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All Journal JURNAL MATEMATIKA STATISTIKA DAN KOMPUTASI GRADIEN Edumatsains Panrita Abdi - Jurnal Pengabdian pada Masyarakat BAREKENG: Jurnal Ilmu Matematika dan Terapan JURNAL PENGABDI Teorema: Teori dan Riset Matematika Jurnal Pengabdian Masyarakat Bumi Raflesia Sainstek : Jurnal Sains dan Teknologi Jurnal Berdaya Mandiri LOSARI: Jurnal Pengabdian Kepada Masyarakat Jurnal Pengabdian kepada Masyarakat TRIBUTE: JOURNAL OF COMMUNITY SERVICES Jurnal Ilmiah Ilmu Terapan Universitas Jambi Mathematical Journal of Modelling and Forecasting Jurnal Integritas Serasan Sekundang Indonesian Journal of Statistics and Its Applications
Siska Yosmar
Department Of Mathematics, Faculty Of Mathematics And Natural Sciences, Bengkulu University, Indonesia
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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.
LQ45 Stock Portfolio Selection using Black-Litterman Model in Pandemic Time Covid-19 Siska Yosmar; S Damayanti; S Febrika
Indonesian Journal of Statistics and Applications Vol 5 No 2 (2021)
Publisher : Statistics and Data Science Program Study, SSMI, IPB University, in collaboration with the Forum Pendidikan Tinggi Statistika Indonesia (FORSTAT) and the Ikatan Statistisi Indonesia (ISI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29244/ijsa.v5i2p343-354

Abstract

The world was shocked by the emergence of a virus that spread very quickly to several countries including Indonesia at the end of 2019. This virus infection is called Corona Virus Disease 2019 (Covid-19). The outbreak of Covid-19 not only threatens human lives but also disrupts various economic, financial, and business activities, especially in Indonesia. A stock portfolio is a collection of financial assets in a unit that is held or created by an investor, investment company, or financial institution. The Black-Litterman model of the stock portfolio is a portfolio model that involves the CAPM equilibrium return and investor views. The purpose of this study is to determine the stock portfolio with the Black-Litterman model using company data listed in the LQ45 stock index from January 2020 to June 2020. Four of the twenty-nine LQ45 stocks were selected as assets in the stock portfolio. The stock portfolio containing the four stocks, namely ICBP, KLBF, MNCN, and TLKM with the Black-Litterman model resulted in an expected return of 2.07% and a risk of 2.82%.
“Data-Driven Decision Making”: Pengenalan Statistika dan Pemanfaatannya di SMA IT Iqra Kota Bengkulu Firdaus; Ramya Rachmawati; Nurul Hidayati; Septri Damayanti; Siska Yosmar
Jurnal Pengabdian Masyarakat Bumi Rafflesia Vol. 8 No. 1 (2025): APRIL: Jurnal Pengabdian Kepada Masyarakat Bumi Raflesia
Publisher : Universitas Muhammadiyah Bengkulu

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Abstract

Kemampuan mengambil keputusan yang baik merupakan kebutuhan esensial bagi siswa. Terdapat banyak metode dalam mengambil keputusan, salah satunya adalah Data-Driven Decision Making, yaitu pengambilan keputusan berdasarkan analisis data. Pentingnya peran data dalam menentukan pengambilan keputusan berdasarkan data belum utuh dipahami oleh siswa SMA IT Iqra Kota Bengkulu karena kurikulum pada jenjang SMA terbatas pada statistika deskriptif yang meliputi pengenalan ukuran pemusatan, ukuran penyebaran, dan visualisasi data. Program Pengabdian Kepada Masyarakat (PkM) dengan judul “Data-Driven Decision Making”: Pengenalan Statistika dan Pemanfaatannya di SMA IT Iqra Kota Bengkulu bertujuan untuk mengenalkan analisis korelasi dan regresi yang dapat digunakan untuk mendukung pengambilan keputusan. Pelaksanaan PkM dilaksanakan secara klasikal dengan penyampaian materi dan pelatihan langsung dengan memanfaatkan Bahasa pemrograman RStudio. Evaluasi program PkM yang dilakukan dengan memberikan pre-test dan post-test menunjukkan bahwa terdapat perbedaan rata-rata hasil pemahaman sebelum dan setelah mengikuti kegiatan. Hal ini dapat diinterpretasikan bahwa kegiatan PkM memberikan pengaruh terhadap pemahaman siswa pengambilan keputusan berdasarkan analisis data.
ANN-ENHANCED ARIMA MODELS FOR SST-BASED SEASONAL FISH-CATCH FORECASTING AND DECISION-SUPPORT IN BENGKULU WATERS, INDONESIA Rizal, Jose; Afandi, Nur; Rahman, Refpo; Astuti, Mulia; Mayasari, Zulfia Memi; Faisal, Fachri; Yosmar, Siska
Jurnal Ilmiah Ilmu Terapan Universitas Jambi Vol. 10 No. 4 (2026): Volume 10, Nomor 4, August 2026
Publisher : LPPM Universitas Jambi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22437/jiituj.v10i4.55311

Abstract

This study presents a comparative evaluation of hybrid ARIMA-family forecasting models combined with Artificial Neural Networks (ANNs) for seasonal fish-catch prediction in Bengkulu waters, Indonesia, while assessing the role of sea surface temperature (SST) as an environmental predictor. Monthly SST data from NASA’s Giovanni portal and pelagic fish-catch records collected between January 2017 and June 2025 were used to develop ARIMA, ARIMAX, SARIMA, and SARIMAX models, whose residuals were subsequently modeled using Feedforward Neural Networks (FFNN) and Long Short-Term Memory (LSTM) networks to capture nonlinear temporal dependencies. Among the evaluated models, the hybrid ARIMA–LSTM achieved the highest forecasting accuracy on the available dataset, with an RMSE of 76.779 and a MAPE of 19.223%, whereas hybrid models that explicitly incorporate SST as a linear exogenous predictor showed lower predictive performance. These findings suggest that although SST remains an ecologically important environmental driver of pelagic fisheries, its predictive contribution may be better captured by nonlinear, lag-dependent relationships embedded in historical fish-catch observations rather than by contemporaneous linear exogenous modeling. Overall, this study provides empirical evidence for selecting appropriate hybrid forecasting models under practical fisheries data conditions and highlights their potential application as analytical components within fisheries Decision Support Systems (DSS) for adaptive fisheries management.
Co-Authors Acnesya, Vivin Ajeng Siti Nurul Kharima Arita Nurul Khotimah Baki Swita Dian Agustina Dion Raja Kusumah Dwi Miranda, Anggel Dyah Setyo Rini Etis Sunandi Fachri Faisal Fajri Ramadan Fanani Haryo Widodo Firdaus Firdaus Gumaratia, Deriani Herlin Fransiska Hisyam Rahmatullah Akbar Jose Rizal Kelfin Hardiansyah Lidya Savitri marsela, esti Meca Nerdika Moradarma Taksiyah Mulia Astuti Nova Asri Ramdhani Nur Afandi Nur Afandi Nur El Hasanah Nurul Hidayati Ramya Rachmawati Ratna Widayati Refpo Rahman Rewan Jayadi Risfa Fadila S Damayanti S Febrika Septri Damayanti Septri Damayanti Swita, Baki Syahrul Akbar Winalia Agwil Zulfia Memi Mayasari