Article Per Year (5 Year)
p-Index From 2021 - 2026
10.906
P-Index
This Author published in this journals
All Journal Media Statistika JURNAL MATEMATIKA STATISTIKA DAN KOMPUTASI SAINSMAT Chemica: Jurnal Ilmiah Kimia dan Pendidikan Kimia LaGeografiA Journal of Marine Research Jurnal Matematika dan Statistika serta Aplikasinya (Jurnal MSA) Jurnal Environmental Science Seminar Nasional Variansi (Venue Artikulasi-Riset, Inovasi, Resonansi-Teori, dan Aplikasi Statistika) BAREKENG: Jurnal Ilmu Matematika dan Terapan Saintifik : Jurnal Matematika, Sains, dan Pembelajarannya Proximal: Jurnal Penelitian Matematika dan Pendidikan Matematika Abdimas: Jurnal Pengabdian Masyarakat Universitas Merdeka Malang Inferensi PENGABDI: Jurnal Hasil Pengabdian Masyarakat Jurnal Dedikasi ARRUS Journal of Mathematics and Applied Science Journal of Multidisciplinary Applied Natural Science Journal of Mathematics: Theory and Applications Pedagogy : Jurnal Pendidikan Matematika Chemistry Education Review Seminar Nasional Pengabdian Kepada Masyarakat SAINSMAT: Jurnal Ilmiah Ilmu Pengetahuan Alam INOVASI: Jurnal Hasil Pengabdian Masyarakat Indonesian Journal of Fundamental Sciences Venn: Journal of Sustainable Innovation on Education, Mathematics and Natural Sciences Seminar Nasional Hasil Penelitian LP2M UNM Ininnawa: Jurnal Pengabdian Masyarakat Parameter: Jurnal Matematika, Statistika dan Terapannya Jurnal Hasil-Hasil Pengabdian dan Pemberdayaan Masyarakat (JHP2M) SMART: Jurnal Pengabdian Kepada Masyarakat Journal of Mathematics, Computation and Statistics (JMATHCOS) Journal of Community Services and Development
Wahidah Sanusi
Jurusan Matematika FMIPA Universitas Negeri Makassar, 90224
Religion Agriculture, Biological Sciences & Forestry Arts Humanities Astronomy Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Immunology & microbiology Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Medicine & Pharmacology Physics Public Health Social Sciences Transportation Other

Published : 104 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 104 Documents
Search

 7   8   9   10   11 
Forecasting Acute Respiratory Infection Incidence in South Sulawesi Province Through a Hybrid ARIMA–RBFNN Model Muthia Ramadhani Rafli; Muhammad Abdy; Wahidah Sanusi
Journal of Mathematics, Computations and Statistics Vol. 9 No. 2 (2026): Volume 09 Issue 02 (June 2026)
Publisher : Jurusan Matematika FMIPA UNM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35580/ca608g80

Abstract

Abstract. Among all notifiable diseases in Indonesia, Acute Respiratory Infection (ARI) consistently registers the highest national burden of illness. Within South Sulawesi Province alone, the eight-month tally from January through August 2023 surpassed 320,942 confirmed cases, underscoring the critical need for reliable case-number projections to guide evidence-based health-service planning. The present work constructs a time series forecasting framework that integrates ARIMA (Autoregressive Integrated Moving Average) with a Radial Basis Function Neural Network (RBFNN) under the hybrid paradigm proposed by Zhang (2003). Monthly ARI incidence data spanning January 2014 to December 2024 provided 132 observations in total. Following a chronological split, the first 96 data points (January 2014–December 2021) served as the training set and the remaining 36 (January 2022–December 2024) as the hold-out evaluation set. ARIMA captured the linear dynamics of the series, whereas RBFNN was subsequently applied to the ARIMA residuals to account for any nonlinear structure that remained unexplained. Minimum-AIC model selection identified ARIMA(2,1,2) as the most suitable linear specification. For the RBFNN stage, a four-lag input vector—derived from the partial autocorrelation function—combined with four hidden units and a multiquadratic basis function delivered the best generalisation performance. Assessed against MAPE, RMSE, and R², the standalone ARIMA(2,1,2) attained 14.19%, 5038.37, and 0.6275, respectively; RBFNN alone produced 15.47%, 4714.93, and 0.5479; and the Hybrid ARIMA–RBFNN yielded 16.11%, 5014.99, and 0.6309. The superior R² of the combined model demonstrates its enhanced capacity to account for data variability. Because all three models returned MAPE values below the 20% threshold, they qualify as good predictors under the Lewis (1982) classification scheme. On this basis, the hybrid approach is put forward as the preferred tool for ARI early-warning and surveillance operations in South Sulawesi.
Comparison of Support Vector Regression and Random Forest Methods for Rainfall Prediction in Makassar City Ilmadinah Kadir; Wahidah Sanusi; Ja'faruddin Ja'faruddin
Journal of Mathematics, Computations and Statistics Vol. 9 No. 2 (2026): Volume 09 Issue 02 (June 2026)
Publisher : Jurusan Matematika FMIPA UNM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35580/e1mjfd50

Abstract

This study aims to compare the performance of Support Vector Regression (SVR) and Random Forest (RF) methods in predicting daily rainfall in Makassar City and to identify the most influential meteorological factors. The dataset consists of daily climate data from 2019 to 2024, including rainfall as the response variable and temperature, humidity, wind speed, and sunshine duration as predictor variables. Data preprocessing was conducted through missing value imputation, time-series structuring, and normalization using the Z-score method for the SVR model. The SVR model was developed using several kernel functions, including linear, polynomial, radial basis function (RBF), and sigmoid, with hyperparameter tuning performed using grid search and k-fold cross-validation. Meanwhile, the Random Forest model was constructed using bootstrap aggregation and random feature selection, with optimal parameters determined based on the minimum out-of-bag (OOB) error. The results show that the SVR model with the RBF kernel achieved the best performance, with RMSE of 16.52 mm and MAE of 9.01 mm, outperforming the Random Forest model, which produced RMSE of 18.15 mm and MAE of 10.93 mm. Furthermore, feature importance analysis indicates that humidity and temperature are the most dominant variables influencing rainfall. Therefore, the SVR method is more accurate and reliable for rainfall prediction in Makassar City.
SDF Mathematical Model as a Solution to Overcoming Difficulties in Completing Thesis of Students of Mathematics Department of Makassar State University Wahidah Sanusi; Syafruddin Side; Muh. Ishaq Firdaus
Journal of Mathematics, Computations and Statistics Vol. 9 No. 1 (2026): Volume 09 Issue 01 (March 2026)
Publisher : Jurusan Matematika FMIPA UNM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35580/qh78b575

Abstract

This study aims to develop and analyze the SDF mathematical model in addressing difficulties faced by students in completing their theses at the Mathematics Department, Universitas Negeri Makassar, in 2023. The SDF model is derived from the SIR model, which is commonly utilized to analyze disease spread, by adapting specific assumptions relevant to the students' circumstances. The analysis of the model focuses on determining equilibrium points, model stability, and the basic reproduction number , which serves as a critical parameter in understanding the dynamics of the issue's spread. Data for the study were collected from students in the Mathematics Department, Faculty of Mathematics and Natural Sciences, Universitas Negeri Makassar, and were used for model simulations employing Maple 18 software. The analysis results indicate a basic reproduction number value of , implying that the number of students experiencing difficulties in completing their theses is expected to increase over time without intervention. These findings provide valuable insights for the university to comprehend the factors influencing the thesis completion rates among students. It is anticipated that this study can inform the design of more effective strategies and interventions to assist students in overcoming their challenges. The SDF model can also serve as a reference for similar studies in other contexts
Comparison of Naïve Bayes and K-Nearest Neighbor (K-NN) Methods in Classifying Stunting in Toddlers in Takalar Regency Wahidah Sanusi; Irwan Thaha; Aliyah Arianti Halim
Inferensi Vol 9 No 1 (2026)
Publisher : Department of Statistics ITS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j27213862.v9i1.9026

Abstract

Stunting is a chronic nutritional problem that has long-term effects on children's physical growth and cognitive development. Therefore, classifying the nutritional status of toddlers is an important step in early detection and determining appropriate interventions. This study aims to compare the performance of two classification methods, namely Gaussian Naïve Bayes and K-Nearest Neighbor (K-NN), in identifying the nutritional status of toddlers. The data used consisted of 14,620 toddler data obtained from the Takalar District Health Office covering 11 sub-districts. Gaussian Naïve Bayes is a probabilistic classification method with the assumption of independence between variables, while K-NN is a nonparametric method that classifies data based on the proximity of the distance between observations. The results showed that Gaussian Naïve Bayes produced an accuracy of 91.76%, but was unable to accurately classify stunting classes due to class imbalance and low posterior probability values in minority classes. In contrast, the K- NN method with an optimal parameter value of k=3 produced an accuracy of 97.00% and showed better performance in identifying toddlers with stunting status. Based on these results, the K-NN method is considered superior to Gaussian Naïve Bayes in classifying the nutritional status of toddlers in Takalar Regency.
Co-Authors A. Armansyah AHMAD FAUZAN RIDHA SUJIONO ahmad yani Ahmad Zaki AHMAD ZAKI Ahmad Zaky Alimuddin Alimuddin Tampa Aliyah Arianti Halim Amal Amal Amal Amal Amal Arfan, Amal Amni Rasyidah Andi Abidah Andi Diki Nurbaldatun Islam Andini, Reski Anggi Ananda Putri Annas, Suwardi Arkas, Amaliah Nurul Asdar Asdar Asdar Asmi, Nurul Asni, Asriani Arsita Asriani Arsita Asni Astuti - Aswi, Aswi Aulia, Hikma Awi Dassa, Awi Beby Fitriani Besse Nur Afni Besse Nur Afni Bohari, Nurul Aulia Bohari, Nurul Aulia Diki Nurbaldatun Islam Elma Selviana Darwis Fausiatul Iffa Febriyanto Saman Fitriadita Fitriyani Fitriyani Fitriyani Folorunso, Serifat Adedamola G. Gunawan H. Hasriani Haekal, Muh. Fahri Hafilah Hardiono Hafilah. H Hamzah Upu Harisahani, Nur Hasan Basri Hasanah, Afifatun Hasnawiyah, Hasnawiyah Hasriani Hikma Aulia Hisyam Ihsan Ihsan U, Wa Irma Al Ika Pratiwi Ilham Minggi Ilmadinah Kadir Irham Aryandi Basir Irham Aryandi Basir Irma Aswani Ahmad, Irma Aswani Irwan Irwan Irwan Irwan Irwan Irwan Irwan Irwan Thaha Jafaruddin Janide, Anugrah Kahvi Nurani Kaito, Nurlaila Katrina Pareallo Lhenny Ardillah Latif Lisca Palerina Mudinillah, Adam Muh. Idris Muh. Ishaq Firdaus Muhammad Abdy Muhammad Abdy Muhammad Abdy Muhammad Abdy Muhammad Abdy Muhammad Abdy Muhammad Ammar Naufal Muhammad Arif Tiro, Muhammad Arif Muhammad Danial Muhammad Danial Muhammad Danial Muhammad Farhan Muhammad Farhan Muhammad Isbar Pratama Muhammad Rakib Muhammad Rakib Muhammad Rakib Muhammad Syahrir Muhjria, Muhjria Mukarram, Trys Musliati Musliati Mustati'atul Waidah Maksum Muthia Ramadhani Rafli N Nurfadillah N Nurwakia Nasrullah Nasrullah Nirwana, St. Risma Ayu Nur Anny S. Taufieq Nur Anny S. Taufieq Nur Anny S. Taufieq Nur Anny Suryaningsih Taufieq Nur Fajri Setiawan Nur Hikmayanti Syam Nur Khaerati Rustan Nur Ridiawati Nur Ridiawati Nurani, Kahvi Nurazizah Nurdin, Nur Izzah Nurfadillah Nurhilaliyah, Nurhilaliyah Nurkhalifah Anwar Nurlaila Kaito Nurul Aulia Bohari Nurul Fadilah Syahrul Nyulle, Rusdianto Oktaviana Oktaviana Padjalangi, Andi Muhammad Ridho Yusuf Sainon Andi Palarungi, Andi Gagah Patahuddin, Sudarmin Patasik, Ghadytha Marie Lucia Pertiwi, Ika Pince Salempa Putri, Siti Choirotun Aisyah R. Rusli Rabiatul Adawiyah Rabiatul Adawiyah Rahman, Muhammad Fatur Rahmat Setiawan Rahmat Syam Rahmawati, Rahmawati Reski Andini Risna Ulfadwiyanti Rosidah Rosidah Ruliana Rustan, Nur Khaerati S Sukmawati Sahlan Sidjara Saiful Bahri Saman, Febriyanto Sari, Yulfiana Serly Diliyanti Restu Ningsih Serly Diliyanti Restu Ningsih Setiawan, Nur Fajri Sidjara, Sahlan Siti Helmyati Sudarmin Sudarmin Sukarna Sukarna Sukarna Sukarna Sukarna Sulaiman Sulaiman Suwardi Annas Syafruddin Side SYahnur, Andi Aulia Syuhri, Ajrian Takdir, Nurfajri Hamdani Talib, Dr. Ahmad Tampa, Alimuddin Taty Sulastri Taty Sulastri Taty Sulastri Thaha, Irwan Trys Mukarram Ulfadwiyanti, Risna Usman Mulbar Utami Priono Wahyuliani, Dwi Wahyuni, Maya Sari Wulandari, Natalia Puspita Yusuf S.A.P., Andi Muh. Ridho Zainal, Zaid