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All Journal Information System for Educators and Professionals : Journal of Information System KOPERTIP: Jurnal Ilmiah Manajemen Informatika dan Komputer Jurnal ICT : Information Communication & Technology Jurnal Informatika Terpadu AMMA : Jurnal Pengabdian Masyarakat Akuntansi: Jurnal Riset Ilmu Akuntansi Jurnal Informatika: Jurnal Pengembangan IT Jurnal Teknik Informatika dan Teknologi Informasi
Nisa Dieanwati Nuris
STMIK IKMI Cirebon
Humanities Chemistry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Engineering Law, Crime, Criminology & Criminal Justice Materials Science & Nanotechnology Nursing Other

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Penerapan Algoritma K-Means Clustering Pada Tingkat Inflasi Kota Di Indonesia Novia Wulandari; Nisa Dienwati Nuris; Saeful Anwar
Akuntansi Vol. 2 No. 2 (2023): Juni: Jurnal Riset Ilmu Akuntansi
Publisher : Lembaga Pengembangan Kinerja Dosen

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55606/akuntansi.v2i2.235

Abstract

Inflation is a general tendency to increase the prices of goods and services, and it happens all the time. when the prices of domestic goods and services rise, inflation will rise. Depreciation causes the prices of goods and services to rise. Uncontrolled inflation can result in losses for society and the government. Therefore, an appropriate study is needed to map the dynamics of inflation in a region. One way to map the inflation rate is clustering. Clustering is dividing data into groups with the same characteristics. The author took the initiative to analyze the urban inflation rate in Indonesia from 2020 to 2022. The data is sourced from the Central Statistics Agency (BPS) website. This analysis uses the K-Means Clustering method with 5 clusters. the group with the highest inflation is in cluster 0, the high inflation group is in cluster 1, the moderate inflation group is in cluster 2, the low inflation group is in cluster 3, and the lowest inflation is in cluster 4. by categorizing the inflation rate of cities in Indonesia, it can be seen which cities in Indonesia have very high, high, medium, low and very low inflation rates.
Co-Authors Ahmad Faqih Bahrul Ulum Dwi Teguh Afandi Edi Tohidi Federicko Ramiro Firjatullah Fifia Nur Handayani Gifthera Dwilestari Haris Abdul Hadi Iin Mohamad Ibnu Abas Muhammad Abdullah Nahdi Muhammad Alfian Nur Rahmat Mulyawan Nana Suarna Nanda Satria Halim Pratama Narasati, Riri Narasati Nining Rahaningsih Nova Novitasari Novia Wulandari Nuryadi Raditya Danar Dana Rano Rano Riska Yati Ningrum Ruli Herdiana Saeful Anwar Yudhistira Arie Wijaya