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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

Author-ID : 2838333

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 Vidio Pembelajaran Matematika Menggunakan Model Flip Learning untuk Meningkatkan Kemampuan Pemecahan Masalah Matematis Peserta Didik Hana Zafirah; Elita Zusti Jamaan; Suherman Suherman; Dony Permana
Jurnal Basicedu Vol 7, No 1 (2023): February
Publisher : Universitas Pahlawan Tuanku Tambusai

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/basicedu.v7i1.4582

Siswa harus diberi kesempatanyuntuk berpikir dan aktif memecahkan berbagai masalah yang diberikan. Penelitian ini didasari oleh rendahnya kemampuan pemecahan masalah matematis peserta didik SMK. Tujuan dari penelitian ini untuk menghasilkan vidio pembelajaran matematika menggunakan model Flip Learning. Proses penelitian ini dilaksanakan dengan model pengembangan Plomp. Penelitian ini dilakukan di SMK Negeri PP Padang Mengatas. Hasil penelitian menunjukkan bahwa video pembelajaran matematika yang dikembangkan menggunakan flipped learning tergolong sangat efektif dengan persentase 86,62% untuk RPP, dan 83,93%runtuk video pembelajaran, dan sangat praktisuasing-masing 87% danu86,85%. Sedangkan berdasarkan hasilrhasil uji soal tes kemampuan pemecahan masalah matematis diperolehu83,33% peserta didik memenuhuirkriteria keberhasilan tes kemampuan pemecahan masalah matematis > 80%, artinya vidio pembelajaran matematika menggunakan model flip learning sangat efektif terhadaprkemampuan pemecahan masalah matematis. Jadi, kemampuan pemecahan masalahumatematis peserta didik dapat ditingkatkan menggunakan video pembelajaran matematika menggunakan model flip learning.

Developing Guided Discovery-Based Learning Device for Improving Middle School Students' Mathematics Problem-Solving Ability Emi Suryani Putri; Dony Permana; Yerizon Yerizon; I Made Arnawa
Jurnal Gantang Vol 7 No 2 (2022): Jurnal Gantang
Publisher : Universitas Maritim Raja Ali Haji

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31629/jg.v7i2.4933

According to the 2011 PISA Mathematics Survey, Indonesia ranked 72nd out of 78 countries. This is a very worrying position for the development and quality of Indonesian education in the eyes of the world. This study aims to create a learning device based on guided discovery to enhance students' valid, practical, and effective problem-solving skills. The developed instructional devices are presented as lesson plans and worksheets for high school math students. This development study uses the Plomp model in three phases: pre-production, prototyping, and evaluation. The research subjects are 7th-grade students of SMPN 2 Northern Rao. Experts conduct verification in mathematics education, educational technology, and the Indonesian Language. The practicality of the teaching aid is evident in the results of practicality surveys in teaching practice and questions from students and teachers. Efficiency can be seen in student learning outcomes. The Data Analysis Result Guided Discovery Learning Device is effective because it meets valid criteria in terms of content and design, is practical in terms of implementation, simplicity, and turnaround time, and can improve learning outcomes. In essence, this research can provide an overview for enhancing the quality of education. In addition, it can be used as an indicator to improve students' problem-solving skills by making math learning more accessible and efficient.

Forecasting Shallot Prices in West Sumatra Province Using the Fuzzy Time Series Method of the Singh Model and the Cheng Model Huriati Khaira; Fadhilah Fitri; Nonong Amalita; Dony Permana
UNP Journal of Statistics and Data Science Vol. 1 No. 1 (2023): UNP Journal of Statistics and Data Science
Publisher : Departemen Statistika Universitas Negeri Padang

Shallots are one of the leading spices that are widely used by humans as food seasoning and traditional medicine. The price of shallots always fluctuates which can affect the buying and selling of consumers and producers. Therefore, forecasting is used as a reference to be able to predict the price of shallots in the future and can provide convenience to the public for the condition of shallot prices in the next period. The forecasting method used is the fuzzy time series (FTS) method. FTS is a method whose forecasting uses data in the form of fuzzy sets sourced from real numbers to the universe set on actual data. Forecasting models used in this study are Singh's FTS model and Cheng's model. The data used is monthly data on shallot prices in West Sumatra Province for the period January 2018 to March 2022. The results obtained in this forecast are the Singh model FTS has a smaller MAPE value of 4.41% with a forecasting accuracy value of 95.59 %. This means that Singh's FTS model is better at predicting the price of shallots in West Sumatra Province.

Time Series Modeling on Stock Return at PT. Telecommunication Indonesia Tbk. Hana Rahma Trifanni; Dony Permana; Nonong Amalita; Atus Amadi Putra
UNP Journal of Statistics and Data Science Vol. 1 No. 1 (2023): UNP Journal of Statistics and Data Science
Publisher : Departemen Statistika Universitas Negeri Padang

One of the time series data modeling is the ARMA model which assumes constant volatility. However, in economic and financial data, there are many cases where volatility is not constant. This results in the occurrence of heteroscedasticity problems in the residuals, so a GARCH model is needed. In addition to heteroscedasticity, another problem with residuals is the asymmetric effect or leverage effect. For that we need asymmetric GARCH modeling. This study aims to compare the accuracy of the ARMA, GARCH, and asymmetric GARCH models. This research is an applied research. The data used is daily stock return data from February 2020 to February 2022 as many as 488 data. The results showed that the best model in modeling stock return volatility is ARMA(0,1). The accuracy of this model is very good with MAD value of 0,0018644 and RMSE value of 0,0025352.

Adding Exogenous Variable in Forming ARIMAX Model to Predict Export Load Goods in Tanjung Priok Port Elvina Catria; Atus Amadi Putra; Dony Permana; Dina Fitria
UNP Journal of Statistics and Data Science Vol. 1 No. 1 (2023): UNP Journal of Statistics and Data Science
Publisher : Departemen Statistika Universitas Negeri Padang

The main idea of world maritime has been launched by the Indonesia’s Government through the development of inter-island connectivity, namely a logistics distribution line system using cargo ships with scheduled routes. However, in terms of inter-island sea transportation connectivity using sea transportation, the number of ships used for loading and unloading activities at Tanjung Priok in 2020 reached 11,876 units, which number decreased by 12.6% compared to the previous year, this figure was not sufficient for transportation of Indonesian loading and unloading goods (exports). This condition is important to note because the implementation of sea transportation, especially for sea toll transportation, if it cannot reach all regions, will cause freight transportation in some areas to be limited and regional economic growth cannot be distributed evenly. The purpose of this study is to predict the number of goods loaded (exported) at the Port of Tanjung Priok, by establishing an export forecasting model. Exogenous variable in the form of the Indonesian Wholesale Price Index. After analyzing the data, the order of the ARIMA model (5,1,1) was obtained as a parameter to estimate the ARIMAX model. From the ARIMAX model (5,1,1), the model's accuracy rate is 13.25% which is quite feasible to use to predict the total export cargo for the period January 2021-December 2021. Forecasting results show better fluctuations than in 2020.

Comparison of Forecasting Using Fuzzy Time Series Chen Model and Lee Model to Closing Price of Composite Stock Price Index Mohammad Reza febrino; Dony Permana; syafriandi; Nonong Amalita
UNP Journal of Statistics and Data Science Vol. 1 No. 2 (2023): UNP Journal of Statistics and Data Science
Publisher : Departemen Statistika Universitas Negeri Padang

Investment is an activity to invest with the hope that someday you will get a number of benefits from theinvestment result. In investing, analyzing is important to see the current situation and condition of stock. Investorscan forecast stock prices by looking at trends based on data movements from stock prices in the past. Fuzzy TimeSeries (FTS) was used in this study to forecast. Fuzzy time series is a forecasting technique that uses patterns frompast data to project future data in areas where linguistic values are formed in the data. This study compares theclosing price of composite stock forecasting using the fuzzy time series chen and lee models. The JCI's closing pricefor the following period is 6,904 and has a Mean Absolute Percentage Error (MAPE) of 4.03%, according to the chenfuzzy time series method. In contrast, utilizing Lee's fuzzy time series method, the predicted JCI closing price for thefollowing period is 7,046, with a MAPE value of 3.10 percent. It can be concluded from the forecasting results of theChen and Lee methods that the Lee model FTS is superior to the Chen model FTS in predicting the JCI closing price.

Comparison of Naive Bayes Method and Binary Logistics Regression on Classification of Social Assistance Recipients Program Keluarga Harapan (PKH) Fanni Rahma Sari; Fadhilah Fitri; Atus Amadi Putra; Dony Permana
UNP Journal of Statistics and Data Science Vol. 1 No. 2 (2023): UNP Journal of Statistics and Data Science
Publisher : Departemen Statistika Universitas Negeri Padang

Population density is one of the causes of economic inequality in society. One of the solutions provided by the government is to distribute social assistance. In 2007 the government created a social assistance program called the “Program Keluarga Harapan” (PKH) with the aim of alleviating poverty. There are several problems in the distribution of social assistance, one of which is receiving aid that is not right on target. Therefore, an appropriate method is needed in classifying the recipients of social assistance properly. This study will use two methods, namely Naive Bayes and Binary Logistic Regression to compare which method is better on the data used. The data used is the DTKS data for PKH assistance recipients in the Anduring Village in 2020. Based on the results obtained, the accuracy of the Naive Bayes method is 70% and Binary Logistic Regression is 73%. So the best method in measuring classification is Binary Logistic Regression.

Comparison of Naïve Bayes and K-Nearest Neighbor for DKI Jakarta Air Pollution Standard Index Classification Nurdalia; Zilrahmi; Dony Permana; Admi Salma
UNP Journal of Statistics and Data Science Vol. 1 No. 2 (2023): UNP Journal of Statistics and Data Science
Publisher : Departemen Statistika Universitas Negeri Padang

Data mining is the process of extracting and searching for useful knowledge and information using certain algorithms or methods according to knowledge or information. The data mining classification methods used in this study are Naïve Bayes and K-Nearest Neighbor. By using the Naïve Bayes and K-Nearest Neighbor methods, it is possible to classify the DKI Jakarta air pollution standard index in 2021 based on six air pollutants, namely dust particles (PM10), dust particles (PM2.5), sulfur dioxide (SO2), carbon monoxide. (CO), ozone (O3) and nitrogen dioxide (NO2). The test was carried out to determine the accuracy in predicting the DKI Jakarta air pollution standard index in 2021 using the confusion matrix evaluation value. So that the best performance of the two methods is found in the Naïve Bayes algorithm with high Naïve Bayes sensitivity values for all categories even though there are data in minority or unbalanced categories, and the frequency of data from each category or in this case the data is not balanced, the Naïve Bayes algorithm shows good performance in accuracy, sensitivity, specificity.

Application of Random Forest to Identify for Poor Households in West Sumatera Province Febri Ramayanti; Dodi Vionanda; Dony Permana; Zilrahmi
UNP Journal of Statistics and Data Science Vol. 1 No. 2 (2023): UNP Journal of Statistics and Data Science
Publisher : Departemen Statistika Universitas Negeri Padang

Poverty is a socioeconomic problem in Indonesia. The number of people who were living in poverty in West Sumatera increases for 26.44 thousands from 2020 to 2021. The government has created programs to cope with poverty by taking into account the criteria for the poor households. These criteria have been developed by using the data obtained through The National Socioeconomic Survey (Susenas). However, instead of.showing the actual location of poor household, the existing data only interprets the number of poor household. Thus make the program less effective. This could be overcome by classification analysis of random forest (RF). RF is collection of many decision trees. Before fitting RF, one has to determine the values if three tuning parameters, mtry, ntree and node size. The result are the smallest OOB’s error rate (%) and Variable Importance Measure(VIM). The classification by RF in this research results in OOB’s error rate was 5.65% or accuracy rate was 94.35% with tuning parameter using mtry=5 and ntree=500. Based on the VIM, the poor household’s criteria include sources of drinking water such as protected or unprotected spring water and surface water, lighting tools such as non-PLN electricity or no usage of electricity, fuel for cooking such as charcoal and firewood, and the head of the household being self-employed, a family worker, or unpaid with at least a junior high degree.

Factors affecting the grade point average students of FMIPA Universitas Negeri Padang with binary logistic regression model Irma Surya Anisa; Dony Permana
International Journal of Trends in Mathematics Education Research Vol 5, No 3 (2022)
Publisher : SAINTIS Publishing

Today, everyone places a high importance on their education. Learning is how education is implemented, and learning allows people to reach their full potential. Since learning is a process and learning achievement is the end consequence of the learning process, learning and learning achievement are inextricably linked. Learning achievement levels are assessed using GPA (Grade Point Average). Allowance, gender, major, status of residence, school location, study time, admission type, duration of gadget use, and personality type are all factors that affect GPA. In order to identify the components that influence academic accomplishment, a model must be developed since it can be understood, explained, controlled, and forecasted. This study's goal is to identify the binary logistic regression model, which describes the variables influencing the faculty of mathematics and natural sciences at Universitas Negeri Padang's GPA. The aim of this study is to identify the logistic regression model that represents the variables that affect the GPA of the Faculty of Mathematics and Natural Sciences at Universitas Negeri Padang. Secondary and primary data were employed in this study, and questionnaires were used to collect the data. The 2020 students made up the study's sample, which included 293 respondents. According to the study's findings, factors such as gender, major, admission type, and duration of gadgets use may have an impact on students' GPAs at the Faculty of Mathematics and Natural Sciences at Universitas Negeri Padang. The percentage of correct predictions between the logistic regression model and training data is 70%.

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

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