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All Journal AKSIOMA: Jurnal Program Studi Pendidikan Matematika Jurnal Edukasi Matematika dan Sains Jurnal Matematika dan Statistika serta Aplikasinya (Jurnal MSA) Jurnal Gantang NUMERICAL (Jurnal Matematika dan Pendidikan Matematika) Jurnal Penelitian Pendidikan IPA (JPPIPA) Jurnal Basicedu Eksakta : Berkala Ilmiah Bidang MIPA FIBONACCI: Jurnal Pendidikan Matematika dan Matematika Jurnal Ilmiah Ilmu Komputer Fakultas Ilmu Komputer Universitas Al Asyariah Mandar Pelita Eksakta Jurnal Ilmiah Pendidikan Matematika Al Qalasadi Jurnal Basicedu Journal of Mathematics UNP UNP Journal of Statistics and Data Science International Journal of Trends in Mathematics Education Research (IJTMER)
Dony Permana
Jurusan Matematika Universitas Negeri Padang
Agriculture, Biological Sciences & Forestry Astronomy Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Energy Engineering Environmental Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Medicine & Pharmacology Physics Social Sciences Other

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An Examination of Determinants Affecting the Survival Duration Pediatric Brain Cancer Patients Through Stratified Cox Regression Analysis Fauzan Al-Hamdani Siregar; Andini Diva Luthfiyah; Tessy Octavia Mukhti; Dony Permana
UNP Journal of Statistics and Data Science Vol. 3 No. 4 (2025): UNP Journal of Statistics and Data Science
Publisher : Departemen Statistika Universitas Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24036/ujsds/vol3-iss4/420

Abstract

Brain cancer is the second most common pediatric malignancy and the leading cause of cancer-related mortality in children. Pediatric brain tumors (PBTs) represent around 25% of all pediatric cancers and consist of clinically and biologically diverse subtypes, with an estimated incidence of 0.3–2.9 cases per 100,000 children annually. The high prevalence emphasizes the importance of identifying factors that influence patient survival. This study aims to identify and analyze the factors that significantly affect the survival duration of pediatric brain cancer patients by applying the Stratified Cox regression model. This study utilized secondary data from the Pediatric Brain Cancer database (www.cbioportal.org). Independent variables included cancer type, ethnicity, other medical conditions, sex, tumor type, and treatment type, while the dependent variables were survival time (OS Months) and patient status (OS Status). Data were analyzed using the Stratified Cox regression method. A total of 203 patients were observed, consisting of 39 uncensored cases (19.21%) and 164 censored cases (80.79%). The majority of patients were male (58.62%), diagnosed with low-grade glioma/astrocytoma (43.35%), classified as non-Hispanic or Latino (93.52%), had no additional medical conditions (51.72%), received new treatment (85.22%), and were categorized with primary tumor type (74.38%). Results from the stratified Cox model indicated that cancer type was a significant predictor of survival. Children with embryonal tumors were found to have 8.9 times greater risk of experiencing an event compared to those with CNS cancer types, whereas children with high-grade glioma/astrocytoma had a 24.85 times higher risk compared to the CNS cancer group.
Peramalan Konsentrasi PM2.5 di Kota Medan Menggunakan Metode ARIMAX dengan Faktor Meteorologi sebagai Variabel Eksogen Fauzan Arrahman; Tessy Octavia Mukhti; Dony Permana; Fenni Kurnia Mutiya
UNP Journal of Statistics and Data Science Vol. 3 No. 4 (2025): UNP Journal of Statistics and Data Science
Publisher : Departemen Statistika Universitas Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24036/ujsds/vol3-iss4/429

Abstract

Particulate Matter 2.5 (PM2.5) is a fine particle measuring less than 2.5 micrometers which is dangerous for human health because it can penetrate the respiratory system and cause cardiovascular disorders. High PM2.5 concentrations reflect a decline in air quality, so forecasting efforts are needed to support pollution control and environmental policies. This study aims to forecast daily PM2.5 concentrations in Medan City using the Autoregressive Integrated Moving Average with Exogenous Variables (ARIMAX) method by considering meteorological factors as exogenous variables. The data used consist of PM2.5 concentrations and average temperature, humidity, rainfall, and wind speed data for the period from June 1, 2024 to June 10, 2025. The analysis results show that the best model is ARIMAX (4,1,0) with exogenous variables of average temperature and rainfall, where temperature has a positive effect and rainfall has a negative effect on PM2.5. This model meets the assumptions of white noise and residual normality, with a MAPE value of 20.635%, indicating a fairly good level of forecasting accuracy. The forecasting results show PM2.5 concentrations in the range of 19–26 µg/m³ with a downward trend at the end of June 2025, indicating improved air quality in Medan City. Thus, the ARIMAX method with meteorological factors is considered effective in modeling and forecasting PM2.5 dynamics in urban areas.
Analisis Pengaruh Penggunaan ChatGPT Terhadap Prestasi Akademik Mahasiswa Dengan Motivasi Sebagai Variabel Intervening Menggunakan Metode SEM-PLS Salsabilla Khairani; Yenni Kurniawati; Dony Permana; Fadhilah Fitri
UNP Journal of Statistics and Data Science Vol. 3 No. 4 (2025): UNP Journal of Statistics and Data Science
Publisher : Departemen Statistika Universitas Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24036/ujsds/vol3-iss4/430

Abstract

This study aims to analyze the factors that influence student academic achievement through the use of ChatGPT using the Structural Equation Modeling (SEM) method based on the Partial Least Square (PLS) approach. In this study, three main factors were identified as elements that can influence the use of ChatGPT, namely knowledge about ChatGPT (PTC), willingness to use the technology (KUMT), and concerns that may arise (KYDT), as well as learning motivation as an intervening variable. The total sampling method was used in this study, where the entire population that met the criteria was designated as respondents. The research population included students in the Statistics Study Program at Padang State University in semesters 4–8 who had used ChatGPT for at least six months, with a total of 216 student respondents. Data were collected through a survey using an online questionnaire. Based on the analysis that has been carried out, the results of the study show that the variables of knowledge about ChatGPT (PTC) and willingness to use the technology (KUMT) have a significant positive effect on learning motivation, while concerns that may arise (KYDT) have no significant effect. Furthermore, only the variable of concerns that may arise (KYDT) had a significant direct effect on academic achievement, while the results of the mediation effect test showed that only the variable of willingness to use the technology (KUMT) had a significant indirect effect on academic achievement through learning motivation.
Forecasting Smallholder Oil Palm Yield in Riau Province through the SARIMA Approach Septrina Kiki Arisandi; Dony Permana; Tessy Octavia Mukhti
UNP Journal of Statistics and Data Science Vol. 4 No. 1 (2026): UNP Journal of Statistics and Data Science
Publisher : Departemen Statistika Universitas Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24036/ujsds/vol4-iss1/436

Abstract

Oil palm stands as one of Indonesia’s major agricultural sectors that plays a vital role in regional economic growth, particularly within Riau Province. However, its production often fluctuates due to seasonal and environmental factors, making accurate forecasting essential for planning and policy formulation. This study aims to forecast smallholder oil palm production in Riau Province through the Seasonal Autoregressive Integrated Moving Average (SARIMA) Approach. The data consist of monthly oil palm production from January 2006 to December 2023 obtained from the Central Bureau of Statistics (BPS) of Riau Province. The modeling process includes identifying the model structure, estimating parameters, performing diagnostic checks, and evaluating forecasting accuracy using the Mean Absolute Percentage Error (MAPE). The best model selected was SARIMA (2,0,0)(0,1,1)[12] with an AIC value of 4980.12 and a MAPE of 11.27%, indicating a good level of accuracy. The model effectively captured both seasonal and long-term trend patterns in production. The forecast results suggest that peak production typically occurs in August–September, while the lowest occurs in February–March. The study concludes that the SARIMA model provides a robust statistical framework for predicting oil palm production and can be applied as a decision-support tool in agricultural and economic planning for the province
Classification of Tuberculosis in Rumah Sakit Paru Sumatera Barat Using the C5.0 Algorithm Meliani Maya Sari; Zilrahmi; Dony Permana; Dwi Sulistiowati
UNP Journal of Statistics and Data Science Vol. 4 No. 1 (2026): UNP Journal of Statistics and Data Science
Publisher : Departemen Statistika Universitas Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24036/ujsds/vol4-iss1/444

Abstract

Tuberculosis (TB) remains a serious public health problem, including in West Sumatra Province, where the number of reported cases has continued to increase in recent years. Consequently, effective methods are required to support early detection and accurate classification of TB patients. This study aims to classify the tuberculosis status of patients at Rumah Sakit Paru Sumatera Barat by applying the C5.0 algorithm. The data used in this study consists of secondary data extracted from patient medical records collected from october to december 2024 with a total of 150 patient medical records. The dataset included eight predictor variables representing clinical symptoms and one target variable, namely sputum smear (BTA) examination results. The research process involved data preprocessing, after which the dataset was divided into training and testing subsets using a 70:30 ratio, a classification model was developed using the C5.0 algorithm, and its performance was evaluated using a confusion matrix. The findings indicate that the C5.0 algorithm achieved an accuracy of 91.11%, with a precision of 95.83%, sensitivity of 88.46%, and specificity of 94.74%. Night sweats were identified as the most influential variable in the construction of the decision tree. These findings indicate that the C5.0 algorithm demonstrates excellent performance and can be applied as a decision support method for classifying tuberculosis based on patients’ clinical symptoms
Co-Authors Ade Eriyen Saputri Afdhal Rezeki Afdhal Afifah Hardi Afifah Zafirah Ahmad Fauzan Alandra, Cindy Resha Aldi Prajela Ali Asmar Andini Diva Luthfiyah april leniati Armiati Arnellis Arnellis Arssita Nur Muharromah Asra Dinul Haq Atus Amadi Putra AULIA YUSWITA Bahri Annur Sinaga Bonita Nurul Afifah Denny Armelia Dewi Febiyanti DHEA PUTRI RIZKIA Dina Fitria Dina Fitria Dodi Vionanda Dodi Vionanda Dwi Putri Amilia Dwi Ratih Listiani Yusri Dwi Sulistiowati Edwin Musdi Elita Zusti Jamaan Elsa Oktaviani Elvina Catria Emi Suryani Putri Fadhilah Fitri Fadhilah Fitri Fadhillah Fitri Fadhillah Meisya Carina Fakhri Kamil Fanni Rahma Sari Farras Luthfyah Nisa Fauzan Al-Hamdani Siregar Fauzan Arrahman Febri Ramayanti Fenni Kurnia Mutiya Gilang Ibnul farizi Hana Rahma Trifanni Hana Zafirah Hanif Khairi Hanifa Hasna Hanifah Nazhiroh haniyathul husna Hefiani Mustika Hasanah Helma Helma Huriati Khaira I Made Arnawa I Made Arnawa iin aini fitri Indonesia Irma Surya Anisa Isra Miraltamirus Kerin Hagia Aidillah Kurnia Andrea Diva M. Farel Rusde Putra Media Rosha Meidiani Sandra Meil Sri Dian Azma Meliani Maya Sari Meliani Putri Mohammad Reza febrino Muhammad Fadlan Rafly Muslimah, Nailul Amani Muthia Sakhdiah Mutiara Amazona Sosiawati nabillah putri Nadya Nadya Nahda Maesya Tsani Nilda Yanti Nisa Ulkhairat Asfar Nonong Amalita Nufhika Fishuri Nur Nur Fadillah Nurdalia Nurul Afifah rahmad revi fadillah Rahmadina Adityana rama novialdi Refenia Usman Refina Rintani Revina Rahmadani Ridha Fajria rios Riry Sriningsih Riska 01 Ronald Rinaldo roza maylinda Salma, Admi Salsabilla Khairani Septrina Kiki Arisandi Siltima Wiska Sindy Amelia Putri Sofni Fajriani SRI RAHAYU Suherman Suherman Suwanda Risky Syafriandi Syafriandi Syafriandi Tessy Octavia Mukhti Tessy Octavia Mukhti Titin Mardianingsih Tri Wahyuni Nurmulyati Ully Martha martha Vidhiya Addini Vinka Haura Nabilla Wahda Aulia Assara Welgi Okta Irawan Widia Handa Riska Widya Febriani Widya Yarman Yarman Yatri Asri Yenni Kurniawati Yerizon Yerizon Yerizon Yoga Perdana Yuli Andari Wulan Yulia Pertiwi Yulia Utami Putri Yulyanti Harisman Yurivo Rianda Saputra Zamahsary Martha Zilrahmi, Zilrahmi