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All Journal dCartesian: Jurnal Matematika dan Aplikasi Jurnal Matematika dan Statistika serta Aplikasinya (Jurnal MSA) JOURNAL OF APPLIED INFORMATICS AND COMPUTING Pelita Eksakta GAUSS: Jurnal Pendidikan Matematika Leibniz: Jurnal Matematika UNP Journal of Statistics and Data Science d'Cartesian: Jurnal Matematika dan Aplikasi Indonesian Journal of Statistics and Its Applications
Zamahsary Martha
Universitas Negeri Padang
Biochemistry, Genetics & Molecular Biology Chemistry Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Environmental Science Mathematics Medicine & Pharmacology Physics Social Sciences Other

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Analisis Tingkat Kejahatan di Jabodetabek Menggunakan Model SARQR Pada Data Yang Mengandung Outlier Martha, Zamahsary; Muharromah, Arssita Nur; Permana, Dony; Mukthi, Tessy Octavia
d\'Cartesian: Jurnal Matematika dan Aplikasi Vol. 13 No. 2 (2024): September 2024
Publisher : Sam Ratulangi University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35799/dc.13.2.2024.57594

Abstract

Jabodetabek memiliki permasalahan tingginya tingkat kejahatan yang berdampak pada permasalahan sosial, kemiskinan, pendidikan, dan lain-lain. Tingkat kejahatan berhubungan dengan wilayah yang saling dipengaruhi oleh wilayah sekitarnya dan datanya mengandung outlier. Metode yang tepat dalam memodelkan permasalahan tersebut dengan menggunakan model Spatial Autoregressive Quantile Regression (SARQR). Tujuannya adalah menentukan faktor-faktor yang mempengaruhi tingkat kejahatan menggunakan model SARQR. Data yang digunakan adalah data tingkat kejahatan tahun 2022 serta faktor-faktor yang diduga mempengaruhinya pada 14 Kab/Kota di Jabodetabek. Model SARQR pada kuantil ke-0.95 merupakan model terbaik dan diperoleh faktor persentase penduduk miskin dan tingkat pengangguran terbuka berpengaruh terhadap tingkat kejahatan di Jabodetabek tahun 2022.
Penerapan Metode Rating-Based Conjoint Analysis dalam Preferensi E-Wallet Mahasiswa Departemen Statistika Universitas Negeri Padang Putra, Dio Afdal; Dodi Vionanda; Yenni Kurniawati; Zamahsary Martha
UNP Journal of Statistics and Data Science Vol. 2 No. 4 (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-iss4/222

Abstract

The rapid development of technology in the era of globalization has influenced the evolution of society's life in terms of economy, social, culture, and education, with the aim of facilitating daily activities, one of which is the ease of transactions using e-wallets. An e-wallet is a payment tool that uses a server-based system. Many factors influence a person's decision to use an e-wallet as a payment method, one of which is the level of security. To identify the factors that affect someone's use of e-wallets, one method is Rating-Based Conjoint Analysis (RBC). Therefore, this study aims to determine what influences a person to use an e-wallet, with the subjects being active students of the Statistics Department at Padang State University. The results of this RBC study indicate that the most influential factor on the e-wallet preferences of statistics students is security level, with a value of 37.70%, followed by transaction speed 23.17%, transfer fees at at 23.07%, features provided at 11.78%, and the least influential factor being promotions at 4.28%.
Prediksi Harga Emas Dunia Menggunakan Metode k-Nearest Neighbor Nanda P, Muhamad Rayhan; Zamahsary Martha; Dodi Vionanda; Admi Salma
UNP Journal of Statistics and Data Science Vol. 2 No. 4 (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-iss4/314

Abstract

This research aims to predict world gold prices using the k-nearest neighbor (KNN) method with secondary data from the London Bullion Market Association (LBMA) in the form of monthly time series data from January 2019 to December 2023. In the analysis process, the data is divided into two parts: 80% for training data (January 2019 - December 2022) and 20% for testing data (January - December 2023). The analysis results show that the Mean Absolute Percentage Error (MAPE) value of the KNN method is 4.5%, which indicates a very good level of accuracy. With a MAPE below 10%, the KNN model is proven to be able to accurately predict world gold prices. Gold price predictions for the period January to December 2024 show a consistent upward trend, which is influenced by factors such as global economic fluctuations, increased gold demand, and geopolitical uncertainty. These results show that the KNN model is reliable as a tool for forecasting future world gold prices.
Regularized Ordinal Regression with LASSO: Identifying Factors in Students' Public Speaking Anxiety at Universitas Negeri Padang Natasya Dwi Ovalingga, natasyalinggaa; Nonong Amalita; Yenni Kurniawati; Zamahsary Martha
UNP Journal of Statistics and Data Science Vol. 2 No. 4 (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-iss4/316

Abstract

Public speaking anxiety is a common issue faced by students, particularly in academic settings. It may arise from a range of factors, including humiliation, physical appearance, preparation, audience interest, personality traits, rigid rules, unfamiliar role, negative result, and mistakes. This research seeks to determine the factors influencing different levels of public speaking anxiety among students at Universitas Negeri Padang through the application of ordinal regression with LASSO regularization. This method allows for automatic selection of significant variables and addressesmulticollinearity issues. The results indicate that eight factors influence low public speaking anxiety levels, while only six factors impact high public speaking anxiety levels. The ordinal regression model with LASSO penalty demonstrates good performance in classifying public speaking anxiety levels, achieving an accuracy of 71.33%. This study is expected to help students and educators better understand and manage public speaking anxiety, thereby enhancing public spekaing competence among students
Workshop on WA-PPG Application (Wolfram Alpha, Photo Math, Padlet, Geogebra) to Support the Pedagogical and Professional Competence of Mathematics MGMP Teachers in 50 Regency Cities in Implementing the Independent Curriculum Suherman, Suherman; Al Aziz, Saddam; Martha, Zamahsary; Fitria, Dina
Pelita Eksakta Vol 7 No 2 (2024): Pelita Eksakta, Vol. 7, No. 2
Publisher : Fakultas MIPA Universitas Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24036/pelitaeksakta/vol7-iss2/233

Abstract

The independent curriculum requires teachers to have professional competence in understanding the material and pedagogical competence in designing teaching modules according to the material. Teachers experience many obstacles. Teachers can overcome these obstacles by utilizing technological developments in learning (TPACK). The solution was provided by a Wolfram Alpha, Photo Math, Padlet, Geogebra, or WA-PPG application workshop for High School Mathematics MGMP teachers, Harau District, 50 City Regency. Based on pre-test and post-test data from 31 teachers, an N-Gain score for professional competence and 0.55 was obtained for pedagogy. This means that workshops are genuinely able to improve teacher competence. Apart from that, the average questionnaire score for the practicality of using the WA-PPG application was 80.24%, which concluded that the WA-PPG application used by teachers during the workshop was practical and easy to use.
Implementation of Association Rule on Agricultural Commodity Exports in Indonesia Using Apriori Algorithm Dinul Haq, Asra; Fitria, Dina; Dony Permana; Zamahsary Martha
UNP Journal of Statistics and Data Science Vol. 3 No. 1 (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-iss1/336

Abstract

Exports of agricultural commodities in Indonesia have the smallest contribution to state revenues and the movement of export values ​​in the last decade has not shown a significant increase compared to other export sectors. This shows that there are weaknesses in the export of agricultural commodities so that an analysis is needed to optimize export results to other countries. These weaknesses can be seen in terms of quality, price, infrastructure and technology. This study uses association rule analysis with the apriori algorithm with the aim of finding out what agricultural commodities are exported simultaneously and the resulting association rules. The apriori algorithm is an algorithm used to find association rules between items in a database by considering two main parameters, namely Support and Confidence. The data used is agricultural commodity export data obtained from the publication of the Central Statistics Agency in Indonesia in 2023. Based on the analysis carried out, there are 32 association rules generated with a minimum Support of 25% and a minimum Confidence of 80%. Then after the Lift Ratio test was carried out, all the rules generated met the Lift Ratio test with a value of more than 1. The association rules produced must have at least 2 to 4 agricultural export commodities in each rule. By knowing the association rules for agricultural commodity exports, it is hoped that export distribution in the agricultural sector can be further optimized for trading abroad so that it can cover existing weaknesses.
Peramalan Curah Hujan Kabupaten Padang Pariaman dengan Menggunakan Metode Fuzzy Time Series Singh Lubis, Riskiani; Martha, Zamahsary; Syafriandi; Salma, Admi
GAUSS: Jurnal Pendidikan Matematika Vol. 8 No. 1 (2025)
Publisher : Universitas Serang Raya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30656/gauss.v8i1.10465

Abstract

Abstrak Penelitian ini bertujuan untuk meramalkan curah hujan di Kabupaten Padang Pariaman, Provinsi Sumatera Barat, menggunakan metode Fuzzy Time Series Singh. Penelitian ini dilatarbelakangi oleh fluktuasi curah hujan yang tinggi di wilayah tersebut, yang menyebabkan bencana seperti banjir dan tanah longsor, yang merugikan sektor pertanian, infrastruktur, kesehatan, dan perekonomian masyarakat. Data yang digunakan adalah data curah hujan bulanan dari Januari 2020 hingga Desember 2024. Metode Fuzzy Time Series Singh dipilih karena sederhana namun efektif dalam meramalkan data runtun waktu berbasis logika fuzzy. Tahapan dalam metode ini meliputi pembentukan himpunan semesta, penentuan interval, fuzzifikasi data, pembentukan hubungan logika fuzzy, dan defuzzifikasi. Berdasarkan hasil penelitian diperoleh bahwa metode ini mampu menghasilkan estimasi curah hujan yang mendekati nilai aktual, dengan MAPE 7,67%. Hasil penelitian dapat digunakan sebagai alat bantu dalam perencanaan mitigasi bencana seperti tanah longsor dan banjir. Kata kunci: Curah Hujan, Peramalan, Fuzzy Time Series Singh Abstract This study aims to forecast rainfall in Padang Pariaman Regency, West Sumatra Province, using the Fuzzy Time Series Singh method. The research is motivated by the high fluctuation of rainfall in the area, which often leads to disasters such as floods and landslides, adversely affecting the agricultural sector, infrastructure, public health, and the local economy. The data used in this study consists of monthly rainfall records from January 2020 to December 2024. The Fuzzy Time Series Singh method was chosen due to its simplicity and effectiveness in forecasting time series data based on fuzzy logic. The stages of this method include the formation of the universe of discourse, interval determination, data fuzzification, formation of fuzzy logical relationships, and defuzzification. The results of the study show that this method is capable of producing rainfall estimates that closely match the actual values, with a MAPE of 7.67%. The findings can be used as a supporting tool for disaster mitigation planning, particularly for landslides and floods. Keywords: Rainfall, Forecasting, Fuzzy Time Series Singh
Grouping of Provinces in Indonesia Based on Active Family Planning Participants Using Modern Methods Using Fuzzy C-Means Ramadhani, Annisa; Tessy Octavia Mukhti; Yenni Kurniawati; Zamahsary Martha
UNP Journal of Statistics and Data Science Vol. 3 No. 2 (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-iss2/365

Abstract

Indonesia’s rapid population growth presents a significant challenge to national welfare and public health. One of the key strategies implemented by the government to address this issue is the Family Planning (FP) program, which emphasizes the use of modern contraceptive methods. However, the utilization of these methods remains uneven across provinces. This study aims to cluster Indonesian provinces based on the number of active participants using modern contraceptive methods in 2023 by applying the Fuzzy C-Means (FCM) clustering algorithm. FCM was selected due to its ability to handle overlapping data characteristics, allowing for a more flexible and representative analysis. The clustering results reveal two main clusters: Cluster 1, which consists of provinces with high levels of active modern contraceptive users, and Cluster 2, which includes provinces with low participation levels. These findings are expected to serve as a reference for more targeted policy formulation to enhance the equity and effectiveness of the FP program across the country.
Pemodelan Geographically Weighted Regression pada Kasus Pneumonia di Indonesia Oktaviani, Bernadita; Amalita, Nonong; Kurniawati, Yenni; Martha, Zamahsary
Leibniz: Jurnal Matematika Vol. 5 No. 02 (2025): Leibniz: Jurnal Matematika
Publisher : Program Studi Matematika - Fakultas Matematika dan Ilmu Pengetahuan Alam Universitas San Pedro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59632/leibniz.v5i02.564

Abstract

Pneumonia adalah penyakit infeksi pernafasan yang menjadi salah satu penyumbang terbesar kasus kematian pada balita dan termasuk dalam  salah satu masalah kesehatan secara global. Kematian balita akibat pneumonia di Indonesia mengalami peningkatan dari 459 kasus pada tahun 2022 menjadi 522 kasus pada  tahun 2023 yang menunjukkan bahwa pneumonia masih menjadi masalah serius bagi kesehatan balita. Geographically Weighted Regression (GWR) adalah metode yang digunakan dalam penelitian ini. Data penelitian ini diperoleh dari publikasi yang diterbitkan oleh Kemenkes RI, yaitu Profil Kesehatan Indonesia 2023. Tujuan penelitian ini untuk mengevaluasi penerapan model GWR dalam memodelkan data spasial dan untuk mengidentifikasi faktor-faktor yang berpengaruh terhadap jumlah kasus pneumonia balita di Indonesia. Hasil analisis menunjukkan bahwa model GWR memberikan hasil yang lebih baik dalam memodelkan jumlah kasus pneumonia pada balita dibandingkan model regresi linier berganda dengan nilai AIC sebesar 15,66953 dan  sebesar 94,66%. Faktor-faktor yang berpengaruh signifikan terhadap jumlah kasus pneumonia pada balita di Indonesia tahun 2023 adalah persentase balita yang mendapat vitamin A, persentase bayi mendapat ASI eksklusif sampai 6 bulan, jumlah puskesmas, persentase bayi yang mendapat imunisasi dasar lengkap, persentase rumah tangga yang memiliki akses terhadap sanitasi layak, persentase penduduk miskin, persentase kejadian gizi buruk pada balita usia 0-59 bulan, dan jumlah bayi berat badan lahir rendah (BBLR).
Comparison of Nadaraya-Watson Method with Local Polynomial in Modeling HDI and Poverty Relationship in Java Island Novi, Yoli Marda; Fadhilah Fitri; Zamahsary Martha
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-iss3/380

Abstract

Poverty remains a critical issue in Indonesia, with the number of poor people reaching 24.06 million in September 2024. The Human Development Index (HDI), which indicates the level of human resource quality, is one of the factors influence poverty. This analysis focuses on the correlation involving HDI also this number of poor people in districts/cities in Java Island by comparing two kernel regresokesion methods, namely Nadaraya-Watson Estimator and Local Polynomial Estimator. Nonparametric regression was chosen thus it does not necessitate this presumption of a certain form of connection among variables, so it is more flexible in capturing complex relationship patterns. Secondary data from Statistics Indonesia (BPS) in 2024 was used in this study. Initial exploration shows, the data distribution does not have a clear pattern, so nonparametric methods are more suitable for use. Modeling is done using the optimal bandwidth obtained through the dpill function in R software. The analysis results show that the local polynomial estimator produces smoother regression curves and lower MSE values. In addition, comparison of different polynomial degrees shows that higher polynomial degrees tended to improve model performance. Among the tested polynomial degrees, the local polynomial with degree five (p=5) produced the lowest MSE value and the highest coefficient of determination. Therefore, the local polynomial estimator with degree 5 is the best method for modeling the relationship between the HDI and poverty levels in Java in 2024
Co-Authors 01, Riska Admi Salma Admi Salma Afifa Lufti Insani Al Aziz, Saddam Anjelisni, Nining Annisa Ramadhani april leniati Arssita Nur Muharromah Atus Amadi Putra Aurumnisva Faturrahmi Bimbim Oktaviandi Carina, Fadhillah Meisya Desi Rahmawati Dina Fitria Dina Fitria, Dina Dinul Haq, Asra Dodi Vionanda Dodi Vionanda Dony Permana Dwi Sulistiowati Fadhilah Fitri Fajri Juli Rahman Nur Zendrato Febiola Putri, Febi Fitri, Fadhilah fitri, silfia wisa Futiah Salsabila Gezi Fajri Hefiani Mustika Hasanah Lubis, Riskiani Maharani, Listia Maulidya Hernanda Muhammad Arief Rivano Muharromah, Arssita Nur Mukhti, Tessy Octavia Mukthi, Tessy Octavia Mulya, Nurul Nadha Ovella Syaqhasdy Nanda P, Muhamad Rayhan Natasya Dwi Ovalingga, natasyalinggaa Nazifatul Azizah Nini Erdiani Nonong Amalita Novi, Yoli Marda Oktaviani, Bernadita Permana, Dony Putra, Dio Afdal Rahma, Dzakyyah Rahmad Wanizal Pastha Rahmanesta, Frandito Raihan Athaya Wudd Rizki Amalia, Annisa Salma, Admi Suherman, Suherman Syafriandi Syafriandi Syafriandi Syahfitrri, Nindi Tessy Octavia Mukhti Tri Wahyuni Nurmulyati Wahda Aulia Assara Yenni Kurniawati Yonggi, Yonggi Septa Pramadia Zilrahmi, Zilrahmi