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All Journal Jurnal Cendekia : Jurnal Pendidikan Matematika EduMatika: Jurnal MIPA Economic: Journal Economic and Business Jurnal Cendekia Ilmiah
Gizka Triyunita Sinaga
Universitas Negeri Medan
Humanities Chemistry Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Mathematics Physics Social Sciences

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Prediksi Pertumbuhan Jumlah Penduduk Indonesia Menggunakan Interpolasi Polinomial Lagrange Michael Dolly Sianturi; Mery Christyn Lubis; Gizka Triyunita Sinaga; Jogi Nicholas Manihuruk
J-CEKI : Jurnal Cendekia Ilmiah Vol. 4 No. 2: Februari 2025
Publisher : CV. ULIL ALBAB CORP

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56799/jceki.v4i2.6766

Abstract

Penelitian ini bertujuan untuk memprediksi jumlah penduduk Indonesia pada periode 2016-2024 dengan menggunakan metode Interpolasi Polinomial Lagrange. Data populasi diperoleh dari Badan Pusat Statistik (BPS) yang kemudian dianalisis menggunakan perangkat lunak MATLAB. Hasil simulasi menunjukkan pola pertumbuhan yang stabil dengan rata-rata peningkatan jumlah penduduk sebesar 0.54% setiap enam bulannya. Metode intepolasi ini memungkinkan estimasi jumlah penduduk pada interval antar tahun yang tidak tersedia dalam data asli, sehingga memberikan wawasan komprehensif terkait tren demografis di Indonesia. Penelitian ini menunjukkan bahwa interpolasi polinomial Lagrange merupakan alat yang andal untuk prediksi populasi berdasarkan data historis, yang dapat menjadi dasar untuk pengambilan kebijakan strategis dalam pengelolaan sumber daya dan perencanaan infrastruktur.
Integrating garch, markowitz mean–variance, and lstm for risk, volatility, and portfolio analysis of four major Indonesian banking stocks 2015 – 2024 Risca Octaviyani Hutapea; Gizka Triyunita Sinaga; Ardicha Appu Sianturi
Economic: Journal Economic and Business Vol. 5 No. 1 (2026): ECONOMIC: Journal Economic and Business
Publisher : Lembaga Riset Mutiara Akbar (LARISMA)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56495/ejeb.v5i1.1401

Abstract

This study examines the risk structure, volatility behavior, and optimal portfolio construction of four major banking stocks in Indonesia BBCA, BBRI, BMRI, and BBNI during the 2015–2024 period through the integration of the GARCH(1,1) model, Markowitz Mean–Variance optimization, and Long Short-Term Memory (LSTM)-based forecasting. Daily closing price data is transformed into log returns and tested for stationarity before further analysis. The GARCH estimation results indicate persistent high volatility across all stocks (?+? close to 1), with BBNI and BBRI the most responsive to market shocks, while BBCA remains the most stable stock. Markowitz optimization produces a Minimum Variance portfolio dominated by BBCA, while the Maximum Sharpe portfolio allocates funds entirely to BBCA due to its superior return efficiency. The LSTM is able to represent price trends well, as evidenced by low prediction error values for BBRI and BBNI and accuracy between 58–61 percent. The integration provides a comprehensive analytical framework for understanding changing market risk dynamics and supporting adaptive investment decision-making in the Indonesian banking sector. These findings confirm that the hybrid approach can improve risk mapping while maximizing portfolio performance through a combination of historical information, dynamic volatility, and price trend predictions.
Analisis Tabel Mortalitas Penduduk Indonesia (TMPI) 2023: Pendekatan Survival, Valuasi Aktuaria, dan Evaluasi Actual-to-Expected (A/E) Ratio M. Shadri Ismaun Lubis; Hafiz Khalik Lubis; Gizka Triyunita Sinaga
Jurnal Cendekia : Jurnal Pendidikan Matematika Vol 10 No 2 (2026): Jurnal Cendekia: Jurnal Pendidikan Matematika Volume 10 Nomor 2 Tahun 2026
Publisher : Mathematics Education Study Program

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/cendekia.v10i2.4915

Abstract

Asumsi mortalitas yang tepat merupakan fondasi perhitungan premi dan cadangan asuransi jiwa; namun belum ada penelitian yang secara bersamaan menghubungkan pemodelan survival, valuasi aktuaria komprehensif, dan evaluasi kualitas tabel menggunakan TMPI 2023. Penelitian ini bertujuan menganalisis TMPI 2023 yang diterbitkan BPJS Kesehatan melalui kerangka enam tahap terintegrasi mencakup pemodelan fungsi survival ( ) dan force of mortality ; valuasi anuitas jiwa diskrit dan kontinu ( ); valuasi asuransi jiwa seumur hidup ( ); pembentukan premi bersih berdasarkan prinsip ekuivalens; formulasi nilai tebus; serta perhitungan cadangan prospektif via 2 rekursi Thiele dengan stress test berbasis Actual-to-Expected (A/E) Ratio. Data sekunder TMPI 2023 dianalisis menggunakan Python dengan asumsi suku bunga = 6,00% (BI Rate Desember 2023) dan asumsi UDD (Uniform Distribution of Deaths). Usia harapan hidup perempuan ( = 78,37 tahun) lebih tinggi 4,63 tahun dari laki-laki ( = 73,74 tahun), menghasilkan selisih premi bersih 22,6% (Rp 6.818,78 vs. Rp 8.360,86 per Rp 1.000.000). Evaluasi A/E Ratio 2018-2022 mengungkap excess mortality neonatal yang persisten (puncak 2020: LK=2,76; PR=2,80) dan rata-rata A/E lima tahun di bawah satu untuk kedua gender, mengindikasikan longevity risk sistemik. Stress test menghasilkan deviasi cadangan hingga -5,57% pada tahun polis ke-29, mengonfirmasi bahwa TMPI 2023 tanpa kalibrasi pengalaman aktual cenderung over-reserved jangka panjang namun under-reserved pada usia neonatal, dengan implikasi langsung bagi kecukupan solvabilitas industri asuransi jiwa Indonesia.
Modeling inflation in indonesia using the adf stationarity test, garch, and random forest regression based on monthly consumer price index data from 2014 to 2024 Ardicha Appu Sianturi; Risca Octaviyani Hutapea; Gizka Triyunita Sinaga; Enjelita Simangunsong
EduMatika: Jurnal MIPA Vol. 5 No. 3 (2025): EduMatika: Jurnal MIPA
Publisher : Lembaga Riset Mutiara Akbar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56495/emju.v5i3.1426

Abstract

Inflation is one of the key macroeconomic indicators that reflects price stability and overall economic conditions of a country. This study aims to examine the characteristics, volatility, and forecasting of monthly inflation in Indonesia over the period 2014–2024 by employing descriptive statistical analysis, time series approaches, and machine learning methods. The descriptive results indicate that Indonesia’s inflation has an average rate of 1.71% with a standard deviation of 1.32, a minimum value of 0.04%, and a maximum value of 8.36%. The inflation distribution is asymmetric, as evidenced by a positive skewness of 1.88 and a kurtosis value of 5.15, suggesting the presence of extreme inflation spikes during certain observation periods. Time series analysis reveals that the inflation data do not fully satisfy the stationarity assumption at the level form, thus requiring differencing prior to further modeling. Volatility estimation using the GARCH model indicates the existence of inflation volatility persistence. Furthermore, forecasting results using the Random Forest method demonstrate that the model is able to closely track actual inflation patterns under normal conditions, although predictive accuracy declines during periods of heightened volatility. Overall, this integrated approach provides a more comprehensive understanding of the dynamics of inflation in Indonesia.
Co-Authors Ardicha Appu Sianturi Enjelita Simangunsong Hafiz Khalik Lubis Jogi Nicholas Manihuruk M. Shadri Ismaun Lubis Mery Christyn Lubis Michael Dolly Sianturi Risca Octaviyani Hutapea