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All Journal LOGIK@ Statistika JURNAL MATEMATIKA STATISTIKA DAN KOMPUTASI CAUCHY: Jurnal Matematika Murni dan Aplikasi Journal of Mathematical and Fundamental Sciences Jurnal Fourier Desimal: Jurnal Matematika BAREKENG: Jurnal Ilmu Matematika dan Terapan Zero : Jurnal Sains, Matematika, dan Terapan AXIOM : Jurnal Pendidikan dan Matematika M A T H L I N E : Jurnal Matematika dan Pendidikan Matematika InPrime: Indonesian Journal Of Pure And Applied Mathematics Majalah Ilmiah Matematika dan Statistika (MIMS) Jambura Journal of Biomathematics (JJBM) Jurnal Eurekamatika Innovative: Journal Of Social Science Research The Indonesian Journal of Computer Science
Nina Fitriyati
UIN Syarif Hidayatullah Jakarta
Agriculture, Biological Sciences & Forestry Humanities Astronomy Chemistry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice Mathematics Mechanical Engineering Physics Public Health Social Sciences Transportation Other

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Comparative Study of Data Generation for Normal, Lognormal, and Gamma Distributions using PLS and Usury Models Handarbeni, Zalfa Talitha; Fauziah, Irma; Fitriyati, Nina
Mathline : Jurnal Matematika dan Pendidikan Matematika Vol. 8 No. 4 (2023): Mathline: Jurnal Matematika dan Pendidikan Matematika
Publisher : Universitas Wiralodra

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31943/mathline.v8i4.543

Abstract

Mathematical modeling in the world of economics and banking has particular relevance, especially in terms of providing capital for business activities, particularly for Micro, Small, and Medium Enterprises (MSMEs). Currently, banking activities are predominantly dominated by conventional banks that apply interest, which in Islamic Sharia is considered as riba, and it is incumbent upon Muslims to avoid riba and conduct all their affairs in accordance with Islamic Sharia. This research discusses a mathematical model of a profit-loss sharing system in accordance with Islamic Sharia using the profit-loss sharing model scheme, which is one of the investment models in Islamic finance with a musyarakah contract. The data is derived from the daily profit and loss of a micro trader for the period of September 2023 in Cijantung, East Jakarta, which is then generated for periods of 35, 40, and 50 days, following normal, lognormal, and gamma distributions, with an investment capital of Rp 1,500,000, Rp 3,000,000, and Rp 4,500,000, and profit-sharing portions of 2%, 5%, and 7%. This research is capable of demonstrating a profit-sharing scheme and optimization functions in profit distribution, as well as determining parameters that are more advantageous for traders and capital owners compared to the usury model because the imposed penalties are considered burdensome for the public. Generating data following a normal distribution provides a more realistic profit-sharing scheme but a lognormal and gamma distribution yields the largest profit-sharing portion for investor and trader.
MODEL MATEMATIKA PENYEBARAN PENYAKIT PULMONARY TUBERCULOSIS DENGAN PENGGUNAAN MASKER MEDIS Inayah, Nur; Manaqib, Muhammad; Fitriyati, Nina; Yupinto, Ikhwal
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 14 No 3 (2020): BAREKENG: Jurnal Ilmu Matematika dan Terapan
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (890.372 KB) | DOI: 10.30598/barekengvol14iss3pp459-472

Abstract

This research developed a model of tuberculosis disease spread using the SIR model with addition of the medical mask usage factor. First, we create a diagram of the tuberculosis disease spread compartment through contact between individuals with medical mask usage. After that, we construct a system of nonlinear differential equations based on the compartment diagram and then find the disease-free equilibrium point, the endemic equilibrium point, and the initial reproduction number . We use linearization to analyze of the disease-free equilibrium point. The disease-free equilibrium point obtained is asymptotically stable at . The simulation result shows that the value of . It means that tuberculosis disease in the future will disappear. But if we reduce the value of medical mask usage and increase the value of tuberculosis disease spread, the value . It means that tuberculosis diseases can become an outbreak.
Evaluasi Performa Metode Exponential Smoothing pada Data Runtun Waktu Hierarkis Alkadrie, Syarifah Syila; Wijaya, Madona Yunita; Fitriyati, Nina
The Indonesian Journal of Computer Science Vol. 14 No. 2 (2025): The Indonesian Journal of Computer Science
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v14i2.4783

Abstract

Penelitian ini bertujuan untuk menyalakan metode Simple Exponential Smoothing (SES), Double Exponential Smoothing (Metode Holt), dan Triple Exponential Smoothing (Holt-Winters) dalam memperkirakan jumlah wisatawan di Australia dari tahun 1998 sampai dengan tahun 2016. Data yang digunakan memiliki struktur hierarki dengan empat tingkat: Australia, negara bagian, kawasan, dan tujuan kunjungan. Pendekatan bottom-up diterapkan untuk menghasilkan ramalan pada tingkat hierarki teratas dengan menggabungkan ramalan dari tingkat terendah. Evaluasi dilakukan dengan menggunakan metrik Symmetric Mean Absolute Percentage Error (SMAPE) pada setiap tingkat hierarki dan cakrawala peramalan. Hasil penelitian menunjukkan bahwa Metode Holt berkinerja terbaik pada tingkat Australia (SMAPE 3,26%–9,28%) dan tingkat negara bagian (6,96%–12,29%). Sementara itu, Holt-Winters mencapai kinerja terbaik pada tingkat wilayah (16,57%–21,43%) dan tingkat tujuan kunjungan (43,98%–47,63%). Penelitian ini menyoroti efektivitas Exponential Smoothing dalam menangkap pola dan tren musiman dalam hierarki data dan pentingnya pendekatan bottom-up dalam menghasilkan prakiraan yang konsisten di semua tingkat hierarkis.
Forecasting Indonesian inflation using a hybrid ARIMA-ANFIS Fitriyati, Nina; Mahmudi, Mahmudi; Wijaya, Madona Yunita; Maysun, Maysun
Desimal: Jurnal Matematika Vol. 5 No. 3 (2022): Desimal: Jurnal Matematika
Publisher : Universitas Islam Negeri Raden Intan Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24042/djm.v5i3.14093

Abstract

This paper discusses the prediction of the inflation rate in Indonesia. The data used in this research is assumed to have both linear and non-linear components. The ARIMA model is selected to accommodate the linear component, while the ANFIS method accounts for the non-linear component in the inflation data. Thus, the model is known as the hybrid ARIMA-ANFIS model. The clustering method is performed in the ANFIS model using Fuzzy C-Mean (FMS) with a Gaussian membership function. Consider 2 to 6 clusters. The optimal number of clusters is assessed according to the minimum value of the error prediction. To evaluate the performance of the fitted hybrid ARIMA-ANFIS model, it can be compared to the classical ARIMA model and with the ordinary ANFIS model. The result reveals that the best ARIMA model for inflation prediction in Indonesia is ARIMA(2,1,0). In the hybrid ARIMA(2,1,0)-ANFIS model, two clusters are optimal. Meanwhile, the optimum number of clusters in the ordinary ANFIS model is six. The comparison of prediction accuracy confirms that the hybrid model is superior to the individual model alone of either ARIMA or ANFIS model.
Indonesia’s total fertility rate (TFR) using the brown and holt double exponential smoothing with grid search Rahma, Chelsea Fatihah; Fitriyati, Nina; Inna, Suma; Adjie, Dharma Syadhi Putra
Desimal: Jurnal Matematika Vol. 8 No. 1 (2025): Desimal: Jurnal Matematika
Publisher : Universitas Islam Negeri Raden Intan Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24042/djm.v8i1.26332

Abstract

The Brown and Holt DES method effectively captures trends in time-series data. Its forecasting accuracy heavily depends on the selection of optimal smoothing parameters. Often, the smoothing parameters are selected manually using trial and error methods. This method is time-consuming, unsystematic, prone to bias, not scalable, less reproducible, and increases the risk of overfitting or underfitting. To overcome these problems, in this study, we propose optimization of smoothing parameters using Grid Search. This new approach will be applied to predict Indonesia’s TFR. Grid Search optimization is employed to systematically explore the parameter space and identify the best combination that minimizes forecasting errors. To ensure model robustness, cross-validation is implemented, allowing the evaluation of model performance across multiple training and validation splits. The results show that the Holt DES method with Grid Search is more accurate than the Brown DES with Grid Search, with the smallest Mean Absolute Percentage Error (MSE) value of 0.00972 at  and . Predictions with Holt DES with Grid Search show a downward trend in the national TFR until 2027, potentially falling below the ideal level of 2.1. TFR predictions at the provincial level show pattern variations, with several regions experiencing significant declines. The difference in results between the Brown and Holt methods emphasizes the importance of optimizing smoothing parameters and selecting an appropriate population-analysis prediction model to support demographic policy.
The Analysis of Epidemic Dynamical Models for Dengue Transmission Considering the Mosquito Aquatic Phase Inayah, Nur; Manaqib, Muhammad; Fitriyati, Nina; Wijaya, Madona Yunita; Fiade, Andrew; Sari, Flori Ratna
Jambura Journal of Biomathematics (JJBM) Volume 6, Issue 3: September 2025
Publisher : Department of Mathematics, Universitas Negeri Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37905/jjbm.v6i3.29332

Abstract

This  study  generalizes the dengue  transmission model  by  considering the dynamics of the human population and  the Aedes  aegypti mosquito  population.  The  mosquito  population is  devided into  two  phases,  i.e.,  the aquatic  phase and the adult  phase.  From  the model,  we seek the disease-free  equilibrium, endemic  equilibrium, and  basic  reproduction number   (R0) points.    The  model  yields a  single   basic  reproduction number   which determines the system’s  behavior.   If  R0    1,  the disease-free  equilibrium is  locally  asymptotically stable, indicating that the disease  will die out.  Conversely, if R0    1, an endemic  equilibrium exists,  and  the disease may  persist  in the  population.    Next,   a  numerical simulation  is  performed  to  geometrically  visualize   the resulting analysis  and  also  to  simulate the  dengue   transmission in  DKI Jakarta   Province,  Indonesia.   The resulting  numerical simulation  supports our  analysis.   Meanwhile, the  simulation in  DKI Jakarta  Province suggests that  the dengue  fever  disappears after  60 days  from  the first  case appearance  after  controlling  the mosquito  population through fogging and the use of mosquito  larvae  repellent.  Lastly, the sensitivity analysis of R0   indicates  that  parameters   related  to  the  mosquito’s  aquatic   phase  have  a  strong   influence   on  dengue transmission, meaning that small  changes  in these parameters  can significantly increase or decrease the value  of R0  and thus the potential  for an outbreak.
Transformation of Traditional Models to AI: SLR on the Application of Machine Learning in Mortality Prediction Nuraini, Vita; Fitriyati, Nina
CAUCHY: Jurnal Matematika Murni dan Aplikasi Vol 10, No 2 (2025): CAUCHY: JURNAL MATEMATIKA MURNI DAN APLIKASI
Publisher : Mathematics Department, Universitas Islam Negeri Maulana Malik Ibrahim Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18860/cauchy.v10i2.35972

Abstract

The application of machine learning (ML) in actuarial science and life insurance has driven digital transformation in mortality risk prediction. This article conducts research using the Systematic Literature Review (SLR) methodology with the PRISMA approach to evaluate the performance comparison between ML methods and traditional actuarial models in predicting mortality risk. This study analyzed publication trends, geographic and institutional distribution, and methodologies used in the literature published between 2019 and 2025. The results from SLR show that ML methods, especially Random Forest and XGBoost, have superior predictive accuracy compared to traditional actuarial models such as Traditional Logistic Regression and Cox Proportional Hazards. However, despite the obvious accuracy advantage, issues of interpretability and long-term stability remain a major challenge in implementing ML in the actuarial industry. This study also identifies the need for a hybrid approach combining the strengths of both methodologies to improve prediction accuracy while maintaining high interpretability. This study suggests the need for further development in the application of ML by the regulation and compliance of the insurance industry. The findings provide insights for actuarial practitioners, regulators, and academics regarding the potential and challenges of using ML in mortality risk prediction.
Performa Naïve Bayes, SVM, dan IndoBERT pada Analisis Sentimen Twitter IndiHome dengan Strategi Penanganan Data Tidak Seimbang Anas Qolbu, Adinda; Fitriyati, Nina; Inayah, Nur
Jurnal Fourier Vol. 14 No. 1 (2025)
Publisher : Program Studi Matematika Fakultas Sains dan Teknologi UIN Sunan Kalijaga Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14421/fourier.2025.141.29-44

Abstract

Penelitian ini bertujuan untuk membandingkan performa tiga pendekatan analisis sentimen, yaitu Naïve Bayes, Support Vector Machine (SVM), dan Indonesian Bidirectional Encoder Representations from Transformers (IndoBERT), pada layanan IndiHome menggunakan data Twitter. Keterbatasan model tradisional melatarbelakangi penelitian ini dalam mengenali opini positif dan tantangan ketidakseimbangan data yang sering muncul dalam analisis berbasis media sosial. Data penelitian berupa 7393 tweet (Januari 2019–Agustus 2024) yang dilabeli secara manual menjadi sentimen positif dan negatif. Model dievaluasi menggunakan stratified 10-fold cross validation dan data uji, dengan penerapan teknik penanganan ketidakseimbangan berupa Synthetic Minority Oversampling Technique (SMOTE) dan pembobotan kelas (class weighting). Hasil menunjukkan IndoBERT unggul dengan akurasi 0,96 dan F1-score makro 0,95 tanpa penanganan khusus, sedangkan SVM mencapai akurasi 0,95 dengan pembobotan kelas, dan Naïve Bayes meningkat dari akurasi 0,89 menjadi 0,92 setelah SMOTE. Analisis tren sentimen menunjukkan opini negatif mendominasi, terutama terkait kecepatan dan kestabilan layanan. Temuan ini menegaskan bahwa IndoBERT lebih efektif dalam memahami konteks bahasa Indonesia, sementara teknik penanganan data tetap relevan untuk meningkatkan performa model tradisional. Hasil penelitian ini penting karena memberikan dasar empiris dalam pemilihan model analisis sentimen yang lebih akurat, adaptif terhadap bahasa Indonesia, dan bermanfaat dalam meningkatkan kualitas layanan.   This study aims to compare the performance of three sentiment analysis approaches, namely Naïve Bayes, Support Vector Machine (SVM), and Indonesian Bidirectional Encoder Representations from Transformers (IndoBERT), on IndiHome services using Twitter data. The limitations of traditional models underlie this study in recognizing positive opinions and the challenge of data imbalance that often arises in social media based analysis. The research data consist of 7,393 tweets (January 2019–August 2024) manually labeled into positive and negative sentiments. Models were evaluated using stratified 10-fold cross validation and test data, with the application of imbalance handling techniques such as Synthetic Minority Oversampling Technique (SMOTE) and class weighting. Results show IndoBERT excels with 0.96 accuracy and 0.95 macro F1-score without special handling, while SVM reaches 0.95 accuracy with class weighting, and Naïve Bayes improves from 0.89 to 0.92 accuracy after SMOTE. Sentiment trend analysis indicates negative opinions dominate, mainly regarding speed and service stability. These findings confirm IndoBERT is more effective in understanding Indonesian context, while data handling remains relevant for improving traditional models. This study’s results are important because they offer an empirical foundation for choosing sentiment analysis models that are more accurate, adaptive to Indonesian language, and useful for improving service quality.  
Allocation of Tabarru's Funds in Endowment Premium Fitriyati, Nina; Zharifah, Zharifah; Zulkifli, Dhea Urfina
ZERO: Jurnal Sains, Matematika dan Terapan Vol 7, No 2 (2023): Zero: Jurnal Sains Matematika dan Terapan
Publisher : UIN Sumatera Utara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30829/zero.v7i2.18078

Abstract

In general, the premiums paid by participants in Islamic insurance are allocated into three components with specific proportions: administrative costs, participants' savings, and Tabarru's funds (mutual aid funds). Participants' savings and Tabarru's funds are invested according to Sharia principles, and the returns are shared (Mudharabah) between the participants and the company. This study analyses the allocation of Tabarru's funds and participant savings about the size of premiums using the Endowment method. Additionally, the investment results from Tabarru's funds and participant savings, as well as their distribution to participants and the insurance company, will be simulated. Premium calculations utilize the Indonesian Mortality Table (TMI) for the year 2011. The simulation results indicate that administrative costs and investment returns from premiums managed for male participants are larger than those for female participants. Therefore, the claim amount for male participants is likely to be higher compared to the claim, amount for female participants.
Pengaruh Early Warning System, Return on Asset, dan Return on Equity Terhadap Kinerja Keuangan Perusahaan Asuransi Jiwa Konvensional dan Asuransi Jiwa Syariah Amelia Emaras, Tamara; Zulkifli, Dhea Urfani; Fitriyati, Nina
Innovative: Journal Of Social Science Research Vol. 4 No. 1 (2024): Innovative: Journal Of Social Science Research
Publisher : Universitas Pahlawan Tuanku Tambusai

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

Abstract

Untuk menumbuhkan kepercayaan nasabah pada industri asuransi di Indonesia, baik asuransi konvensional maupun syariah, penelitian ini berfokus pada pentingnya mengawasi perusahaan asuransi melalui kinerja keuangan. Tujuan dari penelitian ini adalah mengetahui kinerja keuangan perusahaan asuransi jiwa konvensional maupun asuransi jiwa syariah yang berpengaruh pada Early Warning System, Return on Asset, dan Return on Equity. Metode yang digunakan yaitu kuantitatif deskriptif di mana data sekunder menggunakan 4 sampel perusahaan yang terdiri dari 2 perusahaan asuransi jiwa konvensional dan 2 perusahaan asuransi jiwa syariah. Hasil penelitian menunjukkan bahwa berdasarkan Early Warning System perusahaan asuransi jiwa konvensional memiliki kinerja keuangan yang baik pada Rasio Likuiditas, Rasio Beban Klaim, dan Rasio Retensi Sendiri sedangkan Rasio Kecukupan Dana perusahaan asuransi jiwa konvensional dan asuransi jiwa syariah memiliki kinerja keuangan yang kurang baik. Berdasarkan Return on Asset perusahaan asuransi jiwa konvensional memiliki kinerja keuangan yang lebih baik daripada asuransi jiwa syariah. Berdasarkan Return on Equity perusahaaan asuransi jiwa konvensional memiliki kinerja keuangan yang baik. Faktor rasio yang memiliki pengaruh signifikan terhadap kinerja keuangan perusahaan asuransi jiwa konvensional dan asuransi jiwa syariah yaitu pada Early Warning System yang terdiri dari Rasio Likuiditas dan Rasio Kecukupan Dana serta Return on Equity.
Co-Authors . Mahmudi Aceng Komarudin Mutaqin Adjie, Dharma Syadhi Putra Agus Yodi Gunawan Agus Yodi Gunawan Alkadrie, Syarifah Syila Amelia Emaras, Tamara Anas Qolbu, Adinda Andrew Fiade Anggita Septiani Ayu Puji Rahayu Azzahra Benita Bisyri, M. Alvi Devi Ila Octaviyani Dhea Urfina Zulkifli Dino Agustin Putra Eliskar, Yulinda Fachry Abda El Rahman Faizah, Salma Abidah Fathu Rahman Gina Isma Kusuma Handarbeni, Zalfa Talitha Haris Hamzah Inna, Suma Irene, Yanne Irma Fauziah Iskandar Fahmi Khusnul Khotimah Mahmudi Mahmudi Mahmudi Mahmudi Maysun Maysun Maysun, Maysun Muhammad Manaqib, Muhammad Nadilia, Nindita Nindita Nadilia Nur Inayah Nuraini, Vita Nurmaleni Nurmaleni NURTITI SUNUSI Rahma, Chelsea Fatihah Rahman, Fathu Rini Marwati Rustam, Rustam S. Sutawanir Saleh, Khaerudin Sari, Flori Ratna Sri Maliska Sutanto, Taufik Edy Sutawanir Darwis Taufik Edy Sutanto Wahri Irawan Wijaya, Madona Yunita Wulandari, Fitria Sisca Yupinto, Ikhwal Zharifah, Zharifah Zulkifli, Dhea Urfani