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All Journal KUBIK: Jurnal Publikasi Ilmiah Matematika Enthusiastic : International Journal of Applied Statistics and Data Science RENATA Jurnal Pengabdian Masyarakat Kita Semua Parameter: Jurnal Matematika, Statistika dan Terapannya TeknoKreatif: Jurnal Pengabdian kepada Masyarakat Indonesian Actuarial Journal
Andirasdini, Indah Gumala
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Humanities Computer Science & IT Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Mathematics Physics Other

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Implementasi Metode Fuzzy Black-Scholes Real Options Valuation pada Rencana Investasi Smelter Nikel Jalaludin, Paiz; Rahman, Alrafiful; Andirasdini, Indah Gumala
KUBIK Vol 8, No 2 (2023): KUBIK: Jurnal Publikasi Ilmiah Matematika
Publisher : Jurusan Matematika, Fakultas Sains dan Teknologi, UIN Sunan Gunung Djati Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15575/kubik.v8i2.29738

Abstract

Indonesia is the largest nickel producing country in the world in 2022 by contributing 48.48% of the world's total nickel production. Therefore, the Indonesian government pays attention to the development of smelter companies by planning to build around 53 companies in 2024, and 56.60 percent of them are nickel smelters. The potential of the nickel smelter needs attention from various circles, especially academics from various disciplines. One that needs attention is the study of the method of evaluating the economic value of the investment plan in the nickel smelter company. The DCF method, although practical and widely used, still has a drawback, which is that it does not pay attention to the flexibility of managers' decision-making in the middle of the ongoing investment period. As a solution, the real options valuation (ROV) method provides flexibility features in making these decisions. Among the real options methods that are often used is the Black-Scholes formula which is considered the most rigid but more practical real options method. However, this problem can be overcome by implementing the fuzzy number method into the ROV method, making it more flexible. The results of this study show that the fuzzy Black-Scholes ROV method is a practical method, can calculate the risks and projects flexibility, and become a solution when initial information is less available about the characteristics of nickel smelter investment projects.
Pelatihan Pengolahan dan Analisa Data Statistik Untuk Meningkatkan Kompetensi Guru SMPN di Kalianda, Lampung Selatan Andirasdini, Indah Gumala; Sofia, Ayu; Lestari, Fuji; Listiani, Amalia; Yulita, Tiara; Julianty, Dila Tirta; Rivai, Muklas
TeknoKreatif: Jurnal Pengabdian kepada Masyarakat Vol 4 No 2 (2024): TEKNOKREATIF : Jurnal Pengabdian kepada Masyarakat Volume 4 No 2
Publisher : Lembaga Penelitian dan Pengabdian Kepada Masyarakat (LP2M), Institut Teknologi Sumatera, Lampung, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35472/teknokreatif.v4i2.1875

Abstract

Salah satu wujud pengembangan diri seorang guru adalah dengan melakukan dan menulis laporan penelitian. Penelitian dan penulisan laporan hasil penelitian terkait kinerja pembelajaran seorang guru merupakan salah satu upaya evaluasi (refleksi) terhadap kinerja seorang guru di dalam kelas. Evaluasi ini dilakukan dengan melaksanakan Penelitian Tindakan Kelas (PTK). Permasalahan yang terjadi pada PTK adalah kurangnya kompetensi guru dalam melakukan pengolahan data, menggunakan tools, dan menerapkan model-model statistika yang cocok untuk kasus-kasus yang terjadi di kelas. Pada jurnal ini membahas peningkatan kompetensi guru-guru SMPN melalui pelatihan olah data dan analisis data statistik menggunakan JASP. Tingkat pemahaman dan kompetensi guru diukur menggunakan kuesioner yang diberikan pada sebelum dan setelah melakukan pelatihan. Pelatihan ini memberikan perubahan tingkat pemahaman dan kompetensi yang signifikan berdasarkan bidang kompetensi yang ditanyakan. Hal ini dapat dilihat dari gap responden yang diperoleh saat sebelum pelatihan dan setelah pelatihan dilakukan.
Literasi Pemanfaatan Software JASP Untuk Meningkatkan Keterampilan Statistik Guru di MAN 1 Bandar Lampung Andirasdini, Indah Gumala; Sofia, Ayu; Rivai, Muklas; Mahrani, Dwi; Yulita, Tiara; Irwan, Sri Efrinita; Berliana Ratam, Aldila Nur Indah; Gustina K.S., Annisa Hevita; Dewi, Karina Sylfia; Marisa, Marisa; Azzanina, Nanda; Baiti, Putri Isnaini Cahyaning; Rosni, Rosni
RENATA: Jurnal Pengabdian Masyarakat Kita Semua Vol. 3 No. 1 (2025): Renata - April 2025
Publisher : PT Berkah Tematik Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61124/1.renata.147

Abstract

Guru sebagai agen perubahan memiliki peran strategis dalam mengembangkan literasi digital di lingkungan kerja. Salah satu aspek penting dalam literasi digital adalah kemampuan dalam memanfaatkan teknologi dan aplikasi digital untuk mendukung proses pembelajaran dan pengolahan data. Pemanfaatan software JASP (Jeffreys's Amazing Statistics Program) menjadi salah satu cara efektif bagi guru untuk meningkatkan keterampilan statistik seperti mengolah dan menganalisis data. Dengan memanfaatkan JASP, guru dapat melakukan analisis statistik secara intuitif dan efisien, sehingga memudahkan dalam mengajarkan konsep-konsep statistik kepada siswa. Pengabdian dalam bentuk literasi pemanfaatan software JASP ini didasari oleh kebutuhan mendesak akan kemampuan memahami analisis data yang efektif di kalangan pendidik, mengingat pentingnya pengolahan data dalam proses pembelajaran dan evaluasi. Metode yang digunakan dalam pengabdian ini meliputi pelatihan intensif dan workshop yang dirancang untuk memperkenalkan fitur-fitur utama JASP, termasuk analisis statistik dasar hingga lanjutan. Peserta diberikan kesempatan untuk langsung mempraktikkan penggunaan software sehingga diharapkan dapat meningkatkan pemahaman dan keterampilan. Hasil dari kegiatan ini menunjukkan peningkatan kemampuan guru yang signifikan dalam mengolah dan menganalisis data. Hal ini ditunjukkan dari hasil pre-test dan post-test yang dilakukan sebelum dan sesudah kegiatan. Kegiatan pengabdian ini tidak hanya memberikan pengetahuan baru, tetapi juga membangun kepercayaan diri para guru dalam menggunakan teknologi untuk mendukung pengajaran. Kesimpulan dari pengabdian ini menekankan pentingnya pelatihan berkelanjutan dalam literasi data untuk meningkatkan kualitas pendidikan
Claim Reserving Estimation Using the Double Chain Ladder Method with the Bootstrap Approach Josepa , Tiffany Audrey; Sofia, Ayu; Andirasdini, Indah Gumala
Enthusiastic : International Journal of Applied Statistics and Data Science Volume 5 Issue 1, April 2025
Publisher : Universitas Islam Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20885/enthusiastic.vol5.iss1.art3

Abstract

The claim reserve is the amount of funds the insurance company must set aside to pay claims reported by policyholders. Estimation of claim reserves is carried out as a preventive step for failed payment if the reported claim exceeds the insurance company’s capacity. The estimation of claim reserves in this study was performed using the double chain ladder method with a bootstrap approach. The data used was in the form of a run-off triangle of claim counts and claim amounts presented in incremental and cumulative form. The purpose of this research was to determine the estimated value of reported but not settled (RBNS) and incurred but not reported (IBNR) claim reserves through the bootstrap application on the double chain ladder method. After performing the double chain ladder calculation, the estimated RBNS claim reserves amounted to 6,828,456,000 and the IBNR amounted to 3,714,144,000. Meanwhile, using the bootstrap approach, the RBNS claim reserve estimate was 6,777,539,000 and the IBNR was 3,741,979,000. With the conclusion that the greater the nominal claim reserve allocated, the lower the chance of the company going bankrupt.
FAKTOR –FAKTOR YANG MEMENGARUHI RASIO PROFITABILITAS PERUSAHAAN ASURANSI UMUM DI BURSA EFEK INDONESIA TAHUN 2018 –2022 MENGGUNAKAN REGRESI PANEL Simarmata, Gabriel Camoranesa; Sofia, Ayu; Andirasdini, Indah Gumala
Parameter: Jurnal Matematika, Statistika dan Terapannya Vol 3 No 2 (2024): Parameter: Jurnal Matematika, Statistika dan Terapannya
Publisher : Jurusan Matematika FMIPA Universitas Pattimura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/parameterv3i02pp155-168

Abstract

Analisis kinerja keuangan memberikan gambaran hasil bisnis perusahaan pada suatu periode. Penelitian ini bertujuan mengetahui faktor-faktor yang memengaruhi Return on Assets (ROA) dan Return on Equity (ROE) perusahaan asuransi umum di Bursa Efek Indonesia (BEI) dari 2018 hingga 2022 denganregresi panel. Data diambil dari idx.co.id dan emiten.kontan.co.id. Penelitian ini menghasilkandua model, dengan Variabelbebas dan terikat yang berbeda, yang menunjukkan pengaruh signifikan Variabelterhadap ROA dan ROE. Persamaan model ROA yang didapat adalah 푅푂퐴푖,푡=훼푖+0.0053(퐶푅)푖,푡+0.0118(푇퐴푇푂)푖,푡−0.0068(퐷퐸푅)푖,푡+0.0001(퐸푃푆)푖,푡. Sedangkan, persamaan model ROE adalah, 푅푂퐸푖,푡=훼푖+0.0030(퐶푅)푖,푡−0.0137(푇퐴푇푂)푖,푡+0.1999(퐷퐴푅)푖,푡+0.2016(푁푃푀)푖,푡. Dalam persamaan ROA menunjukkan hanya Variabel EPS yang signifikan secara parsial. Sementara itu, dalam persamaan ROE menunjukkan VariabelDAR dan NPM signifikan secara parsial dalam memengaruhi ROE.
Pelatihan Olah Data dan Visualisasi Data Statistik dalam Peningkatan Kompetensi Perangkat Desa Badran Sari, Lampung Selatan Sofia, Ayu; Lestari, Fuji; Rivai, Muklas; andirasdini, indah Gumala; Julianty, Dila Tirta
TeknoKreatif: Jurnal Pengabdian kepada Masyarakat Vol 5 No 1 (2025): TEKNOKREATIF : Jurnal Pengabdian kepada Masyarakat Volume 5 No 1
Publisher : Lembaga Penelitian dan Pengabdian Kepada Masyarakat (LP2M), Institut Teknologi Sumatera, Lampung, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35472/teknokreatif.v5i1.1873

Abstract

The use of data processing skills is something that is very important in various fields. The data processing process can use various applications, one of which is the number/data processing application which is commonly known as the Ms. Excel application. Ms. Excel is a software program that allows users to process and calculate numerical data so that calculations and reading data are no longer done manually. The problem with partners is the lack of competence of village officials regarding the use of technology in processing village data. Based on the problems faced by partners, the PkM team offers a solution, namely providing training in processing and analyzing statistical data using Ms. Excel which aims to help village officials to be able to process data and be able to visualize the data into images/graphs that are more attractive to the community so that able to improve the quality of data processing contained in village officials.
Regression Models with ARMA Errors for Predicting Tabarru Fund in Islamic Insurance: A Normally Distributed Simulation Approach Andirasdini, Indah Gumala; Aliem, Dien Manarul; Sofia, Ayu
Parameter: Jurnal Matematika, Statistika dan Terapannya Vol 4 No 2 (2025): Parameter: Jurnal Matematika, Statistika dan Terapannya
Publisher : Jurusan Matematika FMIPA Universitas Pattimura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/parameterv4i2pp239-248

Abstract

Islamic insurance is a financial protection system based on mutual assistance and risk-sharing, facilitated by a tabarru fund among participants. Effective management of this fund is essential to prevent financial deficits while ensuring sustainability and compliance with Sharia principles. This study aims to predict the value of the tabarru fund by developing a regression model with ARMA errors, incorporating variables such as participant contributions, claim amounts, and investment returns. The Regression model with ARMA errors is a hybrid approach that combines multiple linear regression with ARMA-based residual modeling, effectively addressing autocorrelation in regression residuals. The data used in this study were generated through a normal distribution simulation based on the monthly financial records of a Sharia insurance company over a ten-year period. The analysis results indicated that the regression model with ARMA(1,0) errors could provide predictive values with minimum error of prediction (MAPE value 0.022%). These findings demonstrate the model’s potential for strategic financial planning in Islamic insurance institutions, particularly in optimizing fund allocation and supporting risk-sensitive investment decisions.
Analysis of the Health Social Security Administration (BPJS Kesehatan) Claim Amount using Random Forest Regression Andirasdini, Indah Gumala; Saputra, Desta; Rivai, Muklas; Putra, Septia Eka Marsha
Indonesian Actuarial Journal Vol. 1 No. 1 (2025): Indonesian Actuarial Journal
Publisher : Persatuan Aktuaris Indonesia

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

Abstract

Claims paid by hospitals need to be identified to verify the accuracy of health services, maintain service quality, and optimize services provided to the Health Social Security Administration (BPJS Kesehatan) participants. This aligns with the third goal of the Sustainable Development Goals (SDGs), which is to ensure healthy lives and promote well-being for all ages, particularly in the context of universal health coverage. The difference in tariffs set by BPJS Kesehatan (INA-CBGs) compared to the amount paid by hospitals has led to a problem that can harm health facilities, such as delayed claim payments. This study aims to analyze the amount of claims paid by a regional hospital to BPJS Kesehatan participants using machine learning with the Random Forest Regression method. Based on this modeling, it was found that the severity of patients, length of stay, and type of illness are the most significant factors in determining the amount of claims. This study has an accuracy value of 81.89%, an adjusted R-square value of 80.4%, and a Mean Absolute Percentage Error (MAPE) of 18.11% in estimating the amount of claims.
Co-Authors Aliem, Dien Manarul Azzanina, Nanda Baiti, Putri Isnaini Cahyaning Berliana Ratam, Aldila Nur Indah Dewi, Karina Sylfia Dila Tirta Julianty Dwi Mahrani Fuji Lestari Fuji Lestari Gustina K. S, Annisa Hevita Irwan, Sri Efrinita Josepa , Tiffany Audrey Listiani, Amalia Marisa Marisa Paiz Jalaludin Rahman, Alrafiful Rivai, Muklas Rosni, Rosni Saputra, Desta Septia Eka Marsha Putra Simarmata, Gabriel Camoranesa Sofia, Ayu Yulita, Tiara