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Saefullah, Rifki

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Author-ID : 1913438

Religion Humanities Computer Science & IT Decision Sciences, Operations Research & Management Dentistry Economics, Econometrics & Finance Education Energy Engineering Environmental Science Health Professions Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mathematics Medicine & Pharmacology Nursing Physics Public Health Social Sciences Transportation Other

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Sentiment Analysis of Maxim App User Reviews in Indonesia Using Machine Learning Model Performance Comparison Saefullah, Rifki; Yohandoko, Setyo Luthfi Okta; Prabowo, Agung
International Journal of Quantitative Research and Modeling Vol 5, No 3 (2024)
Publisher : Research Collaboration Community (RCC)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46336/ijqrm.v5i3.762

User reviews can vary widely in language and writing style, which can make accurate sentiment modeling difficult. Selecting the right machine learning model and comparing performance between models can be challenging, given that each model has its own strengths and weaknesses. The method used involved data collection by scraping 5000 reviews from the Google Play Store, followed by data pre-processing including data cleaning, tokenization, stemming, and feature engineering using TF-IDF. The data was divided into training (70%) and testing (30%) sets, with the SMOTE oversampling technique applied to address class imbalance. Three machine learning models were used: Random Forest, Support Vector Machine (SVM), and Naive Bayes. The results showed that the majority of reviews were positive, with a high average app rating. Word cloud analysis revealed that “service”, “driver”, “price”, and “time” were the most frequently discussed aspects in the reviews. In terms of model performance, SVM performed the best with an accuracy of 91.3%, followed by Random Forest (89%) and Naive Bayes (78%). Maxim was generally well received by users in Indonesia, with the majority of reviews being positive. The SVM model proved to be the most effective in classifying review sentiment, outperforming other models in accuracy and precision.

Determining the Pure Premium at Jasa Raharja Insurance Company Purwakarta Branch using Fast Fourir Transform (FFT) through Estimated Aggregate Loss Distribution Saefullah, Rifki; Ibrahim, Riza Andrian
International Journal of Quantitative Research and Modeling Vol 5, No 4 (2024)
Publisher : Research Collaboration Community (RCC)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46336/ijqrm.v5i4.815

Insurance is a contractual agreement between two parties, namely the insured party (customer) and the insurer (insurance company), in which the insured party pays a premium to the insurer, then in return, the insurer will provide compensation (claim) to the insured party if an insured event occurs. Each customer is required to pay a premium as an obligation stated in the insurance agreement by paying a premium, the customer fulfills his obligations and is entitled to the benefits stated in the policy. Therefore, the Insurance Company needs to carry out a scheme in the process of paying pure premiums for the sustainability of the insurance company. When determining the premium, it is done by estimating the aggregate loss distribution. This research will calculate thepure premium at the Purwakarta Branch of Jasa Raharja Insurance Company. The model used in this study is the distribution of aggregate loss with a compound distribution of claim frequency and claim size. Many claims follow the Poisson distribution and large claims follow the Lognormal distribution. In the process of estimating the probability of aggregate loss with the compound distribution model, the Inverse method with the Fast Fourier Transform (FFT) algorithm is used. This research will provide understanding and insight to insurance companies in determining the amount of premium that must be charged to customers.

Determining the Pure Premium at Jasa Raharja Insurance Company Purwakarta Branch using Fast Fourir Transform (FFT) through Estimated Aggregate Loss Distribution Saefullah, Rifki; Ibrahim, Riza Andrian
International Journal of Quantitative Research and Modeling Vol. 5 No. 4 (2024)
Publisher : Research Collaboration Community (RCC)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46336/ijqrm.v5i4.815

Insurance is a contractual agreement between two parties, namely the insured party (customer) and the insurer (insurance company), in which the insured party pays a premium to the insurer, then in return, the insurer will provide compensation (claim) to the insured party if an insured event occurs. Each customer is required to pay a premium as an obligation stated in the insurance agreement by paying a premium, the customer fulfills his obligations and is entitled to the benefits stated in the policy. Therefore, the Insurance Company needs to carry out a scheme in the process of paying pure premiums for the sustainability of the insurance company. When determining the premium, it is done by estimating the aggregate loss distribution. This research will calculate thepure premium at the Purwakarta Branch of Jasa Raharja Insurance Company. The model used in this study is the distribution of aggregate loss with a compound distribution of claim frequency and claim size. Many claims follow the Poisson distribution and large claims follow the Lognormal distribution. In the process of estimating the probability of aggregate loss with the compound distribution model, the Inverse method with the Fast Fourier Transform (FFT) algorithm is used. This research will provide understanding and insight to insurance companies in determining the amount of premium that must be charged to customers.

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Journal : International Journal of Quantitative Research and Modeling