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All Journal Jurnal Al-Qardh BAREKENG: Jurnal Ilmu Matematika dan Terapan Dinamisia: Jurnal Pengabdian Kepada Masyarakat
Sukmawaty, Yuana
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Religion Humanities Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Energy Engineering Mathematics Mechanical Engineering Physics Social Sciences Transportation Other

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CLUSTERING ANALYSIS FOR GROUPING SUB-DISTRICTS IN BOJONEGORO DISTRICT WITH THE K-MEANS METHOD WITH A VARIETY OF APPROACHES Nurdiansyah, Denny; Ma'ady, Mochamad Nizar Palefi; Sukmawaty, Yuana; Utomo, Muchammad Chandra Cahyo; Mutiani, Tia
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 18 No 2 (2024): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol18iss2pp1095-1104

Abstract

Population data is an important piece of information that is useful for regional planning and development. Insight into the state of an area is more straightforward to observe if there are grouped sub-districts. In this case, data mining techniques can identify patterns and relationships in population data. The K-Means algorithm is a clustering technique that divides data into groups or clusters based on similar characteristics. This research aims to apply the K-Means method with various approaches to clustering sub-districts in the Bojonegoro district according to population data. The research method used is a quantitative method with an exploratory study in the application of the K-Means method with a variety of approaches, namely the use of the Kernel K-Means method by utilizing the mapping function to map data to a higher dimension before the clustering process. In addition, the Fast K-Means method is used, which reduces the model training time to improve the cluster-centered recalibration problem as the amount of data increases. The data source used in this research is secondary population data in the form of birth, death, migrant, and moving variables obtained from the Satu Data Bojonegoro website developed by the Bojonegoro Regency Government. It is found that the best K-Means approach is the Kernel K-Means method with a number of clusters of 5. The performance of the cluster method is evaluated by measuring the average distance within the cluster. The data coordinate pattern in the Kernel K-means method clustering shows a smooth initial trend when the value of the number of clusters is 5 so that the clusters formed are obtained clearly. The conclusion from this study's results is that the K-Means method's best approach in grouping sub-districts in Bojonegoro district is the Kernel K-Means approach.
ANALISIS KASUS KEMISKINAN DI PROVINSI KALIMANTAN TENGAH DENGAN PENDEKATAN PRINCIPAL COMPONENT ANALYSIS Halida, Annisa; Pradita, Nadya Farah; Sukmawaty, Yuana
AL-QARDH Vol 5 No 2 (2020): AL-QARDH
Publisher : Fakultas Ekonomi dan Bisnis Islam Institut Agama Islam Negeri Palangka Raya

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Abstract

According to a report by the Central Statistics Agency, the percentage of poor people in urban areas in September 2019 was 6.89%. Meanwhile, the percentage of poor people in rural areas in September 2019 was 13.10%. The data above is only a national percentage, there are still many provinces that have a poverty percentage above the national percentage, especially in Central Kalimantan Province. There are many factors that influence poverty, including education, employment status, working sector, and per capita income. This paper focuses on a study to determine the factors that have the greatest influence on poverty in Central Kalimantan Province in 2019. The results of this study simplify the poverty factor into 2 (two) factors, namely the first factor consisting of variables of education, work status, and sector. work. Meanwhile, the second factor consists of the variable per capita income for food. In addition, the greatest eigen value was obtained in the education variable of 465.67, which indicates that the education variable has the greatest influence on poverty in Central Kalimantan Province in 2019.
Improving the Competence of Pekapuran Raya Village Apparatus in Managing Maternal and Child Health Data: Peningkatan Kompetensi Perangkat Kelurahan Pekapuran Raya dalam Mengelola Data Kesehatan Ibu dan Anak Anggraini, Dewi; Asmu’i, Asmu’i; Sukmawaty, Yuana; Maisarah, Maisarah; Maulida, Maisya; Sinambela, Talenta Parasian; Zaskia, Novia Ramadhani Putri; Situmorang, Amsal Halomoan; Cahyadi, Rizqa Nabiilah; Azkia, Shofia; Nurhaliza, Alya
Dinamisia : Jurnal Pengabdian Kepada Masyarakat Vol. 9 No. 3 (2025): Dinamisia: Jurnal Pengabdian Kepada Masyarakat
Publisher : Universitas Lancang Kuning

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31849/dinamisia.v9i3.25618

Abstract

This community service program has addressed data management challenges faced by Posyandu health workers in Pekapuran Raya Sub District, Banjarmasin City. The primary objective is to enhance digital data management capabilities among healthcare workers' through scientific and technical training using Microsoft Excel. The training was conducted at the Central Bureau of Statistics in Banjarmasin on September 3, 2024. Based on the collected data analysis from pre-test and post-test showed improvements in the three core competencies: data management ability (from 57.7% to 84.6%), data presentation capability (from 57.7% to 92.3%), and dashboard development skills (from 61.5% to 80.8%). Statistical analysis using the Wilcoxon test confirmed the significance of these improvements (p < 0.05). Furthermore, participants’ evaluation data analysis indicated that this program was highly effective, with 88.5% of participants reporting positive feedback on the training methodology, materials, and facilities. This intervention has effectively demonstrated the capacity of structured technology training to enhance community healthcare data management systems, especially for monitoring maternal and child health.
Co-Authors Asmu’i, Asmu’i Azkia, Shofia Cahyadi, Rizqa Nabiilah Denny Nurdiansyah Dewi Anggraini Halida, Annisa Maisarah Maisarah, Maisarah Maulida, Maisya Mochamad Nizar Palefi Ma’ady Mutiani, Tia Nurhaliza, Alya Pradita, Nadya Farah Sinambela, Talenta Parasian Situmorang, Amsal Halomoan Utomo, Muchammad Chandra Cahyo Zaskia, Novia Ramadhani Putri