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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 Imajiner: Jurnal Matematika dan Pendidikan Matematika 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 Education Environmental Science Languange, Linguistic, Communication & Media Mathematics Medicine & Pharmacology Physics Social Sciences Other

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Classification of Stroke Disease at Dr. Drs. M. Hatta Brain Hospital Bukittinggi With Decision Tree Algorithm C4.5 Futiah Salsabila; Zamahsary Martha; Atus Amadi Putra; Admi Salma
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-iss1/135

Abstract

Stroke is a health condition that has vascular disorders where brain  function is related to problems with blood vessels that carry blood to the brain. Several factors that can influence stroke include unhealthy eating habits, lack of physical activity, smoking behavior, alcohol consumption, and obesity. The symptoms experienced are headache, nausea, vomiting, blurred vision and difficulty swallowing. The researcher’s aim is to determine the risk faktors that affect the incidence of stroke hospitalization based on stroke diagnoses at Rumah Sakit Otak Dr. Drs. M. Hatta Bukittinggi city by classifying each variable using a decision tree. A decision tree is a flowchart that resembles a branching tree. The C4.5 algorithm is used in this research, which can process numerical and categorical data, can handle missing attribute values, and produces rules that are easy to interpret. The results of the analysis show that the attribute that is a risk factor for stroke is the heart. The model created using the C4.5 algorithm was tested using a counfusion matrix resulting in an accuracy of 64.54%, a precision of 53.34% for classifying ischemic stroke patients correctly, and a recall of 72.73% for classifying hemorrhagic patients correctly.  
Forecasting Gold Prices in Indonesia using Support Vector Regression with the Grid Search Algorithm Nindi Syahfitrri; Nonong Amalita; Dodi Vionanda; Zamahsary Martha
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-iss1/145

Abstract

Investment is an effort to increase economic growth in Indonesia.  A popular investment in the community is gold investment.  The value of gold investments tends to increase but is not immune from price fluctuations, therefore it is important to forecast the price of gold in Indonesia. The method that can be used to make this forecast is Support Vector Regression (SVR).  SVR is a method that looks for a function that has a deviation of no more than ε to get the target value from all training data. The best SVR model with a linear kernel was obtained from a combination of parameters C=0,0625 and ε=0,001 with a RMSE value of 0,19734 and a value of 0,974112.  So, the SVR method is appropriate to use for forecasting gold prices in Indonesia.
Artificial Neural Network Model for Estimating the Poor Population in Indonesia as an Effort to Alleviate Poverty Febi Febiola Putri; Atus Amadi Putra; Yenni Kurniawati; Zamahsary Martha
UNP Journal of Statistics and Data Science Vol. 2 No. 2 (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-iss2/154

Abstract

Forecasting the poverty rate in Indonesia is one of the activities that is considered to be able to help various parties, such as being able to help the government in planning more effective and efficient poverty alleviation programs. In this study, forecasting the poverty rate in Indonesia was carried out using the backpropagation artificial neural network method. The purpose of this research is to model and predict the poverty rate using the backpropagation artificial neural network model, and to determine the accuracy of the forecasting results produced by this method. This research is an applied researc. The data used is annual data on proverty in Indonesia from 2917-2021. The data is then divided into two parts, namely training data and test data. The results show that the best artificial network model is BP (7,7,2) with 7 neurons in the input layer, 7 neurons in the hidden layer, and 2 neurons in the output layer. The accuracy of this model is good with a MAPE value of 0.07633%. The forecasting results in the next period show that the highest number of poor people is East Java province with a value of 3604.1698 thousand people in the first semester (March) of 2022 and has increased in the second semester period (September) of 2022 with a value of 3698.822 thousand people
Analisis Sentimen Pengguna Aplikasi X terhadap Konflik antara Israel dan Palestina Menggunakan Algoritma Support Vector Machine Fadhillah Meisya Carina; Admi Salma; Dony Permana; Zamahsary Martha
UNP Journal of Statistics and Data Science Vol. 2 No. 2 (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-iss2/170

Abstract

The conflict between Israel and Palestine is the Middle East's longest-running conflict since 1917 and is still ongoing today. This is one of the international conflicts that involves many Arab countries and Western countries in the dispute. The conflict between Israel and Palestine has caused countries in the world to be divided into two camps, namely the pro Palestinian independence camp and the contra camp. The impact of this conflict also creates polarization among Indonesians and forms diverse public opinions on the social media application X. The purpose of this research is to find out how the classification of sentiment of X application users affects the conflict between Israel and Palestine. An analysis that is utilized to convert text-based public opinion data into information is sentiment analysis. The chosen algorithm to separate data classes is the Support Vector Machines algorithm, which can classify data by determining the best hyperplane to provide a separator between opinions that are pro Israel or pro Palestine. After the preprocessing stage, 1000 tweets data were obtained with 800 training data and 200 testing data. The accuracy rate is 93%, precision is 92.93%, recall is 100%, and f-measure is 96.33%. From the results of testing 200 data points, there were 198 pro Palestine opinions and 2 pro Israel opinions, so that it might be said that more individuals favor or support Palestinian independence in the conflict that occurred between Israel and Palestine.
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.
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.
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.
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
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).
Penerapan Vector Error Correction Model dalam Menganalisis Dampak Faktor Makroekonomi terhadap Inflasi di Indonesia Anjelisni, Nining; 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.v5i02.654

Abstract

Penelitian ini bertujuan menganalisis dampak faktor makroekonomi terhadap inflasi di Indonesia pada periode Januari 2020–Maret 2025 dengan menggunakan pendekatan matematis melalui metode Vector Error Correction Model (VECM). Data diperoleh dari situs resmi Badan Pusat Statistik (BPS) dan Bank Indonesia (BI), yang meliputi variabel inflasi, jumlah uang beredar, BI Rate, kurs, ekspor, dan impor. Hasil analisis menunjukkan terdapat empat hubungan kointegrasi signifikan, dengan pengaruh positif dari jumlah uang beredar, kurs, dan ekspor terhadap inflasi, serta pengaruh negatif dari BI Rate dan impor. Dalam jangka pendek, ekspor (lag 1) secara statistik signifikan memengaruhi inflasi, sedangkan variabel lainnya belum signifikan. Model VECM yang dibangun terbukti stabil dan valid melalui berbagai uji kelayakan, serta menunjukkan akurasi tinggi dalam peramalan dengan nilai MAPE sebesar 9,23%. Prediksi inflasi untuk enam bulan ke depan memperlihatkan tren kenaikan bertahap, sehingga diperlukan penguatan ekspor dan pengendalian kebijakan moneter untuk menjaga stabilitas harga. Kontribusi utama penelitian ini adalah penerapan model matematis VECM sebagai alat analisis kuantitatif yang komprehensif dalam studi dinamika inflasi.
Co-Authors Afifa Lufti Insani Al Aziz, Saddam Anjelisni, Nining Annisa Ramadhani Annisa Rizki Amalia april leniati Arssita Nur Muharromah Asra Dinul Haq Atus Amadi Putra Aurumnisva Faturrahmi Bimbim Oktaviandi Bunga Nafandra Desi Rahmawati Dina Fitria Dina Fitria Dina Fitria, Dina Dio Afdal Putra Dodi Vionanda Dodi Vionanda Dony Permana Dwi Sulistiowati Dzakyyah Rahma Fadhilah Fitri Fadhilah Fitri Fadhillah Meisya Carina Fajri Juli Rahman Nur Zendrato Febi Febiola Putri Fitri, Fadhilah Futiah Salsabila Gezi Fajri Hefiani Mustika Hasanah Khairisa Putri, Nadya Lubis, Riskiani Maulidya Hernanda Muhamad Rayhan Nanda P Muhammad Arief Rivano Muharromah, Arssita Nur Mukthi, Tessy Octavia Nadha Ovella Syaqhasdy natasyalinggaa Natasya Dwi Ovalingga Nazifatul Azizah Nindi Syahfitrri Nini Erdiani Nonong Amalita Novi, Yoli Marda Nurul Mulya Oktaviani, Bernadita Olga Afrilly Putri Permana, Dony Rahmad Wanizal Pastha Rahmadani Rahmadani Rahmanesta, Frandito Raihan Athaya Wudd Riska 01 Salma, Admi silfia wisa fitri Suherman, Suherman Syafriandi Syafriandi Syafriandi Syafriandi Syafriandi Tessy Octavia Mukhti Tessy Octavia Mukhti Tri Wahyuni Nurmulyati Wahda Aulia Assara Yenni Kurniawati Yoli Marda Novi Yonggi Septa Pramadia Yonggi Zilrahmi, Zilrahmi