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All Journal MATEMATIKA JURNAL MATEMATIKA STATISTIKA DAN KOMPUTASI Jurnal Ilmu Sosial dan Humaniora CAUCHY: Jurnal Matematika Murni dan Aplikasi Matematika dan Sains Journal of Information Systems Engineering and Business Intelligence Prosiding SI MaNIs (Seminar Nasional Integrasi Matematika dan Nilai-Nilai Islami) Syntax Literate: Jurnal Ilmiah Indonesia MUST: Journal of Mathematics Education, Science and Technology BAREKENG: Jurnal Ilmu Matematika dan Terapan JTAM (Jurnal Teori dan Aplikasi Matematika) Zero : Jurnal Sains, Matematika, dan Terapan Cakrawala: Jurnal Litbang Kebijakan PengabdianMu: Jurnal Ilmiah Pengabdian kepada Masyarakat Jurnal Aplikasi Statistika & Komputasi Statistik JP2M (Jurnal Pendidikan dan Pembelajaran Matematika) G-Tech : Jurnal Teknologi Terapan Inferensi Contemporary Mathematics and Applications (ConMathA) Jurnal Keselamatan Transportasi Jalan (Indonesian Journal of Road Safety) Jurnal Layanan Masyarakat (Journal of Public Service) PROCEEDINGS OF THE INTERNATIONAL CONFERENCE ON DATA SCIENCE AND OFFICIAL STATISTICS Jurnal Keselamatan Transportasi Jalan Limits: Journal of Mathematics and Its Applications
Toha Saifudin
Departement Of Mathematics, Faculty Of Science And Technology, Airlangga University
Agriculture, Biological Sciences & Forestry Arts Humanities Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Energy Engineering Environmental Science Law, Crime, Criminology & Criminal Justice Materials Science & Nanotechnology Mathematics Mechanical Engineering Physics Public Health Social Sciences Transportation Other

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Pemodelan Indeks Kebahagiaan di Indonesia Berdasarkan Pendekatan Mixed Geographically Weighted Regression Alfredi Yoani; Fina Insyiroh; Leni Sartika Panjaitan; Toha Saifudin; Suliyanto
G-Tech: Jurnal Teknologi Terapan Vol 8 No 1 (2024): G-Tech, Vol. 8 No. 1 Januari 2024
Publisher : Universitas Islam Raden Rahmat, Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33379/gtech.v8i1.3639

Abstract

The well-being of society, involving the fulfilment of basic needs and opportunities for education and employment, can be measured through the happiness index. This research aims to assist the Indonesian government in achieving Sustainable Development Goal 3 related to Health and Well-being. It is hoped that by comprehending these factors, the government can improve the health and well-being of the Indonesian population. The happiness index varies across different geographical regions due to factors such as culture, social dynamics, and the environment, which can have different impacts from one region to another. Given the randomness in data patterns stemming from the diverse provinces in Indonesia, this study employs the Mixed Geographically Weighted Regression (MGWR) method. Results reveal that the MGWR model, utilizing a fixed Gaussian kernel weight, yields the lowest Akaike’s Information Criterion Corrected and the highest at 87.2%, underscoring its precision in modeling the happiness index in Indonesia.
Classification Of Country Status In 2022 Based On Social Indicators With Ordinal Logistic Regression Sugha Faiz Al Maula; Alfi Nur Nitasari; Mochamad Rasyid Aditya Putra; Maelcardino Christopher Justin; Salma Bethari Andjani Sumarto; Suliyanto Suliyanto; Toha Saifudin
Jurnal Matematika, Statistika dan Komputasi Vol. 20 No. 3 (2024): May 2024
Publisher : Department of Mathematics, Hasanuddin University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20956/j.v20i3.32356

Abstract

This research examines the classification of country status in 2022 by applying ordinal logistic regression on various social indicators including education, health and economic. The urgency of the research is to know the country determine factors with specific factors in the form of research variables that can be useful for policy makers, unlike the existing classification which is only divided based on GDP per capita or HDI score only. By dividing 3 country status classes, namely not developed, developing and developed countries using the world bank classification baseline, the accuracy results were obtained at 72,5% but there were several variables that were not significant. After re-modelling, the accuracy was found increased to 76.4% with the odds ratio results for the minimum wage variable being 42,32 in the high class compared to the middle class and 11,66 for the middle class compared to the lower class, which means that the higher the minimum wage tends to be classify countries as developed countries. Another variable that has significance level is the birth rate with an odds ratio of 0,71 in the high and middle classes and 0.89 in the middle and lower classes comparison, which shows that this variable has a negative effect because the odds ratio is <1, which means that the higher the birth rate tends to make the country will be classified as a non-developed country.
Application of Geographically Weighted Logistic Regression in Modeling the Human Development Index in East Java Saifudin, Toha; Panjaitan, Leni Sartika; Falasifah, Sabrina; Yan Dwi
Jurnal Aplikasi Statistika & Komputasi Statistik Vol 16 No 1 (2024): Jurnal Aplikasi Statistika & Komputasi Statistik
Publisher : Politeknik Statistika STIS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34123/jurnalasks.v16i1.587

Abstract

Main Objectives: pinpoint the factors influencing HDI, taking into consideration location and spatial factors. Problems: The Human Development Index (HDI) in East Java often fails to reflect actual conditions accurately, as disparities exist among districts and cities, with some falling below government expectations. Novelty: GWLR extends logistics regression by incorporating spatial factors, allowing for the identification of regional differences and influential factors affecting HDI based on actual data. Methods: To address this issue, the Geographically Weighted Logistic Regression (GWLR) method is employed. The independent variables used are Expected Years of Schooling (X1), Open Unemployment Rate (X2), and Morbidity Rate ( X3) in 2021, while dependent variable is the Human Development Index (Y). Finding/Results: The study reveals that GWLR provides a superior model compared to Ordinary Logistic Regression, indicated by a lower Akaike Information Criterion (AIC) of 28.72. Additionally, the GWLR model with Fixed Gaussian Kernel weights outperforms other weighting methods. At 90% confidence level, the significant variables influencing HDI are expected years of schooling (X1) and the open unemployment rate (X2). Given the relatively low HDI in Indonesia, the East Java Government should focus on improving these key areas to enhance HDI across districts and cities in the region.
Geographically Weighted Poisson Regression for Modeling the Number of Maternal Deaths in Papua Province Saifudin, Toha; Nur Rahmah Miftakhul Jannah; Risky Wahyuningsih; Gaos Tipki Alpandi
Jurnal Aplikasi Statistika & Komputasi Statistik Vol 16 No 1 (2024): Jurnal Aplikasi Statistika & Komputasi Statistik
Publisher : Politeknik Statistika STIS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34123/jurnalasks.v16i1.598

Abstract

Introduction/Main Objectives: Maternal Mortality Rate (MMR) in Indonesia is one of the main focuses in achieving the third Sustainable Development Goals (SDGs) in 2030. Background Problems: The Central Statistics Agency states that the MMR in Papua Province is the highest, reaching 565. Novelty: Given the diverse geographical conditions of each district/city in Papua Province, an analysis was carried out. Research Methods: Using the Geographically Weighted Poisson Regression (GWPR) method with the response variable being maternal mortality rates and variables predictors of health, social, and environmental factors. Finding/Results: Fixed Gaussian kernel GWPR is the best model with an AIC value of 27.6. Variable significantly influencing MMR include the percentage of households with access to adequate sanitation, the number of recipients of food assistance programs, and the number of doctors.
Modeling PLN Inc. Customer Receivables Based on Geographically Weighted Regression Approach Shalwa Oktavrilia Kusuma; Marshanda Aprilia; Toha Saifudin; Sa’idah Zahrotul Jannah
G-Tech: Jurnal Teknologi Terapan Vol 8 No 3 (2024): G-Tech, Vol. 8 No. 3 Juli 2024
Publisher : Universitas Islam Raden Rahmat, Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33379/gtech.v8i3.4439

Abstract

PLN Inc. implements a postpaid service that has resulted in many customer receivables issues. Customer receivables disrupt PLN Inc.'s cash flow, requiring the government to inject funds from the state budget. If the state budget experiences a deficit, it can increase the national debt. National debt impacts the achievement of SDG goals, namely sustainable economic growth (SDG 8), reducing inequalities (SDG 10), and financing infrastructure that supports development (SDG 9). The largest receivables occur on the island of Java, where many companies have high electricity consumption, while outside Java, electricity consumption is lower due to the scarcity of companies. This indicates a spatial influence on the size of PLN Inc.'s customer receivables, so this research was conducted using the Geographically Weighted Regression (GWR) method. The study found that the best weighting was fixed Gaussian with an R² value of 96.94%, which is better than the global regression value of 44.34%.
FORECASTING THE NUMBER OF SEARCH AND RESCUE OPERATIONS FOR SHIP ACCIDENTS IN INDONESIA USING FOURIER SERIES ANALYSIS (FSA) Recylia, Rien; Saifudin, Toha; Chamidah, Nur
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 19 No 2 (2025): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol19iss2pp1023-1036

Abstract

As an archipelago country, Indonesia is a national and international route. This position makes high ship mobility which also increases the risk of ship accidents. To address this issue, based on these conditions, a prediction is required to forecast ship accidents in Indonesia for the upcoming period using an effective method. Through data forecasting, we can map the readiness of Basarnas resources in conducting search and rescue operations for ship accidents. Forecasting data for search and rescue operations in ship accidents is important because it can predict the quantity of needed search and rescue operations. These can be effective measures to reduce casualties in accidents of this type. This research uses the Fourier Series Analysis (FSA) method, which doesn’t require parametric assumption. Additionally, the FSA method can be used for data with unknown patterns. The data used is divided into training data and testing data. The training data used in this research is the number of search and rescue operations from January 2021 to December 2022, while the testing data is from January 2023 to December 2023. The analysis results of this study indicate that forecasting using the FSA method has a MAPE of 25.758%, which falls into the category of reasonable forecasting accuracy and with an optimal and a GCV of 166.586. The results of future predictions are in the form of a mathematical model that can be used by entering the time variable that you want to predict. The anticipated benefits of this research are to contribute to Basarnas’s planning and execution of search and rescue operations for shipwrecks, enrich academic literature on forecasting methodologies, and enhance public awareness of search and rescue operations in Indonesia
COMPARISON FORECASTING BETWEEN SINGULAR SPECTRUM ANALYSIS AND LOCAL LINEAR METHOD FOR SHIP ACCIDENT SEARCH AND RESCUE OPERATIONS IN INDONESIA Recylia, Rien; Saifudin, Toha; Chamidah, Nur; Mardianto, M. Fariz Fadillah
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 19 No 2 (2025): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol19iss2pp1329-1340

Abstract

As a maritime country strategically located along the world's leading transportation routes, Indonesia often faces increased ship accidents. Based on the Basarnas Statistics Book, ship accidents handled by Basarnas from 2021 to 2023 increased by 3%. This condition requires an effective forecasting method to carry out SAR operations to predict ship accidents in the Indonesian region in the future and assess the readiness and needs of Basarnas resources. This study compares the forecasting results obtained using the Singular Spectrum Analysis (SSA) and the Local Linear methods. Both methods do not require parametric assumptions. The data used in this study are divided into training data and test data. This data is secondary data obtained from the Basarnas Statistics Book. The training data in this study is the number of SAR operations from January 2021 to December 2022, while the testing data is from January 2023 to December 2023. From the analysis results, it is known that the method with the smallest MAPE is the Local Linear method with a MAPE of test data of 18.67% (good forecasting category), optimal bandwidth (h) = 4.299, and CV (h) = 231.39 where bandwidth is used to determine the level of smoothness of the estimate, while the CV (h) value is used to select the optimal bandwidth that minimizes the estimation error. At the same time, the SSA method has a MAPE of 40.27% (fair forecasting category). This shows that the Local Linear method provides a more accurate forecast of the number of SAR operations related to ship accidents in Indonesia. This research contributes to the SDGs to make Basarnas an effective and accountable institution and improve the planning and decision-making process in SAR operations through accurate forecasting research is relevant to accurate forecasting.
MODELLING MATHEMATICS LEARNING OUTCOMES USING A MULTIPREDICTOR SEMIPARAMETRIC REGRESSION APPROACH BASED ON SPLINE ESTIMATOR Purnama, Titania Faisha; Chamidah, Nur; Saifudin, Toha
JP2M (Jurnal Pendidikan dan Pembelajaran Matematika) Vol 11, No 1 (2025)
Publisher : Universitas Bhinneka PGRI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29100/jp2m.v11i1.6999

Abstract

Education is one of the points of Indonesia's SDGs which is stated in goal number 4. Mathematics is one of the subjects that contributes to the realizing national education goals. In the independent curriculum, the success of the learning process at school can be seen from the criteria for achieving learning objectives. In this article, we analyzed students’ mathematics learning outcomes using a multi predictor semiparametric regression approach and interpreted the results with Spline estimator. The results shows that the differences between the types of classes greatly influence outcomes in learning mathematics, where social classes experienced a decrease of 2.435 percent compared to science classes. To increase outcomes in learning mathematics, the percentage of learning motivation must be more than 88 percent. Apart from that, high or low IQ cannot determine whether students’ mathematics learning outcomes. Furthermore, by combining linear and nonlinear components in the model effectively, the overall accuracy based on the MAPE value is 7.87 percent, so that the model can be predict the actual value high accurately. Thus, the multi predictor semiparametric regression approach based on spline estimator can explain the mathematics learning outcomes model very well.
Gender Equality in Indonesian Employment: Multivariate Adaptive Regression Spline (Mars) Analysis Dita Amelia; Toha Saifudin; Suliyanto Suliyanto; Aditya Syarifudin Akbar; Muhammad Rosyid Ridho Az Zuhro
Jurnal Ilmu Sosial dan Humaniora Vol 13 No 2 (2024)
Publisher : Universitas Pendidikan Ganesha

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23887/jish.v13i2.76687

Abstract

Based on data from the Central Bureau of Statistics, laborers in Indonesia, men are still more dominant for a career in the world of work. This contradicts the prevailing regulations, where gender equality is also a government development priority in realizing equitable development. This study aims to identify the factors that influence the percentage of women who work in Indonesia. The Multivariate Adaptive Regression Spline (MARS) Method is suitable for extensive data and can model relationships of interactions between various variables. The variables analyzed were the average wage of female workers, gross regional domestic product at constant prices, female workers with at least a high school education, life expectancy, provincial minimum wage, and female workers who are heads of households. Based on the research results, the best model was obtained with a coefficient of determination of 90.8%. Some influential variables in the base function are Minimum Wage for Female Workers, Female Workers with at least a high school education, Life Expectancy, and Female Workers who are Heads of Families. Based on the significant basis functions, the function that appears the most is Basis Function 5, which contains the predictor variable Average Wage of Female Workers, which shows a positive relationship with the Percentage of Female Workers with Labor Status in Indonesia. Meanwhile, based on the importance of the predictor variables, the top two are Female Workers with at least a high school education and Female Workers who are Heads of Households.
LEPROSY CASE MODELING IN EAST JAVA USING SPATIAL REGRESSION WITH QUEEN CONTIGUITY WEIGHTING Saifudin, Toha; Rifada, Marisa; Makhbubah, Karina Rubita; Ramadhanty, Devira Thania
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 19 No 3 (2025): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol19iss3pp2141-2154

Abstract

Leprosy, a highly contagious disease caused by the bacterium Mycobacterium leprae, can result in permanent disability if left untreated. It remains a significant public health issue in many regions, particularly tropical countries like Indonesia. Despite ongoing control efforts, incidence rates are still high in some areas. In 2023, East Java had the highest number of leprosy cases in Indonesia, with 2,124 out of 7,166. To understand the factors contributing to these cases, this study explores various influences and offers policy recommendations to reduce leprosy in East Java. The study uses spatial modeling with a weighting scheme based on queen contiguity, selected because leprosy spreads through human interactions and movement, creating spatial dependencies. It examines spatial, social, economic, educational, and environmental factors based on cross-sectional data from 38 regencies/cities in East Java for 2023. Among the regression models tested, the spatial error regression model proved most effective, showing an R-Square value of 67.14% and an AIC of 213.023. Key findings identified () average years of schooling and () healthcare worker ratios as significant factors influencing leprosy cases. These results aim to guide policymakers in developing stronger leprosy control strategies and offer a basis for further research in East Java.
Co-Authors Abdul Aziz Aditya Syarifudin Akbar Adyatma, Isryad Yoga Afifa, Fitriana Nur Aflaha, Nabila Shafa Aisharezka, Mutiara Aisyah, Arlisya Shafwan Al Hasri, Ilham Maulana Aldawiyah, Najwa Khoir Alfi Nur Nitasari Alfredi Yoani Alpandi, Gaos Tipki Ana, Elly Andini Putri Mediani Angga Kusuma Bayu Viargo Aniq Atiqi Any Tsalasatul Fitriyah Ardi Kurniawan Ardi Kurniawan Ariani, Fildzah Tri Januar Ariyawan, Jovansha Arrofah, Aini Divayanti Aulia, Niswa Faizah Auliyah, Nina Ayuning Dwis Cahyasari Azis, Aurelia Islami Azizah, Khansa Baihaqi, Mochamad Belindha Ayu Ardhani Budijono, Gabriella Agnes Chaerobby Fakhri Fauzaan Purwoko Christiano Ginzel, Bryan Given Christopher Andreas Dewanti, Maria Setya Dewanty, Sanda Insania Diah Puspita Ningrum Dita Amelia Dita Amelia, Dita Easyfa Wieldyanisa, Ezha Elly Ana Elly Pusporani Erfiana Erfiana Faiza, Atikah Fajrina, Sofia Falasifah, Sabrina Fatmawati Fatmawati Fauzi, Doni Muhammad Fauziah, Nathania Fina Insyiroh Firmansyah, Mochamad FIRMANSYAH, MOCHAMMAD Fitriani, Mubadi'ul Fortunata, Regina Gaos Tipki Alpandi Gaos Tipki Alpandi Hardiansyah, Fernanda Rizky Herdianto, Muhammad Hendra Ilma Amira Rahmayanti Indrasta, Irma Ayu Insania Dewanty, Sanda Januarta, R. Arya Khairian, Farhan Aldan Kholidiyah, Azizatul Leni Sartika Panjaitan Lensa Rosdiana Safitri M. Fariz Fadillah Mardianto Maelcardino Christopher Justin Mahadesyawardani, Arinda Maharani, Prima Makhbubah, Karina Rubita Marisa Rifada Marpaung, Josua Ronaldo Davico Marshanda Aprilia Marthabakti, CitraWani Mediani, Andini Putri Mochamad Rasyid Aditya Putra Muhammad Rosyid Ridho Az Zuhro Muzakki, Naufal Nahar, Muhammad Hafidzuddin Naura, Sheila Sevira Asteriska Nugraha, Galuh Cahya Nur Chamidah Nur chamnidah Nur Rahmah Miftakhul Jannah Nurdin, Nabila Nurrohmah, Zidni 'Ilmatun Oktavia, Sabrina Salsa Panjaitan, Leni Sartika Pratama, Fachriza Yosa Purnama, Titania Faisha Puspasari, Laili Rahayu, Rizky Dwi Kurnia Ramadhani, Azzah Nazhifa Wina Ramadhanti, Aulia Ramadhanty, Devira Thania Ramadhina, Fidela Sahda Ilona Recylia, Rien Risky Wahyuningsih Sa'idah, Andini Safitri, Lensa Rosdiana Salma Bethari Andjani Sumarto Salsabila, Fatiha Nadia Sa’idah Zahrotul Jannah Sediono, Sediono Sentosa, Martha Ayu Setyawan, Muhammad Daffa Bintang Shalwa Oktavrilia Kusuma Siagian, Kimberly Maserati Sihite, Rivaldi Siti Maghfirotul Ulyah Sugha Faiz Al Maula Suliyanto Suliyanto Suliyanto Syaugi Sungkar, Salman Tiani Wahyu Utami Trisa, Nadya Lovita Hana Ubadah, Mohammad Noufal Valida, Hanny Victory, Johanna Tania Wahyuli, Diana Widyawati, Ayu Wieldyanisa, Ezha Easyfa Wulandari, Indana Zulfa Yan Dwi Zhafira, Azizah Atsariyyah