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All Journal Multi Proximity: Jurnal Statistika Universitas Jambi IJASDS: Indonesian Journal of Applied Statistics and Data Science
Alfarez, Dzaki Ade
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Economics, Econometrics & Finance Environmental Science Mathematics Public Health Social Sciences

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Pengaruh Angka Harapan Hidup saat lahir (AHH), Harapan Lama Sekolah (HLS) dan Rata-rata Lama Sekolah (RLS) terhadap Indeks Pembangunan Manusia di Provinsi Jambi. Arif, Arif; Alfarez, Dzaki Ade; Ramadhan, M. Rizky; Mardhotillah, Bunga
Multi Proximity: Jurnal Statistika Vol. 2 No. 2 (2023): Applied Statistics
Publisher : Universitas Jambi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22437/multiproximity.v2i2.28408

Abstract

Indeks pembangunan manusia (IPM) adalah indikator yang digunakan untuk mengukur kemajuan sosial dan ekonomi dalam suatu negara atau wilayah dengan fokus pada kesejahteraan manusia Penulis ingin melihat pengaruh dari variabel independen yaitu Angka Harapan Hidup (AHH), Harapan Lama Sekolah (HLS), dan Rata -rata Lama Sekolah (RLS) terhadap variabel dependen yaitu Indeks Pembangunan Manusia (IPM), dimana data diambil pada web BPS di 11 kabupaten/kota di Provinsi Jambi. Data yang diperoleh tersebut kemudian dianalisis menggunakan analisis regresi linier berganda pada software JASP. Langkah pertama dilakukan dengan mengetahui normalisasi data dari model regresi yang telah, kemudian melakukan uji asumsi klasik yaitu uji Multikolinearitas dan uji heteroskedastisitas. Pada uji multikolinearitas, hanya variabel X1 saja yang tidak multikolinearitas. Langkah selanjutnya yaitu melakukan uji F dan uji t dimana nilai signifikansi atau p-value nya < 0,05. Dari hasil penelitian tersebut diperoleh bahwa Angka Harapan Hidup, Harapan Lama Sekolah, dan Rata-rata Lama Sekolah memiliki pengaruh terhadap Indeks Pembangunan Manusia jika dipengaruhi secara bersama-sama (simultan). Angka Harapan Hidup juga bisa secara sendiri mempengaruhi Indeks Pembangunan Manusia, tetapi untuk Harapan Lama Sekolah dan Rata-rata Lama Sekolah tidak bisa mempengaruhi Indeks Pembangunan Manusia secara mandiri.
Pengklasifikasian 10 Kabupaten/Kota di Provinsi Nusa Tenggara Barat untuk Kasus Kemiskinan Tahun 2022 Menggunakan Analisis Cluster Metode K-Means Sabina, Sabna Zulfaa; Alfarez, Dzaki Ade; Graha, Syifa Salsabila Satya; Auladi, Muhammad Yuzaul; Lisa , Harsyiah; Lisa Harsyiah
Indonesian Journal of Applied Statistics and Data Science Vol. 1 No. 1 (2024): November
Publisher : Universitas Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/ijasds.v1i1.5415

Abstract

Poverty is a very serious problem for countries in the world, especially for developing countries like Indonesia. Poverty will have a big impact if it occurs in the long term with different factors. One province that is still in the spotlight for high levels of poverty is West Nusa Tenggara Province. Even though the number of poor people in West Nusa Tenggara Province has decreased, conditions of the ground show that there are still many people whose lives are far from decent. Therefore, the government must immediately find a solution to overcome the problem of poverty. To overcome cases of poverty in a region, we can group the characteristics of these regions based on poverty indicators into several clusters. Grouping in this case is carried out with data that will be analyzed using the K-Means cluster analysis method. So the results obtained by analysis using the K-Means cluster method for grouping 10 regencies/cities in West Nusa Tenggara Province based on poverty in 2022 formed 3 clusters, namely cluster 1 consisting of West Sumbawa Regency, Bima City and Mataram City, cluster 2 consisting of Bima Regency, Dompu Regency, West Lombok Regency, Central Lombok Regency, East Lombok Regency, and Sumbawa Regency, and cluster 3 consists of North Lombok Regency. Apart from that, the characteristics of each cluster were also obtained, namely cluster 1 containing the districts/cities with the highest PPM values. While RLS, AHH, and TPT have very high numbers in 2022, cluster 2 contains districts/cities that have quite low PPM, RLS, AHH, and TPT numbers in 2022, and cluster 3 contains a group of districts/cities with RLS, AHH, and TPT has quite low numbers compared to the high PPM in 2022.
Co-Authors Arif Arif Auladi, Muhammad Yuzaul Graha, Syifa Salsabila Satya Harsyiah, Lisa Lisa , Harsyiah M. RIZKY RAMADHAN Mardhotillah, Bunga Sabina, Sabna Zulfaa