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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 Journal Research of Social Science, Economics, and Management 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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PENGEMBANGAN MULTIMEDIA INTERAKTIF BERBASIS PROBLEM BASED LEARNING PADA MATERI STATISTIKA Refenia Usman; Elita Zusti Jamaan; Arnellis Arnellis; Dony Permana; Afifah Zafirah
AKSIOMA: Jurnal Program Studi Pendidikan Matematika Vol 14, No 1 (2025)
Publisher : UNIVERSITAS MUHAMMADIYAH METRO

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24127/ajpm.v14i1.8634

Abstract

Peningkatan Kemampuan Pemecahan Masalah Matematis (KPMM) dapat dicapai melalui pembelajaran yang terintegrasi dengan teknologi, salah satunya melalui multimedia interaktif. Penggunaan multimedia ini menjadikan proses pembelajaran lebih menarik dan interaktif. Selain itu, model pembelajaran Problem Based Learning (PBL) juga berperan penting dalam mendukung pengembangan KPMM peserta didik. Tujuan dari penelitian ini adalah untuk mengembangkan multimedia interaktif berbasis PBL yang valid, praktis, dan efektif dalam memfasilitasi pengembangan KPMM peserta didik. Penelitian ini menerapkan model Plomp, yang meliputi tiga fase: fase investigasi awal, fase pengembangan atau pembuatan prototipe, dan fase penilaian. Peserta didik kelas X di salah satu SMA di kota Padang menjadi subjek penelitian ini. Instrumen penelitian yang digunakan mencakup pedoman wawancara, lembar angket, lembar observasi, dan tes KPMM. Analisis data yang digunakan yaitu teknik deskriptif dan statistik deskriptif. Hasil penelitian menunjukkan bahwa multimedia interaktif berbasis PBL telah terbukti valid dengan skor 88,88%, praktis digunakan oleh pendidik matematika dan peserta didik dengan skor masing-masing 82,19% dan 80,00%, serta efektif dalam memfasilitasi pengembangan KPMM dengan skor rata-rata 80,00%.The improvement of Mathematical Problem-Solving Ability (MPSA) can be achieved through technology-integrated learning, one of which is through interactive multimedia. the use of multimedia  enhances the learning experience by making it more engaging and interactive. Additionally, the Problem-Based Learning (PBL) model is also essential in supporting the development of students' MPSA. This study aims to create PBL-based interactive multimedia that is valid, practical, and effective in supporting the development of students' MPSA. This study utilizes the Plomp model, which consists of three phases: the initial investigation phase, the development or prototyping phase, and the assessment phase. Tenth-grade students from a high school in Padang served as the study's subjects. The research instruments used include interview guidelines, questionnaires, observation sheets, and MPSA tests. Data analysis involved descriptive and descriptive statistical techniques. The study results show that PBL-based interactive multimedia has proven to be valid with a score of 88.88%, practical for use by mathematics teachers and students with scores of 82.19% and 80.00%, respectively, and effective in facilitating MPSA development with an average score of 80.00%. 
Application of the ARIMA Method for Forecasting the Average Corn Production in Padang Pariaman Regency Alandra, Cindy Resha; Dony Permana
Jurnal MSA (Matematika dan Statistika serta Aplikasinya) Vol 13 No 1 (2025): VOLUME 13 NO 1 TAHUN 2025
Publisher : Universitas Islam Negeri Alauddin Makassar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24252/msa.v13i1.55288

Abstract

Jagung memegang peranan penting dalam sektor pertanian dan menempati peringkat ketiga sebagai tanaman pokok terpenting di dunia setelah beras dan gandum. Di Indonesia, jagung merupakan komoditas strategis yang banyak digunakan sebagai bahan pangan, pakan ternak, dan bahan baku industri. Produksi jagung di Kabupaten Padang Pariaman berfluktuasi dari waktu ke waktu, sehingga memerlukan peramalan yang akurat untuk mendukung perencanaan dan pembuatan kebijakan pertanian. Penelitian ini bertujuan untuk meramalkan rata-rata produksi jagung di Kabupaten Padang Pariaman selama kurun waktu lima tahun (2022–2026) dengan menggunakan model Autoregressive Integrated Moving Average (ARIMA). Beberapa langkah yang dilakukan dalam penelitian ini meliputi pengumpulan data, pengujian stasioneritas, pemilihan model, pemeriksaan diagnostik, dan peramalan. Hasil penelitian menunjukkan bahwa model ARIMA yang paling sesuai adalah ARIMA(1,0,0), yang menunjukkan bahwa model ini paling sesuai untuk memprediksi tren masa depan rata-rata produksi jagung di Kabupaten Padang Pariaman. Hasil prakiraan menunjukkan penurunan produksi jagung selama lima tahun ke depan, dengan Mean Absolute Percentage Error (MAPE) sebesar 7,57%, yang menunjukkan tingkat akurasi prakiraan yang tinggi. Temuan ini menyoroti perlunya intervensi strategis dari pemerintah dan petani untuk mengatasi masalah ini secara efektif.
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.
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.
Effectiveness of Use of Islamic Integrated Mathematics E-Modules To Improve Mathematical Problem Solving Capability Hanif Khairi; Dony Permana; Yerizon; I Made Arnawa
Jurnal Penelitian Pendidikan IPA Vol 9 No 12 (2023): December
Publisher : Postgraduate, University of Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/jppipa.v9i12.6247

Abstract

The unavailability of teaching materials that can support mathematical problem-solving abilities and are integrated with Islamic teaching values makes it difficult for SMP IT educators to hone students' mathematical problem-solving abilities and difficulties in instilling Islamic values in students. One solution that educators can use in learning is to use e-modules. E-module is a teaching material that has the characteristics of independent learning principles. This research aims to see the effectiveness of using mathematics e-modules that are integrated with Islamic values in facilitating students' mathematical problem-solving abilities. This type of research is pre-experimental research and the research design is one group pre-test-post test design. The sampling method is purposive sampling, where one class is taken directly from the population as a research sample. The subjects of this research were 24 students in class VIII.1 at SMP IT Qurrata A'yun Batusangkar. The effectiveness of the mathematics e-module can be seen from the comparison of the results of the pre-test and post-test of students' mathematical problem-solving abilities. The final results of the student's mathematical problem-solving ability test obtained were 57% of the total maximum score, while the results of the student's initial mathematical problem-solving ability test were only 22% of the maximum score. Because 57%>22%, these results indicate that the use of Islamic integrated mathematics e-modules is effective in facilitating students' mathematical problem-solving abilities.
Stock Price Forecasting of PT Bank Rakyat Indonesia (Persero) Tbk Using the Support Vector Regression Method Widya Febriani Widya; Dony Permana
UNP Journal of Statistics and Data Science Vol. 4 No. 2 (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-iss2/476

Abstract

Stock price forecasting is an important activity in the capital market because stock price movements tend to be nonlinear and volatile over time. PT Bank Rakyat Indonesia (Persero) Tbk (BBRI) is a blue-chip stock with high liquidity and strong fundamentals, making it an appropriate subject for forecasting research. This study aims to predict BBRI’s stock price using the Support Vector Regression (SVR) method, which is known for its ability to model nonlinear relationships and minimize overfitting. The data used consist of BBRI’s daily closing prices from January 2020 to December 2024. Before modeling, the data were normalized using the Min–Max method and divided into training and testing sets with an 80:20 ratio.The initial baseline model employed an SVR with a linear kernel. The model was then optimized using the Radial Basis Function (RBF) kernel through Grid Search Optimization combined with time-series cross-validation to determine the best parameter combination. Optimal parameters were selected based on the lowest Root Mean Square Error (RMSE). The results show that the SVR RBF model outperformed the linear model in capturing the nonlinear patterns of BBRI’s stock price. During testing, the optimized model achieved an RMSE of 0.022054, indicating high predictive accuracy. The optimized SVR model was subsequently used to forecast stock prices for the next period and demonstrated relatively stable yet dynamic price movements. Overall, the findings confirm that the SVR method is effective and reliable for stock price forecasting and can serve as a valuable reference for investors and future financial research.
Markov Chain Model Application for Rainfall Pattern in Padang City haniyathul husna; Dony Permana; Nonong Amalita; Fadhilah Fitri
UNP Journal of Statistics and Data Science Vol. 2 No. 3 (2024): 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/vol2-iss3/179

Abstract

Rainfall is a natural phenomenon that includes climate variables and is observed every time in every place. Daily rainfall data is a time series data, which is random. It is a data transfer from one time to another which can be expressed as a state of light, medium, heavy or very heavy rainfall intensity. Rainfall prediction is needed for people's lives and supports the economy. In addition, rainfall prediction is an anticipation of prevention if high rain intensity will occur in a long time. One of the rainfall prediction methods that can be used is the stochastic process approach. Markov chain is part of the stochastic process that can be used for prediction of rainfall at the present time based on one previous time. The focus of this research is the application of Markov Chains for rainfall prediction. Through Markov chains, long-term opportunities for rainfall phenomena are obtained. This study will look at the rainfall pattern of Padang City using Markov chains and also to predict rainfall in Padang City. The results of predicting the weather conditions of Padang City with any rainfall conditions today are 36.9% for the chance of no rain tomorrow, 46% for the chance of light rain tomorrow, 10% for the chance of moderate rain tomorrow, 5.3% for the chance of heavy rain tomorrow, and 1.8% for the chance of very heavy rain tomorrow.The results of this study are expected to be a recommendation for parties directly involved in taking preventive measures due to rainfall.
Classification of Harvest - Non Harvest in Rice Plant Image Using Convolutional Neural Network Algorithm Revina Rahmadani; Yenni Kurniawati; Dony Permana; Dina Fitria
UNP Journal of Statistics and Data Science Vol. 2 No. 3 (2024): 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/vol2-iss3/181

Abstract

The Area Sample Framework (ASF) survey is an area based survey carried out by direct observation of sample parts whose locations have been determined. Every month ASF officers take photos of observation results using an Android based cellphone, where the results of the photos will be classified manually by supervision officers and sent to a central server for processing. The large amount of rice plant image data included can hinder officers in classifying rice growth phases. Therefore, to speed up the classification process, the Convolution Neural Network (CNN) method is used. In this research, the CNN model built consists of 3 convolution layers, 3 pooling, ReLU and Sigmoid activation functions, with several other parameters such as batch size and epoch value. The training results show that the accuracy value for the training data is 92.86% with an epoch value of 120. Meanwhile, the accuracy value for the validation data is 69.01%. Model evaluation shows a precision value of 21.34% and a recall value of 32.20%. This shows that the CNN model has poor performance in predicting harvest and non-harvest in rice plant images.
Analysis of the Population of Sumatera Island Using Profile Analysis Sri Rahayu; Dony Permana; Yenni Kurniawati; Dina Fitria
UNP Journal of Statistics and Data Science Vol. 2 No. 3 (2024): 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/vol2-iss3/185

Abstract

The distribution of the population in each province according to age groups in Sumatra Island has tended to change over time. Therefore, an analysis is needed to provide a comparative overview of the characteristics between the populations of each province with different age groups. This analysis can help to understand the variations in these characteristics in relation to the population. Profile analysis is a technique within multivariate analysis of variance that can be used to examine the differences between two or more populations, where each population is influenced by several treatments (variables) tested. This method has been applied in various fields, including government, to understand the characteristics of specific regions. This study aims to identify the characteristics of the population in each province on the island of Sumatra based on sixteen age groups. Sumatra is one of the largest islands in Indonesia, comprising ten provinces. In this research, profile analysis is utilized to compare the population profiles of each province in Sumatra based on the sixteen age groups. Based on the profile parallelism test, it was found that the profiles of the ten provinces are not parallel, indicating differences in the average population numbers or trend patterns among the provincial profiles in Sumatra based on age groups. Further testing using Tukey's HSD method was conducted to compare each pair of provinces based on specific age groups. The testing revealed that there are significant differences in several provinces in Sumatra for each age group.
Penerapan Rantai Markov pada Data Curah Hujan Harian di Kota Semarang Nahda Maesya Tsani; Dony Permana; Yenni Kurniawati; Admi Salma
UNP Journal of Statistics and Data Science Vol. 2 No. 3 (2024): 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/vol2-iss3/189

Abstract

Rainfall is a measure of the amount of water that falls on the earth's surface in a given period of time. High rainfall can cause flooding in certain areas, while low rainfall can leave areas vulnerable to drought. Semarang City is one of the largest cities in Java Island that is often hit by floods. Efforts can be made to anticipate the risk of flooding, one of which is by studying the pattern of rainfall. This study will determine the chances of rainfall transition in Semarang City in steady state conditions using Markov chains. The results are expected to be used to anticipate the risk of flooding in Semarang City. The probability of daily rainfall transition in Semarang City in each state for the next period of time is 90.5% chance of staying in the light rain state, 7.97% chance of staying in the medium rain state and 1.50% chance of staying in the heavy rain state.
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 Fenni Kurnia Mutya 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 Rifa Trisna Putri 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