1 (2018): Jurnal Statistika Universitas Muhammadiyah Semarang Vol 6, No 1 (2018): Jurnal Statistika Vol 5, No 2 (2017): Jurnal Statistika Universitas Muhammadiyah Semarang Vol 5, No 2 (2017): Jurnal Statistika Vol 5, No 1 (2017): Jurnal Statistika Universitas Muhammadiyah Semarang Vol 4, No 2 (2016): Jurnal Statistika Vol 4, No 2 (2016): Jurnal Statistika Universitas Muhammadiyah Semarang Vol 4, No 1 (2016): Jurnal Statistika Vol 4, No 1 (2016): Jurnal Statistika Universitas Muhammadiyah Semarang Vol 3, No 2 (2015): Jurnal Statistika Universitas Muhammadiyah Semarang Vol 3, No 2 (2015): Jurnal Statistika Vol 3, No 1 (2015): Jurnal Statistika Universitas Muhammadiyah Semarang Vol 3, No 1 (2015): Jurnal Statistika Vol 2, No 2 (2014): Jurnal Statistika Universitas Muhammadiyah Semarang Vol 2, No 2 (2014): Jurnal Statistika Vol 2, No 1 (2014): Jurnal Statistika Universitas Muhammadiyah Semarang Vol 2, No 1 (2014): Jurnal Statistika Vol 1, No 2 (2013): Jurnal Statistika Universitas Muhammadiyah Semarang Vol 1, No 2 (2013): Jurnal Statistika Vol 1, No 1 (2013): Jurnal Statistika Vol 1, No 1 (2013): Jurnal Statistika Universitas Muhammadiyah Semarang More Issue