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All Journal Jurnal Gizi dan Pangan Jurnal Matematika dan Statistika serta Aplikasinya (Jurnal MSA) BAREKENG: Jurnal Ilmu Matematika dan Terapan EIGEN MATHEMATICS JOURNAL Majalah Ilmiah Matematika dan Statistika (MIMS) Journal of Mathematics: Theory and Applications STATISTIKA Pangulu Abdi: Jurnal Pengabdian Kepada Masyarakat Journal of Mathematics, Computation and Statistics (JMATHCOS)
Nurfadilah, Khalilah
Universitas Islam Negeri Alauddin Makassar
Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Energy Engineering Environmental Science Health Professions Mathematics Mechanical Engineering Medicine & Pharmacology Physics Social Sciences Transportation Other

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Penerapan model regresi multilevel untuk data ketepatan waktu lulus mahasiswa Ula, Rahmatul; Ibnas, Risnawati; Nurfadilah, Khalilah; Nawawi, M. Ichsan; Asfar, Asfar
Majalah Ilmiah Matematika dan Statistika Vol 23 No 1 (2023): Majalah Ilmiah Matematika dan Statistika
Publisher : Jurusan Matematika FMIPA Universitas Jember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.19184/mims.v23i1.34479

Abstract

Multilevel logistic regression is one of the alternatives to solving a problem that has a nested data structure like the student data in Alauddin in 2016. The data indicates that students are nested in each different study program. This condition allows the students in the same study program tend to have similar characteristics. The study aims to gain a student graduating model of punctuality using multilevel regression analysis and recognize factors that have a significant impact on student graduating time. Based on our research, we find the best model that fits the data to be the random intercepts model with a random slope of gender variable. The variables that have significant effects are gender, cumulative achievement index, educational background, and accredited program. Keywords: logistic regression, nested, multilevel logistic regression, graduation of studentMSC2020: 62J05
Negative Binomial and Generalized Poisson Regression Model for Death Due to Dengue Hemorrhagic Fever Data Risnawati Ibnas; Satriani Satriani; Khalilah Nurfadilah
Eigen Mathematics Journal Vol. 6 No. 1 Juni 2023
Publisher : University of Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/emj.v6i1.153

Abstract

Data on the number of deaths due to Dengue Fever in statistics is count data often approximated by a Poisson distribution. However, if overdispersion occurs, Poisson regression is no longer sufficient, so the Negative Binomial and Generalized Poisson Regression approaches are used. From the two models, the best model was chosen based on the smallest AIC value, 66.50, namely the Negative Binomial Regression model. From this model, factors that have a significant effect are determined based on the p-value, and the factor ratio of health facilities per 100,000 population  is obtained.
Analisis Runtun Waktu untuk Peramalan Banyaknya Kejadian Kecelakaan Lalu Lintas Di Kabupaten Soppeng Yusran, Andi Muhammad Fauzan Shadiq; Irwan; Nurfadilah, Khalilah
Jurnal MSA (Matematika dan Statistika serta Aplikasinya) Vol 13 No 1 (2025): VOLUME 13 NO 1 TAHUN 2025
Publisher : Universitas Islam Negeri Alauddin Makassar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24252/msa.v13i1.56782

Abstract

Analisis runtun waktu merupakan salah satu metode statistika yang banyak digunakan dalam meramalkan kejadian di masa mendatang. Beberapa metode yang popular adalah metode Autoregressive Integrated Moving Average (ARIMA) dan fuzzy time series. Metode-metode ini dapat diaplikasikan dalam peramalam jumlah kecelakaan lalu lintas sebagai antisipasi terhadap terjadinya, serta adanya peningkatan terhadap jumlah kecelakaan. Tujuan dari penelitian ini adalah mengimplementasikan model ARIMA, serta fuzzy time series dalam data jumlah kecelakaan lau lintas di Kabupaten Soppeng. Metode yang terbaik dipilih berdasarkan nilai MAPE dan MAD terkecil. Berdasarkan hasil analisis diketahui model time series yang terbaik dihasilkan oleh metode Fuzzy Time Series model Singh dengan nilai MAPE 12,817% dan MAD 0,77. Model ini kemudian digunakan untuk meramalkan jumlah kecelakaan lalu lintas di kabupaten Soppeng dengan hasil ramalan banyaknya kejadian di setiap bulan adalah 10 kejadian.
Model Penyebaran Covid-19 di Provinsi Sulawesi Selatan Menggunakan Poisson Inverse Gaussian Regression (PIGR) Munawwarah; Adnan Sauddin; Nurfadilah, Khalilah; Asfar
Jurnal MSA (Matematika dan Statistika serta Aplikasinya) Vol 13 No 1 (2025): VOLUME 13 NO 1 TAHUN 2025
Publisher : Universitas Islam Negeri Alauddin Makassar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24252/msa.v13i1.56944

Abstract

Artikel ini membahas tentang model penyebaran Covid-19 di Provinsi Sulawesi Selatan menggunakan regresi Poisson Inverse Gaussian (PIG). Covid-19 merupakan penyakit menular yang berpotensi menimbulkan kedaruratan kesehatan masyarakat, hal ini disebabkan karena penyakit tersebut dapat menular melalui droplet yang keluar dari dari batuk, bersin, hingga napas orang yang terinfeksi Covid-19. Tujuan penelitian ini yaitu untuk mendapatkan model penyebaran Covid-19 di Provinsi Sulawesi Selatan menggunakan regresi Poisson Inverse Gaussian (PIG). Model rata-rata penyebaran Covid-19 di Provinsi Sulawesi Selatan menggunakan regresi Poisson Inverse Gaussian (PIG)
MULTILEVEL REGRESSIONS FOR MODELING MEAN SCORES OF NATIONAL EXAMINATIONS Nurfadilah, Khalilah; Aidi, Muhammad Nur; Notodiputro, Khairil A.; Susetyo, Budi
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 18 No 1 (2024): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol18iss1pp0323-0332

Abstract

National Exam known as UN score is the final evaluation to determine the achievement of national graduate competency standards in the school. The determinants of the achievement of the standards can’t be separated from the role of schools and local governments in which this regard is known as nested. In the field of statistics, this phenomenon can be described with a multilevel model, where level-1 is the school while level-2 is the district where the school is located. Several multilevel models are used to describe the phenomenon, the result shows that the two-level regression model without interaction is selected as the best model and the variables which affect the UN average scores significantly at level-1 are school status , the ratio between laboratories and students , while the variable at level-2 is expenditure per capita of district/city . From this study, that educational institutions' steps in achieving a graduation standard can be right on the target.
Exploring Diabetes Mellitus Risk Patterns with Multiple Correspondence Analysis at Torabelo Hospital, Central Sulawesi Asfar; Fadjryani; Ambarwati B. Sarabi , Valina; Jannah , Miftahul; Sartika, Dewi; Setiawan, Aldi; Nurfadilah, Khalilah
Jurnal MSA (Matematika dan Statistika serta Aplikasinya) Vol 13 No 2 (2025): VOLUME 13 NO 2, 2025
Publisher : Universitas Islam Negeri Alauddin Makassar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24252/msa.v13i2.59040

Abstract

This study aims to explore the pattern of diabetes mellitus risk factors among patients at Torabelo Regional Hospital in Sigi using Multiple Correspondence Analysis (MCA). A total of 465 patients were analyzed based on eight categorical variables, including age, gender, blood glucose levels, and lipid profiles. MCA was applied to identify inter-category relationships and visualize them in a low-dimensional space. The results show that most diabetes patients were female, aged 46 years and above, and had high fasting glucose, low HDL, and high LDL levels. The analysis identified two main patterns: a group with a low-risk metabolic profile who were not diagnosed with diabetes, and a group with a combination of high-risk metabolic categories who were more likely to already have diabetes. A distinct subgroup with extremely high triglyceride levels was also identified, indicating a rare but significant metabolic pattern. The first two dimensions of the MCA explained more than 40% of the data variation, providing sufficient support for meaningful visual interpretation. These findings demonstrate that MCA is effective in simplifying complex categorical data and supports risk-based segmentation strategies for early intervention planning in primary healthcare services, particularly in regions with high diabetes prevalence.
Logistik Regression Analysis of Factor Influencing Drung Abuse Cases for Inmates in Class IIA Parepare Prison Ale Miftahulhaer, Ale Miftahulhaer; Wahidah Alwi; Adnan Sauddin; Khalilah Nurfadilah
Journal of Mathematics, Computations and Statistics Vol. 8 No. 2 (2025): Volume 08 Nomor 02 (Oktober 2025)
Publisher : Jurusan Matematika FMIPA UNM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35580/jmathcos.v8i2.9448

Abstract

Drugs are addictive substances that can have a negative impact on the body, especially on the central nervous system. Drug abuse can be caused by various factors including parental influence, knowledge, attitudes, family structure, peer pressure, and community environment. The purpose of this study was to identify the factors asociated with drug abuse cases in Class IIA Parepare Prison. The sample consisted of 85 respondebts from cases related to drug abuse in the prison. Logistic regression analysis was ued, with drug abuse status (using drugs/not using drugs) as the dependent variable and gender, age, knowledge, family, peers, and community environment as independent variables. The results of this study indicate that a high level of knowledge has a regression odds ratio of 13.6489. This indicate that inmates with higher knowledge about drugs had a significantly greater likelihood of avoiding drug abuse compared to those with lower knowledge.
Analilis Pendekatan Metode Vector Autoregressive (VAR) dalam Meramalkan Jumlah Pengadaan Beras di Sulawesi Selatan Yulia, Yulia Novita Sari; Adnan Sauddin; Khalilah Nurfadilah
Jurnal MSA (Matematika dan Statistika serta Aplikasinya) Vol 12 No 2 (2024): VOLUME 12 NO 2 TAHUN 2024
Publisher : Universitas Islam Negeri Alauddin Makassar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24252/msa.v12i2.32340

Abstract

This study discusses forecasting the amount of rice procurement in South Sulawesi. The need increases every year because it is one of the staple foods of the Indonesian population that is consumed everyday. Related to this, to ensure the availability of stock of rice suppiles throughout the South Sulawesi region, this is done by estimating the number of procurements in terms of rice prices and rice production. The purpose of this study is to determine the forecasting model, the variables that have a relationship between variables and the results of forecasting the amount of rice procurement. The results obtained in this study indicate that the smallest AIC value is found in the length of lag 2 so that the model used is the VAR model (3). In addition, all the variables used have a significant effect. Then from the forecasting results obtained, the rice price variabes (Y1) and the amount of rice production (Y2) have MAPE values of 20,4 % dan 14,0 %, which means the forecasting results are good. The results of the forecast number of procurement based on the price in term of the price of rice and the amount of production in the text five years has increased every year.
Identifying the Characteristics of Pregnant Women with Inflammation/Infection in Indonesia Nur Aidi, Muhammad; Efriwati, Efriwati; Suryanty, Santy; Rahman, La Ode Abdul; Nurfadilah, Khalilah; Ernawati, Fitrah
Jurnal Gizi dan Pangan Vol. 17 No. 3 (2022)
Publisher : The Food and Nutrition Society of Indonesia in collaboration with the Department of Community Nutrition, IPB University

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (406.816 KB) | DOI: 10.25182/jgp.2022.17.3.177-186

Abstract

Infection in pregnant women is common and one of the highest causes of death in Indonesia. Reducing infection conditions through early infection prevention needs to be done, one of which is by knowing the characteristics that contribute to the incidence of infection in pregnant women in Indonesia. This study used the Classification and Regression Tree (CART) method to determine the pregnant women with infections and not infections characteristics and classify them. The results of the CART analysis found that seven variables contributed to separating infected and not-infected status in pregnant women, they are nutritional status based on Body Mass Index (BMI), history of anemia, pregnancy distance, Chronic Energy Deficiency (CED) status, ages, socioeconomic and gestational age. Characteristics of the highest incidence of infection, namely 79%, occurred in the group of pregnant women with overweight – obese (BMI>25.0), anemia and pregnancy distance <3 years. The classification analysis of the CART method in this study resulted in the accuracy of identification performance which was still not good, with an accuracy value of 52.78%. It is necessary analysis with other classification methods such as the Chi-square Automatic Interaction Detection (CHAID) in the future.
Co-Authors Adiatma Adnan Sauddin Ahmad Zikir Aldi Setiawan, Aldi Ale Miftahulhaer, Ale Miftahulhaer Ambarwati B. Sarabi , Valina Andi Mariani Anugrawati, Sri Dewi Asfar Asfar Asfar, Asfar Asmia Astrid Putri Shafira Asyifah Qalbi Azlam Nas Bagus Sartono Basri, Nurzarina Budi Susetyo C, Fery Ramadhan DEWI SARTIKA Efriwati Efriwati Ermawati Fadjryani, Fadjryani Fitrah Ernawati FITRI RAHMAWATI Hasmira Irwan Irwan Kasse Iyut Wahyu Saputri Jannah , Miftahul Khairil Anwar Notodiputro La Ode Abdul Rahman Lestari, Nila Lukmar T Muafikah Tul Azizah Muh. Algifari Alayubi Muhammad Ihsan Salim Muhammad Nur Aidi Munawwarah Munira Munira Anwar Musdalifah Nawawi, M. Ichsan Nur Aidi, Muhammad Nur Ismi Tahir Nur Pratiwi Baso Nurfadilah Nurhikma Nurma, Try Azisah Nurul Qalbi Putri Saras Rayyan, Ismi Risnawati Ibnas Satriani Satriani Suryanty, Santy Tiara Anastasya Halik Ula, Rahmatul Wahidah Alwi Wahidah Alwi Winda Saputri Yuli Hastuti Yulia, Yulia Novita Sari Yusran, Andi Muhammad Fauzan Shadiq Zulfikar Alifahmi