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All Journal Proximal: Jurnal Penelitian Matematika dan Pendidikan Matematika J-Intech (Journal of Information and Technology) Causa: Jurnal Hukum dan Kewarganegaraan
Oktavia, Grace
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Humanities Computer Science & IT Decision Sciences, Operations Research & Management Engineering Law, Crime, Criminology & Criminal Justice Library & Information Science Mathematics

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Analisis Perhitungan Retensi Optimal Reasuransi Stop Loss dengan Metode Value at Risk (VaR) Oktavia, Grace; Addini, Fida Fathiyah; Prihandoko, Dedy Irawan
Proximal: Jurnal Penelitian Matematika dan Pendidikan Matematika Vol. 7 No. 1 (2024): Sustainable Development Goal in Mathematics and Mathematics Education
Publisher : Universitas Cokroaminoto Palopo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30605/proximal.v7i1.3322

Abstract

Insurance companies do not cover the entire risk of policyholders. The risk is generally transferred in part to the reinsurance company. Stop loss reinsurance is a form of reinsurance contract where there is a limit on the risk value that can be borne by the insurance company. This value is the retention value or retention limit in the reinsurance contract, which is the maximum risk or insurance value that can be borne by the insurance company. Determining the right retention value is very important. Optimizing the Value at Risk risk measure is one approach in calculating optimal retention. Optimal retention criteria were identified in this research so that optimal retention values ​​could be calculated. This research uses a large claim data sample with the Weibull distribution. Determining optimal retention depends on the distribution of claim sizes and loading factors (additional factors in the insurance policy). With a 95% confidence level, for loading factors of 10%, 15%, and 20%, the estimated optimal retention values ​​are $1,080.56, $1,393.54, and $1,567.20. This means that the risk transferred to the reinsurer is the remaining claim amount, if the claim size exceeds the optimal retention value.
PENERAPAN PRINSIP LEX LOCI CONTRACTUS DALAM KASUS SENGKETA KONTRAK INTERNASIONAL DI INDONESIA Putri, Salsabella Vanisa; Renanda, Shandya Alonso Eka; Oktavia, Grace; Yuniar, Anggita; Setyawan, Dio
Causa: Jurnal Hukum dan Kewarganegaraan Vol. 8 No. 12 (2024): Causa: Jurnal Hukum dan Kewarganegaraan
Publisher : Cahaya Ilmu Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.3783/causa.v8i12.8092

Abstract

Kontrak internasional telah berkembang sebagai hasil dari perluasan substansial perdagangan internasional dan hubungan ekonomi di era globalisasi. Meskipun demikian, perselisihan sering kali terjadi akibat perbedaan kerangka hukum dan latar belakang budaya. Dalam hal penyelesaian sengketa kontrak internasional, gagasan Lex Loci Contractus yang berkaitan dengan hukum di wilayah tempat kontrak dibuat menjadi sangat penting. Mengkaji penggunaan Lex Loci Contractus dalam sengketa kontrak internasional di Indonesia, khususnya dalam kerangka sistem hukum nasional, merupakan tujuan dari penelitian ini. Prinsip-prinsip internasional, variasi sistem hukum, keterbatasan sumber daya, serta pertimbangan politik dan ekonomi akan ditelaah dalam penelitian ini. Dalam rangka memaksimalkan penerapan Lex Loci Contractus, studi ini juga membahas pentingnya memahami hukum internasional, memperkuat hukum domestik, memanfaatkan pilihan ketentuan hukum, dan meningkatkan kemampuan peradilan. Hasil dari penelitian ini memiliki potensi untuk memajukan hukum kontrak Indonesia dan memberikan panduan bagi para pengusaha dan profesional hukum yang terlibat dalam transaksi lintas batas.
Identifikasi Tanda Tangan Dengan Menggunakan Metode Convolution Neural Network (CNN) Indriani.S, Dechy Deswita; Sinaga, Elya Juni Arta; Oktavia, Grace; Syahputra, Hermawan; Ramadhani, Fanny
J-INTECH (Journal of Information and Technology) Vol 12 No 1 (2024): J-Intech : Journal of Information and Technology
Publisher : LPPM STIKI MALANG

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32664/j-intech.v12i1.1273

Abstract

This research aims to develop and evaluate a Convolutional Neural Network (CNN) model for signature identification. The CNN method is chosen for its capability to extract and analyze complex visual features from signature images. The data used in this study consists of a collection of signature images divided into training and testing sets. The proposed CNN model comprises several convolutional, pooling, and fully connected layers optimized for classification tasks. Evaluation results indicate that the CNN model achieves excellent performance with an accuracy of 0.97, demonstrating high accuracy and precision in signature recognition. With these results, CNN proves to be an effective and reliable method for signature identification, making a significant contribution to the field of biometric identity verification. These findings open opportunities for further applications in security and authentication systems requiring automatic signature recognition.
Analisis Perhitungan Retensi Optimal Reasuransi Stop Loss dengan Metode Value at Risk (VaR) Oktavia, Grace; Addini, Fida Fathiyah; Prihandoko, Dedy Irawan
Proximal: Jurnal Penelitian Matematika dan Pendidikan Matematika Vol. 7 No. 1 (2024): Sustainable Development Goal in Mathematics and Mathematics Education
Publisher : Universitas Cokroaminoto Palopo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30605/proximal.v7i1.3322

Abstract

Insurance companies do not cover the entire risk of policyholders. The risk is generally transferred in part to the reinsurance company. Stop loss reinsurance is a form of reinsurance contract where there is a limit on the risk value that can be borne by the insurance company. This value is the retention value or retention limit in the reinsurance contract, which is the maximum risk or insurance value that can be borne by the insurance company. Determining the right retention value is very important. Optimizing the Value at Risk risk measure is one approach in calculating optimal retention. Optimal retention criteria were identified in this research so that optimal retention values ​​could be calculated. This research uses a large claim data sample with the Weibull distribution. Determining optimal retention depends on the distribution of claim sizes and loading factors (additional factors in the insurance policy). With a 95% confidence level, for loading factors of 10%, 15%, and 20%, the estimated optimal retention values ​​are $1,080.56, $1,393.54, and $1,567.20. This means that the risk transferred to the reinsurer is the remaining claim amount, if the claim size exceeds the optimal retention value.
Co-Authors Addini, Fida Fathiyah Hermawan Syahputra Indriani.S, Dechy Deswita Prihandoko, Dedy Irawan Putri, Salsabella Vanisa Ramadhani, Fanny Renanda, Shandya Alonso Eka Setyawan, Dio Sinaga, Elya Juni Arta Yuniar, Anggita