MODEL DISCRETE TIME MARKOV CHAIN (SEIRD) PENYAKIT PNEUMONIA PADA BALITA DENGAN PENGARUH VAKSINASI | ELECTRONIC THESES AND DISSERTATION

Electronic Theses and Dissertation

Universitas Syiah Kuala

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MODEL DISCRETE TIME MARKOV CHAIN (SEIRD) PENYAKIT PNEUMONIA PADA BALITA DENGAN PENGARUH VAKSINASI


Pengarang

Shalsabila Kamalsyah Putri - Personal Name;

Dosen Pembimbing

Rini Oktavia - 197010121995122002 - Dosen Pembimbing I
Syarifah Meurah Yuni - 198006072008122001 - Dosen Pembimbing II



Nomor Pokok Mahasiswa

2108101010010

Fakultas & Prodi

Fakultas MIPA / Matematika (S1) / PDDIKTI : 44201

Penerbit

Banda Aceh : Fakultas mipa., 2025

Bahasa

Indonesia

No Classification

511.1

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Pneumonia menjadi penyebab utama kematian balita di Indonesia. Penelitian ini mengembangkan model Discrete Time Markov Chain (DTMC) SEIRD untuk memodelkan penyebaran pneumonia dengan lima kompartemen Susceptible (S), Exposed (E), Infected (I), Recovered (R), dan Death (D). Transisi antar kompartemen dimodelkan stokastik menggunakan probabilitas transisi dari data epidemiologi 2011 sampai 2023 dan cakupan vaksinasi PCV. Simulasi dilakukan dengan bilangan acak uniform random pada dua skenario: tanpa vaksinasi v sama dengan 0 dan dengan vaksinasi v sama dengan 0,0000448. Hasil menunjukkan tanpa vaksinasi puncak infeksi rata-rata 811,44 kasus dengan total kematian 82,3 individu per 1.000 populasi. Dengan vaksinasi, puncak infeksi turun sedikit menjadi 807,94 kasus, sementara total kematian berkurang signifikan hingga 42,47 individu. Penurunan infeksi pada skenario vaksinasi lebih lambat karena proporsi vaksinasi sangat kecil, sesuai kondisi program PCV nasional yang baru berjalan sejak 2019. Kesimpulannya, vaksinasi efektif menekan angka kematian hingga 50 persen, meski dampak terhadap penurunan infeksi jangka panjang masih terbatas pada cakupan vaksinasi rendah.

Kata Kunci: Pneumonia, DTMC, SEIRD,Vaksinasi, Stokastik

Pneumonia is the leading cause of infant mortality in Indonesia. This study developed a Discrete Time Markov Chain (DTMC) SEIRD model to model the spread of pneumonia with five compartments: Susceptible (S), Exposed (E), Infected (I), Recovered (R), and Death (D). Transitions between compartments were modeled stochastically using transition probabilities from 2011 to 2023 epidemiological data and PCV vaccination coverage. Simulations were performed with uniform random numbers in two scenarios: without vaccination v is equal to 0 and with vaccination v is equal to 0.0000448. The results showed that without vaccination, the peak infection averaged 811.44 cases with a total death of 82.3 individuals per 1,000 population. With vaccination, the peak infection decreased slightly to 807.94 cases, while the total death was significantly reduced to 42.47 individuals. The decline in infections in the vaccination scenario was slower due to the very small proportion of vaccinated individuals, in line with the national PCV program, which has only been running since 2019. In conclusion, vaccination is effective in reducing mortality by up to 50 percent, although the impact on long-term infection reduction is still limited due to low vaccination coverage. Keywords: Pneumonia, DTMC, SEIRD, Vaccination, Stochastic.

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