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All Journal Imajiner: Jurnal Matematika dan Pendidikan Matematika Pelita Eksakta GAUSS: Jurnal Pendidikan Matematika Leibniz: Jurnal Matematika Jurnal Riset Statistika Journal of Mathematics UNP Rangkiang Mathematics Journal UNP Journal of Statistics and Data Science 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 Education Languange, Linguistic, Communication & Media Mathematics Physics Social Sciences Other

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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
Pengelompokan Kabupaten/Kota Maluku dan Nusa Tenggara Barat Berdasarkan Faktor Kemiskinan Menggunakan Self Organizing Maps Aulia, Yuke; Sulistiowati, Dwi; Kurniawati, Yenni; 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.v5i02.607

Abstract

Provinsi Maluku dan Nusa Tenggara Barat masih menghadapi tantangan serius dalam upaya pengentasan kemiskinan. Kedua provinsi ini tidak hanya mengalami peningkatan persentase penduduk miskin, tetapi juga termasuk sebagai wilayah dengan persentase penduduk miskin tertinggi di Indonesia. Persentase penduduk miskin di Provinsi Maluku pada tahun 2023 mencapai 16,42%, naik sebesar 0,45%. Sementara itu, persentase penduduk miskin di Provinsi Nusa Tenggara Barat mencapai 13,85%, naik sebesar 0,17%. Angka-angka ini masih jauh dari target pemerintah yang menetapkan 6%-7% untuk persentase kemiskinan nasional. Penelitian ini bertujuan untuk mengelompokkan kabupaten/kota di Provinsi Maluku dan Nusa Tenggara Barat berdasarkan faktor yang memengaruhi kemiskinan serta mengidentifikasi karakteristik hasil klaster yang terbentuk. Penelitian ini menggunakan metode Self Organizing Maps (SOM). Data penelitian ini bersumber dari publikasi Badan Pusat Statistik (BPS), yaitu Maluku dalam Angka 2024 dan Nusa Tenggara Barat dalam Angka 2024. Hasil analisis menunjukkan terbentuknya 3 klaster wilayah yang divalidasi menggunakan pendekatan validasi internal (Connectivity, Dunn, dan Silhouette). Klaster 1 terdiri dari 2 kota ditandai oleh keunggulan dalam indikator pendidikan, kesehatan, dan ekonomi. Klaster 2 terdiri dari 15 kabupaten/kota yang dicirikan dengan potensi tenaga kerja yang tinggi, namun mengahadapi tantangan jumlah penduduk yang besar. Sementara itu, klaster 3 terdiri dari 4 kabupaten memiliki keterbatasan dalam berbagai aspek, termasuk pendidikan, kesehatan, ekonomi, dan infrastruktur.
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.
Multidimensional Poverty Clustering using K-Means Algorithm with Dimensionaly Reduction by Principal Component Analysis Salma, Admi; Zilrahmi, Zilrahmi
Rangkiang Mathematics Journal Vol. 4 No. 2 (2025): Rangkiang Mathematics Journal
Publisher : Department of Mathematics, Universitas Negeri Padang (UNP)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24036/rmj.v4i2.101

Abstract

The level of Multidimensional poverty in each province in Indonesia varies, similar policies is ineffective to reduce the poverty. Several poverty indicators also influence other factors. General policies established to overcome poverty have proven ineffective, making it urgent to identify the needs of each province in overcoming this condition. Grouping provinces based on similar multidimensional poverty which use cluster analysis, will help address this situation. The aim of this study is to group provinces based on multidimensional poverty indicators using the k-means clustering method. Principal Component Analysis (PCA) was also used to reduce variables and multicollinearity. The clustering results showed seven clusters. The highest multidimensional poverty was found in cluster 2, which consisted of one province, namely Papua Pegunungan. This province shows deficiencies in education, health, and living standards compared to other clusters. Meanwhile, the lowest multidimensional poverty was found in cluster 7. There are three provinces in this cluster, namely Bali, Jakarta, and DIY Jogjakarta. These provinces experience minimal multidimensional poverty which is able to provide a better quality of life. The policies and development strategies in these provinces could serve as role models to develop other provinces based on their specific deficiencies and needs.   Each cluster is well separated, as Davies Bouldin Index (DB) is lover, at 0.4.
Handling Unbalanced Data with SMOTE Algorithm for Unemployment Classification in Lima Puluh Kota Regency Using CART Method Aldwi Riandhoko; Amalita, Nonong; Vionanda, Dodi; Salma, Admi
Indonesian Journal of Statistics and Applications Vol 8 No 2 (2024)
Publisher : Statistics and Data Science Program Study, SSMI, IPB University, in collaboration with the Forum Pendidikan Tinggi Statistika Indonesia (FORSTAT) and the Ikatan Statistisi Indonesia (ISI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29244/ijsa.v8i2p166-177

Abstract

Unemployment is a problem that occurs in the labor force, where high unemployment is caused by the low ability of the labor force. A region that is still experiencing unemployment problems in West Sumatera is Lima Puluh Kota Regency. Unemployment in Lima Puluh Kota Regency is caused by the low competence of human resources to fulfill employment market requirements. Based on the results of the Sakernas survey in August 2023, Lima Puluh Kota Regency has more employed labor force than unemployed labor force, so this results in unbalanced data. A method that can overcome unbalanced data is Synthetic Minority Oversampling Technique (SMOTE). SMOTE is a technique with addition of synthetic data in minority class so that the proportion is balanced. Data imbalance conditions need to be handled so as to improve the performance of the classification model. Classification and Regression Trees (CART) is a classification technique with a decision tree method that can obtain the characteristics of a classification. The purpose of this research is to compare the CART model before and after applying SMOTE which can be measured by comparing the highest Area Under Curve (AUC) value. The AUC value in the CART method before SMOTE applied has a value of 62.1% while the AUC value in the CART method after SMOTE applied has a value of 70.2%. Therefore, it can be concluded that the CART classification analysis after SMOTE applied is able to provide better performance compared to the CART classification analysis before SMOTE applied.
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.
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 Dina Fitria, Dina Dodi Vionanda Dodi Vionanda Dony Permana Dwi Sulistiowati, Dwi Eujeniatul Jannah Fadhilah Fitri Fadhilah Fitri Fadhillah Fitri Fadhillah Meisya Carina 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 Lubis, Riskiani 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 rahmad revi fadillah Reny Wulandari Rheizma Dewi Adiningtiyas Riska Ramadani Riwi Dyah Pangesti Seif Adil El-Muslih Sherly Amora Jofipasi Silvia Agustina Silvia Triana Suliswati, Yeni Susi Jumiati 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