UNP Journal of Statistics and Data Science

Comparison of Cox Proportional Hazard Models with Interaction and Without Interaction in Heart Failure Patients

Sat, 31 May 2025 00:00:00 +0000

Heart failure is one of the disorders that attack the heart and is a major cause of morbidity and mortality. There is a 5% prevalence of heart failure in Indonesia in 2020. By utilizing survival analysis, this study aims to compare the Cox proportional hazard model with interaction and without interaction, and identify factors that significantly affect the survival time of heart failure patients. The research data is secondary data consisting of 299 heart failure patient data with several variables including high blood pressure, anemia status, and age. Through the stages of analysis that have been carried out, it is found that the variables of high blood pressure and age have a significant effect on the survival time of heart failure patients, while the anemia variable and the interaction between independent variables do not have a significant relationship with survival time. In addition, based on the AIC value, it is also found that the model without interaction is better than the model with interaction, which is characterized by a smaller AIC value in the model without interaction. Based on the best model, patients with high blood pressure have a 1.52 times higher chance of dying than patients without high blood pressure. In addition, the probability of death increased by 4.33% for every one-year increase in patient age. This study concludes that the model without interaction is more suitable for describing the relationship between independent variables and survival time in heart failure patients.

Cox-Stratified Model in Relationship Analysis between Employee Mental Health and Resignation Decision

Sat, 31 May 2025 00:00:00 +0000

This study examines the relationship between employee mental health and turnover decisions using the Cox Stratified model. Utilizing secondary worker turnover data from Kaggle, the research investigates the impact of anxiety and self-control on job tenure. Results indicate that the Cox Proportional Hazard model significantly explains this relationship, with self-control emerging as a key factor negatively associated with turnover risk. Stratification of profession variables, which did not meet the proportional hazard assumption, revealed variations in survival rates across different professions. Professions requiring strong self-control, such as HR and sales, exhibited higher survival probabilities, whereas high-pressure professions like consulting andshowed lower survival rates. A reduced model confirmed the importance of self-control in employee retention. The findings suggest that interventions aimed at enhancing self-control could serve as an effective strategy for mitigating turnover, especially in high-stress occupations. Elevated job pressure can negatively impact employee mental well-being, potentially disrupting self-control and increasing anxiety levels. Future research could incorporate additional influential factors, such as job satisfaction, work environment, and social support, to further develop this research. Furthermore, the implementation of real-time data collection could enable continuous monitoring of mental conditions, behaviors, and relevant factors such as self-control and anxiety, providing dynamic insights over short time intervals.

Survival Analysis of Heart Failure Patients Using the Cox Proportional Hazard Model And Weibull Regression

Sat, 31 May 2025 00:00:00 +0000

Cardiovascular disesase is the leading cause of death globally, claiming around 17,9 million lives each year, accounting for 31% of all deaths worldwide. Hearth failure is a common event caused by cardiovascular disease. Hearth failure is one of the principal health issues with excessive mortality and morbidity costs. Heart failure is the main reason of mortality worldwide. This take a look ambitions to analyze the factors influencing the survival of heart failure patients using the Cox proportional hazard Cox (PH) model and the Weibull regression. The main purpose of this study is to provide information on the causes of heart failure deaths and what effects occur when having heart disease. It is hoped that the results of this study can provide the general public to be more careful in order to prevent heart failure disease. The data used are secondary data from Kaggle consisting of 299 patients with the variables anemia, diabetes, hypertension, gender and smoking status. The analysis showed that only hypertension significantly increased the risk of events in both models, whereas other variables were not statistically significant. The selection of the best model is based on the assumptions of proportional hazard, flexibility, and Akaike information criterion (AIC) values. The Cox-PH model was chosen as the model of choice because it is more flexible and does not require certain fundamental assumptions regarding risk distribution. This study provides important insight into the risk factors that influence the prognosis of heart failure patients.

Binary Logistic Regression to Factors Affecting Unmet Need for Limiting in East Java, Indonesia

Sri Wahyuni, Yenni Kurniawati, Sepniza Nasywa, Ardiyatul Putri — Sat, 31 May 2025 00:00:00 +0000

East Java, Indonesia's second most populated province, is anticipated to see significant annual population growth in the future, potentially resulting in a population explosion. The elevated birth rate facilitates the swift increase in population size. The unmet need for knowledge-based information among women of reproductive age has posed obstacles for the execution of family planning initiatives aimed at reducing birth rates. This study used binary logistic regression to identify the factors affecting the unmet demand for family planning among women of reproductive age in East Java Province in 2017.The investigation revealed that the woman's age, employment status, and husband's educational level significantly influenced the unmet need for constraint. Moreover, women aged 15-24 who are unemployed, lack schooling, have an illiterate partner, and reside in rural regions are more prone to experiencing an unmet need for contraception. Women aged 15-19 years compared to women aged 45-49 years were at 3,182 times higher risk of having an unmet need for family planning compared to a met need for family planning. Women aged 20-24 years compared to women aged 45-49 years were at 1,316 times higher risk of having an unmet need for family planning compared to a met need for family planning. Women who did not work compared to women who worked were 1,311 times more likely to have an unmet need for family planning compared to a met need for family planning. The binary logistic analysis model that was formed provided a good accuracy of 92,135% in predicting

Application of K-Modes Clustering Method to Identify Low Birth Weight Factors in Central Sulawesi Province

Celsy Aprotama, Yenni Kurniawati, Muhammad Arief Rivano, Devi Yopita Sipayung — Sat, 31 May 2025 00:00:00 +0000

Low birth weight (LBW) has long-term effects on maternal and child health, with a high prevalence in Central Sulawesi Province. This study aims to identify factors influencing the occurrence of LBW in the region using the k-modes clustering method. The data used in this research is derived from the 2017 Indonesian Demographic and Health Survey. The analyzed variables include the husband's education level, miscarriage rate, maternal smoking habits, child's gender, husband's occupation, type of residence, and wealth index. The analysis revealed two distinct clusters. The first cluster mainly consisted of husbands with a secondary education level or equivalent to junior high school, working in the agricultural sector, residing in urban areas, and having a medium wealth index. In contrast, the second cluster was dominated by husbands with only primary education or equivalent to elementary school, living in rural areas, and having a very low wealth index. The findings of this study emphasize the need for comprehensive efforts to improve education, enhance environmental conditions, and expand healthcare access to reduce poverty and lower the incidence of LBW in Central Sulawesi. This research also contributes to initiatives aimed at improving maternal and child health in the region.

Logit And Complementary Log-Log Modeling (Case Study: Factors Influencing Birth Control Use in Papua 2017)

Riza Sasmita, Yenni Kurniawati, Sri Wahyuni, Celsy Aprotama — Sat, 31 May 2025 00:00:00 +0000

Research was conducted to determine the factors that influence the use of family planning in Papua Province in 2017. Indonesia has the 4th largest total population in the world, facing the challenge of a fairly high and uncontrolled population growth rate, which can have an impact on the welfare of the community, especially Papua Province. This study used secondary data from the 2017 SDKI. The population of this study was all women of childbearing age in the province of Papua. The research was conducted using logit logistic regression and cloglog logistic regression methods and took the best model to analyze the factors affecting family planning use in Papua Province. The results showed that the cloglog logistic regression model proved to be the best model based on AIC and accuracy. The accuracy of this cloglog logistic regression model is 78.54%. With the results of the cloglog logistic regression analysis, it was found that there was a relationship between region of residence, husband's education, and wife's education. The odds of a woman who has a husband with more than a junior high school education having an unmet need for family planning is 1.688 times higher than a woman who has a husband with less than a junior high school education. The odds of a woman with a junior high school education or above having an unmet need for family planning is 0.496 times higher than a woman with less than a junior high school education.

Mortality Trends in Heart Failure Patients : A Study Using Cox Regression Models

Sat, 31 May 2025 00:00:00 +0000

Heart failure is classified as a cardiovascular disease, which is the leading cause of death worldwide. In Indonesia, heart failure has a high mortality rate, which in 2019 became the second leading cause of death after stroke. One method that can be used to examine the factors affecting mortality in heart failure patients is the cox proportional hazards regression. Cox proportional hazards regression is one of the most commonly used methods for analyzing survival data to date. The study data consisted of 299 observations involving 5 predictor variables, such as age, serum creatinine, serum sodium, high blood pressure, and diabetes. The conclusion of the analysis indicates that the variables of age, serum creatinine, serum sodium, and high blood pressure are significant. High blood pressure and serum creatinine are the factors that most affect the death of heart failure patients. Patients with high blood pressure have a 56,71% higher risk of death than patients without high blood pressure, and every 1 mg/dL in creatinine in the blood, the risk of death for heart failure patients will increase by 29,77%.

Sentiment Analysis of the MSIB Program on Application X (Twitter) Using the Naïve Bayes Algorithm

Nabila Husni, Dodi Vionanda, Nur Leli, Syafriandi Syafriandi — Sat, 31 May 2025 00:00:00 +0000

Certified Internships and Independent Studies (MSIB) is one of the programs of the Independent Learning-Independent Campus (MBKM) curriculum as a policy of the Kemendikbudristek. A government policy, especially in terms of education, will of course give rise to stigmas or feedback from the public regarding the policy. This research aims to find out public opinion regarding the MSIB program in the X (Twitter) application by sentiment analysis using the Naive Bayes Classifier algorithm. From this analysis, it was found that 84.6% of reviews had positive sentiments, while 16.4% of reviews had negative sentiments. Evaluation using the Naïve Bayes Classifier model shows that this model succeeded in classifying 85% of all data correctly, showing quite good performance in classifying the sentiment of these reviews.

An Application X-bar Chart and Statistical Process Control With R Package

Niswatul Rizkiah, Yenni Kurniawati — Sat, 31 May 2025 00:00:00 +0000

Quality control is a critical aspect of ensuring that production processes meet established standards and customer requirements. One widely used approach in Statistical Quality Control (SQC) is the control chart, particularly the X̄ and s charts, which monitor process stability based on the mean and variability of the data. This study aims to evaluate the quality and variation of the feed water boiler process using X̄ and s control charts, as well as to assess process capability with the aid of the R programming language and the qcc package. The dataset comprises hardness measurements of water collected over 25 consecutive days, three times per day, resulting in 75 observations. Initial analysis revealed one data point outside the control limit in the X̄ chart, which, when excluded, improved overall process stability. The s chart indicated more consistent stability compared to the X̄ chart. Process capability analysis yielded Cp and Cpk values of 0.5844 and 0.5600, respectively, indicating that the process is not yet capable of fully meeting product specifications and exhibits relatively high variability.These findings highlight the need for process improvement through variation reduction and six sigma approaches.The use of R/qcc proved to be an effective tool for monitoring and analyzing quality control in production processes.

K-Means Cluster Analysis for Grouping Small and Medium Enterprises (SMEs) in Pesisir Selatan Regency

nailul arrahmi, Chairina Wirdiastuti, Yenni Kurniawati — Sat, 31 May 2025 00:00:00 +0000

Small and Medium Industries (SMEs) play an important role in national economic growth through job creation, improving regional economies, and triggering entrepreneurial spirit. Although most SMEs operate on a limited scale with simple technology, this sector has great potential to grow if it receives sustainable support. However, SMEs in Pesisir Selatan Regency face various challenges, such as limited human resources, difficulty in accessing capital, and low utilization of technology. This study aims to analyze the grouping of SMEs in Pesisir Selatan Regency using the clustering method. Using secondary data on six types of SMEs in 15 sub-districts in 2023, this study applies the K-Means algorithm to group SMEs based on the characteristics of the dominant sector. The clustering results produce three main groups: first, sub-districts with high SME activity in the textile and food sectors; second, sub-districts with low SME activity in almost all sectors; and third, sub-districts with balanced SME activity in various sectors, such as apparel, beverages, furniture, and non-metallic minerals. These findings are expected to provide insight for local governments in formulating more targeted policies for the development of SMEs and equitable distribution of economic growth in Pesisir Selatan Regency.

Grouping of Provinces in Indonesia Based on Active Family Planning Participants Using Modern Methods Using Fuzzy C-Means

Annisa Ramadhani, Tessy Octavia Mukhti, Yenni Kurniawati, Zamahsary Martha — Sat, 31 May 2025 00:00:00 +0000

Indonesia’s rapid population growth presents a significant challenge to national welfare and public health. One of the key strategies implemented by the government to address this issue is the Family Planning (FP) program, which emphasizes the use of modern contraceptive methods. However, the utilization of these methods remains uneven across provinces. This study aims to cluster Indonesian provinces based on the number of active participants using modern contraceptive methods in 2023 by applying the Fuzzy C-Means (FCM) clustering algorithm. FCM was selected due to its ability to handle overlapping data characteristics, allowing for a more flexible and representative analysis. The clustering results reveal two main clusters: Cluster 1, which consists of provinces with high levels of active modern contraceptive users, and Cluster 2, which includes provinces with low participation levels. These findings are expected to serve as a reference for more targeted policy formulation to enhance the equity and effectiveness of the FP program across the country.

Comparison of Double Moving Average and Double Exponential Smoothing (Brown) Methods for Open Unemployment Rate in Padang Panjang Municipality

Nufhika Fishuri, Fadhilah Fitri, Dony Permana — Sat, 31 May 2025 00:00:00 +0000

The Open Unemployment Rate (TPT) is the percentage of unemployed people in the total labor force. The population included in the labor force is the population aged 15 years and over who has a job but is temporarily not working. Unemployment occurs because of a mismatch between the demand for employment and the qualifications of job seekers. Many job vacancies require graduates with a diploma or degree, so unemployment is one of the problems faced by Padang Panjang City. To overcome TPT in Padang Panjang City, one of the needs is to do forecasting to see how the TPT rate will occur in the coming year. This research uses a forecasting method by comparing the Double Moving Average (DMA) and Double Exponential Smoothing (DES) forecasting values of the Unemployment Rate in Padang Panjang City from 2006 to 2023. This forecasting is done to provide insight into the future condition of the workforce in Padang Panjang City. The results of the forecasting indicate that in 2024, there will be an increase of 0.42%, and for the next 2 years, there will be a decrease

Forecasting Analysis of Total Coconut Production in Padang Pariaman Using the Double Exponential Smoothing Holt

Della Amelia, Zilrahmi, Fitri Mudia Sari — Sat, 31 May 2025 00:00:00 +0000

Kelapa merupakan buah khas daerah tropis yang memiliki banyak manfaat. Kelapa memiliki arti penting yang strategis bagi Indonesia. Sumatera Barat merupakan salah satu provinsi penghasil kelapa di Indonesia dengan total produksi sebesar 88 ribu ton pada tahun 2023. Dimana Kabupaten Padang Pariaman merupakan kabupaten penghasil kelapa terbesar di Provinsi Sumatera Barat dengan total produksi sebesar 38.794 ton pada tahun 2022. Kelapa merupakan salah satu komoditas utama dan sumber perekonomian di Kabupaten Padang Pariaman. Melihat pentingnya peranan kelapa di Kabupaten Padang Pariaman, maka perlu dilakukan peramalan produksi kelapa untuk mengetahui kondisi hasil perkebunan tersebut. Double Exponential Smoothing merupakan metode yang sesuai digunakan dalam peramalan jumlah produksi kelapa di Kabupaten Padang Pariaman. Hal ini dikarenakan metode ini sesuai dengan data yang memiliki pola trend. Hasil peramalan menunjukkan bahwa produksi kelapa pada tahun 2024 sampai dengan tahun 2028 adalah sebesar 39.506,16 ton, 39.943,43 ton, 40.380,7 ton, 40.817,97 ton, dan 41.255,24 ton. Dimana hasil tersebut menunjukkan bahwa produksi kelapa mengalami peningkatan setiap tahunnya sekitar 1% dengan nilai MAPE sebesar 16,19% yang menunjukkan bahwa hasil peramalan tersebut termasuk dalam kriteria akurat.

Application of Extreme Gradient Boosting Algorithm with ADASYN for Classification of Households Receiving Program Keluarga Harapan in West Sumatra Province

Amelia Susrifalah, Dodi Vionanda, Yenni Kurniawati, Dwi Sulistiowati — Sat, 31 May 2025 00:00:00 +0000

Program Keluarga Harapan (PKH) is a form of social protection provided by the government to overcome poverty in Indonesia. However, challenges remain in accurately predicting eligible households. Therefore, a data-based classification method is needed to identify PKH recipients based on their factors. This research was conducted in West Sumatra Province using variables from the Data Terpadu Kesejahteraan Sosial (DTKS) variable group contained in SUSENAS 2024. Based on data from Badan Pusat Statistik (BPS) of West Sumatera Province, there are 1.790 PKH recipient households and 9.810 non-recipient households, indicating a class imbalance. Considering the large amount of data and complex variables, PKH can be analyzed using the Extreme Gradient Boosting (XGBoost) algorithm because of its ability to handle large-scale data and produce high classification performance. To address data imbalance, Adaptive Synthetic (ADASYN) was applied before analysis. The application of XGBoost with the scale_pos_weight parameter shows low classification performance, with sensitivity value of 12.3% and balanced accuracy of 55.2%. To overcome this, unbalanced data was handled using the ADASYN method. The application of XGBoost after data balancing with ADASYN showed significant performance improvement, with sensitivity value 80.4% and balanced accuracy 88.1%. In classifying PKH recipient households, the variables that make an important contribution are the age of the head of household, floor area, diploma of the head of household, floor material and number of household Members. This research shows that the combination of XGBoost and ADASYN is effective in overcoming data imbalance and improving PKH recipient classification performance.

Application of Singular Spectrum Analysis for Predicting Indonesia’s Total Export Value

Ronald Rinaldo, Yenni Kurniawati, Dony Permana, Dina Fitria — Sat, 31 May 2025 00:00:00 +0000

Forecasting export data presents unique challenges due to seasonal fluctuations and complex global economic dynamics. Inaccurate forecasts may lead to misguided economic policies, particularly in the export sector, which plays a critical role in national economic growth. This study aims to forecast the total export value of two major sectors in Indonesia from January to December 2024 using the Singular Spectrum Analysis (SSA) method. Forecasting is essential in supporting economic policy planning and strategic decision-making. SSA is chosen for its ability to decompose time series data into interpretable components such as trend, seasonality, and noise. The forecasting model's performance is evaluated using the Mean Absolute Percentage Error (MAPE), which provides an intuitive accuracy interpretation in percentage terms. The optimal parameter for SSA was found at L=28L = 28L=28, yielding a MAPE of 16.63%, indicating good forecasting accuracy. The forecasted export values show that the highest export is expected in December 2024 (USD 39,578.67 million), and the lowest in January 2024 (USD 21,689.14 million). These findings suggest that SSA is effective in forecasting economic time series data, particularly Indonesia’s export values. This study contributes to the practical application of SSA in economics and serves as a reference for future research and policymakers in formulating export strategies.