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All Journal Media Statistika JURNAL MATEMATIKA STATISTIKA DAN KOMPUTASI Edumatsains AKSIOMA BAREKENG: Jurnal Ilmu Matematika dan Terapan JTAM (Jurnal Teori dan Aplikasi Matematika) Jurnal Pengabdian Masyarakat Bumi Raflesia InPrime: Indonesian Journal Of Pure And Applied Mathematics Journal of Midwifery Jurnal Statistika dan Aplikasinya Jurnal Kesehatan Tambusai Griya Journal of Mathematics Education and Application Indonesian Journal of Community Empowerment and Service J-KOMA : Jurnal Ilmu Komputer dan Aplikasi Journal of Statistics and Data Science Journal for Quality in Women's Health
Ramya Rachmawati
Jurusan Matematika Fakultas Matematika Dan Ilmu Pengetahuan Alam Universitas Bengkulu
Agriculture, Biological Sciences & Forestry Humanities Chemistry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Economics, Econometrics & Finance Education Energy Engineering Health Professions Languange, Linguistic, Communication & Media Mathematics Mechanical Engineering Medicine & Pharmacology Nursing Physics Public Health Social Sciences Transportation Other

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Handling Missing Data in Bivariate Gamma Generation Data Using the Random Forest Method Muhammad Arib Alwansyah Arib; Ramya Rachmawati Ramya
J-KOMA : Jurnal Ilmu Komputer dan Aplikasi Vol 8 No 02 (2025): J-KOMA : Jurnal Ilmu Komputer dan Aplikasi
Publisher : Universitas Negeri Jakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21009/JKOMA.082.02

Abstract

Missing data is a common problem in data analysis that can reduce the quality and accuracy of study results if not handled properly. This study aims to evaluate the performance of the Random Forest (RF) imputation method at various levels of missing value proportions, namely 5%, 10%, 15%, and 20%. The data used are Bivariate Gamma data of 200 observations with two variables, generated using RStudio software. Evaluation of imputation performance is carried out by considering the correlation value between the imputed data and the original data, the p-value as an indicator of the significance of the difference, and the error measures Mean Absolute Percentage Error (MAPE) and Root Mean Square Error (RMSE).
Markov Chain Model for Daily Rainfall Modeling in Bengkulu City Rachmawati, Ramya; Firdaus; Ratna Widayati; Siska Yosmar; Risfa Fadila; Ajeng Siti Nurul Kharima
EduMatSains : Jurnal Pendidikan, Matematika dan Sains Vol 10 No 4 (2026): April
Publisher : Fakultas Keguruan dan Ilmu Pendidikan, Universitas Kristen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33541/edumatsains.v10i4.8000

Abstract

Bengkulu City is a region in Indonesia that is particularly vulnerable to shifts in rainfall patterns, which can have significant impacts on the agricultural sector, water resource management, and disaster mitigation. The uncertainty in rainfall patterns often complicates long-term planning. Hence, it is necessary to adopt a statistical approach that can model and predict rainfall characteristics with greater accuracy. This research aims to develop a Markov Chain model to represent the daily rainfall regime in Bengkulu City. The daily rainfall data are categorized into rainfall intensity states, namely: no rain, light, moderate, heavy, or very heavy rainfall. By leveraging historical daily rainfall data, this model is expected to identify the transition probabilities between these states. Based on the obtained steady-state probabilities, it can be concluded that regardless of today’s rainfall condition in Bengkulu City, the long-term probabilities for tomorrow’s weather are as follows: 38% for no rain, 43% for light rain, 13.8% for moderate rain, 4.2% for heavy rain, and 1% for very heavy rain.
Optimal Control Analysis of a Human-Animal Nipah Virus Transmission Model Ratna Widayati; Ramya Rachmawati; Yulian Fauzi; Septri Damayanti; Arlin Marsyanda
InPrime: Indonesian Journal of Pure and Applied Mathematics Vol. 8 No. 1 (2026)
Publisher : Department of Mathematics, Faculty of Sciences and Technology, UIN Syarif Hidayatullah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15408/inprime.v8i1.49631

Abstract

This study proposes a novel multi-host SIRS optimal control model for Nipah virus transmission involving bats, pigs, and humans, extending classical SIR/SEIR frameworks by explicitly incorporating cross-species transmission, reinfection, and heterogeneous contact pathways supported by ecological evidence. Unlike uncontrolled epidemic models, the proposed framework integrates three time-dependent interventions: infected-pig culling, human–animal protective measures, and isolation of infected humans, allowing simultaneous evaluation of epidemiological impact and implementation cost. The existence of an optimal control set is established, and Pontryagin’s Maximum Principle is applied to derive the necessary optimality system. Numerical simulations show that coordinated interventions substantially outperform no-control scenarios. The peak number of infected humans decreases from more than 650,000 to approximately 70,000, while the peak number of infected pigs declines from over 68,000 to about 16,000. Control trajectories indicate that strong early implementation of all measures is the most effective strategy, followed by gradual relaxation as prevalence declines, leading to more efficient resource allocation over time. Compared with classical single-host SIR/SEIR models, the proposed model better captures interspecies spillover dynamics and enables evaluation of integrated response policies that cannot be represented in simpler frameworks. These findings demonstrate the importance of coordinated multi-component strategies for mitigating Nipah outbreaks across interacting host populations. A limitation of this study is the use of assumed parameter values in several transmission processes; future work may incorporate data-driven calibration, stochastic effects, spatial mobility, and uncertainty analysis to improve predictive accuracy. AbstrakPenelitian ini mengusulkan model optimal kontrol SIRS multi-host yang baru untuk penularan virus Nipah yang melibatkan kelelawar, babi, dan manusia, sebagai pengembangan dari kerangka klasik SIR/SEIR dengan secara eksplisit memasukkan penularan antarspesies, reinfeksi, serta jalur kontak heterogen yang didukung oleh bukti ekologis. Berbeda dengan model epidemi tanpa kontrol, kerangka yang diusulkan mengintegrasikan tiga intervensi bergantung waktu, yaitu pemusnahan babi terinfeksi, tindakan perlindungan manusia–hewan, dan isolasi manusia terinfeksi, sehingga memungkinkan evaluasi simultan terhadap dampak epidemiologis dan biaya implementasi. Keberadaan himpunan kontrol optimal dibuktikan, dan Prinsip Maksimum Pontryagin diterapkan untuk menurunkan sistem syarat optimalitas yang diperlukan. Simulasi numerik menunjukkan bahwa intervensi terkoordinasi secara signifikan lebih unggul dibandingkan skenario tanpa kontrol. Puncak jumlah manusia terinfeksi menurun dari lebih dari 650.000 menjadi sekitar 70.000, sedangkan puncak babi terinfeksi turun dari lebih dari 68.000 menjadi sekitar 16.000. Profil kontrol menunjukkan bahwa penerapan kuat pada tahap awal untuk seluruh tindakan merupakan strategi paling efektif, kemudian dikurangi secara bertahap seiring menurunnya prevalensi sehingga menghasilkan alokasi sumber daya yang lebih efisien dari waktu ke waktu. Dibandingkan model klasik SIR/SEIR satu-host, model yang diusulkan lebih mampu menggambarkan dinamika spillover antarspesies serta mengevaluasi kebijakan respons terpadu yang tidak dapat direpresentasikan pada model yang lebih sederhana. Temuan ini menegaskan pentingnya strategi multi-komponen yang terkoordinasi dalam mitigasi wabah Nipah pada populasi host yang saling berinteraksi. Keterbatasan penelitian ini adalah penggunaan beberapa nilai parameter asumsi pada proses transmisi tertentu; penelitian selanjutnya dapat memasukkan kalibrasi berbasis data, efek stokastik, mobilitas spasial, dan analisis ketidakpastian untuk meningkatkan akurasi prediksi.Kata Kunci: Penularan antarspesies; Nipah virus; Kontrol optimal; Dinamika populasi; Model SIRS. 2020MSC: 49J15
DATA IMPUTATION FOR BIVARIATE GAMMA-GENERATED DATA USING PREDICTIVE MEAN MATCHING AND RANDOM FOREST METHODS Muhammad Arib Alwansyah Arib; Jose Rizal Jose; Ramya Rachmawati Ramya
Jurnal Statistika dan Aplikasinya Vol. 10 No. 1 (2026): Jurnal Statistika dan Aplikasinya
Publisher : LPPM Universitas Negeri Jakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21009/JSA.10103

Abstract

Missing data is a common problem in data analysis and can reduce the quality and accuracy of research results if not handled properly. This study aims to compare the Predictive Mean Matching (PMM) and Random Forest (RF) imputation methods in handling missing data with missing levels of 5%, 10%, 15%, and 20% using correlation indicators, p-values, and observing the smallest Mean Absolute Percentage Error (MAPE) and Root Mean Square Error (RMSE) values. The results show that both methods differ at each level of missing data. At 5% missing data, both methods show significant differences to the original data with a p-value smaller than α = 0.05, but the RF method produces smaller MAPE and RMSE values ​​than PMM. At 10% missing data, the PMM method still shows significant differences to the original data, while the RF method does not. At 15% missing data, the PMM method showed results that were not significantly different from the original data and had smaller MAPE and RMSE values ​​than RF. Meanwhile, at 20% missing data, the RF method produced the highest correlation value of 0.7788 compared to PMM at 0.7638. In general, the results of the study indicate that the greater the proportion of missing data, the imputation error rate also tends to increase. Therefore, the selection of imputation methods needs to be adjusted to the characteristics and proportion of missing data to obtain optimal imputation results.
Co-Authors Abda Abda Agwil, Winalia Ajeng Siti Nurul Kharima Alwansyah, Muhammad Arib APRIANISA, TRI Arlin Marsyanda Asmariyah Deni Maryani Dewiani, Kurnia Dwi Kumala, Siska Faisal, Fahri Fakhry Asad Agusfrianto Firdaus Fransiska, Welly Jose Rizal Jose Rizal Jose Jumeysi Herlina Irawan Muhammad Arib Alwansyah Muhammad Arib Alwansyah Arib Muhammad Chairul Imam Novianti Novianti Nur Afandi Nurul Hidayati Putriasari, Novi Rafflesia, Ulfa Ratna Widayati Ratna Widayati Ratna Widayati Rewan Jayadi Rina Filia Sari Rina Widyasari Risfa Fadila Saniyyah, Nabilah Sarumpaet, Mey Yanti Septa, Oon Septri Damayanti Septri Damayanti Setiawan, Aisyah Nooravieta Sigit Nugroho Sigit Nugroho Siska Yosmar Sitohang, Yosep O Suci Rahmawati Wijuniamurti, Susi Yulian Fauzi Yusanti, Linda