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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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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 RevenuePrediction Based on Total Transactions and Total WasteVolume Maliki, Naufal Ridho; Khaulasari, Hani; Novitasari, Dian Candra Rini; Pramono, Wahyu Joko
Desimal: Jurnal Matematika Vol. 9 No. 1 (2026): Desimal: Jurnal Matematika
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.
An Integrated K-Means++–Davies–Bouldin Index Approach for Educational Resource-Based District Clustering: A Case Study of Districts in Surabaya Subaekti, Hendrik; Hakim, Lutfi; Khaulasari, Hani; Yuliati, Dian
Jambura Journal of Mathematics Vol 8, No 1: February 2026
Publisher : Department of Mathematics, Universitas Negeri Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37905/jjom.v8i1.35412

Abstract

Equitable distribution of educational resources is an important prerequisite to ensure that all communities benefit from human resource development. Access to education through the availability of schools and teachers at every level, plays a role in reducing the gap between regions. This study aims to group educational resources at the elementary and junior high school levels in 31 sub-districts of Surabaya City and evaluate the quality of grouping using the Davies–Bouldin Index (DBI). The analysis was carried out using secondary data from the Surabaya City Education Office which included the number of schools, teachers, and students based on education level in each sub-district. The clustering method used is K-Means++, which improves the centroid initialization process to produce more stable clustering. The results of the analysis identified three clusters, namely Development Education Areas (17 sub-districts), Elementary Focused Areas with Limited Junior High Schools (7 sub-districts), and Priority Education Areas (7 sub-districts: Rungkut, Sukolilo, Wonokromo, Sukomanunggal, Genteng, Kenjeran, and Krembangan). The quality of the grouping was validated with a DBI value of 0.752, which indicates a good cluster separation These findings can directly inform the Surabaya City Government in formulating targeted policies for educational equity, especially in teacher placement, student quota adjustment, and infrastructure development.
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