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All Journal Imajiner: Jurnal Matematika dan Pendidikan Matematika Pelita Eksakta GAUSS: Jurnal Pendidikan Matematika Leibniz: Jurnal Matematika Jurnal Riset Statistika Rangkiang Mathematics Journal UNP Journal of Statistics and Data Science Indonesian Journal of Statistics and Its Applications
Salma, Admi
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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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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, IPB University, 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.
Co-Authors Aldwi Riandhoko Amalia, Nonong Aulia, Yuke Dina Fitria, Dina Dodi Vionanda Dwi Sulistiowati, Dwi Faulina Fitri, Fadhilah Lubis, Riskiani Mawaddah, Nurul Mukhti, Tessy Octavia Nonong Amalita Permana, Dony Safitri, Melda Sondriva, Wilia Suliswati, Yeni Syafriandi Syafriandi Syafriandi Tsani, Nahda Maesya Winartha, Mardia Wirdiastuti, Chairina Yenni Kurniawati Zamahsary Martha Zilrahmi, Zilrahmi