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All Journal Jurnal Matematika dan Statistika serta Aplikasinya (Jurnal MSA) Euler : Jurnal Ilmiah Matematika, Sains dan Teknologi Enthusiastic : International Journal of Applied Statistics and Data Science Jurnal Pengabdian Masyarakat Bangsa Journal of Oikonomia Jurnal KALANDRA
Simanjuntak, Erica Grace
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Premium Estimation Using a Spliced Gamma-Gamma Distribution for Long-Tail Insurance Claims Simanjuntak, Erica Grace; Madonna, Nora; Hayati, Ma'rufah
Jurnal MSA (Matematika dan Statistika serta Aplikasinya) Vol 13 No 2 (2025): VOLUME 13 NO 2, 2025
Publisher : Universitas Islam Negeri Alauddin Makassar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24252/msa.v13i2.60648

Abstract

Determining fair premiums that accurately reflect actual risks is a crucial element in insurance risk management, particularly when claim data exhibits long-tail characteristics that are challenging to model using a single distribution. This study aims to develop a premium estimation model using the spliced Gamma-Gamma distribution, which can capture the behavior of small to large claims more flexibly. This model is applied to a collective risk model framework, focusing on calculating the expected value and variance of aggregate claims as the basis for premium estimation. Premium estimation is conducted using three actuarial principles: the expected value principle, the variance principle, and the standard deviation principle. The research indicates that the standard deviation principle yields the most accurate premium estimation, as it accurately reflects the risk level while striking a balance between premium adequacy and affordability for policyholders. This approach considers both the expected loss and its volatility, making it more adaptive to extreme claim risks. This study demonstrates that claim modelling using splicing distributions, combined with volatility-based premium estimation principles, can be a practical and realistic approach to managing risk and estimating premiums more accurately.
Pelatihan Pemanfaatan Looker Studio dalam Analisis Data dan Dashboard Statistik bagi Peningkatan Kompetensi Siswa SMKS Nurul Huda Pringsewu Rosni; Mahrani, Dwi; Fitriawati , Andi; Sofia, Ayu; Yulita, Tiara; Irawan, Agus; Mt, Ma’rufah Hayati; Mahkya, Dani Al; Nasrullah; Simanjuntak, Erica Grace; Irfan, Miftahul; Madonna, Nora; Alfian, Muhammad Nuril; Siregar, Abian Avisena; Lestari, Yushinta Cahya
KALANDRA Jurnal Pengabdian Kepada Masyarakat Vol 4 No 6 (2025): November
Publisher : Yayasan Kajian Riset Dan Pengembangan Radisi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55266/jurnalkalandra.v4i6.605

Abstract

This Community Service (PkM) program aims to enhance students’ competencies in data analysis and statistical dashboard management through the utilization of the Looker Studio application. The training was conducted at SMKS Nurul Huda Pringsewu, involving students as participants. The training methods included lectures, demonstrations, and hands-on practice in processing data and presenting it in the form of interactive dashboards. The results of the program showed that students were able to understand the basic concepts of data exploration, the purpose of data visualization, and the use of key features in Looker Studio. In addition, students’ skills in selecting appropriate chart types according to analytical needs improved significantly. Based on the satisfaction survey, most participants rated the activity as very satisfactory (63%) and satisfactory (16%), although a small proportion expressed dissatisfaction (16%) or were not satisfied (5%). Overall, this PkM activity successfully contributed to improving students’ data literacy and digital skills, which are expected to support them in facing both academic challenges and the demands of a data-driven workforce
Pemanfaatan Looker Studio untuk Mengembangkan Kompetensi Analisis dan Visualisasi Data Siswa SMKS Nurul Huda Pringsewu Rosni, Rosni; Madonna, Nora; Fitriawati, Andi; Al Mahkya, Dani; Irawan, Agus; Simanjuntak, Erica Grace; Hayati, Ma’rufah; Nasrullah, Nasrullah; Irfan, Miftahul; Mahrani, Dwi; Sofia, Ayu; Yulita, Tiara; Rivai, Muklas
Jurnal Pengabdian Masyarakat Bangsa Vol. 4 No. 1 (2026): Maret
Publisher : Amirul Bangun Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59837/jpmba.v4i1.4194

Abstract

Kemampuan dalam memanfaatkan teknologi, khususnya dalam eksplorasi dan visualisasi data, menjadi tantangan signifikan dalam dunia pendidikan di era digital saat ini. SMKS Nurul Huda Pringsewu sebagai mitra kegiatan pengabdian menunjukkan adanya keterbatasan kompetensi siswa dalam mengolah data dan menyajikannya dalam bentuk visual yang informatif. Menanggapi permasalahan tersebut, tim Pengabdian kepada Masyarakat (PkM) melaksanakan pelatihan menggunakan aplikasi Looker Studio untuk eksplorasi data dan pembuatan dashboard statistik. Looker Studio merupakan platform berbasis web yang memudahkan pengguna dalam mengolah data numerik dan menyajikannya secara visual, sehingga proses pembacaan data tidak lagi dilakukan secara manual. Pelatihan ini bertujuan untuk meningkatkan kemampuan siswa dalam mengolah dan memvisualisasikan data menggunakan teknologi terkini. Hasil dari kegiatan ini menunjukkan peningkatan kompetensi siswa dalam mengeksplorasi data statistik serta peningkatan kualitas pembuatan dashboard yang lebih informatif dan menarik. Kegiatan ini diharapkan dapat menjadi langkah awal dalam membekali siswa dengan keterampilan digital yang relevan dengan kebutuhan industri saat ini.
Optimasi Penentuan Basis Risiko pada Jaringan Saham Keuangan Menggunakan Dimensi Metrik untuk Estimasi Value at Risk Saefulloh, Annisa Hevita Gustina Kumalasari; Baiti, Putri Isnaini Cahyaning; Simanjuntak, Erica Grace; Latifah, Rahmatika Zaqiatul
Euler : Jurnal Ilmiah Matematika, Sains dan Teknologi Volume 14 Issue 1 April 2026
Publisher : Universitas Negeri Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37905/euler.v14i1.37004

Abstract

This study aims to optimize systemic risk monitoring in the Indonesian financial sector network by determining the minimum risk basis using the Metric Dimension concept. The high complexity of inter-asset correlations requires a dimension reduction method that maintains structural information regarding risk exposure. Daily stock price data from 10 financial issuers (banking, insurance, and financing) for the five-year period from January 1, 2021, to December 31, 2025, were used. The data were transformed into a weighted graph through a log-return correlation matrix converted into metric distances. The resolving set (W) was determined using a greedy algorithm to identify the optimal basis. Validation was performed by analyzing the correlation between the metric coordinates of each issuer and its 95% Value at Risk (VaR). The results showed that the financial network has a metric dimension of dim(G) = 1, with ADMF.JK selected as the optimal resolving set (basis). Actuarial validation revealed a significant negative correlation (−0.5495) between the metric distance and VaR. This implies that the metric distance from the basis can linearly map the magnitude of market risk, offering an efficient strategy for investment managers to monitor portfolio stability through a single reference entity.
Analisis Volatilitas nilai Tukar USD/IDR dan Impikasinya terhadap Stabilitas Ekonomi Indonesia:Pendekatan GARCH dan Hybrid ARIMA-ANN Miftahul Irfan; Nora Madonna; Erica Grace Simanjuntak
Oikonomia Vol 4 No 1 (2026): March
Publisher : Edu Partner Indonesia

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

Abstract

Penelitian ini bertujuan untuk menganalisis volatilitas serta mengevaluasi kinerja peramalan return nilai tukar USD/IDR menggunakan model GARCH dan pendekatan hybrid ARIMA-ANN. Data yang digunakan adalah data harian periode 2010–2025 yang diperoleh dari Yahoo Finance. Metode analisis meliputi transformasi log return, uji stasioneritas menggunakan Augmented Dickey-Fuller (ADF), uji ARCH untuk mendeteksi heteroskedastisitas, serta estimasi model GARCH(1,1) untuk memodelkan volatilitas. Selanjutnya, model ARIMA digunakan untuk menangkap pola linear, sementara residualnya dimodelkan menggunakan Artificial Neural Network (ANN) untuk membentuk model hybrid. Hasil penelitian menunjukkan bahwa data return bersifat stasioner dan mengandung efek ARCH yang signifikan. Estimasi GARCH menunjukkan bahwa volatilitas bersifat sangat persisten dengan nilai α₁ + β₁ mendekati satu. Perbandingan kinerja model menunjukkan bahwa hybrid ARIMA-ANN menghasilkan nilai RMSE yang lebih rendah dibandingkan ARIMA, yang mengindikasikan peningkatan akurasi peramalan. Temuan ini menunjukkan bahwa dinamika nilai tukar tidak hanya dipengaruhi oleh pola linear, tetapi juga oleh hubungan nonlinear yang kompleks, sehingga pendekatan hybrid lebih efektif dalam meningkatkan akurasi prediksi. This study aims to analyze volatility and evaluate the forecasting performance of the USD/IDR return using the GARCH model and a hybrid ARIMA-ANN approach. The data used are daily exchange rate data from 2010 to 2025 obtained from Yahoo Finance. The analysis includes log return transformation, stationarity testing using the Augmented Dickey-Fuller (ADF) test, and ARCH testing to detect heteroskedasticity. The GARCH(1,1) model is employed to capture volatility dynamics. Furthermore, the ARIMA model is used to capture linear patterns, while its residuals are modeled using Artificial Neural Networks (ANN) to form a hybrid model. The results indicate that the return data are stationary and exhibit significant ARCH effects. The GARCH estimation shows that volatility is highly persistent, with α₁ + β₁ approaching one. The comparison results demonstrate that the hybrid ARIMA-ANN model produces a lower RMSE than the ARIMA model, indicating improved forecasting accuracy. These findings suggest that exchange rate dynamics are influenced not only by linear patterns but also by complex nonlinear relationships, making the hybrid approach more effective in enhancing prediction accuracy.
Utilizing Geographically Weighted Regression with a Gaussian Kernel to Analyze Unemployment Hayati, Ma'rufah; Madonna, Nora; Simanjuntak, Erica Grace; Nikmah, Rohmatun
Enthusiastic : International Journal of Applied Statistics and Data Science Volume 6 Issue 1, April 2026
Publisher : Universitas Islam Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20885/enthusiastic.vol6.iss1.art3

Abstract

Unemployment is a major challenge in economic development, reflecting an imbalance between labor supply and available job opportunities. This study aimed to examine the spatial variation of factors influencing the open unemployment rate (OUR) in Lampung Province, Indonesia, and to compare the performance of a global regression model with the geographically weighted regression (GWR) model in explaining these variations. The GWR method, using a fixed Gaussian kernel, was applied to capture spatial heterogeneity across regions. Secondary data were obtained from the Statistics Indonesia of Lampung Province in 2023, including economic growth (EG), human development index (HDI), and labor force participation rate (LFPR). The results showed that in the global regression model, LFPR was the only variable that significantly reduced unemployment, while EG and HDI were not statistically significant. The Breusch–Pagan test confirmed spatial heterogeneity, supporting the use of the GWR. The GWR model performed better, with Akaike information criterion (AIC) of 40.8262 and R² of 0.6059. Spatial analysis indicated that EG and HDI positively affected unemployment in several districts, suggesting limited job absorption and possible skill mismatches, whereas LFPR consistently showed a negative relationship with the open unemployment rate (OUR) across regions.
Co-Authors Agus Irawan Alfian, Muhammad Nuril Andi Fitriawati Ayu Sofia Baiti, Putri Isnaini Cahyaning Dani Al Mahkya Dwi Mahrani Fitriawati , Andi Hayati MT, Ma’rufah Irfan, Miftahul Latifah, Rahmatika Zaqiatul Lestari, Yushinta Cahya Madonna, Nora Mt, Ma’rufah Hayati Nasrullah Nasrullah Nasrullah Nikmah, Rohmatun Nora Madonna Rivai, Muklas Rosni, Rosni Saefulloh, Annisa Hevita Gustina Kumalasari Siregar, Abian Avisena Yulita, Tiara