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All Journal Martabe : Jurnal Pengabdian Kepada Masyarakat Eksakta : Berkala Ilmiah Bidang MIPA Pelita Eksakta Journal of Mathematics UNP UNP Journal of Statistics and Data Science

Fadhilah Fitri

Universitas Negeri Padang

Author-ID : 2756574

Agriculture, Biological Sciences & Forestry Arts Humanities Astronomy Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Decision Sciences, Operations Research & Management Education Energy Engineering Environmental Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Physics Social Sciences Other

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Application Of Mathematical Literacy In Mathematics Learning For Elementary School Fadhilah Fitri; Dina Fitria; Fridgo Tasman; Defri Ahmad; Suherman Suherman
Pelita Eksakta Vol 2 No 2 (2019): Pelita Eksakta Vol. 2 No. 2
Publisher : Fakultas MIPA Universitas Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24036/pelitaeksakta/vol2-iss2/75

Mathematical literacy requires individuals to solve a problem and also apply mathematics in everyday problems, which results in the ability to interpret solutions to those problems. In PISA it is known that Indonesia's mathematics literacy score is among the lowest, as well as in Guguk District Lima Puluh Kota Regency. One way to overcome this is to start introducing literacy to students early on. The introduction of literacy must be instilled in students since they are still in elementary school. Based on this, a training program and workshop was held regarding the application of mathematical literacy in mathematics learning in elementary schools in Guguak District with elementary school mathematics teacher partners who are members of the KKG SD Gugus III Kecamatan Guguak Kabupaten Lima Puluh Kota.

Infant Mortality Case: An Application of Negative Binomial Regression in order to Overcome Overdispersion in Poisson Regression Fadhilah Fitri; Fitri Mudia Sari; Nurul Fiskia Gamayanti; Iut Tri Utami
EKSAKTA: Berkala Ilmiah Bidang MIPA Vol. 22 No. 3 (2021): Eksakta : Berkala Ilmiah Bidang MIPA (E-ISSN : 2549-7464)
Publisher : Faculty of Mathematics and Natural Sciences (FMIPA), Universitas Negeri Padang, Indonesia

Infant mortality is an indicator to determine the degree of public health. Infant mortality is death that occurs in the period from birth to before the age of one. The high rate of infant mortality indicates that the quality of public health services is not optimal. The number of infant deaths is an example of count data that follows a Poisson distribution, so it can be analyzed using Poisson Regression. The assumption that must be met when using this method is the equidispersion or variance of the response variable is equal to mean. However, this condition rarely occurs because usually the counted data has a greater variance than the mean or it is called overdispersion. One way to solve this problem is to use the Negative Binomial Regression method. The data used in this study is the case of infant mortality in the city of Padang. First, we model the data using Poisson Regression, then we check the assumption, if there is overdispersion, we handle it by modeling the data with Negative Binomial Regression. The results showed that the equidispersion assumption could not be met so that the data was modeled with Negative Binomial Regression.

Peramalan Kurs Rupiah Terhadap Dolar Amerika Menggunakan Jaringan Saraf Tiruan Rifani Rizki Amelia; Fadhilah Fitri
Journal of Mathematics UNP Vol 7, No 3 (2022): Journal Of Mathematics UNP
Publisher : UNIVERSITAS NEGERI PADANG

The Indonesian rupiah (IDR) exchange rate is used to gauge Indonesia's economic stability. Maintaining the IDR exchange rate's stability is critical since it has a direct impact on Indonesia's national monetary situation, particularly during the Covid-19 pandemic. Forecasting is one way to assess government policy in terms of lowering the exchange rate. The goal of this study is to use the backpropagation artificial neural network model to model and predict the IDR exchange rate. This study uses daily data on the US Dollar (USD) to Indonesian Rupiah (IDR) exchange rate from March 2020 to December 2021. The best BPNN model is BP (2,5,1) with 2 neurons in the input layer, 5 neurons in the hidden layer, and 1 neuron in the output layer. The accuracy of prediction of this model is very good with an RMSE value is 33,66 and MAPE value is 0,1796%.

PELATIHAN PINJAMAN ONLINE: KENALI YANG LEGAL DAN ILEGAL, HINDARI JEBAKAN Devni Prima Sari; Fadhilah Fitri; Yuki Fitria
Martabe : Jurnal Pengabdian Kepada Masyarakat Vol 8, No 1 (2025): MARTABE : JURNAL PENGABDIAN KEPADA MASYARAKAT
Publisher : Universitas Muhammadiyah Tapanuli Selatan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31604/jpm.v8i1.279-285

Meningkatnya penggunaan pinjaman online di masyarakat, terutama di kalangan pelajar dan pendidik, menimbulkan masalah baru dalam literasi keuangan. Kegagalan untuk memahami perbedaan antara pinjaman online yang legal dan ilegal, beserta risiko-risiko yang menyertainya, membuat banyak orang rentan terjebak dalam utang yang berbahaya. Di SMAN 3 Padang Panjang, instruksi khusus diberikan mengenai dimensi hukum pinjaman online. Program ini mencakup peserta tentang perbedaan antara pinjaman yang legal dan melanggar hukum, strategi untuk menghindari jebakan utang, dan kriteria untuk memilih pinjaman yang sesuai. Hasil penilaian menunjukkan adanya peningkatan pemahaman peserta terhadap dimensi hukum pinjaman online, yang diharapkan dapat meningkatkan literasi keuangan dan memfasilitasi penilaian keuangan yang lebih bijaksana di masa depan.

PENINGKATAN KEMAMPUAN GURU DALAM VISUALISASI DATA UNTUK PENELITIAN TINDAKAN KELAS MELALUI PELATIHAN MICROSOFT EXCEL DAN QUIZIZZ Devni Prima Sari; Fadhilah Fitri; Maulani Meutia Rani
Martabe : Jurnal Pengabdian Kepada Masyarakat Vol 8, No 1 (2025): MARTABE : JURNAL PENGABDIAN KEPADA MASYARAKAT
Publisher : Universitas Muhammadiyah Tapanuli Selatan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31604/jpm.v8i1.286-294

Pelatihan bertujuan untuk meningkatkan keterampilan guru dalam menganalisis dan memvisualisasikan data, mendukung pengambilan keputusan berbasis bukti di kelas. Fokus pelatihan ini adalah penggunaan alat analisis data, seperti Microsoft Excel dan Quizizz, yang membantu guru memahami dan menyajikan data secara efektif. Hasil pelatihan menunjukkan peningkatan signifikan dalam keterampilan visualisasi data peserta, di mana guru-guru lebih mampu mengaplikasikan fitur grafik dan diagram untuk menampilkan hasil pembelajaran secara jelas dan menarik. Dengan peningkatan ini, guru-guru menjadi lebih siap dalam merencanakan dan melaksanakan Penelitian Tindakan Kelas (PTK), yang memungkinkan mereka menghasilkan solusi berbasis bukti untuk meningkatkan efektivitas pembelajaran. Pelatihan ini diharapkan dapat memperkuat peran guru sebagai agen perubahan dalam pendidikan, mendorong peningkatan kualitas pendidikan di sekolah dan masyarakat secara keseluruhan.

A Predicting the Future: A Forecast of Bukittinggi's Original Local Revenue from 1996 to 2024 Fedisha Elfiri Fedisha; Fadhilah Fitri; Zilrahmi
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-iss2/473

In the past decade, Bukittinggi City’s locally generated revenue (PAD) has experienced considerable instability. A significant decline occurred during the 2020 pandemic, followed by external disruptions such as the 2024 Mount Marapi eruption. These conditions complicate regional financial planning and highlight the importance of reliable forecasting. This study aims to forecast PAD for the 2025–2029 period using the ARIMA (Autoregressive Integrated Moving Average) method. Annual data from 1996–2024 were obtained from official publications of Indonesia’s Central Bureau of Statistics (BPS) Bukittinggi. The analysis procedure included exploratory data analysis, variance stationarity testing using Box-Cox transformation, mean stationarity testing through the Augmented Dickey-Fuller test supported by ACF and PACF plots, tentative model identification, parameter estimation, residual diagnostics using the Ljung-Box and Shapiro-Wilk tests, and model selection based on the smallest MAPE value. The results showed that the data became stationary after Box-Cox transformation and second-order differencing. Among the candidate models, ARIMA(3,2,0) was selected as the best model because all parameters were statistically significant (p-value < 0.05), the residuals satisfied the white noise assumption, and the model produced the lowest MAPE value. Forecasting results indicate an increasing PAD trend from approximately 240.23 million Rupiah in 2025 to 429.57 million Rupiah in 2029. However, prediction intervals widened over time, indicating increasing uncertainty in long-term forecasts. Therefore, the local government should implement adaptive fiscal policies and strengthen regional revenue sources to anticipate future PAD fluctuations

Sentiment Analysis of Public Opinion on Rupiah Redenomination on Twitter Using Naive Bayes Classification FIGO RAHMATULLAH; Dila Sari; Rahmat Kurniawan; Fadhilah Fitri
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-iss2/484

This study examines public opinion on the Rupiah redenomination policy through sentiment analysis of Twitter data. Redenomination refers to the simplification of currency denominations without changing their real value, a policy that often triggers varied public responses due to concerns such as inflation perception and money illusion. In the digital era, Twitter (currently X) serves as a major platform for real-time public expression, generating large volumes of unstructured textual data suitable for analysis. The objective of this research is to classify public sentiment toward the Rupiah redenomination policy into positive, negative, and neutral categories using the Naive Bayes Classifier, as well as to evaluate the model’s performance. The dataset consists of Indonesian-language tweets collected via the Twitter API using keywords related to redenomination. Data processing involves several stages, including data cleaning, manual labeling, text preprocessing (case folding, tokenization, stopword removal, and stemming), and feature extraction using Term Frequency–Inverse Document Frequency (TF–IDF). The classification results are evaluated using a confusion matrix. The Naive Bayes Classifier achieved an accuracy of approximately 74.84% and a precision of 80%, indicating that the model performs adequately in identifying sentiment patterns. The findings show that neutral sentiment dominates the discussion, suggesting that most users tend to provide informational or observational opinions rather than strong support or opposition. These results are expected to provide insights for policymakers, particularly Bank Indonesia and the government, regarding public acceptance of the redenomination policy, while also contributing to the development of sentiment analysis research on Indonesian social media data.

Comparison of K-Means and Fuzzy C-Means Algorithms for Clustering Based on Happiness Index Components Across Provinces in Indonesia Inna Auliya; Fadhilah Fitri; Nonong Amalita; Tessy Octavia Mukhti
UNP Journal of Statistics and Data Science Vol. 2 No. 1 (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-iss1/150

Cluster analysis is a statistical technique used to group objects based on their shared characteristics. This research aims to assess how 34 provinces in Indonesia are clustered using happiness index indicators for the year 2021. The study compares two non-hierarchical cluster analysis methods, K-Means and Fuzzy C-Means. K-Means categorizes objects into clusters based on their proximity to the nearest cluster center, while Fuzzy C-Means employs a fuzzy grouping model assigning membership degrees from 0 to 1. The results indicate that both methods form three clusters. Evaluating standard deviation values and ratios, Fuzzy C-Means proves superior, displaying a larger standard deviation between groups and a smaller ratio (0.6680004) compared to K-Means. Consequently, the study concludes that the Fuzzy C-Means method is more optimal than K-Means.

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

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.

Pengelompokan Wilayah Potensi Kebakaran Hutan dan Lahan di Pulau Sumatera Berdasarkan Titik Panas Menggunakan Metode CLARA Melda Safitri; Admi Salma; 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-iss3/180

Sumatera Island is one of the areas with the potential for forest and land fires in Indonesia. Sumatra Island has the largest oil palm plantation in Indonesia. The vast land area of oil palm plantations in Indonesia can increase the risk of fires due to land expansion by burning. In addition, the burning of peatlands in Sumatra can exacerbate the impact of forest and land fires. Forest and land fires on the island of Sumatra that occur every year can cause various negative impacts, indicating the need for countermeasures and prevention efforts to minimize the impact of forest and land fires. Hotspots can be used to detect fires in a region and help with prevention and countermeasures to reduce the impact of land and forest fires. Clustering the hotspot data allows one to obtain information on the presence of a fire in a given area as well as its potential status high, medium, or low. The clustering method used is the CLARA method. The CLARA method is a clustering method that breaks the dataset into groups. The advantages of the CLARA method are robust to outliers and effective for large data sets. The results of this research show that the CLARA method can be used for hotspot clustering with a silhouette coefficient of 0.53 in the use of 2 clusters. The analysis of the clustering results shows that cluster 1 is a cluster with low fire potential while cluster 2 is a cluster with high fire potential.

Co-Authors Admi Salma Afifah Nabilah Asyifa Dwi Ayshah Atus Amadi Putra Bunga Miftahul Barokah Chairina Wirdiastuti Defri Ahmad Devni Prima Sari Dhiyaa Fitri Yafe Diah Triwulandari Dila Sari Dina Fitria Dodi Vionanda Dony Permana Faddiah Gusti Handayani Faulina Fedisha Elfiri Fedisha Fenni Kunia Mutiya Fenni Kurnia Mutiya FIGO RAHMATULLAH Fitri Mudia Sari Fridgo Tasman haniyathul husna Herlena Purnama Sari Iga Mawarni Inna Auliya Iut Triutami khairunnisa Pane Kurnia Andrea Diva Latifah Jayatri Febiola Maulani Meutia Rani Mayrita Addila Putri Mayrita Meil Sri Dian Azma Melda Safitri Musthafa Imran Nonong Amalita Nufhika Fishuri Nur Leli Nurul Fiskia Gamayanti Olivin Adelia Huqmi Rahmadina Adityana Rahmat Kurniawan Rheizma Dewi Adiningtiyas Rifani Rizki Amelia Riska 01 Rosa Salsabila Azarine Salsabilla Khairani Salwa Hifa Fadilah Sarmilah Mila Septrina Kiki Arisandi Suherman Suherman Syafriandi Syafriandi Tessy Octavia Mukhti Tessy Octavia Mukhti yazid permana Yoli Marda Novi Yuki Fitria Zamahsary Martha Zilrahmi, Zilrahmi

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