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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 JBMP (Jurnal Bisnis, Manajemen dan Perbankan) 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 Journal of Mathematics Education and Science Euler : Jurnal Ilmiah Matematika, Sains dan Teknologi Enthusiastic : International Journal of Applied Statistics and Data Science 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 Mathematics Mechanical Engineering Physics Social Sciences Transportation

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Service Quality to the Level of Customer Satisfaction and Loyalty at Banking in Surabaya Using the SEM Method: Kualitas Layanan hingga Tingkat Kepuasan dan Loyalitas Pelanggan di Perbankan di Surabaya Menggunakan Metode SEM Farida, Yuniar; Nadiyah, Fithrotun; Khaulasari, Hani
JBMP (Jurnal Bisnis, Manajemen dan Perbankan) Vol. 11 No. 2 (2025): September: JBMP Vol.11 No. 2 2025
Publisher : Universitas Muhammadiyah Sidoarjo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21070/jbmp.v11i2.2122

Abstract

Service quality in the banking sector has become a primary focus to ensure customer satisfaction and loyalty. This study aims to analyze the influence of service quality on customer satisfaction, customer satisfaction on customer loyalty, and service quality on customer loyalty at the Banking in Surabaya Branch. The sample obtained from the questionnaire consists of 160 customers. The data analysis technique used in this study is the SEM method, which analyzes the relationships between variables in a model. The SEM method also includes the role of a mediating variable, which is customer satisfaction, between service quality and customer loyalty. The results of this study indicate that service quality significantly impacts customer satisfaction, and customer satisfaction also significantly influences customer loyalty. Additionally, service quality has a direct impact on customer loyalty. Moreover, service quality indirectly affects customer loyalty through the mediating variable of customer satisfaction at the Banking in Surabaya Branch. The benefits of this research include developing business strategies for competitive advantage and strengthening the relationship between customers.
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.
THE GENERALIZED SPACE-TIME ARIMA (GSTARIMA) MODEL FOR PREDICTING NITROGEN MONOXIDE TO MITIGATE EID AL- FITR AIR POLLUTION IN SURABAYA Khaulasari, Hani; Rini Novitasari, Dian Candra; Setyawati, Maunah; Maulana, Jeneiro; Mohd Fauzi, Shukor Sanim
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 20 No 1 (2026): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol20iss1pp0069-0086

Abstract

Air quality is a crucial factor due to its significant impact on environmental sustainability and public health. One of the major pollutants affecting air quality is Nitrogen Monoxide (NO), especially during periods of increased human mobility such as Eid al-Fitr. Monitoring and predicting NO levels are essential for early mitigation efforts. This study aims to evaluate the performance of the Generalized Space-Time Autoregressive Integrated Moving Average (GSTARIMA) model with three types of spatial weighting schemes and compare it with other forecasting methods, namely ARIMA, VARIMA, and Support Vector Regression (SVR), in predicting NO concentrations in Surabaya for April 2024. The data used in this study consist of daily NO concentration measurements obtained from the Surabaya City Environment Agency’s monitoring stations located at SPKU Tandes, SPKU Wonorejo, and SPKU Kebonsari, covering the period from January 2023 to March 2024. The GSTARIMA model was selected for its capability to capture both spatial and temporal dependencies across monitoring locations. As an extension of the ARIMA model, GSTARIMA incorporates spatial weight matrices to model spatial heterogeneity. Parameter estimation was conducted using the Ordinary Least Squares (OLS) method. The results indicate that the GSTARIMA model with Inverse Distance Weighting (IDW) and order (3,1,0)₁ in the first spatial order yields the most accurate predictions, outperforming ARIMA, VARIMA, and SVR models. The model produced the lowest Symmetric Mean Absolute Percentage Error (sMAPE) of 0.93% and Root Mean Square Error (RMSE) of 5.32. A notable spike in NO concentrations was observed between April 23 and 25, 2024, coinciding with the post-Eid al-Fitr return flow, indicating a surge in population mobility.
OPTIMIZATION OF PARAMETERS IN MEWMV AND MEWMA CONTROL CHARTS FOR CLEAN WATER QUALITY CONTROL AT PP KRAKATAU TIRTA GRESIK Hafiyusholeh, Moh.; Khaulasari, Hani; Firmansyah, Fery; Ulinnuha, Nurissaidah
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 20 No 1 (2026): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol20iss1pp0729-0742

Abstract

Water is a vital resource whose quality directly affects public health. In Gresik Regency, water treatment processes must be closely monitored, particularly during production. PT PP Krakatau Tirta, a key provider of clean water in the region, plays a strategic role in treating raw water from the heavily polluted Bengawan Solo River. Ensuring that the treated water consistently meets health standards is crucial, highlighting the need for an effective process. This study aims to evaluate the clean water production process and assess the process capability in maintaining the quality of water produced by PT PP Krakatau Tirta Gresik. Laboratory data on key parameters, including pH, dissolved iron, and total dissolved solids, were collected daily from November 25, 2022, to May 31, 2023. These mandatory indicators were analyzed using Multivariate Exponentially Weighted Moving Variance (MEWMV) and Moving Average (MEWMA) control charts to assess process performance. A key contribution of this research lies in optimizing smoothing parameters to enhance control chart performance. Sixteen combinations of (ω,λ) were tested for MEWMV, with the optimal configuration found at (λ = 0.4) and (ω = 0.4), indicating that process variability is statistically stable. For MEWMA, nine values of λ were evaluated, and the optimal weight (λ=0.9) was identified as optimal, yielding a stable process mean after removing two out-of-control points. PT PP Krakatau Tirta, which plays a strategic role in treating raw water from the polluted Bengawan Solo River, was selected as a case study to evaluate the effectiveness of advanced monitoring methods. The results indicate that its clean water production process is well-controlled and capable, with water quality consistently meeting health and safety standards.
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.
OPTIMIZATION OF ARIMA RESIDUALS USING LSTM IN STOCK PRICE PREDICTION OF PT MEDCO ENERGI INTERNASIONAL TBK Ababil, Achmad Fachril Yusuf; Hamid, Abdulloh; Khaulasari, Hani; Novitasari, Dian Candra Rini; Utami, Wika Dianita
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 20 No 2 (2026): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol20iss2pp1405-1420

Abstract

The capital market plays an important role in the economy by providing a means for companies to obtain capital and as a place to invest. Stocks are one of the popular investment instruments because their potential profits are attractive to investors. The stocks used in this study are PT Medco Energi Internasional Tbk (MEDC) shares. The purpose of this study is to obtain the optimal ARIMA-LSTM residual optimization model, how much the accuracy, and to predict Medco stock prices for the next 8-month period. The data used starts from January 4, 2021, to October 31, 2024, was obtained from the yahoofinance.com website. The ARIMA model, which is known to be effective in handling linear data, will be combined with LSTM. The use of residuals in the LSTM model can help LSTM capture patterns in the entire stock data so as to increase prediction accuracy. The research results obtained are the optimal ARIMA-LSTM optimization model, namely, ARIMA ([5,9],1,[5,9,11]) and LSTM with the best hyperparameter, namely, hidden layer 64, batch size 16, and learning rate 0.01. The accuracy of the ARIMA-LSTM optimization model is classified as very accurate, with a MAPE value of 0.3%. Medco Energi’s stock price for the next 8-month period is predicted to increase from IDR1312 to IDR1430 or an increase of 9%.
MODELLING THE NUMBER OF CRIMES IN EAST JAVA USING A TRUNCATED SPLINE SEMIPARAMETRIC REGRESSION APPROACH Saputra, Yahya Vigo Tri; Hafiyusholeh, Moh.; Khaulasari, Hani; Farida, Yuniar; Intan, Putroue Keumala
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 20 No 2 (2026): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol20iss2pp1627-1642

Abstract

High crime rates can lead to unrest and financial losses for the community. East Java is one of the provinces with high crime rates, with a total of 21,046 reported crimes in 2023. This study aims to identify the factors that influence crime rates in East Java and evaluate the goodness of the model through truncated spline semiparametric regression. Truncated spline semiparametric regression is a combination of parametric and nonparametric methods that can adjust changes in data patterns through the presence of knot points. The data used in this study were sourced from the Central Statistics Agency, including variables such as the number of people living in poverty, average years of schooling, gross regional domestic product, population, Gini ratio, per capita expenditure, and open unemployment rate. The results of the analysis indicate that the predictor variables have a significant influence on the number of crimes simultaneously. Partially, the variables that influence the number of crimes in East Java Province are average years of schooling, population, Gini ratio, per capita expenditure, and open unemployment rate. The best regression model is obtained using the combination knot point (4,2,4,3) with a minimum GCV value of 49636.60. The coefficient of determination obtained is 93.60%, indicating that the predictor variables can explain 93.60% of the variation in the crime rate, while the remaining 6.40% is attributed to variables outside the scope of the study.
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.
Co-Authors Ababil, Achmad Fachril Yusuf 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 Yuliati Efendi, Havid Fajar Setiawan FAJAR SETIAWAN Farmita, Mayandah Ferdani, Ayu Fery Firmansyah Firda Sari Firda Yunita Sari Hafiyussholeh, Moh. Hamidah, Isna Inayah, Jauharotul Lutfi Hakim M. Imron M. Lail Kurniawan Maharani Kuntari Maharani sukma Kuntari Maliki, Naufal Ridho Maulana, Aashif Amiruddin Maulana, Jeneiro Moh. Hafiyusholeh Moh. Hafiyusholeh Mohammad Rizal Abidin Mohd Fauzi, Shukor Sanim Muhammad Thohir Nadiyah, Fithrotun Nafa Nur Nanda Adifia Novitasari, Dian C Rini Nur Aulia, Shofinatul Wahdah Nurissaidah Ulinnuha Pramesti, Diah Devi Pramono, Wahyu Joko pratiwi, Yuniar Ines Purhadi Purhadi Putri Krismadewi Putroue Keumala Intan Ratna Pangastuti Rudi Antonius S, Eka Eliyana Saputra, Yahya Vigo Tri Sari, Dian Candra Rini Novita Sari, Lia Puspita Setyawati, Maunah Siti Nurlela SNM, Diva Ayu Subaekti, Hendrik Suhaeri, Fadjar Susanto, Hugeng 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