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All Journal Jurnal Sains dan Seni ITS Jurnal Statistika Universitas Muhammadiyah Semarang Techno.Com: Jurnal Teknologi Informasi Jurnal Buana Informatika Jurnas Nasional Teknologi dan Sistem Informasi Jurnal Ilmiah Teknik Industri Jurnal Pendidikan Agama Islam Jurnal Fourier Prosiding SI MaNIs (Seminar Nasional Integrasi Matematika dan Nilai-Nilai Islami) Journal of Information Technology and Computer Science Desimal: Jurnal Matematika Al-MARSHAD: Jurnal Astronomi Islam dan Ilmu-Ilmu Berkaitan BAREKENG: Jurnal Ilmu Matematika dan Terapan MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer Jambura Journal of Mathematics MAJAMATH: Jurnal Matematika dan Pendidikan Matematika Contemporary Mathematics and Applications (ConMathA) Journal of Mathematics Education and Science Euler : Jurnal Ilmiah Matematika, Sains dan Teknologi Enthusiastic : International Journal of Applied Statistics and Data Science MATHunesa: Jurnal Ilmiah Matematika Jurnal Matematika Integratif STATISTIKA International Journal of Computing Science and Applied Mathematics-IJCSAM
Hani Khaulasari
Universitas Islam Negeri Sunan Ampel, Surabaya, Indonesia
Arts Humanities Astronomy Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Materials Science & Nanotechnology Mathematics Mechanical Engineering Physics Social Sciences Transportation

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Analysis of Factors that Influence Maternal Mortality Rates Using Generalized Poisson Regression pratiwi, Yuniar Ines; Khaulasari, Hani; Farida, Yuniar; Ferdani, Ayu
Enthusiastic : International Journal of Applied Statistics and Data Science Volume 5 Issue 2, October 2025
Publisher : Universitas Islam Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20885/enthusiastic.vol5.iss2.art2

Abstract

Maternal Mortality Rate (MMR) is the number of deaths of women within 42 days after childbirth or during pregnancy. Objective: This study aims to identify factors affecting MMR in East Java and compare the performance of the Generalized Poisson Regression (GPR) model with Poisson regression. The method used is Generalized Poisson Regression, a regression model for count data, which extends Poisson regression to overcome the problem of overdispersion or underdispersion with data derived from the East Java Health Office, including MMR as the dependent variable, as well as five variables that are thought to affect it in 38 districts/cities. The GPR model proved superior to Poisson regression with an Akaike Information Criterion (AIC) value of 239.515 to identify factors affecting maternal mortality. Factors such as delivery handled by health workers, K6 visits by pregnant women, provision of diphtheria-tetanus immunization, and obstetric complications affect MMR in East Java in 2022.
Model Geographically Weighted Regression Menggunakan Adaptive Gaussian Kernel untuk Pemetaan Faktor Penyebab Stunting Vianti, Febi; Khaulasari, Hani; Farida, Yuniar; Swantika, Cicik; Efendi, Havid
Euler : Jurnal Ilmiah Matematika, Sains dan Teknologi Volume 12 Issue 2 December 2024
Publisher : Universitas Negeri Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37905/euler.v12i2.28072

Abstract

Stunting is a child growth disorder that is evident from a lack of height for age. Jember Regency has a stunting prevalence rate of 34.90% in 2022, making it the region with the highest stunting cases in East Java. The purpose of this research is to map the factors that influence stunting in Jember Regency with a spatial analysis approach. The method applied in this study is Geographically Weighted Regression (GWR) to analyze the spatial relationship between predictors and responses. GWR uses an optimal kernel to determine the spatial weights based on distance accurately, as well as the AIC and  goodness criteria to calculate the goodness of the model. The research variables include the number of stunting cases in Jember Regency as the response variable (Y), and the predictor variables (X) are chronic energy deficiency pregnant women (), anemic pregnant women (), exclusive breastfeeding (), proper sanitation (), pregnant women consuming TTD at least 90 days (), complete basic immunization (), and wasting (). The results of the study using the adaptive gaussian kernel with the minimum CV compared to other kernels can improve accuracy, so it can be applied to data analysis.  The GWR model obtained an accuracy of 80.59% and AIC 360.  indicates the ability to explain 80.59% of the variability of the response data, and the AIC value is 360, which reflects the efficiency and suitability of the model to spatial data. From the GWR parameters, 14 groups were formed where there are several different factors in each area in the sub-districts in Jember Regency.
Modelling the Effect of Calendar Variation in the GSTARIMAX For Predicting Nitrogen Monoxide Air Quality Khaulasari, Hani; Akbar, Jeneiro Rezkyansyah Maulana
Euler : Jurnal Ilmiah Matematika, Sains dan Teknologi Volume 13 Issue 3 December 2025
Publisher : Universitas Negeri Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37905/euler.v13i3.33830

Abstract

Nitrogen monoxide (NO) pollution has had a devastating impact on the environment and public health in Surabaya. This study aims to determine the best prediction model and forecast nitrogen monoxide concentrations in the April 2024 period. The method used is the GSTARIMAX model, which integrates the influence of calendar variation as well as spatial weight. Calendar factors such as school holidays, Christmas, New Year, and Eid al-Fitr are included as pseudo-exogenous variables (dummy). Data was obtained from three air quality monitoring points in Surabaya, namely SPKU Wonorejo, Kebonsari, and Tandes, throughout January 2023 to March 2024. Parameter estimation in the GSTARIMAX model used the Generalized Least Squares (GLS) and Ordinary Least Squares (OLS) approaches. This study also compares three types of spatial weights and compares the performance of the GSTARIMAX model with other models that consider or ignore calendar variations. The results of the analysis show that significant parameters are derived from the AR(1) model, so that the GSTARIX-SUR(1) model with first-order spatial lag and cross-normalized correlation weight provides the best performance, indicated by the sMAPE value below 10% and the lowest RMSE value. In addition, this model also meets the assumptions of white noise and normal distribution. Fluctuations in nitrogen monoxide concentrations during April 2024 show fairly high volatility, with a significant spike occurring on April 12–14, 2024. The increase is correlated with the return flow of people from outside the city to Surabaya after the Eid al-Fitr holiday.
Implementasi K-Means Clustering Melalui Pemanfaatan Sampling Kombinasi Pada Pengelompokan Pola Kesehatan Mental Mahasiswa Sains dan Teknologi Firda Sari; Maharani Kuntari; Winda Yati; Hani Khaulasari; Moh. Hafiyusholeh
Jurnal Nasional Teknologi dan Sistem Informasi Vol 11 No 1 (2025): April 2025
Publisher : Departemen Sistem Informasi, Fakultas Teknologi Informasi, Universitas Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25077/TEKNOSI.v11i01.2025.9-16

Abstract

Kesehatan mental merupakan aspek kesehatan penting selain kesehatan fisik. Mahasiswa merupakan individu yang berada pada usia remaja akhir sampai dewasa awal yang pada masa ini akan mengalami tekanan secara emosional karena masalah-masalah sosial, akademik, dan personal. Perlu diadakan pengecekan dini pada kesehatan mental mahasiswa seperti asesmen psikologi yang dilakukan untuk pencegahan gangguan mental yang dihadapi mahasiswa sehingga dapat mengurangi angka bunuh diri. Tujuan dari penelitian ini adalah untuk mendapatkan kelompok pola kesehatan mental mahasiswa untuk diidentifikasi pola dan tren dengan algoritma K-Means clustering dan dievaluasi dengan silhouette coefficient untuk memastikan keakuratan dan validitas dari hasil clustering. Data penelitian diperoleh dari pengisian angket mengenai kondisi kesejahteraan psikologis  dan tekanan psikologis  yang maing-masingnya terdiri dari 5 pertanyaan. Penelitian ini memperoleh hasil setelah dikelompokkan menjadi 3 cluster yaitu tertekan (C1), netral/stabil (C2), dan bahagia (C3), pada mahasiswa sistem informasi tidak ada cluster yang dominan karena di setiap cluster memiliki jumlah data yang sama, mahasiswa arsitektur dan matematika dominan mahasiswa yang memiliki kesehatan mental yang tertekan, mahasiswa biologi dominan mahasiswanya memiliki kesehatan mental yang netral. Berdasarkan 4 program studi hasil evaluasi cluster pada program studi system informasi dan matematika memiliki struktur yang lemah, sedangkan pada program studi arsitektur dan biologi memiliki struktur yang sedang.
Clustering Couples of Childbearing Age to Get Family Planning Counseling Using K-Means Method Yuniar Farida; Adam Fahmi Khariri; Dian Yuliati; Hani Khaulasari
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol. 22 No. 1 (2022)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/matrik.v22i1.1888

Abstract

Couples of Childbearing Age (CCA) in the Madiun Regency have increased in the last three years. It caused the population in Madiun to overgrow with the newborn, which implies the economic, social, and environmental aspects. This study aims to cluster villages in Madiun with CCA case studies instead of birth control participants who will give birth and want children to determine the priority of getting Family Planning (in Indonesia, namely Keluarga Berencana/KB) counseling. K-Means clustering is used in this study because it has a linear space of complexity that can be executed quickly and easily. The result of this study is four (4) CCA clusters. CCA cluster 1 is a very high level of giving birth and wanting children, consisting of 7 villages. CCA cluster 2 is a high level of giving birth and wanting children with 119 villages. CCA cluster 3 is a medium level of giving birth and wanting children in 50 villages, and CCA cluster 4 is a low level of giving birth and wanting children, including 34 villages. So, cluster 1, which includes seven villages, is the most prioritized to get Family Planning counseling because it is the CCA cluster with the most birthing rate and wants children. This research obtained a silhouette coefficient of 0.42, which belongs to the medium level.
Comparison of Support Vector Machine Performance with Oversampling and Outlier Handling in Diabetic Disease Detection Classification Firda Yunita Sari; Maharani sukma Kuntari; Hani Khaulasari; Winda Ari Yati
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol. 22 No. 3 (2023)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/matrik.v22i3.2979

Abstract

Diabetes mellitus is a disease that attacks chronic metabolism, characterized by the body’s inability to process carbohydrates, fats so that glucose levels are high. Diabetes mellitus is the sixth cause of death in the world. Classifying data about diabetes mellitus makes it easier to predict the disease. As technology develops, diabetes mellitus can be detected using machine learning methods. The method that can be done is the support vector machine. The advantage of SVM is that it is very effective in completing classification, so it can quickly separate each positive and negative point. This study aimed to obtain the best SVM classification model based on accuracy, sensitivity, and precision values in detecting diabetes by adding Synthetic Minority Over-Sampling Technique (SMOTE) and handling outliers. The SMOTE method was applied to handle class imbalance. The Support Vector Machine (SVM) method aimed to produce a function as a dividing line or what can be called a hyperplane that matches all input data with the smallest possible error. The data studied were indications of diabetes, consisting of 8-factor variables and 1 class variable. The test results show that the SVM-SMOTE scenario produces the best accuracy. The SVM SMOTE scenario produced an accuracy value of the RBF kernel of 88% with an error of 12%, and this is obtained from the division of test data and training data of 90:10. This SVM-SMOTE scenario produced a precision value of 0.880 and a sensitivity value of 0.880. The research results showed that factor classification was more accurate if it is carried out using the support vector machine (SVM) method with imbalance data handling (SMOTE), and it can be concluded that the distribution of test data and training data influences a test scenario.
Analyzing Factors Contributing to Gender Inequality in Indonesia using the Spatial Geographically Weighted Logistic Ordinal Regression Model Hani Khaulasari; Yuniar Farida
(IJCSAM) International Journal of Computing Science and Applied Mathematics Vol. 10 No. 2 (2024)
Publisher : LPPM Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j24775401.ijcsam.v10i2.4529

Abstract

Abstract—Gender inequality is a condition of discrimination caused by social systems and structures. The main objective of this research is to identify factors that influence gender inequality in each province in Indonesia and obtain classification accuracy values using Geographically Weighted Ordinal Logistic Regres- sion (GWOLR). The dataset used in this research consists of a response variable, namely the gender inequality index where theindex value is divided into ordinal categories (low, medium, and high) and four predictor variables from the dimensions of health,education, human empowerment, social-culture, and work. Theresults of this study show that the classification accuracy of theGWOLR model is 85%. The mapping of provinces in Indonesiabased on influential variables forms three groups. The first group(brown) is influenced by the percentage of women who givebirth with the assistance of health workers (X 1 ) and the femaleHuman Development Index (HDI) (X3 ). The second group (blue)is influenced by the ratio of women’s Pure Participation Rate(APM) (X 2 ) and the percentage of rape crimes against women(X 4 ). The third group (red) is influenced by the percentage ofwomen who give birth with the assistance of health workers (X1),the ratio of women’s Pure Participation Rate (APM) (X2 ), thepercentage of women’s Human Development Index (HDI) ratio(X 3 ), and the percentage of women’s rape crimes (X4 ).
Prediction of Wastewater Treatment Revenue Based on Volume and Number of Transactions Using the Long Short-Term Memory (LSTM) Method Maulana, Aashif Amiruddin; Khaulasari, Hani; Novitasari, Dian Candra Rini; Pramono, Wahyu Joko
Journal of Information Technology and Computer Science Vol. 10 No. 3: Desember 2025
Publisher : Faculty of Computer Science (FILKOM) Brawijaya University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jitecs.2025103806

Abstract

This study aims to develop a prediction model for the total Revenue value of the operational activities of the Keputih Surabaya Sewage Sludge Treatment Plant (IPLT) using the Long Short-Term Memory (LSTM) method. The data used is daily data on total transactions and total Revenue from January 2022 to April 2025. Data normalization using the Min-Max method and outlier detection and handling using the IQR and median imputation techniques are examples of preprocessing steps. The model input structure is formed by utilizing Partial Autocorrelation Function (PACF) analysis to ascertain the number of lags. In this study, 405 model combinations are tested with different parameters, including activation function, number of Epochs, learning rate, and ratios of training and testing data. According to the findings, the model that has the optimal parameters a training and testing data ratio of 80:20, 50 Epochs, a learning rate of 0.002, a Tanh activation function, and 100 neurons can produce predictions for total Revenue with a Mean Absolute Percentage Error (MAPE) of 18.18%. The revenue for the following six months was then forecast using this model; the highest revenue forecast was IDR 3,740,085.00, while the lowest was IDR 1,966,628.25. According to these results, LSTM can accurately forecast time series-based income fluctuations and may find use in the waste management industry's financial decision-making and strategic planning processes.
Optimalisasi Blended Learning Model Flipped Classroom pada Perkuliahan Time Series di Prodi Matematika Khaulasari, Hani
MAJAMATH: Jurnal Matematika dan Pendidikan Matematika Vol. 5 No. 1 (2022): Vol. 5 No. 1 Maret 2022
Publisher : Prodi Pendidikan matematika Universitas Islam Majapahit (UNIM), Mojokerto, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36815/majamath.v5i1.1756

Abstract

Metode blended learning model Flipped Classroom merupakan proses belajar mengajar dengan cara memadukan pembelajaran tatap muka (synchronous) dan (asynchronous) berbasis Learning Management System serta model pembelajaran terbalik dari metode tradisional. Tujuan penelitian adalah mengevaluasi dari penerapan optimalisasi blended learning flipped classroom pada perkuliahan time series. Sampel penelitian adalah mahasiswa Prodi Matematika yang mengambil mata kuliah Time Series semester Ganjil 2021/2022 sebanyak 33 mahasiswa. Hasil belajar Mahasiswa sebelum (KUIS 1) dan sesudah (UTS) diterapkan metode blended learning model Flipped Classroom di uji paired t-test kemudian melakukan analisis kualitas pembelajaran dengan menghitung indeks kualitas layanan dan analisis GAP. Penerapan blended learning Flipped Classroom telah terbukti optimal dalam meningkatkan hasil belajar mahasiswa karena hasil belajar mahasiswa setelah penerapan pembelajaran blended learning Flipped Classroom lebih tinggi daripada nilai hasil belajar mahasiswa sebelum penerapan pembelajaran blended learning Flipped Classroom. Kualitas layanan pembelajaran blended learning Flipped Classroom sudah baik, akan tetapi ada beberapa indikator kualitas yang perlu diperbaiki yakni Fasilitas hotspot/Paket data internet (A1), Pengembalian hasil koreksi tugas, kuis, UTS dan UAS kepada mahasiswa (b5) dan Intensitas dosen untuk ditemui dalam rangka konsultasi (c1).
Application of Support Vector Regression (SVR) for Revenue Prediction Based on Total Transactions and Total Waste Volume Maliki, Naufal Ridho; Khaulasari, Hani; Novitasari, Dian Candra Rini; Pramono, Wahyu Joko
Desimal: Jurnal Matematika Vol. 9 No. 1 (2026): Desimal
Publisher : Universitas Islam Negeri Raden Intan Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24042/djm.v9i1.29190

Abstract

Reliable revenue forecasting is critical for ensuring the financial sustainability of urban sanitation infrastructure, particularly in publicly managed fecal sludge treatment systems where demand fluctuates and operational planning depends on daily service variability. However, revenue patterns in such systems are typically nonlinear, volatile, and influenced by interrelated operational factors, limiting the effectiveness of conventional linear forecasting approaches. This study develops a data-driven predictive framework using Support Vector Regression (SVR) to model daily retribution revenue at the Keputih Fecal Sludge Treatment Plant (IPLT Keputih), Surabaya. The dataset comprises 1,213 daily observations from January 2022 to April 2025, incorporating total transactions and total sludge volume as predictor variables and total revenue as the response variable. Three kernel configurations—Linear, Polynomial, and Radial Basis Function (RBF)—were systematically evaluated following Min–Max normalization and chronological training–testing separation. Model performance was assessed using Mean Absolute Percentage Error (MAPE). The results demonstrate that the SVR model with the RBF kernel achieved the highest predictive accuracy, yielding a MAPE of 17.17%, outperforming the Linear and Polynomial kernels in capturing nonlinear revenue dynamics. Forecast projections further reveal cyclical seasonal tendencies with direct implications for operational scheduling and short-term budget allocation. By integrating machine learning–based forecasting into public sanitation revenue modeling, this study contributes to advancing data-driven financial planning strategies for sustainable urban service management.
Co-Authors Abd Rachman Assegaf Abdulloh Hamid Achmad Teguh Wibowo Adam Fahmi Khariri Ahmad Hanif Asyhar Akbar, Jeneiro Rezkyansyah Maulana Amjad Ali Aris Fanani Armunanto, Firman Dian Candra Rini Novitasari Dian Yuliati Efendi, Havid FAJAR SETIAWAN Fajar Setiawan Farmita, Mayandah Ferdani, Ayu Firda Sari Firda Yunita Sari Hafiyussholeh, Moh. Hamidah, Isna Inayah, Jauharotul Karisma Indra Pitaloka Lutfi Hakim M. Imron M. Lail Kurniawan Maharani Kuntari Maharani sukma Kuntari Maliki, Naufal Ridho Maulana, Aashif Amiruddin Micha Annata Shinami Moh. Hafiyusholeh Mohammad Rizal Abidin Muhammad Thohir Nafa Nur Nanda Adifia Novitasari, Dian C Rini Nur Aulia, Shofinatul Wahdah Pramesti, Diah Devi Pramono, Wahyu Joko pratiwi, Yuniar Ines Purhadi Purhadi Putri Krismadewi Putroue Keumala Intan Ratna Pangastuti Rudi Antonius S, Eka Eliyana Sari, Lia Puspita Siti Nurlela SNM, Diva Ayu Subaekti, Hendrik Suhaeri, Fadjar Susanto, Hugeng Susilo Ari Wardani Swantika, Cicik Tiarra Dellaviyanie Muryanto Vianti, Febi Wahyudi, Sharenada Norisdita Wika Dianita Utami Wika Dianita Utami Winda Ari Yati Winda Yati Yuliati, Dian Yuniar Farida Zumrotul Muallifah