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All Journal EKSAKTA: Journal of Sciences and Data Analysis Prosiding Seminar Nasional MIPA JURNAL MATEMATIKA STATISTIKA DAN KOMPUTASI Dharmakarya : Jurnal Aplikasi Ipteks Untuk Masyarakat Jurnal Ekonomi & Kebijakan Publik Jurnal Teknik Industri: Jurnal Keilmuan dan Aplikasi Teknik Industri Jurnal Matematika UNAND Variance : Journal of Statistics and Its Applications Jurnal Perspektif Pembiayaan dan Pembangunan Daerah Jurnal Matematika Integratif Indonesian Journal of Statistics and Its Applications Data Enthusiast: Jurnal Ilmiah Sains Data
Lienda Noviyanti
Actuarial Science, Department Of Statistics, Universitas Padjadjaran, Indonesia
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PREMI ASURANSI PENDIDIKAN DENGAN MEMPERHITUNGKAN PENGARUH WAIVER OF PREMIUM DAN RETURN OF CASH VALUE Suherman, Sandi Nurhibatulloh; ZANBAR SOLEH, ACHMAD; NOVIYANTI, LIENDA; INDRAYATNA, FAJAR
Jurnal Matematika UNAND Vol. 12 No. 3 (2023)
Publisher : Departemen Matematika dan Sains Data FMIPA Universitas Andalas Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25077/jmua.12.3.244-257.2023

Abstract

Asuransi pendidikan adalah kombinasi antara tabungan pendidikan dan asuransi jiwa berjangka. Asuransi Pendidikan melibatkan dua orang yaitu orang tua (Ayah/Ibu) sebagai tertanggung dan anak sebagai penerima beasiswa. Asuransi Pendidikan memberikan manfaat berupa (1) dana tabungan pendidikan yang akan diberikan dalam 4 periode waktu yaitu di akhir tahun polis saat anak berusia 5 tahun, 11 tahun, 14 tahun, dan 17 tahun, (2) manfaat proteksi jiwa apabila orang tua meninggal dunia dalam masa asuransi. Pada penelitian ini akan memperhitungkan pengaruh manfaat lain yaitu (3) waiver of premium yang merupakan polis bebas premi apabila orang tua meninggal dunia, namun manfaat tetap dibayarkan sesuai kesepakatan awal, dan (4) return of cash value apabila anak yang dibeasiswakan meninggal dunia. Penelitian ini menentukan besaran premi kotor tahunan didasarkan pada konsep fully-discrete dengan anuitas joint-life. Dengan penambahan manfaat waiver of premium dan return of cash value akan meningkatkan premi kotor menjadi lebih mahal, akan tetapi manfaat yang diberikan sesuai dengan kebutuhan pemegang polis.
Predicting future inflation in Indonesia using Dynamic Model Averaging (DMA) Sari, Shania Puspita; Ginanjar, Irlandia; Noviyanti, Lienda
Jurnal Perspektif Pembiayaan dan Pembangunan Daerah Vol. 12 No. 2 (2024): Jurnal Perspektif Pembiayaan dan Pembangunan Daerah
Publisher : Program Magister Ilmu Ekonomi Pascasarjana Universitas Jambi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22437/ppd.v12i2.31817

Abstract

The features of Indonesia's inflation data, which make it extremely susceptible to shocks like those felt in 2005 and 2008, as well as extensive potential influencing factors, lead to problems in forecasting inflation. These problems include time variation in coefficients, models that can change over time, and many predictors to consider. Dynamic Model Averaging (DMA) solves these problems since it has evolved coefficients and models that change over time. This study uses DMA to predict future inflation by involving eight macroeconomic indicators as exogenous variables. The results of the in-sample analysis show that six predictors are significant in forecasting inflation, with posterior inclusion probability (PIP) being above 40%. Although the remaining predictors have PIP means below 40%, they can still be considered important. The out-of-sample results suggest that DMA performs better than dynamic model selection and models that don’t include exogenous variables, such as autoregressive models. The forecast results indicate a consistent pattern over the 12 months studied. The attempt to control inflation can be achieved by prioritizing the money supply factor, which has the highest PIP value, indicating that it is the most important factor.
Skema Dana Pensiun Menggunakan Matriks Transisi Markov Fajar Indrayatna; Hasna Afifah Rusyda; Achmad Zanbar Soleh; Lienda Noviyanti
Data Enthusiast: Jurnal Ilmiah Sains Data Vol. 1 No. 1 (2025): Data Enthusiast: Jurnal Ilmiah Sains Data
Publisher : Fakultas Sains dan Teknologi Universitas Koperasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Makalah ini memodelkan skala gaji yang akan digunakan dalam menentukan gaji terakhir seorang karyawan berdasarkan matriks peluang transisi markov . Gaji terakhir ini akan dijadikan dasar penentuan manfaat pensiun yang akan diberikan setiap tahun setelah memasuki usia pensiun hingga meninggal dunia. Skema dana pensiun yang dirancang akan melibatkan penentuan biaya normal yang disesuaikan akibat supplemental liability. Supplemental liability ini terjadi karena adanya perubahan kebijakan yaitu: Perbedaan pemodelan skala gaji sebelumnya dan perubahan manfaat pensiun yang meningkat setiap tahunnya yang mengakibatkan increasing annuity.
Estimation of Value at Risk by Using GJR-GARCH Copula Based on Block Maxima Hasna Afifah Rusyda; Fajar Indrayatna; Lienda Noviyanti
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.v5i2p405-414

Abstract

This paper will discuss the risk estimation of a portfolio based on value at risk (VaR) using a copula-based asymmetric Glosten – Jagannathan – Runkle - Generalized Autoregressive Conditional Heteroskedasticity (GJR-GARCH). There is non-linear correlation for dependent model structure among the variables that lead to the inaccurate VaR estimation so that we use copula functions to model the joint probability of large market movements. Data is GEV distributed. Therefore, we use Block Maxima consisting of fitting an extreme value distribution as a tail distribution to count VaR. The results show VaR can estimate the risk of portfolio return reasonably because the model has captured the data properties. Data volatility can be accommodated by GJR-GARCH, Copula can capture dependence between stocks, and Block maxima can accommodate extreme tail behavior of the data.
Identifying stock market regimes in Indonesia's non-primary consumer goods sector using a Gaussian Hidden Markov Model Hartanti, Anisah Dwi; Nathanael, Jovanka; Sunaga, Jessica Aurelia; Nadhifah, Fairuz; Noviyanti, Lienda
Jurnal Perspektif Pembiayaan dan Pembangunan Daerah Vol. 14 No. 4 (2026): Jurnal Perspektif Pembiayaan dan Pembangunan Daerah (In Progress)
Publisher : Program Magister Ilmu Ekonomi Pascasarjana Universitas Jambi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22437/ppd.v14i4.55906

Abstract

Stock price movements in Indonesia's non-primary consumer goods sector reflect a complex interaction between market sentiment and macroeconomic fluctuations. This study identifies latent market phases and evaluates price forecasting for three selected stocks—MNCN, FAST, and ACES—which span different sub-industries and liquidity profiles, using a Gaussian Hidden Markov Model (GHMM) calibrated on daily open, high, low, and close (OHLC) prices over 620 trading days (January 2024–August 2026). Model selection via the Bayesian Information Criterion, assessed through re-estimation with multiple random seeds to examine convergence stability, identifies five latent states for MNCN and six for both FAST and ACES, indicating that regime structure varies across issuers even within the same sector. A likelihood-based forecasting procedure is then evaluated over an extended out-of-sample period of 124 trading days (February–August 2026), yielding mean absolute percentage error (MAPE) values of 1.79% for MNCN, 4.65% for FAST, and 6.22% for ACES. However, the GHMM forecasts did not outperform a random-walk benchmark for any of the three stocks. The findings therefore highlight the GHMM's value in characterising issuer-specific market regimes and monitoring risk, rather than improving short-horizon point forecasts.
Co-Authors Achmad Zabar Soleh Achmad Zanbar Soleh, Achmad Zanbar Adri Arisena Anna Chadidjah David Eurico FAJAR INDRAYATNA Fajar Indrayatna Hartanti, Anisah Dwi Hasna Afifah Rusyda Irlandia Ginanjar Krisdiantha Krisdiantha M. Jibril Khalifatullah Muhammad Syamsuddin Nadhifah, Fairuz Nathanael, Jovanka Rizki Maulana Sandi Nurhibatulloh Suherman Sandra Kezia Sari, Shania Puspita Sibarani, Triana Sucova Suherman, Sandi Nurhibatulloh Sunaga, Jessica Aurelia Tione Daffaxa Dumamika Triana Sucova Sibarani