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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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Inflation Prediction In Indonesia Using Extreme Learning Machine and K-Fold Cross Validation Wahda Aulia Assara; Zamahsary Martha; Dony Permana; Dina Fitria
UNP Journal of Statistics and Data Science Vol. 3 No. 3 (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-iss3/412

Abstract

Inflation rate forecasting is an important aspect in supporting economic policies and price control by the government. This study aims to evaluate the performance of the Extreme Learning Machine (ELM) algorithm in forecasting the inflation rate in Indonesia and provide inflation prediction results for 2025. The data used is historical data on Indonesia's inflation rate for the period 2003–2024. The analysis process begins with data normalization to ensure a uniform scale, followed by data partitioning using 10-Fold Cross Validation. The ELM model was built with 30 hidden neurons, a sigmoid activation function, and a regularization parameter of 0.8. The test results show that the ELM algorithm has superior performance. This is evidenced by the average MAPE value of 1.71%, RMSE of 0.0359, and coefficient of determination (R²) of 0.9833, indicating very high accuracy. The inflation prediction for January to December 2025 is in the range of 1.517%–1.761%, with an average approaching 1.663%, indicating a relatively stable pattern throughout the year. Based on these results, the ELM algorithm can be used as an effective alternative method for forecasting time series data, particularly in the context of inflation. This research is expected to serve as a reference for the government in establishing inflation control policies and for other researchers interested in applying artificial intelligence models to economic analysis.
Peramalan Jumlah Uang Beredar di Indonesia Menggunakan Jaringan Saraf Tiruan Muslimah, Nailul Amani; Dony Permana; Syafriandi; Zilrahmi
JURNAL ILMU KOMPUTER Vol 9 No 1 (2023): Edisi April
Publisher : LPPM Universitas Al Asyariah Mandar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35329/jiik.v9i2.253

Abstract

ABSTRACT Inflation is one of the economic problems that has a strong correlation with people's welfare, especially for people with a low income fixed income class. Inflation will have a complicated impact on people with a low economy as well as the government. The money supply is an indicator that influences the rise and fall of the inflation rate in Indonesia. Therefore, controlling the money supply needs to be done to determine strategic policies that can be implemented by the government when the money supply is outside the stability limit. This study aims to predict the money supply using Backpropagation Neural Networks. The results of the analysis show that the most optimal Backpropagation model has 12 input layer units, 6 hidden layer units and 1 output layer unit or is written as BP model(12,6,1). The MAPE value resulting from forecasting with the BP(12,6,1) model is 7.53% and an accuracy of 92.47%. The BP(!2,6,1) model is a very good model for forecasting. Keywords— Forecasting, Money Supply, Inflation, Neural Networks.
Peramalan Harga Beras di Kota Padang untuk Tahun 2025 Menggunakan Jaringan Syaraf Tiruan dengan Metode Backpropagation Nisa, Farras Luthfyah; Dony Permana; Denny Armelia
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-iss4/381

Abstract

Rice is a staple food commodity in Indonesia that significantly influences economic stability and food security. In Padang City, rice price fluctuations frequently occur due to high dependence on external supply sources and limited local production, highlighting the need for a reliable predictive system. This study aims to forecast the monthly average retail price if rice in Padang City for the year 2025 using an Artificial Neural Network (ANN) based on the Backpropagation algorithm. The forecasting model is developed using historical rice price data from January 2017 to December 2024. In addition to building the forecasting model, this study evaluates the model’s accuracy in capturing the complex and nonlinear patterns of rice price fluctuations. The forecasting results are expected to serve as a valuable reference for local policymakers, market participants, and consumers in making strategic decisions to anticipate future price volality.
Klasterisasi Kabupaten/Kota Berdasarkan Faktor-Faktor yang Mempengaruhi Kemiskinan di Sumatera Barat Menggunakan Metode K-Medoids Hardi, Afifah; Dony Permana; Denny Armelia
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-iss4/382

Abstract

Poverty remains a significant issue in Indonesia, particularly in West Sumatra Province, where regional disparities persist despite a national decline in poverty rates. This study aims to classify the 19 regencies/cities in West Sumatra based on key socioeconomic indicators to support more targeted and effective poverty alleviation policies. Using a quantitative descriptive approach, the research applies the K-Medoids clustering method to group regions according to four indicators: Gross Regional Domestic Product (GRDP) per capita, Human Development Index (HDI), Open Unemployment Rate (OUR), and Gini Ratio. Secondary data for the year 2024 were obtained from the official website of the Central Bureau of Statistics of West Sumatra. Prior to clustering, data standardization using Z-score transformation was performed, and multicollinearity was tested using the Variance Inflation Factor (VIF). The silhouette method indicated that the optimal number of clusters is four. The clustering analysis revealed four distinct groups: (1) underdeveloped areas with low income and human development but high inequality; (2) moderately developed areas with stable unemployment and low income inequality; (3) urbanized areas with high income and human development but also high unemployment and inequality; and (4) a single metropolitan area with high economic and human development and moderate inequality. The findings highlight the importance of region-specific strategies in addressing poverty, considering the diverse economic and social conditions across regions. The results can serve as a basis for designing equitable and effective socioeconomic development policies.
An Examination of Determinants Affecting the Survival Duration Pediatric Brain Cancer Patients Through Stratified Cox Regression Analysis Siregar, Fauzan Al-Hamdani; 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.
Using AI for the Personalization of Mathematics and Science Education in Students Titin Mardianingsih; Dony Permana; Armiati; Yulyanti Harisman
Jurnal Penelitian Pendidikan IPA Vol 11 No 11 (2025): November
Publisher : Postgraduate, University of Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/jppipa.v11i11.12557

Abstract

This research review explores the role of artificial intelligence (AI) in personalizing mathematics and science education to enhance student learning experiences and outcomes. The study synthesizes current research to examine how AI-driven technologies—such as adaptive learning systems, intelligent tutoring, and real-time feedback mechanisms—support individualized instruction aligned with students’ learning styles, paces, and cognitive needs. Findings indicate that AI significantly improves engagement, conceptual understanding, and problem-solving skills by leveraging data analytics and machine learning to deliver tailored content. These systems are grounded in established educational theories, including Mastery Learning and the Zone of Proximal Development. However, challenges remain, including unequal access to technology, algorithmic bias, data privacy concerns, and limited teacher preparedness, which hinder equitable implementation. The review also identifies gaps in longitudinal and context-specific research, particularly in under-resourced educational settings. The study concludes that while AI holds transformative potential for STEM education, its effective integration requires ethical design, inclusive policies, teacher training, and pedagogical alignment. For sustainable impact, AI should be implemented as a supportive tool within human-centered educational frameworks rather than a standalone solution.
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 01, Riska Addini, Vidhiya Ade Eriyen Saputri Admi Salma Admi Salma Afdhal, Afdhal Rezeki Afifah Zafirah Ahmad Fauzan Aidillah, Kerin Hagia Alandra, Cindy Resha Aldi Prajela Ali Asmar Andini Diva Luthfiyah april leniati Armiati Arnellis Arnellis Arssita Nur Muharromah Atus Amadi Putra Azma, Meil Sri Dian Bahri Annur Sinaga Bonita Nurul Afifah Carina, Fadhillah Meisya Denny Armelia Dewi Febiyanti Dina Fitria Dina Fitria, Dina Dinul Haq, Asra 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 Fadhillah Fitri Fadlan Rafly, Muhammad Fanni Rahma Sari Fauzan Arrahman Febri Ramayanti Fenni Kurnia Mutiya Fishuri, Nufhika Hana Rahma Trifanni Hana Zafirah haniyathul husna Hardi, Afifah Hasna, Hanifa Hefiani Mustika Hasanah Helma Helma Huriati Khaira I Made Arnawa Ibnul farizi, Gilang iin aini fitri Indonesia Irma Surya Anisa Isra Miraltamirus Kamil, Fakhri Kurnia Andrea Diva martha, Ully Martha Media Rosha Meidiani Sandra Meliani Maya Sari Meliani Putri Mohammad Reza febrino Muslimah, Nailul Amani Muthia Sakhdiah Mutiara Amazona Sosiawati nabillah putri Nadya Nadya nazhiroh, hanifah Nilda Yanti Nisa Ulkhairat Asfar Nisa, Farras Luthfyah Nonong Amalita Nur Fadillah, Nur Nurdalia Nurul Afifah Putra, M. Farel Rusde rahmad revi fadillah rama novialdi Refenia Usman Refina Rintani Revina Rahmadani Ridha Fajria rios Riry Sriningsih RIZKIA, DHEA PUTRI Ronald Rinaldo roza maylinda Salsabilla Khairani Septrina Kiki Arisandi Siltima Wiska Siregar, Fauzan Al-Hamdani Sofni Fajriani SRI RAHAYU Suherman Suherman Suwanda Risky Syafriandi Syafriandi Syafriandi Tessy Octavia Mukhti Titin Mardianingsih Tri Wahyuni Nurmulyati Vinka Haura Nabilla Wahda Aulia Assara Welgi Okta Irawan Widia Handa Riska Yarman Yarman Yatri Asri Yenni Kurniawati Yerizon Yerizon Yoga Perdana Yuli Andari Wulan Yulia Pertiwi Yulia Utami Putri Yulyanti Harisman Yurivo Rianda Saputra YUSWITA, AULIA Zamahsary Martha Zilrahmi, Zilrahmi