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Dinamika dan Probabilitas Transisi Curah Hujan Harian di MKG Itera Menggunakan Model Rantai Markov

Indonesian Journal of Applied Mathematics · Vol. 6 No. 1 (2026) · pp. 49–57

DOI
10.35472/indojam.v6i1.2728
ISSN
2774-2016, 2774-2067
Published
2026-04-30
Publisher
Institut Teknologi Sumatra
License
creativecommons.org/licenses/by-nc/4.0

Abstract

This study aims to analyze the characteristics of daily rainfall in the Meteorology, Climatology, and Geophysics (MKG) Itera region during the period from January to October 2025 using a Markov Chain model. Daily rainfall data are classified into four states based on rainfall intensity, namely No Rain, Light Rain, Moderate Rain, and Heavy Rain. The analysis begins with descriptive statistics to identify general characteristics of the data. The average daily rainfall is 6.50 mm/day, with the highest recorded intensity reaching 82.8 mm on February 21, 2025, and a rainfall variance of 195.68 mm². Subsequently, a transition probability matrix is constructed to model the dynamics of rainfall condition changes between states from day to day. In addition, the steady-state probabilities are calculated to determine the long-term tendencies of rainfall conditions. The results indicate that daily rainfall exhibits a fluctuating pattern with relatively high variability. Most observations fall into the No Rain (48%) and Light Rain (40%) states, indicating that more than 88% of rainfall conditions are of low intensity. The transition probability matrix shows that the highest transition probability occurs from Moderate Rain to Light Rain at 66.7%, suggesting that moderate rainfall tends to decrease to light rainfall on the following day. Furthermore, the probabilities of remaining in the No Rain and Light Rain states are relatively high, at 61.1% and 46.7%, respectively, indicating persistence in dry or low-intensity rainfall conditions. The steady-state distribution shows that, in the long run, the probabilities are 47.6% for No Rain, 40.4% for Light Rain, 8.9% for Moderate Rain, and 3.0% for Heavy Rain. These findings suggest that rainfall patterns in the MKG Itera region are predominantly characterized by dry to low-intensity conditions. Overall, the Markov Chain model is capable of effectively representing the dynamics of daily rainfall and provides a comprehensive description of long-term rainfall characteristics in the MKG Itera region.

Collaboration network of the authors

40 authors · 91 co-authorship links · showing the 40 most connected

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  • State University of Semarang
  • Bandung Institute of Technology
  • Universitas Halu Oleo
  • Affiliation unknown
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
Show as table
Co-authorOrganisationJoint articles
Danni Gathot HarbowoAffiliation unknown2
Sri SulistyoriniState University of Semarang2
Intan Tamara Azka ZanubaAffiliation unknown1
Zinni FitriasihAffiliation unknown1
Lestari Citra AliniAffiliation unknown1
Dewi SuhikaBandung Institute of Technology1
Sri Efrinita IrwanAffiliation unknown1
Edi CahyonoUniversitas Halu Oleo1
. KartonoAffiliation unknown1
Fuji LestariAffiliation unknown1
Mika Alvionita SAffiliation unknown1
Ardika SatriaAffiliation unknown1
Nabila Nurita PutriAffiliation unknown1
Muhammad Ammar AlkautsarAffiliation unknown1
Sekar Anisa SelyAffiliation unknown1
Della Najunda K.SAffiliation unknown1
Rofi Ilmi FadhillahAffiliation unknown1
Alfredo Edward LazuardiAffiliation unknown1
Andre Markus Fernando LubisAffiliation unknown1
Juan Daniel TurnipAffiliation unknown1
Erina Rosalia IrdaAffiliation unknown1
Adelia AzahraAffiliation unknown1
Yanti MaritoAffiliation unknown1
Meysi SupmawatiAffiliation unknown1
Eli RahmawatiAffiliation unknown1
Naila Tsamrotul AulaAffiliation unknown1
Barokah IsdaryantiState University of Semarang1
Farid AhmadiAffiliation unknown1
ANGGI CINTYA LESTARIAffiliation unknown1
Aldina Eka AndrianiAffiliation unknown1
Dewi Nilam TyasAffiliation unknown1
Sri HartatiAffiliation unknown1
Isti Nur HayanahAffiliation unknown1
Wahana Adinindya NugrahaeniAffiliation unknown1

Affiliations come from OpenAlex; journal OAI-PMH metadata carries none. Authors without an OpenAlex ID are matched by name, which can merge or split people.

This record comes from Crossref (what the publisher deposited), because the journal's own OAI-PMH endpoint could not be harvested.

Metadata source: Crossref API (cadangan, berdasarkan ISSN) https://api.crossref.org/journals/2774-2016/works, first harvested 5 Nov 2026, last seen 8 Nov 2026.
Metadata as oai_dc, converted from Crossref
<oai_dc:dc xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:oai_dc="http://www.openarchives.org/OAI/2.0/oai_dc/"><dc:title>Dinamika dan Probabilitas Transisi Curah Hujan Harian di MKG Itera Menggunakan Model Rantai Markov</dc:title><dc:creator>Muliawati, Triyana</dc:creator><dc:creator>Angela Putri Larasati</dc:creator><dc:creator>Desi Wulandari</dc:creator><dc:creator>Robecca Miranda Agustina Gultom</dc:creator><dc:creator>Setia Hot Dameria Purba</dc:creator><dc:creator>Suci Ayu Pangestuti</dc:creator><dc:description>&lt;jats:p&gt;This study aims to analyze the characteristics of daily rainfall in the Meteorology, Climatology, and Geophysics (MKG) Itera region during the period from January to October 2025 using a Markov Chain model. Daily rainfall data are classified into four states based on rainfall intensity, namely No Rain, Light Rain, Moderate Rain, and Heavy Rain. The analysis begins with descriptive statistics to identify general characteristics of the data. The average daily rainfall is 6.50 mm/day, with the highest recorded intensity reaching 82.8 mm on February 21, 2025, and a rainfall variance of 195.68 mm². Subsequently, a transition probability matrix is constructed to model the dynamics of rainfall condition changes between states from day to day. In addition, the steady-state probabilities are calculated to determine the long-term tendencies of rainfall conditions. The results indicate that daily rainfall exhibits a fluctuating pattern with relatively high variability. Most observations fall into the No Rain (48%) and Light Rain (40%) states, indicating that more than 88% of rainfall conditions are of low intensity. The transition probability matrix shows that the highest transition probability occurs from Moderate Rain to Light Rain at 66.7%, suggesting that moderate rainfall tends to decrease to light rainfall on the following day. Furthermore, the probabilities of remaining in the No Rain and Light Rain states are relatively high, at 61.1% and 46.7%, respectively, indicating persistence in dry or low-intensity rainfall conditions. The steady-state distribution shows that, in the long run, the probabilities are 47.6% for No Rain, 40.4% for Light Rain, 8.9% for Moderate Rain, and 3.0% for Heavy Rain. These findings suggest that rainfall patterns in the MKG Itera region are predominantly characterized by dry to low-intensity conditions. Overall, the Markov Chain model is capable of effectively representing the dynamics of daily rainfall and provides a comprehensive description of long-term rainfall characteristics in the MKG Itera region.&lt;/jats:p&gt;</dc:description><dc:publisher>Institut Teknologi Sumatra</dc:publisher><dc:date>2026-04-30</dc:date><dc:type>info:eu-repo/semantics/article</dc:type><dc:format>application/pdf</dc:format><dc:identifier>https://doi.org/10.35472/indojam.v6i1.2728</dc:identifier><dc:identifier>10.35472/indojam.v6i1.2728</dc:identifier><dc:source>Indonesian Journal of Applied Mathematics; Vol 6 No 1 (2026); 49-57</dc:source><dc:source>2774-2016</dc:source><dc:source>2774-2067</dc:source><dc:relation>https://journal.itera.ac.id/index.php/indojam/article/download/2728/776</dc:relation><dc:rights>http://creativecommons.org/licenses/by-nc/4.0</dc:rights></oai_dc:dc>