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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 200 Documents
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FOURIER SERIES APPLICATION FOR MODELING “CHOCOLATE” KEYWORD SEARCH TRENDS IN GOOGLE TRENDS DATA Andrea Tri Rian Dani; Fachrian Bimantoro Putra; Muhammad Aldani Zen; Vita Ratnasari; Qonita Qurrota A'yun
Jurnal Statistika Universitas Muhammadiyah Semarang Vol 11, No 1 (2023): 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.11.1.2023.1-9

Abstract

In some cases of regression modeling, it is very common to find a repeating pattern. To model this, of course, the approach used must be in accordance with the characteristics of the data. The Fourier series is one of the proposed approaches, because it has advantages in modeling relationship patterns that tend to repeat, such as cosine sine waves. The Fourier series is a subset of nonparametric regression, which has good flexibility in modeling. In this study, the Fourier series approach was applied to model search trend data for the keyword "Chocolate" sourced from Google Trends. Generalized Cross-Validation (GCV) is used as model evaluation criteria. Based on the results of the analysis, the best Fourier series nonparametric regression model is obtained with the number of oscillations of five, which is indicated by the minimum GCV value.
Analysis of Enabling Factors on Safety Awareness and Compliance of The Use of PPE in Plastic Sack Industry Workers Using The PLS-SEM Method Clarissa Addikko Febryantie Yasmine; Dewi Kurniasih; Farizi Rachman
Jurnal Statistika Universitas Muhammadiyah Semarang Vol 11, No 2 (2023): 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.11.2.2023.1-11

Abstract

A work accident is an unwanted and unanticipated event that can result in loss of life or property. The high incidence of work accidents can be caused by a lack of worker awareness of work safety. One of the elements in the PRECEDE-PROCEED behavior change theory, namely enabling factors, are factors that facilitate behavior or activities, such as providing facilities and infrastructure for workers to prevent work accidents. This study aims to ascertain the effect of enabling factors consisting of the availability of personal protective equipment (PPE) and training on safety awareness and adherence to the use of personal protective equipment (PPE) in plastic sack industry workers. This research is analytic using the PLS-SEM method. In this study, 81 participants were selected using a proportionate random sampling technique so that the number of samples used was comparable to the population in each sub-group using a questionnaire. Significant influences were observed in the results regarding the relationship between enabling factors and compliance with the utilization of personal protective equipment (PPE) (p-value = 0.000), as well as safety awareness (p-value = 0.000). Additionally, a significant influence was found between safety awareness and compliance with the use of personal protective equipment (PPE) (p-value = 0.019). Moreover, enabling factors were determined to influence compliance with PPE utilization through awareness (p-value = 0.035).
THE EFFECT OF JOB INSECURITY ON MULTIDIMENTIONAL FATIGUE DUE TO WORK IN ENGINEERING SUPPORTING WORKERS FOR POWER PLANT REPAIR SERVICES Faren Rizka Samara; Dewi Kurniasih; Farizi Rachman
Jurnal Statistika Universitas Muhammadiyah Semarang Vol 11, No 2 (2023): 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.11.2.2023.51-62

Abstract

Power Plant Repair Service Engineering Supporting Workers are workers who provide service solution services such as Maintenance and Overhaul of power plants. In practice, work placements tend to move according to the needs of the maintenance schedule, as well as the demands for completion of work which tend to be short resulting in continuous overtime work during the work period. Based on the results of measuring psychological factors in 2022 at work, it is known that workers have an excess of quantitative and qualitative burdens and role ambiguity as the dominant triggersv that cause work stress/burnout/fatigue. From the explanation above, there is a potential indication of multidimensional fatigue due to work which is hypothesized to have a influence with job insecurity. This study aims to analyze whether or not there is a influence between latent variables in this case job insecurity on multidimensional fatigue through the AMOS Structural Equation Modeling (SEM) method in order to obtain overall test results for the constructs tested. The test results show that Job Insecurity has a significant effect on all indicators (Concerns About Transferring to Another Job (JI1), Concerns About Changes in Job Descriptions (JI2), Concerns About Work Schedules (JI33), Concerns About Decreases in Salary (JI4) and Concern About Job Prospects (JI5)) with (p-value = 0.001) and Job Insecurity have a significant effect on Multidimensional Work-related Fatigue (p-value = 0.001)
K-Means Algorithm for Grouping Provinces in Indonesia Based on Macroeconomic and Criminality Indicators Andrea Tri Rian Dani; Fachrian Bimantoro Putra; Meirinda Fauziyah; Sifriyani Sifriyani; Suyitno Suyitno; M Fathurahman
Jurnal Statistika Universitas Muhammadiyah Semarang Vol 11, No 2 (2023): 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.11.2.2023.12-21

Abstract

Cluster analysis is a method in multivariate analysis to group n observations into K groups (K ≤ n) based on their characteristics. One of the well-known algorithms in cluster analysis is K-Means. K-Means uses the non-hierarchical principle where at the initial initiation, it is necessary to determine the number of groups in advance. The K-Means algorithm can be applied to classify provinces in Indonesia based on macroeconomic indicators (percentage of poor people, open unemployment rate, and Gini ratio) and crime rate (Crime rate). The ultimate goal of this research is of course to get optimal grouping results. The similarity measure used is Euclidean Distance. The number of groups tested K=2,3,4,…,10 and the optimal number of groups with the highest Silhouette value was selected. Based on the results of the analysis, the optimal number of clusters is four. These four clusters have characteristics that distinguish one cluster from another.
OPTIMIZATION OF NAÏVE BAYES USING BACKWARD ELIMINATION FOR HEART DISEASE DETECTION Amri, Saeful; Ningrum, Ariska Fitriyana; Arum, Prizka Rismawati
Jurnal Statistika Universitas Muhammadiyah Semarang Vol 11, No 2 (2023): 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.11.2.2023.44-50

Abstract

Heart disease is the main cause of death in humans. Even though preventive measures have been taken such as regulating food (diet), lowering cholesterol, and treating weight, diabetes, and hypertension, heart disease remains a major health problem. There are several factors that cause heart disease, including age, type of chest pain, high blood pressure, sugar levels, ECG test values, maximum heart rate, and induced angina. To reduce the percentage of deaths due to heart disease, we need a system that can predict heart disease. The algorithm used in this research is a combination of the Backward Elimination and Naive Bayes algorithms to increase accuracy in diagnosing heart disease. According to the results of this research, the Naive Bayes algorithm has an accuracy value of 78.90% and an Area Under Curve (AUC) value of 0.86, which is included in the good classification category. Combining the Backward Elimination and Naïve Bayes algorithms has an accuracy value of 82.31% and an Area Under Curve (AUC) value of 0.88.
K-MEDOIDS ALGORITHM CLUSTERING WITH PRINCIPAL COMPONENT ANALYSIS (PCA) (CASE STUDY: DISTRICTS/CITIES ON THE BORNEO ISLAND BASED ON POVERTY INDICATORS IN 2021) Muhammad Yafi; Rito Goejantoro; Andrea Tri Rian Dani
Jurnal Statistika Universitas Muhammadiyah Semarang Vol 11, No 2 (2023): 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.11.2.2023.31-43

Abstract

Cluster analysis is a technique in data mining that aims to group data (object) based on the information in the data. This research is used a non-hierarchical grouping named K-Medoids algorithm to group districts/cities in Borneo island based on poverty indicators and Principal Component Analysis (PCA) method to reduce research variable. This research is also do a cluster validity test to see how many cluster there are has the best grouping result using Silhouette Coefficient (SC) method. Based on the results of the analysis there is 3 optimal Principal Component (PC) were obtained with eigen value criteria of greater than or equal to 1. Furthermore, districts/cities on Borneo island were grouped based on the PC that formed and obtained 2 optimal clusters with an SC value of 0.61. The K-Medoids algorithm obtain 2 cluster, cluster 1 consisting of 49 districts/cities and cluster 2 consisting of 7 cities.
FORECASTING THE OCCUPANCY RATE OF STAR HOTELS IN BALI USING THE XGBOOST AND SVR METHODS Damaliana, Aviolla Terza; Muhaimin, Amri; Prasetya, Dwi Arman
Jurnal Statistika Universitas Muhammadiyah Semarang Vol 12, No 1 (2024): Jurnal Statistika Universitass 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.12.1.2024.24-33

Abstract

The hotel occupancy rate indicator has become a concern in recent years as it goes hand in hand with the rapid growth of the global tourism industry. A way to maintain or even improve this indicator is to carry out managerial planning using forecasting methods. The forecasting methods used in this research are XGBoost and SVR. The advantage of this modelling is that it achieves high accuracy and processing speed. Meanwhile, the benefit of SVR is that it will produce good prediction because can overcome overfitting. The steps in this research are exploring data, separating training data and testing data, transforming data, modelling data, forecasting data, and evaluating forecasting results using RMSE, MAE, and MAPE. The results show that MAPE value from both methods is smaller than 10%, which means that both methods can predict the occupancy rate of star hotels in Bali very accurately. Apart from that, the SVR method has smaller values for all model evaluation criteria than the XGBoost method, which means that the SVR method is better than XGBoost for predicting the occupancy rate of star hotels in Bali.
THE PERFORMANCE ANALYSIS OF THE BEST MACHINE LEARNING MODEL FOR SULFUR DIOXIDE IN DKI JAKARTA Kuswanaji, Panji
Jurnal Statistika Universitas Muhammadiyah Semarang Vol 12, No 1 (2024): Jurnal Statistika Universitass 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.12.1.2024.34-47

Abstract

A good clean air is one of crucial things for humans health. A place with good and clean air can prevent humans from various kinds of respiratory diseases. One of the factors that can influence the cleanliness of the air in an area is the composition of Sulfur Dioxide (SO2). This research focuses on analyzing sulfur dioxide (SO2) compositions in Jakarta over an eleven-year period. The objective is to identify the most effective model in predicting SO2 compositions, which is critical for public health and environmental management. The study incorporates quantitative methods, machine learning techniques, and statistical analysis. From this research there are three best models that has top performance, these are huber, exponential smoothing, and naïve forecaster. The result shows that naive model has the best performance with MASE of 0.3864, RMSSE of 0.3098, MAE of 2.8857, RMSE of 3.7735, MAPE of 0.0593, and SMAPE of 0.0623.
CLUSTERING DISTRICT/CITY IN WEST KALIMANTAN BASED ON FACTORS CAUSING STUNTING USING K-HARMONIC MEANS METHOD Imanni, Rahmania Andarini Hatti; Sulistianingsih, Evy; Perdana, Hendra
Jurnal Statistika Universitas Muhammadiyah Semarang Vol 12, No 1 (2024): Jurnal Statistika Universitass 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.12.1.2024.%p

Abstract

Stunting is a chronic nutritional problem caused by inadequate dietary intake over time. The results of the Indonesian Nutrition Status Survey (SSGI) 2021 show that the percentage of stunting in West Kalimantan is 29.8%, higher than the national average. Based on the high number of stunting cases in West Kalimantan, it is necessary to group districts/cities in West Kalimantan based on the factors that cause stunting. This study aims to analyze the clustering of districts/cities in West Kalimantan based on the factors that cause stunting using the K-Harmonic Means method and analyze the number of optimal clusters using the silhouette coefficient. The percentage of households without access to clean drinking water , the rate of exclusive breastfeeding , the percentage of low birth weight babies born safely , the percentage of households without proper sanitation facilities  in 2021 are the variables analyzed in this study. The analysis results show that the optimal number of clusters is 4 with a silhouette coefficient value of 0.744, indicating a solid structure in the grouping. Cluster 1 is a cluster with a very high causal factor for stunting. The most influential factors in cluster 1 are households without access to clean drinking water, lack of exclusive breastfeeding, and low birth babies born safely.
Determining Sister City Regency/City Non-Sample Cost of Living Survey (SBH) and Clustering Analysis of Consumption Patterns in West Java using the Machine Learning Method Novidianto, Raditya; Tanur, Erwin; Dani, Andrea Tri Rian; Putra, Fachrian Bimantoro
Jurnal Statistika Universitas Muhammadiyah Semarang Vol 12, No 1 (2024): Jurnal Statistika Universitass 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.12.1.2024.%p

Abstract

Inflation is a significant data source in policy making. However, not all Regency/cities have inflation figures. As a result, Regency/cities must borrow inflation figures from dietary characteristics, GDP per capita, population, and distance between Regency and cities; this is called a sister city. With the help of machine learning, the similarity level method using distance measures, namely Euclidean distance, CID distance, and ACF distance, can help Regency/cities find sister cities. Furthermore, grouping was carried out using a biclustering algorithm to see the characteristic variables in West Java from the same consumption pattern data. The biclustering parameter with tuning parameter ????=0.1 is the best bicluster with a total of 3 biclusters with a value of MSR/V=0.02433 with identical characteristic variables, namely Average Fish Consumption (X3), Average Meat Consumption (X4), Average Consumption of Eggs and Milk (X5), Average Consumption of Vegetables (X6), Average Consumption of Fruit (X8), Average Consumption of Oil and Coconut (X9), Average Consumption of Housing and Household Facilities (X15), Average Consumption of Various Goods and Services and Average Consumption of Taxes (X16), Levies and Insurance (X19).

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