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All Journal Pythagoras: Jurnal Matematika dan Pendidikan Matematika Media Statistika JURNAL MATEMATIKA STATISTIKA DAN KOMPUTASI SAINSMAT Jurnal Statistika Universitas Muhammadiyah Semarang CAUCHY: Jurnal Matematika Murni dan Aplikasi TELKOMNIKA (Telecommunication Computing Electronics and Control) Bulletin of Electrical Engineering and Informatics Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) Matematika dan Sains Jurnal Ketahanan Nasional Journal of Information Systems Engineering and Business Intelligence Prosiding SI MaNIs (Seminar Nasional Integrasi Matematika dan Nilai-Nilai Islami) MUST: Journal of Mathematics Education, Science and Technology BAREKENG: Jurnal Ilmu Matematika dan Terapan JTAM (Jurnal Teori dan Aplikasi Matematika) Limits: Journal of Mathematics and Its Applications Zeta - Math Journal J Statistika: Jurnal Ilmiah Teori dan Aplikasi Statistika Zero : Jurnal Sains, Matematika, dan Terapan Cakrawala: Jurnal Litbang Kebijakan Jurnal Aplikasi Statistika & Komputasi Statistik JCRS (Journal of Community Research and Service) Jurnal Ilmiah Manajemen dan Bisnis JP2M (Jurnal Pendidikan dan Pembelajaran Matematika) G-Tech : Jurnal Teknologi Terapan Inferensi Contemporary Mathematics and Applications (ConMathA) Jurnal Layanan Masyarakat (Journal of Public Service) Enthusiastic : International Journal of Applied Statistics and Data Science SAINSMAT: Jurnal Ilmiah Ilmu Pengetahuan Alam Aurelia: Jurnal Penelitian dan Pengabdian Masyarakat Indonesia Indonesian Vocational Research Journal PROCEEDINGS OF THE INTERNATIONAL CONFERENCE ON DATA SCIENCE AND OFFICIAL STATISTICS Jurnal Pengabdian Nasional (JPN) Indonesia Feelings: Journal of Counseling and Psychology Jurnal Teknologi Informasi untuk Masyarakat (Jurnal Teknokrat) Indonesian Journal of Statistics and Its Applications Jurnal Ilmu Sosial dan Humaniora Limits: Journal of Mathematics and Its Applications
Nur Chamidah
Departemen Matematika, Fakultas Sains Dan Teknologi, Universitas Airlangga
Agriculture, Biological Sciences & Forestry Arts Humanities Biochemistry, Genetics & Molecular Biology Chemistry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Physics Public Health Social Sciences Transportation Other

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ANALYZING THE RELATIONSHIP BETWEEN FREQUENCY IN USING SOCIAL MEDIA AND THE ANXIETY LEVEL OF BODY SHAMING AND HARASSMENT VICTIMS Julianto, Agnes Happy; Putra, Mochamad Rasyid Aditya; Rahmatika, Nabila Syahfitri; Widyangga, Pressylia Aluisina Putri; Chamidah, Nur
Journal of Community Research and Service Vol. 7 No. 1: January 2023
Publisher : Universitas Negeri Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24114/jcrs.v7i1.41142

Abstract

In this sophisticated digital era, humans are easier to connect to everything. Unfortunately, this convenience is often used for things that should not be, such as insulting other people through chatting. This makes cyberbullying rampant, especially body shaming and harassment which cause depression, anxiety, and lack of confidence. The aim of this research is analyzing the relationship between the frequency in using social media and the anxiety level as the impact of body shaming and harassment. The research method used in this research is quantitative research with a correlational method. The results of this study are there is no relationship between the frequency of using social media and the anxiety level for both victims of body shaming and harassment, but if viewed based on the results of the respondents it is found that the possibility of cyberbullying is due to the level of education of the perpetrators, attitudes, and behavior. Advice that can be given is the need of further research on the main factors that cause cyberbullying and advice for readers is to control attitudes and behavior while using social media.
Local Polynomial Estimator in The Nonparametric Model of Inflation in Indonesia Aziz, Abdul; Chamidah, Nur; Saifudin, Toha
CAUCHY: Jurnal Matematika Murni dan Aplikasi Vol 10, No 1 (2025): CAUCHY: JURNAL MATEMATIKA MURNI DAN APLIKASI
Publisher : Mathematics Department, Universitas Islam Negeri Maulana Malik Ibrahim Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18860/cauchy.v10i1.27625

Abstract

Inflation is a general and continuous increase in prices of goods and services over a certain period.  Nonparametric regression analysis can be used to model inflation data that does not form a particular pattern. This study applies a local polynomial nonparametric method to model the rate of change rate in the inflation over a period considering two factors influencing inflation: the rate of change in the BI interest rate and the rate of change rate in the money supply from the previous period. The bivariate local polynomial method estimates the nonparametric regression function by considering the optimum Gaussian kernel bandwidth and polynomial order using the Taylor series expansion and WLS estimator. The optimal local polynomial nonparametric regression model was obtained based on a minimum GCV value of  0.015108 with two optimum Gaussian kernel bandwidth values of 0.1 and 0.03 in polynomial order of 1. The best model had a MAPE value of 3.45%, showing that all the prediction models were highly accurate. The benefits gained are additional information and consideration for determining monetary policy, especially inflation in Indonesia, by determining the BI interest rate and money supply.
Optimizing Data Collection Strategies for Effective Poverty Alleviation: Insights from the Social Welfare System - Next Generation (SIKS-NG) Chamidah, Nur; Muchsin, Slamet; Sunariyanto, Sunariyanto
Jurnal Ilmiah Manajemen & Bisnis Vol 8 No 2 (2023)
Publisher : Universitas Pendidikan Nasional

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.38043/jimb.v8i2.4535

Abstract

Malang Regency is the second-largest region in East Java with a diversity of geographical, social, and economic conditions. Data from the Malang Regency Social Service in 2018 shows that the number of impoverished families in Malang Regency reached 230,081 families. The highest number of impoverished families is found in the Dampit District, totaling 12,053 families, while the lowest is in the Kromengan District with 3,061 families. This indicates that the Dampit District has a significant number of impoverished families, surpassing other districts in Malang Regency. This makes the Dampit District a relevant location for research on poverty and its impacts. This research has dual objectives: first, to provide reliable, accurate, and valid information on existing poverty indicators, which will serve as a strong foundation for supporting poverty alleviation programs in Malang Regency. Second, this research aims to formulate strategies in the form of activity guidelines that will enhance the competence of employees in conducting poverty data collection, ultimately supporting poverty alleviation efforts. Data collection methods include interviews, documentation, observation, and Focus Group Discussions to obtain more in-depth information. In-depth interviews were conducted using purposive sampling techniques involving stakeholders related to poverty alleviation in the Dampit District Office, Malang Regency. The analysis results indicate that the government of the Dampit District and the villages in the area have not fully succeeded in implementing the Next Generation Social Welfare System (SIKS-NG) optimally. The poverty data collection has predominantly focused on collecting data on the poor only. Therefore, improvements in the approach and implementation of SIKS-NG are needed to enhance the effectiveness of poverty alleviation programs. The findings of this research highlight the importance of a participatory approach in collecting poverty data, involving village governments, utilizing information infrastructure like BDT, and the data consolidation process to ensure the effectiveness of poverty alleviation programs. Additionally, appropriate training for data collection teams is crucial in improving the accuracy of poverty data.
Peran Peran Mahasiswa dalam Program Asistensi Mengajar di TK Tri Murti Surabaya Alfiatur Rakhma, Syavrilia; Nur Chamidah
Jurnal Teknologi Informasi untuk Masyarakat Vol. 2 No. 2 (2024): Jurnal Teknologi Informasi untuk Masyarakat (Teknokrat)
Publisher : Universitas Hamzanwadi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29408/jt.v2i2.28814

Abstract

The Teaching Assistance Program is an implementation of the Merdeka Belajar Kampus Merdeka (MBKM) initiative aimed at enhancing education quality while providing practical experience for university students. This study documents the implementation of teaching assistance by undergraduate Statistics students of Universitas Airlangga at TK Tri Murti Surabaya. Through observation, teaching, and mentoring activities, students contributed to improving the learning process and addressing individual needs of early childhood students, including supporting their cognitive, social, and motoric development. The results show dual benefits: accelerating students’ learning comprehension and providing university students with valuable experience in classroom management and interaction with diverse student backgrounds. This program highlights the effectiveness of collaboration between higher education institutions and educational organizations in supporting equitable early childhood education quality
Prediction of PM2.5 in DKI Jakarta Using Ordinary Kriging Method Salsabilla, Shafira; Fitri Syaharani, Amadea; Chamidah, Nur
Enthusiastic : International Journal of Applied Statistics and Data Science Volume 3 Issue 1, April 2023
Publisher : Universitas Islam Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20885/enthusiastic.vol3.iss1.art5

Abstract

Air pollution is a serious matter that must be addressed promptly and quickly. One of the most dangerous pollutants in the air is PM2.5. This pollutant is particulates dust measuring 2.5 micrometers. PM2.5 can cause environmental and health problems such as acute respiratory infections, lung cancer, cardiovascular cancer, and premature death. Air pollution occurs in big cities such as the capital city of Indonesia, DKI Jakarta, which is the city with the highest PM2.5 levels in Indonesia. There are 6 six stations in DKI Jakarta that measure PM.2.5 level at 6 areas. The ordinary kriging is one of spatial methods  that can be used to predict PM2.5 level in outside the existing stations, for example in the Pulogadung industrial area. This area was selected because there are many factories in this area that can increase levels of PM2.5 in the air. To predict the concentration of PM2.5 in one area could be done by calculating the surrounding PM2.5 concentrations that were not available to measure air quality. In study, we use mean an absolute percentage error ( MAPE ) value to evaluate Ordinary Kriging performance for predicting PM2.5 level in DKI Jakarta.
Multimodal deep learning from sputum image segmentation to classify Mycobacterium tuberculosis using IUATLD assessment Saurina, Nia; Chamidah, Nur; Rulaningtyas, Riries; Aryati, Aryati
Bulletin of Electrical Engineering and Informatics Vol 14, No 2: April 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i2.9250

Abstract

Tuberculosis (TB) continues to be a major global health issue, especially in areas with limited resources where diagnostic tools are often insufficient. Traditional TB detection methods are slow and lack sensitivity, particularly for early-stage or low bacterial load cases. This study introduces a new multimodal deep learning model that integrates sputum image segmentation across RGB, hue, saturation, and value (HSV), and CIELAB color channels, using the YOLOv8 model for real-time detection and segmentation. The model uses the International Union Against Tuberculosis and Lung Disease (IUATLD) grading scale for accurate Mycobacterium tuberculosis (MTB) classification. Our approach shows high accuracy (92.24%) and precise forecasting (mean absolute percent error (MAPE) of 0.23%), greatly enhancing diagnostic speed and reliability. This research offers a novel method for classifying MTB using a multimodal deep learning model that integrates sputum image segmentation across RGB, HSV, and CIELAB color channels. By using the YOLOv8 model for real-time bounding box detection and segmentation, and the IUATLD grading scale for classification, our method achieves high accuracy and precision in identifying TB bacteria. Our findings indicate that this multimodal deep learning approach significantly improves diagnostic accuracy and speed, providing a reliable tool for early TB detection.
Comparison of Logistic Regression and Support Vector Machine in Predicting Stroke Risk Safitri, Lensa Rosdiana; Chamidah, Nur; Saifudin, Toha; Firmansyah, Mochammad; Alpandi, Gaos Tipki
Inferensi Vol 7, No 2 (2024)
Publisher : Department of Statistics ITS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j27213862.v7i2.20420

Abstract

The issue of health is the third goal of Indonesia's Sustainable Development Goals (SDGs) which is state to ensuring a healthy life and promoting prosperity for all people at all ages. One of the SDGs’s concerns is deaths caused by non-communicable diseases (NCDs) including strokes. One prevention that can be done is by making a prediction of stroke for early detection. There are various methods available which are statistical methods and machine learning methods. In this research work, we aim to compare the two methods based on statistical method and machine learning method on stroke risk prediction. The data used in this research is primary data from Universitas Airlangga Hospital (RSUA) from June until August 2023. In this research, we compare the statistical method that is Logistic Regression (LR), and the machine learning method which is Support Vector Machine(SVM). We use Phyton to analyze all methods in this research. The results show that SVM with Radial Basis Kernel is better than LR in predicting stroke risk based on three goodness criteria namely sensitivity, F-1 score and accuracy where these three goodness criteria values of SVM are greater than those of LR.
Prediksi Risiko Gagal Bayar Kredit Kepemilikan Rumah dengan Pendekatan Metode Random Forest Ulandari, Kartini Putri; Chamidah, Nur; Kurniawan, Ardi
Sainsmat : Jurnal Ilmiah Ilmu Pengetahuan Alam Vol 13, No 2 (2024): September
Publisher : Fakultas Matematika dan Ilmu Pengetahuan Alam Universitas Negeri Makassar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35580/sainsmat132630212024

Abstract

Home Ownership Credit (KPR) is a credit facility provided by banks to individual customers who want to buy or repair a house. KPR also has problems with credit payment failures. This research aims to predict the risk of fraud on home ownership loans by applying the Random Forest method. Random Forest (RF) is a method that can increase accuracy results in generating attributes for each node which is done randomly. Based on the analysis results, it was found that the model with the smallest classification error was using mtry 2 and ntree 50 using a combination of training and testing data of 60%:40%. By applying the random forest algorithm, we obtained an accuracy rate of 84.75% with an Area Under the Curve (AUC) value of 84.32%, which is included in the very good classification category.
Analysis of Geographically Weighted Logistic Regression Models with A Bisquare Weighting Matrix on Poverty Status in West Java Saifudin, Toha; Chamidah, Nur; Aldawiyah, Najwa Khoir; Marthabakti, Citrawani; Ramadhanti, Aulia; Nahar, Muhammad Hafidzuddin; Muzakki, Naufal
CAUCHY: Jurnal Matematika Murni dan Aplikasi Vol 10, No 2 (2025): CAUCHY: JURNAL MATEMATIKA MURNI DAN APLIKASI
Publisher : Mathematics Department, Universitas Islam Negeri Maulana Malik Ibrahim Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18860/cauchy.v10i2.36315

Abstract

This research addresses the first Sustainable Development Goal and aims to analyze poverty status in West Java Province, which has the second highest number of poor people in Indonesia. The study employs Geographically Weighted Logistic Regression (GWLR) and compares it with global logistic regression. Influential variables include GDP, unemployment, population density, access to safe water, and roof type (bamboo/wood). Results show that 55.6% of regions are classified as poor, with the GWLR model using a Fixed Bisquare kernel achieving 81.4% accuracy, outperforming global logistic regression at 66.7%. Significant variables vary by region: unemployment rate in Bogor, Depok, and Bekasi; population density in Bekasi, Karawang, and Purwakarta; water access in Sukabumi; and roof type in Indramayu and Bogor. These spatial variations suggest that poverty reduction requires a region-specific approach. Consequently, policies should be formulated considering the priorities and characteristics of each region in West Java Province.
Pemodelan Harga Emas Berdasarkan Kurs Rupiah Terhadap USD dengan Pendekatan Regresi Polinomial Lokal Halimatuzzahro, Fitria; Ramadhita, Ghina; Chamidah, Nur
Limits: Journal of Mathematics and Its Applications Vol. 22 No. 3 (2025): Limits: Journal of Mathematics and Its Applications Volume 22 Nomor 3 Edisi No
Publisher : Pusat Publikasi Ilmiah LPPM Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Modeling gold prices based on the exchange rate of the rupiah against the USD is important because it can be used in making investment decisions as well as a reference for formulating economic policy. This study aims to apply local polynomial regression in modeling gold prices in Indonesia based on the rupiah exchange rate against the USD. In this study, gold price modeling was carried out using nonparametric regression with local polynomials. The data used in the study are monthly data of exchange rates as predictor variables (X) and gold prices as response variables (Y) observed from January 2014 to October 2024. Applying local polynomial regression starts with collecting data, analyzing data descriptively, and then modeling and estimating gold price data in Indonesia based on the rupiah exchange rate against the USD using the R program. The results showed that gold price modeling based on the rupiah exchange rate against the USD was obtained on insample data with the best local polynomial estimator of order 2 with an optimal bandwidth of 800 with a MAPE of 9.85% which was classified as very good while for outsample data the MAPE value was 24.87% so that the model estimate for outsample data was classified as sufficient. Overall, the MAPE value related to the prediction of gold prices in January 2014 - October 2024 is 11.01% which is classified as good.
Co-Authors A Meylin Abdul Aziz Abidin, Qumadha Zaenal Afriani Agus Satmoko Adi Aisharezka, Mutiara Akbar, Aditya Syarifudin Al Farizi, Muhammad Fikry Al Hasri, Ilham Maulana Aldawiyah, Najwa Khoir Alexandra, Victoria Anggia Alfiatur Rakhma, Syavrilia Alfinda Novi Kristanti Alpandi, Gaos Tipki Amanda, Yulia Aminuyati Aminy, Aisyah Ana, Elly Ananda Dwi Andini Putri Mediani Andriani, Putu Eka Andriani, Putu Eka Angga Kusuma Bayu Viargo Angga Kusuma Bayu Viargo Anies Yulinda W Anisa Laila Azhar Any Tsalasatul Fitriyah Ardi Kurniawan Ardi Kurniawan Ardiyanto, Figo Surya Aryati Aryati Auliyah, Nina Azizah, Khansa Azzen, Fiyadika Amalia Nurizah Baihaqi, Muhammad Rizaldy Baktiar Aris Belindha Ayu Ardhani Brenda Bunga Prasenda Budi Lestari Budi Lestari Christopher Andreas D Lestari Darmawan, Kezia Eunike Dhohirrobbi, Achmad Dhyana Venosia Dhyana Venosia Diah Puspita Ningrum Diana Ulya Dita Amelia Dita Amelia, Dita Easyfa Wieldyanisa, Ezha Eko Tjahjono Elfhira Juli Safitri Fachrian, Muhammad Nadhil Faiza, Atikah Faizun, Nurin Fajrina, Sofia Fajrina, Sofia Andika Nur Fajrina, Sofia Andika Nur Fania, Azzahra Farida Farida Farizi, Muhammad Fikry Al Fatmawati Fatmawati Fatmawati Fatmawati Fauziah, Nathania Feevrinna Yohannes Harianto Fibryan, Muhammad Hilmi FIRMANSYAH, MOCHAMMAD Fitri Syaharani, Amadea Fitri, Marfa Audilla Fitri, Marfa Audilla Gaos Tipki Alpandi Halimatuzzahro, Fitria Hammami, Martha Sayyida Hariadi, Salsabila Niken Hendrawan, Ardana Tegar Herdianto, Muhammad Hendra Hidayat, Rizky Ismaul Uyun Hilma, Dzuria Hilma Qurotu Ain Horidah Horidah Huda, Mi'rojul I Nyoman Budiantara Insania Dewanty, Sanda Islamudin, Mohamad Mujahid IZZAH, NURUL Julianto, Agnes Happy Juniar, Muhammad Althof Kamiilah, Nadhira Safa Kamil, M. Aqil Zaidan Kamila, Yasmin Kinanti Hanugera Gusti Larasati, Berliani Lensa Rosdiana Safitri Lilik Hidayati, Lilik Listyaningsih Listyaningsih M. Fariz Fadillah Mardianto Mahadesyawardani, Arinda Mahadesyawardani, Arinda Marbun, Barnabas Anthony Philbert Marisa Rifada Marthabakti, CitraWani Maula, Sugha Faiz Al Maulidya, Utsna Rosalin MAYA MUSTIKA KARTIKA SARI, MAYA Mediani, Andini Putri Mediani, Andini Putri Melati Tegarina Mohamad David Hermawan Muhammad Falah El Fahmi Mutiara Aisharezka Muzakki, Naufal N. A. Aprilianti Nadia Murbarani Nahar, Muhammad Hafidzuddin Naufal Ramadhan Al Akhwal Siregar Nia Saurina Nitasari, Alfi Nur Nur Azizah Rahayu Ningsih Prasetyo, Juan Krisfigo Pratama, Bagas Shata Pratama, Fachriza Yosa Purnama, Titania Faisha Putra, Mochamad Rasyid Aditya Qumadha Zainal Abidin Rahayu, Rizky Dwi Kurnia Rahma, Alma Khalisa Rahmatika, Nabila Syahfitri Ramadhanti, Aulia Ramadhina, Fidela Sahda Ilona Ramadhita, Ghina Recylia, Rien Reiza Sahawaly Rico Ramadhan, Rico Rimuljo Hendradi Riries Rulaningtyas Rizza Sulistiana Rohim, Achmad Yazid Busthomi S, Salma Bethari Andjani Sa'idah, Andini Sabrina Falasifah Safitri, Lensa Rosdiana Salsabilla, Shafira Salsabylla Nada Apsariny Sasmia Desinta Wulandari Sa’idah, Andini Sediono, Sediono Sely Novika Norrachma Septia Sari, Ni Wayan Widya Setyawan, Muhammad Daffa Bintang Setyowati, Raden Roro Nanik Siagian, Kimberly Maserati Siburian, Cynthia Anggelyn Siregar, Naufal Ramadhan Al Akhwal Siti Maizul Habibah Slamet Muchsin Soewignjo, Steven Subiyanto, Marcel Laverda Sufyan Ats Tsauri Suliyanto Sunariyanto, Sunariyanto Suryono, Alda Fuadiyah Suryono, Alda Fuadiyah Suwarno Suwarno Syifaun Nadhiro Thohari, Habib Nihla Tiani Wahyu Utami Toha Saifudin Toha Saifudin Trias Novia L. Trisa, Nadya Lovita Hana Ulandari, Kartini Putri Ulya, Diana Umi Tri Ruhana Usmi, Rianda Valida, Hanny Wahyuli, Diana Warsono Warsono Widyangga, Pressylia Aluisina Putri Widyawati, Ayu Wieldyanisa, Ezha Easyfa Wulandari, Nuryuliana Yolanda Swastika Yolanda Swastika Yonani Zahrotul Azizah Zidni Ilmatun Nurrohmah