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All Journal Imajiner: Jurnal Matematika dan Pendidikan Matematika Pelita Eksakta GAUSS: Jurnal Pendidikan Matematika Leibniz: Jurnal Matematika Journal of Science and Technology Jurnal Riset Statistika Journal of Mathematics UNP Rangkiang Mathematics Journal UNP Journal of Statistics and Data Science Journal of Authentic Research Indonesian Journal of Statistics and Its Applications
Salma, Admi
Universitas Negeri Padang
Biochemistry, Genetics & Molecular Biology Chemistry Computer Science & IT Decision Sciences, Operations Research & Management Earth & Planetary Sciences Education Languange, Linguistic, Communication & Media Mathematics Physics Public Health Social Sciences Other

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Implementasi Metode Naïve Bayes dengan Random Oversampling pada Klasifikasi Keluarga Berisiko Stunting Suliswati, Yeni; Mukhti, Tessy Octavia; Syafriandi, Syafriandi; Salma, Admi
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.v5i02.610

Abstract

Stunting masih menjadi salah satu masalah kesehatan serius yang memiliki dampak jangka panjang terhadap tumbuh kembang dan kognitif anak. Keluarga memiliki peran penting dalam mencegah terjadinya stunting, sehingga identifikasi dini keluarga yang berisiko melahirkan anak stunting menjadi langkah awal dalam upaya pencegahan. Penelitian ini bertujuan untuk mengklasifikasikan keluarga berisiko stunting menggunakan metode Naïve Bayes serta mengevaluasi pengaruh teknik Random Oversampling (ROS) terhadap performa model pada data tidak seimbang. Data pada penelitian ini terdiri dari 7 variabel independen dan 1 variabel dependen yang bersumber dari Perwakilan Badan Kependudukan dan Keluarga Berencana Nasional (BKKBN) Sumatera Barat. Hasil evaluasi menunjukkan bahwa model Naïve Bayes memiliki akurasi sebesar 92,46% dan sensitivitas 100% serta spesifisitas 69,14% yang menunjukkan kelemahan dalam mengidentifikasi keluarga berisiko. Metode ROS-Naïve Bayes menunjukkan peningkatan performa model dimana diperoleh akurasi sebesar 99,87%, sensitivitas 99,83%, dan spesifisitas 100%. Hal ini menunjukkan bahwa implementasi Naïve Bayes dengan ROS efektif dalam mengatasi ketidakseimbangan data dan meningkatkan performa model. Faktor utama yang memengaruhi risiko stunting meliputi keikutsertaan KB modern, sanitasi, usia ibu dan jumlah anak.
Pengelompokkan Kabupaten/Kota di Provinsi Sumatera Barat Berdasarkan Indikator Kesejahteraan Rakyat Menggunakan Algoritma SOM Winartha, Mardia; Wirdiastuti, Chairina; Salma, Admi
Jurnal Riset Statistika Volume 5, No. 1, Juli 2025, Jurnal Riset Statistika (JRS)
Publisher : UPT Publikasi Ilmiah Unisba

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29313/jrs.v5i1.6707

Abstract

Abstract. People's welfare is the main indicator in measuring the success of a region's development. Welfare reflects the fulfillment of people's basic needs, both material and spiritual, as measured through indicators of people's welfare. West Sumatra Province still shows a welfare gap between districts/municipalities, which can be seen from significant differences in indicators such as employment and poverty. Therefore, the purpose of this article is to cluster districts/cities in West Sumatra Province and recognize the characteristics of each cluster according to the people's welfare indicators in 2023 using the self-organizing maps algorithm. The results of the analysis show that 3 clusters are the optimal number of clusters. Cluster 1 includes 7 districts/cities with higher welfare levels, cluster 2 includes 7 districts/cities with medium welfare levels, and cluster 3 includes 5 districts/cities with lower welfare levels. This article is expected to help create better and more equitable policies that will support the improvement of people's welfare in West Sumatra Province. Abstrak. Kesejahteraan rakyat menjadi indikator utama dalam mengukur keberhasilan pembangunan suatu wilayah. Kesejahteraan mencerminkan kondisi terpenuhinya kebutuhan dasar masyarakat, baik material maupun spiritual yang diukur melalui indikator-indikator kesejahteraan rakyat. Provinsi Sumatera Barat masih menunjukkan kesenjangan kesejahteraan antar kabupaten/kota yang terlihat dari perbedaan signifikan pada indikator seperti ketenagakerjaan dan kemiskinan. Oleh sebab itu, tujuan dari artikel ini adalah untuk mengelompokkan kabupaten/kota di Provinsi Sumatera Barat dan mengenali karakteristik setiap cluster sesuai dengan indikator kesejahteraan rakyat pada tahun 2023 menggunakan algoritma self-organizing maps. Hasil analisis menunjukkan bahwa 3 cluster adalah jumlah cluster optimal. Cluster 1 meliputi 7 kabupaten/kota dengan tingkat kesejahteraan yang lebih tinggi, cluster 2 meliputi 7 kabupaten/kota dengan  tingkat kesejahteraan menengah, dan cluster 3 meliputi 5 kabupaten/kota dengan tingkat kesejahteraan yang lebih rendah. Artikel ini diharapkan dapat membantu menciptakan kebijakan yang lebih baik dan merata yang akan mendukung peningkatan  kesejahteraan rakyat di Provinsi Sumatera Barat.
Analisis Pola Curah Hujan Di Kota Bengkulu Menggunakan Model Rantai Markov Mawaddah, Nurul; Permana, Dony; Amalia, Nonong; Salma, Admi
Imajiner: Jurnal Matematika dan Pendidikan Matematika Vol 7, No 4 (2025): Imajiner: Jurnal Matematika dan Pendidikan Matematika
Publisher : Universitas PGRI Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26877/imajiner.v7i4.23892

Abstract

Curah hujan merupakan komponen penting dalam sistem iklim tropis yang berperan dalam menjaga keseimbangan ekosistem serta mendukung sektor pertanian, perikanan, transportasi, dan mitigasi bencana hidrometeorologi. Kota Bengkulu sebagai wilayah pesisir di barat Pulau Sumatera memiliki karakteristik curah hujan yang fluktuatif. Penelitian ini bertujuan untuk menganalisis pola transisi curah hujan harian di Kota Bengkulu tahun 2023 menggunakan model rantai Markov. Penelitian dilakukan dengan pendekatan kuantitatif deskriptif menggunakan data curah hujan harian dari Stasiun Meteorologi Fatmawati Soekarno Bengkulu selama periode 1 Januari hingga 31 Desember 2023. Tahapan analisis meliputi analisis deskriptif, kategorisasi data berdasarkan intensitas hujan, penyusunan tabel frekuensi dan peluang transisi, pembentukan matriks transisi, perhitungan peluang transisi n-step, serta penentuan kondisi steady state. Hasil penelitian menunjukkan bahwa hujan ringan merupakan kondisi yang paling dominan dengan peluang stabil sebesar 89,33%, disusul oleh hujan sedang (8,33%) dan hujan lebat (2,34%). Peluang transisi terbesar terjadi pada hujan ringan yang tetap hujan ringan sebesar 90,2%, sedangkan transisi ke hujan sedang dan lebat masing-masing sebesar 7,5% dan 2,3%. Temuan ini mengindikasikan bahwa Kota Bengkulu cenderung mengalami hujan ringan secara konsisten, sementara intensitas hujan yang lebih tinggi terjadi secara sporadis. Hasil ini bermanfaat dalam mendukung pengelolaan sumber daya air, mitigasi risiko bencana, serta perencanaan adaptasi perubahan iklim di wilayah pesisir.
Digital-Based Interactive Learning Transformation Optimization of Canva: A Case Study at SMPN 3 Padang Salma, Admi
Pelita Eksakta Vol 8 No 2 (2025): Pelita Eksakta, Vol. 8, No. 2
Publisher : Fakultas MIPA Universitas Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24036/pelitaeksakta/vol8-iss2/290

Abstract

The implementation of digital-based interactive learning in the classroom has the potential to increase student engagement and motivation in the learning process. One of the main problems at SMP N 3 Padang is that teachers have varying levels of basic skills in creating technology-based interactive learning. As a result, digital learning cannot be implemented effectively in the classroom. Therefore, it is very important to improve teachers' skills in creating digital instructional media. Canva is one of the most user-friendly digital learning tools and is accessible to users with limited technical expertise. The study conducted at SMP 3 Padang aimed to address teachers' challenges by providing Canva optimization training. The objective of this study was to enhance teachers' ability to utilize Canva for creating digital-based interactive learning. The results show that teachers' ability to create interactive instructional media with Canva has significantly improved.
A Self-Organizing Map Approach for Clustering Provinces Based on Multisectoral Indicators of Stunting Determinants Admi Salma; Riwi Dyah Pangesti; Reny Wulandari
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-iss2/487

Abstract

Stunting is a national issue in Indonesia and also a global challenge.  It becomes one of the key priorities outlined in the Sustainable Development Goals (SDGs). The heterogeneity of multisectoral conditions across provinces also contributes to the variation in stunting prevalence in Indonesia. The implementation of uniform policies to address stunting may not yield optimal results due to the diverse needs of each province. Therefore, specific interventions are required to overcome stunting issues. Based on this condition, it is important to cluster provinces based on their characteristics so that the government can determine appropriate interventions for each provincial cluster. Visualization of stunting conditions and multisectoral indicators can also enrich the understanding of each cluster. This study aims to construct clusters of provinces with similar characteristics in terms of multisectoral indicators of stunting determinants. This study applies cluster analysis using a Self-Organizing Map (SOM) algorithm to group provinces. The research steps include data preprocessing, clustering using the SOM algorithm, SOM mapping, and cluster characterization analysis. The results of this study show that three clusters were obtained. The first cluster consists of three provinces characterized by a high maternal mortality rate and a high percentage of exclusive breastfeeding. The second cluster includes nine provinces and is characterized by high risks in maternal and child health as well as economic vulnerability. In addition, the third cluster consists of 26 provinces characterized by relatively good living conditions and quality education.
Penanganan Ketidakseimbangan Multikelas pada Dataset Survei Kerangka Sampel Area menggunakan Metode SCUT Wilia Sondriva; Yenni Kurniawati; Nonong Amalita; Admi Salma
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-iss2/163

Abstract

Area Sampling Frame (ASF) is a survey used by the Indonesian government to measure rice productivity in Indonesia. ASF survey is important data because accurate and high-quality rice productivity data is highly needed. There is extreme imbalance in the ASF survey data, thus requiring handling of this imbalance. SMOTE and Cluster-based Undersampling Technique (SCUT) is a method that can be used to address the dataset imbalance. SCUT combines oversampling using SMOTE and undersampling using CUT. The results from SCUT show that the number of data points in each class becomes balanced. Subsequently, a two-sample mean test is conducted to observe the mean differences between the original dataset and the dataset after handling. The results show that in the early vegetative, late vegetative, and harvest phases, the means are significantly similar between the original dataset and the dataset after handling, but in the generative phase, the means are not significantly similar. Therefore, synthetically generated data using the SCUT method generally exhibit similar mean characteristics.
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

Abstract

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.
Classification of Dropout Rates in West Sumatra Using the Random Forest Algorithm with Synthetic Minority Oversampling Technique Anita Fadila; Syafriandi Syafriandi; 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-iss3/183

Abstract

This study aims to classify school dropout rates in West Sumatra Province using the Random Forest algorithm with the Synthetic Minority Oversampling Technique (SMOTE). Based on 2021 data from the Ministry of Education, Culture, Research, and Technology (Kemdikbudristek), the dropout rate in West Sumatra is above the national average. Despite efforts to reduce dropout rates, results remain suboptimal. Therefore, this study seeks to identify the causes of student dropouts and compare the performance of the Random Forest algorithm with and without SMOTE. The study uses the 2021 dropout data from West Sumatra, which has a significant class imbalance. SMOTE is applied to balance the data. The dataset is split into training and testing sets in an 80%:20% ratio, and parameter tuning is performed to optimize mtry and the number of trees (ntree). The model is evaluated using a confusion matrix to compare performance. The results show that Random Forest with SMOTE outperforms the version without SMOTE, with improvements in precision, recall, and F1-score. The presence of the biological mother ( ) is identified as the most significant factor influencing student dropouts, based on the Mean Decrease Gini value. The study concludes that using SMOTE in the Random Forest algorithm helps reduce classification bias and enhances the model's ability to detect students at risk of dropping out.
Vector Error Correction Model to Analyze the Impact of Exchange Rates and Money Supply on Inflation in Indonesia Faulina; Fadhilah Fitri; Nonong Amalita; 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-iss3/188

Abstract

This study analyzes inflation in Indonesia in relation to the influence of exchange rates and the money supply (M2), which pose challenges in controlling inflation amidst rapid economic growth. Data from the Ministry of Trade of the Republic of Indonesia (Kemendag) were used to investigate the relationship between exchange rates and the money supply (M2) on inflation using the Vector Error Correction Model (VECM). The results indicate that in the short term, inflation tends to decrease towards stability, with a strong exchange rate capable of reducing inflation, while an increase in the money supply slightly raises inflation. However, in the long term, inflation demonstrates a strong self-correction mechanism, with the influence of exchange rates and the money supply becoming limited. This model proves effective in forecasting inflation for the period from March to August 2024, with a Mean Absolute Percentage Error (MAPE) of 19.59%.
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 Aldwi Riandhoko Amalia, Nonong Anita Fadila Atus Amadi Putra AULIA YUSWITA Aulia, Yuke Bahri Annur Sinaga Belia Mailien Chairina Wirdiastuti Darwas Denia Putri Fajrina dhea afrila harelvi Dina Fitria Dina Fitria Dodi Vionanda Dodi Vionanda Dony Permana Dwi Sulistiowati, Dwi Eujeniatul Jannah Fadhilah Fitri Fadhilah Fitri Fadhillah Fitri Fadhillah Meisya Carina Fathina Nafisa Putri Fathina Nafisa Putri Faulina Fitria Panca Ramadhani Futiah Salsabila Gilang Ibnul farizi Hanifa Hasna Hariati Ainun Nisa Ihsan Dermawan Ihsanul Fikri Indah Lestari Jimmi Darma putra Kurnia Andrea Diva Mardhiatul Azmi Maulidya Hernanda Mawaddah, Nurul Melda Safitri Muhamad Rayhan Nanda P Muthia Sakhdiah Nahda Maesya Tsani Nonong Amalita Nurdalia Okia Dinda Kelana Permana, Dony Putri fajriyanti nur Putri, Deya Junida rahmad revi fadillah Reny Wulandari Rheizma Dewi Adiningtiyas Riska Ramadani Riskiani Lubis Riwi Dyah Pangesti Seif Adil El-Muslih Sherly Amora Jofipasi Silvia Agustina Silvia Triana Suliswati, Yeni Susi Jumiati Syafriandi Syafriandi Syafriandi Syafriandi Syafriandi Syafriandi Tessy Octavia Mukhti Tessy Octavia Mukhti Ulya Syafitri.J Vidhiya Addini Wafiq Alya Aufa Wilia Sondriva Winartha, Mardia Wulan Septya Zulmawati Yenni Kurniawati Yenni Kurniawati Yulia Pertiwi Zamahsary Martha Zilrahmi, Zilrahmi