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All Journal IKRA-ITH Informatika : Jurnal Komputer dan Informatika IKRA-ITH Teknologi Jurnal Sains dan Teknologi JMM (Jurnal Masyarakat Mandiri) Sebatik Jurnal Sisfokom (Sistem Informasi dan Komputer) IKRA-ITH ABDIMAS Jurnal Mnemonic Unri Conference Series: Community Engagement Buletin Poltanesa Kresna: Jurnal Riset dan Pengabdian Masyarakat IJCOSIN : Indonesian Journal of Community Service and Innovation Jurnal Karya Abdi Masyarakat Jurnal Pengabdian Masyarakat Tekno Proceeding of The International Conference on Management, Entrepreneurship, and Business
Windihastuty, Wiwin
Universitas Budi Luhur
Religion Agriculture, Biological Sciences & Forestry Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Public Health Social Sciences Other

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Prediction of Claim Fund Reserves in Insurance Companies Using the ARIMA Method Brotosaputro, Goenawan; Japriadi, Yohanes Setiawan; Windihastuty, Wiwin; Ahsani, Rivai
Jurnal Sisfokom (Sistem Informasi dan Komputer) Vol. 14 No. 1 (2025): JANUARY
Publisher : ISB Atma Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32736/sisfokom.v14i1.2331

Abstract

Insurance is a financial protection contract between a customer and an insurance company which is stated in the form of an insurance policy. Prediction of insurance claim reserve funds is necessary because the claim amount varies and the claim time can be the same. If at any time there is a claim that is so large that it exceeds the available claim reserve fund plus the claim occurs at the same time, it can cause the company to fail to pay the claim. This will certainly make the company's conduct decline, customer trust will be lost, and can cause the company to go bankrupt. The problem can be solved if the insurance company has sufficient claim fund reserves. Claim fund reserves are an important issue in insurance companies. This study aims to predict the claim fund reserves in insurance companies to anticipate varying claim amounts. Historical analysis of the value of claims with the ARIMA model approach is used to predict future claim values. We use claim value data that has been scaled in millions. 2020 to 2022 as training data and 2023 as test data. The Root Mean Square Error (RMSE) metric obtained is IDR 25,780.71; Mean Absolute Deviation (MAD) of IDR 14,421.89, and Mean Absolute Percentage Error (MAPE) of IDR 5,967.27; while the total actual claim value in 2023 is IDR 161,700.51 and the total predicted claim value is IDR 166,227.36; which means that an accuracy of 97% is obtained. The result of claim prediction value in one periodic year can give a favor to the management to make a decision, how much the claim funds should be prepared.
Classification Methods in Sentiment Analysis of Customers Statisfaction as a Services Improvement Strategy Wiwin Windihastuty; Yani Prabowo; M.N. Farid Thoha
Proceeding of the International Conference on Management, Entrepreneurship, and Business Vol. 1 No. 1 (2024): June : Proceeding of the International Conference on Management, Entrepreneursh
Publisher : Asosiasi Riset Ilmu Manajemen Kewirausahaan dan Bisnis Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61132/icmeb.v1i1.74

Abstract

Customer satisfaction is a crucial indicator in assessing the quality of a company's products, services and overall experience. This research aims to identify the level of customer satisfaction and optimize the available data for effective use in sentiment analysis. In this study, we analyzed 4,353 customer reviews collected over the past year, with 3,481 reviews used as training data and 871 reviews as testing data. The analysis process was conducted using the Cross-Industry Standard Process for Data Mining (CRISP-DM) approach and leveraged the Logistic Regression algorithm to build a predictive model. Model evaluation using the confusion matrix yielded an accuracy of 94.60%, a precision of 94.26%, and a recall of 94.60%. The analysis was conducted using Jupyter Notebook and the Python programming language. The results indicate that sentiment analysis is effective in identifying and predicting customer satisfaction levels, which in turn can help a company’s products improve its service strategies. The optimization of previously underutilized data now provides deeper insights into customer perceptions and expectations, enabling the company to make more targeted decisions and enhance overall customer satisfaction.
Co-Authors Ahsani, Rivai Bambang Pujiyono Brotosaputro, Goenawan Bullion Dragon Dwi Kristanto Goenawan Brotosaputro Japriadi, Yohanes Setiawan Joko Sutrisno Krisna Adiyarta Krisna Adiyarta M.N. Farid Thoha Mulyati Mulyati Musafa, Akhmad Peby Wahyu Purnawan R, Jan Everhart Rahayu, Sri Retno Wulandari Rezza Anugrah Mutiarawan Samsinar Samsinar Samsinar Samsinar Suwasti Broto Suwasti Broto Swasti Broto Thoha, M. Nuur Farid Titin Fatimah Titin Fatimah Wendi Usino Wendy Usino Windarto Windarto Yani Prabowo