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All Journal JURNAL MATEMATIKA STATISTIKA DAN KOMPUTASI Kontinu: Jurnal Penelitian Didaktik Matematika NUMERICAL (Jurnal Matematika dan Pendidikan Matematika) Zero : Jurnal Sains, Matematika, dan Terapan AL-TANZIM : JURNAL MANAJEMEN PENDIDIKAN ISLAM El-Usrah: Jurnal Hukum Keluarga Jurnal Absis : Jurnal Pendidikan Matematika dan Matematika Jurnal Lebesgue : Jurnal Ilmiah Pendidikan Matematika, Matematika dan Statistika Journal of Fundamental Mathematics and Applications (JFMA) Fraction: Jurnal Teori dan Terapan Matematika The Indonesian Journal of Computer Science
Dhea Urfina Zulkifli
Universitas Islam Negeri Syarif Hidayatullah Jakarta
Agriculture, Biological Sciences & Forestry Humanities Chemistry Computer Science & IT Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mathematics Physics

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SURVIVAL ANALYSIS OF THE FIRST JOB WAITING TIME FOR GRADUATES USING THE COX PROPORTIONAL HAZARD MODEL BASED ON THE MAXIMUM LIKELIHOOD ESTIMATION PRINCIPLE: SURVIVAL ANALYSIS OF THE FIRST JOB WAITING TIME FOR GRADUATES USING THE COX PROPORTIONAL HAZARD MODEL BASED ON THE MAXIMUM LIKELIHOOD ESTIMATION PRINCIPLE Dhea Urfina Zulkifli; Riaman; Kankan Parmikanti; Bambang Ruswandi
Fraction: Jurnal Teori dan Terapan Matematika Vol. 2 No. 2 (2022): Fraction: Jurnal Teori dan Terapan Matematika
Publisher : Jurusan Matematika, Fakultas Teknik, Universitas Bangka Belitung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33019/fraction.v2i2.41

Abstract

Every university graduate certainly expects to obtain the desired job as soon as possible. But in reality, because of high competitiveness in job markets many graduates have long waiting time to get a job. Survival analysis can be used to analyse the length of waiting time to obtain the first job. Thus, the objectives of this research are to get Cox Proportional Hazard model parameter on the length of waiting time of the graduate of Faculty of Social and Political Sciences Syarif Hidayatullah State Islamic University Jakarta to obtain the first job based on Maximum Likelihood Estimation principle and to explain factors influencing the graduate’s length of waiting time to obtain the first job by analysing the variable of gender, GPA, and study program. Data used in this research are from document of the faculty. The research found Cox Proportional Hazard model parameter on the graduate’s length of waiting time to obtain the first job and its significant influential factors, namely GPA and study program.
PREDIKSI KINERJA KEUANGAN PERUSAHAAN ASURANSI SYARIAH MENGGUNAKAN METODE ARIMA, EXPONENTIAL SMOOTHING, DAN HYBRID Devita Apriliani; Nina Fitriyati; Dhea Urfina Zulkifli
Jurnal Lebesgue : Jurnal Ilmiah Pendidikan Matematika, Matematika dan Statistika Vol. 4 No. 3 (2023): Jurnal Lebesgue : Jurnal Ilmiah Pendidikan Matematika, Matematika dan Statistik
Publisher : LPPM Universitas Bina Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46306/lb.v4i3.496

Abstract

Insurance is a contract made by an insurance company with a policy holder. One way to assess the stability of company is its financial performance. The aim of this study is to predict the financial performance of sharia insurance company using two factors, namely ROA (Return on Assets) and ROE (Return on Equity). The secondary data used in this research is derived from the quarterly financial reports of an Islamic insurance company from the year 2013 to 2022. The study used ARIMA (Autoregressive Integrated Moving Average), Exponential Smoothing (ETS), and Hybrid methods. The results showed that the ARIMA model is the best way to better predict both ROA and ROE. Compared to other methods that have been evaluated, the ARIMA model showed more accurate results in predicting financial  performance measured through ROAs and ROEs. Predictions show that the ROA value is relatively stable, indicating that the company is efficient in managing resouces while the ROE value tends to fail, which indicates that the identified company is less good at delivering returns to shareholders. Therefore, it can be one of considerations for people who wants to buy insurance or invest
MATHEMATICAL MODEL OF MEASLES DISEASE SPREAD WITH TWO-DOSE VACCINATION AND TREATMENT Manaqib, Muhammad; Yuliawati, Ayu Kinasih; Zulkifli, Dhea Urfina
Journal of Fundamental Mathematics and Applications (JFMA) Vol 6, No 2 (2023)
Publisher : Diponegoro University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/jfma.v6i2.20091

Abstract

This study developed a model for the spread of measles based on the SEIR model by adding the factors of using the first dose of vaccination, the second dose of vaccination, and treatment. Making this model begins with making a compartment diagram of the spread of the disease, which consists of seven subpopulations, namely susceptible subpopulations, subpopulations that have received the first dose of vaccination, subpopulations that have received the second dose vaccination, exposed subpopulations, infected subpopulations, subpopulations that have received treatment, and subpopulations healed. After the model is formed, the disease-free equilibrium point, endemic equilibrium point, and basic reproduction number (R_0) are obtained. Analysis of the stability of the disease-for equilibrium point was locally asymptotically stable when (R_0)<1. The backward bifurcation analysis occurs when (R_C) is present and R_C<R_0. Numerical simulations of disease-free and endemic equilibrium points are carried out to provide an overview of the results analyzed with parameter values from several sources. The results of the numerical simulation are in line with the analysis carried out. From the model analysis, the disease will disappear more quickly when the level of vaccine used and individuals who carry out treatment are enlarged.
Modeling Rainfall in Jakarta with Hybrid ARIMAX-ANN Model Mahmudi, Mahmudi; Safitri, Issa Bella; Zulkifli, Dhea Urfina
JURNAL PENDIDIKAN MATEMATIKA Vol 8, No 1: Mei 2024
Publisher : Universitas Islam Sultan Agung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30659/kontinu.8.1.1-20

Abstract

Abstract. Forecasting Indonesia's rainy and dry seasons is increasingly complex, requiring effective forecasting methods. This research is focused on rainfall forecasting in Jakarta, which includes Kemayoran Station and Tanjung Priok Station, using the Hybrid ARIMAX-ANN model. In ARIMAX modeling, the exogenous variable involved is the humidity variable. The accuracy results of the Hybrid ARIMAX-ANN model will be compared with the ARIMAX model. The results of this study show that the Hybrid ARIMAX-ANN model provides better accuracy. At Kemayoran Station, the Hybrid ARIMAX-ANN (1,0,0) model with 1 hidden layer shows a lower MAPE, 21.145%, than the ARIMAX model. Meanwhile, at Tanjung Priok Station, the Hybrid ARIMAX-ANN (1,0,0) model with 2 hidden layers has a lower MAPE, which is 37.416% compared to the ARIMAX model. The results show that the Hybrid ARIMAX-ANN model provides better accuracy than the ARIMAX model in rainfall modeling in Jakarta. Applying the Hybrid ARIMAX-ANN model at Kemayoran Station produces better accuracy than the application at Tanjung Priok Station.Keywords: ARIMAX, Hybrid ARIMAX-ANN, Rainfall
Deteksi Komunitas, Analisis Topik, dan Sentimen Isu Palestina-Israel Fathnin Nur Azmina; Muhaza Liebenlito; Dhea Urfina Zulkifli
Jurnal Matematika, Statistika dan Komputasi Vol. 21 No. 2 (2025): JANUARY 2025
Publisher : Department of Mathematics, Hasanuddin University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20956/j.v21i2.41308

Abstract

The combination of community detection, topic modeling, and sentiment analysis provides deep insights into conversation data on the social media platform X (formerly Twitter) regarding the Palestine-Israel issue. The data, collected in Indonesian using several keywords, resulted in 108,969 tweets. The analysis process began with community detection using the Leiden algorithm, which identified five communities. The three dominant communities identified are Community 1 comprising 37.13% of users, Community 2 with 26.95%, and Community 3 with 19.76%. Topic modeling using LDA revealed that these communities focused on various aspects of the conflict. Sentiment analysis using the IndoBERT model uncovered that the majority of users expressed negative attitudes such as disappointment and anger. This study provides insights into public opinions and social dynamics surrounding the conflict.
Perbandingan Deteksi Alzheimer: ViT, CNN dan ViT dengan Bobot pada Citra Medis Salsabila, Aisyah Nur; Liebenlito, Muhaza; Zulkifli, Dhea Urfina
The Indonesian Journal of Computer Science Vol. 13 No. 1 (2024): The Indonesian Journal of Computer Science (IJCS)
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v13i1.3765

Abstract

Penyakit Alzheimer merupakan suatu tipe demensia yang berpengaruh terhadap ingatan, cara berpikir, dan perilaku. Gejala-gejala tersebut dapat menjadi cukup parah sehingga dapat mempengaruhi kegiatan sehari-hari. Dalam penelitian ini, diperkenalkan aplikasi Convolutional Neural Network (CNN) sederhana dan pre-trained model Vision Transformer (ViT) untuk menganalisis data MRI Scan Alzheimer dengan empat kelas, yaitu Mild Demented, Moderate Demented, Non Demented, dan Very Mild Demented. Pada penelitian ini, dilakukan perbandingan pengaplikasian CNN dengan bobot dan ViT yang dilakukan dengan menggunakan dua cara, yaitu dengan bobot dan tidak. Hasil dari penelitian ini menunjukkan bahwa pengaplikasian ViT dengan bobot menghasilkan akurasi yang lebih tinggi dibanding dengan metode lainnya. Dari penelitian ini, diharapkan dapat menganalisa dan mendeteksi penyakit Alzheimer dalam bidang kesehatan dengan efisien.
Pemodelan Indeks Kualitas Udara PM2.5 di Kemayoran, Jakarta, dengan Faktor Meteorologi Menggunakan ARFIMAX Oktavia Laras Dianingati; Mahmudi; Dhea Urfina Zulkifli
The Indonesian Journal of Computer Science Vol. 14 No. 1 (2025): The Indonesian Journal of Computer Science (IJCS)
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v14i1.4663

Abstract

Poor air quality that significantly impacts on health due to fine particles PM2.5 has become one of the serious problems in urban areas such as Kemayoran, Jakarta. This study aims to predict the Air Quality Index of PM2.5 pollutant in Kemayoran, Jakarta, with the ARFIMAX model which will be compared with the ARFIMA model. The ARFIMAX modeling involves meteorological factors as exogenous variables. The research results showed that the ARFIMAX(1,0,33,1) model with significant exogenous variables, namely minimum temperature, average temperature, and wind direction at maximum speed provides better prediction accuracy with a Mean Absolute Percentage Error (MAPE) value of 23.69%, compared to ARFIMA with a MAPE value of 25.76%. This decrease in MAPE value indicates that the addition of exogenous variables in the model can improve the accuracy of air quality forecast.
Analysis of Factors Affecting the Human Development Index in Papua Province Using the Geographically Weighted Panel Regression Model Mahmudi; Firdha Wulandari; Dhea Urfina Zulkifli
Numerical: Jurnal Matematika dan Pendidikan Matematika Vol. 8 No. 1 (2024)
Publisher : Universitas Ma'arif Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25217/numerical.v8i1.5029

Abstract

The level of human quality development between regions or countries can be measured using the Human Development Index (HDI) value. The higher the value of the HDI, the better the quality of human development in the region. Some variables affect the value of the HDI. This study will test six independent variables using the Geographically Weighted Panel Regression (GWPR) method. This GWPR method combines panel data regression with the Geographically Weighted Regression (GWR) method. This GWPR method combines the dimensions of location and time to determine the effect of the independent variable on the dependent variable. Therefore, the purpose of this study is to see which variables have a significant effect on the value of the HDI in Papua Province. By using panel data regression, the best model that can be formed is the Fixed Effect Model (FEM). However, the FEM model that was formed did not meet the heteroscedasticity assumption test on the residuals, so further modeling was carried out using the GWPR model. GWPR modeling on this data uses a kernel weighting function, whereas previously, data transformation was carried out by the concept of the FEM model. The GWPR model with the best kernel weighting function is fixed exponential. In selecting the best model based on the coefficient of determination , the GWPR model is better than the FEM model. Regarding the significance of model parameters, nine groups of districts/cities based on independent variables significantly affect the HDI. In all districts/cities of Papua Province, the per capita expenditure variable significantly affects the HDI's value.
Evaluation of the performance of TBATS and SARIMA methods in forecasting air temperature in Indonesia Aprilia, Ananda; Wijaya, Madona Yunita; Zulkifli, Dhea Urfina
Jurnal Absis: Jurnal Pendidikan Matematika dan Matematika Vol. 8 No. 2 (2025): Jurnal Absis
Publisher : Program Studi Pendidikan Matematika Universitas Pasir Pengaraian

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30606/absis.v8i2.3052

Abstract

Indonesia is a tropical archipelago located along the equator, where air temperature patterns exhibit seasonal trends and unstable fluctuations. This instability can impact several sectors, including agriculture—making it difficult for farmers to determine planting and harvesting times—electricity demand, which increases during hotter periods, and public health, as erratic weather may reduce productivity and elevate the risk of diseases such as dehydration, asthma, and respiratory infections. This study aims to evaluate the performance of the Trigonometric, Box-Cox Transformation, ARMA Errors, Trend, and Seasonal (TBATS) model and the Seasonal Autoregressive Integrated Moving Average (SARIMA) model in forecasting air temperature in Indonesia. The dataset used comprises 2-meter air temperature records in Indonesia from January 1940 to August 2024, obtained from ECMWF. The evaluation method applied is cross-validation with a rolling basis. The results show that the RMSE for the TBATS model is 21.3843%, while the SARIMA model has an RMSE of 21.2958%. These results indicate that SARIMA has a slightly better performance than TBATS. However, both methods perform well in forecasting air temperature in Indonesia, as their RMSE percentages are within an acceptable range. This research is expected to contribute to the scientific literature on air temperature forecasting in Indonesia and encourage further studies on hybrid models that integrate TBATS and SARIMA. Additionally, it may support efforts to mitigate the adverse impacts of air temperature changes in the country.
Social Capital and Family Resilience to The Pandemic: A Systematic Review in the Contemporary World Zulkifli; Nuryaman; Zulkifli, Dhea Urfina; Fahri, Muhamad
El-Usrah: Jurnal Hukum Keluarga Vol. 8 No. 2 (2025): EL-Usrah: Jurnal Hukum Keluarga
Publisher : Universitas Islam Negeri Ar-Raniry Banda Aceh

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22373/rj290a90

Abstract

The management of large-scale societal crises is often impeded by a complex interplay of factors, yet the strategic role of social capital in mitigating such unforeseen global challenges remains inadequately conceptualized. This study aims to systematically examine the contribution of social constitutive capital to the development of family resilience during the pandemic in the contemporary world. Employing a Systematic Literature Review (SLR) methodology, this research analyzed 20 pertinent articles sourced from the Scopus database. The results delineate a multifaceted typology of contributions: social capital functions through direct mechanisms, as a mediating variable alongside other factors, and through indirect pathways. The analysis establishes that social capital (operationalized through trust, networks, norms, and social organization) served as a fundamental pillar of family resilience. Its efficacy was significantly amplified when synergized with local belief systems, technological adoption, entrepreneurial initiatives, and institutional support. Furthermore, social capital acted as a critical mediator in enhancing relational capacities within marginalized demographics. A cross-national analysis of 27 countries highlighted the pivotal role of social trust in alleviating psychological distress, thereby bolstering collective resilience. Conversely, the study also revealed that the impact of social capital on individual resilience was comparatively less significant than that of socioeconomic status, demographic vulnerability, and robust physical infrastructure. These findings provide a seminal framework for understanding the multidimensional utility of social capital in crisis response.
Co-Authors Aprilia, Ananda Bambang Ruswandi Bambang Ruswandi, Bambang Devita Apriliani Fahri, Muhamad Fathnin Nur Azmina Fatihunnada, Fatihunnada Firdha Wulandari Kankan Parmikanti MAHMUDI Mahmudi Mahmudi Muhammad Manaqib, Muhammad Muhaza Liebenlito Nina Fitriyati Nina Fitriyati Nuryaman Oktavia Laras Dianingati Riaman Safitri, Issa Bella Salsabila, Aisyah Nur Wijaya, Madona Yunita Yuliawati, Ayu Kinasih Zainudeen, Musah Issa Zharifah, Zharifah Zulkifli Zulkifli