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All Journal BAREKENG: Jurnal Ilmu Matematika dan Terapan Jurnal Pemberdayaan Masyarakat Indonesia (JPMI)
Joewita, Vanessa
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Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Energy Engineering Mathematics Mechanical Engineering Physics Social Sciences Transportation

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Pemberdayaan Siswa SMK Negeri 2 Tasikmalaya melalui Optimalisasi Pemasaran Bengkel Teaching Factory Kusmulyono, M. Setiawan; Yahya, Alessandro Stefan; Wen, Audric; Natalia, Cindy; Ryan, Karen; Vincent, Nathanael; Fauzi, Olivia; Joewita, Vanessa
Journal Pemberdayaan Masyarakat Indonesia Vol 3 No 1 (2021): Jurnal Pemberdayaan Masyarakat Indonesia (JPMI)
Publisher : Pusat Pengabdian kepada Masyarakat (PPKM) Universitas Prasetiya Mulya

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (3019.41 KB) | DOI: 10.21632/jpmi.3.1.56-77

Abstract

Penelitian ini bertujuan untuk mengetahui bagaimana kegiatan program kemitraan Community Development dari Universitas Prasetiya Mulya yang dijalankan oleh Kelompok B006, dan secara deskriptif menggambarkan serta menjelaskan hasil evaluasi dari program tersebut. Penelitian ini menggunakan metode penelitian deskriptif kualitatif dan pengumpulan data yang diperoleh melalui wawancara, observasi, dan dokumentasi. Community Development merupakan program yang wajib dilaksanakan karena program ini merupakan bagian dari penilaian mahasiswa, sehingga ada kelemahan pada program ini yaitu dengan waktu yang bersamaan dengan pandemi COVID-19, maka program ini tidak dapat mempertemukan kedua belah pihak, Kelompok B006 dan mitra SMKN 2 Tasikmalaya. Namun, hasil penelitian menunjukan bahwa kegiatan program kemitraan pemberdayaan usaha mikro, kecil, dan menengah (UMKM) ini dapat dikatakan berhasil. Hal ini dapat dilihat dari tercapainya tujuan dari perencanaan program yang bervariasi yaitu semakin meningkatnya usaha mitra (bengkel otomotif) yang mendapat bantuan dari program kemitraan, baik melalui bantuan modal, pelatihan serta keterampilan kepada mitra usaha, guna mengembangkan usaha bengkel otomotif yang telah ada. Teori yang digunakan dalam penelitian ini mencakup segala teori bisnis terkait pemasaran, keuangan, sumber daya manusia, dsb.
APPLICATION OF EXPECTED CREDIT LOSS MODEL AND MARKOV CHAIN TO CALCULATE NET SINGLE PREMIUM OF UNSECURED CREDIT INSURANCE Lieus, Hansen Juni; Tedja, Devin; Joewita, Vanessa; Hidayat, Agus Sofian Eka; Silalahi, Alexander R. J
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 17 No 4 (2023): BAREKENG: Journal of Mathematics and Its Applications
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol17iss4pp2161-2170

Abstract

Transferring credit risk to an insurance company is a way to mitigate risk. Premiums should be calculated accurately to attain economic value for both the lender and the guarantor. The aim of this study was to determine the net single premium (NSP) values for an unsecured credit insurance product using the expected credit loss (ECL) method from IFRS 9. This study used data generated through simulation of insurance policies issued in 2015 or 2016. Their state classifications were monthly observed from 2016 to 2020. The probability of disbursed claim (PDC) parameter replaced the probability of default parameter on the ECL model, whereas the PDC model was constructed based on the components of a state-transition probability matrix, obtained with the Markov chain approach using the cohort method: = 0.999181, = 0.000130, and = 0.000689. The PDC model validation showed relatively decent results, whereas MSE = 2.457% and zs = 0.608 with a = 5%. These results indicated that the PDC model was a good fit to calculate ECL. 5,000 iterations were done as part of the cash flow simulation process, whereas debtors’ loan amounts were randomly generated during each iteration, and the average NPV of these iterations was -Rp564.419.305. Based on model sensitivity analysis, cash flow values were most sensitive to the variable used to construct the PDC model (). Thus, the 5,000-iteration process was repeated with the newly adjusted PDC value, which were = 0.998924 and = 0.000946. The new average NPV of these iterations was Rp409,877,840, indicating that the constructed ECL model was a good fit to calculate NSP values for unsecured credit insurance products.
Co-Authors Fauzi, Olivia Hidayat, Agus Sofian Eka Lieus, Hansen Juni Muhammad Setiawan Kusmulyono Natalia, Cindy Ryan, Karen Silalahi, Alexander R. J Tedja, Devin Vincent, Nathanael Wen, Audric Yahya, Alessandro Stefan