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All Journal Jurnal Riset Informatika Limits: Journal of Mathematics and Its Applications
R. Mohamad Atok
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Computer Science & IT Mathematics

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Active Learning Query by Committee Labeling Method to Increase Accuracy and Efficiency of Sentiment Analysis Classification Dipa Anasta Iskandar; R. Mohamad Atok
Jurnal Riset Informatika Vol. 7 No. 4 (2025): September 2025
Publisher : Kresnamedia Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1427.54 KB) | DOI: 10.34288/jri.v7i4.386

Abstract

This study proposes the Query by Committee (QBC) labeling method to improve the accuracy of classification models—specifically XLM-RoBERTa—and to increase labeling efficiency compared to manual, supervised labeling, which generally requires more time and resources. The dataset consists of unannotated healthcare-industry application reviews scraped from Google Play. Six distinct labeling strategies were applied as input for fine-tuning XLM-RoBERTa models under identical hyperparameter settings. The six labeling approaches were evaluated namely Rating-based labeling, Lexicon-based labeling, QBC for Rating-Vader labeling, QBC for Rating-Pseudo labeling, QBC for Vader-Pseudo labeling, and QBC triplet for Rating-Pseudo-Vader labeling. Each labeled dataset was split using stratified random sampling, and class weights were set to “auto” during training to address label imbalance. All models were subsequently tested on the IndoNLU SmSA test dataset, with performance compared in terms of accuracy, precision, recall, and F1-score. Results indicate that the triplet QBC approach (combining Rating, VADER, and Pseudo labeling) outperformed all other methods, achieving an accuracy of 91.4%, a precision of 91.28%, a recall of 91.4%, and an F1-score of 91.21%. These findings demonstrate that the QBC labeling method can serve as an effective and efficient alternative to manual annotation for similar classification tasks
Proyeksi Tingkat Kematian di Indonesia Menggunakan Metode Holt-Winters Smoothing Exponential dan Moving Average Ulil Azmi; R. Mohamad Atok; Wawan Hafid Syaifudin; Galuh Oktavia Siswono; Imam Safawi Ahmad; Nuri Wahyuningsih
Limits: Journal of Mathematics and Its Applications Vol. 20 No. 1 (2023): Limits: Journal of Mathematics and Its Applications Volume 20 Nomor 1 Edisi Ma
Publisher : Pusat Publikasi Ilmiah LPPM Institut Teknologi Sepuluh Nopember

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

Abstract

Inaccurate predictions would cause the insurance companies to incur huge losses and may lead to expensive premiums for which low-income consumers are unable to insure themselves. The ability to predict mortality rates accurately allows the insurance companies to take preventive steps to introduce new policies with reasonable prices. It is hoped that by carrying out mortality projections, losses caused by longevity risk in the life insurance industry would be minimized. This study used secondary data obtained from the World Health Organization (WHO) website in the Mortality and Global Health Estimates category with the sub-topic Life Table by Country Indonesia. In this paper, several models are used to predict the mortality rate in a case study population in Indonesia, namely the Moving Average and Exponential Smoothing forecasting methods. The results obtained are the best method for predicting mortality rates is by using the Exponential Smoothing method with the MAPE value of Exponential Smoothing is smaller than the MAPE value on the Moving Average. The results of this mortality projection will later be used to obtain the distribution of life expectations and the premium price of life annuities.
Co-Authors Dipa Anasta Iskandar Galuh Oktavia Siswono Imam Safawi Ahmad Nuri Wahyuningsih Ulil Azmi Wawan Hafid Syaifudin