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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) Limits: Journal of Mathematics and Its Applications Zero : Jurnal Sains, Matematika, dan Terapan Cakrawala: Jurnal Litbang Kebijakan 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
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 Social Sciences Transportation Other

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CUBIC SPLINE ESTIMATOR IN MULTIPREDICTOR NONPARAMETRIC REGRESSION MODELS WITH LOGNORMAL ERRORS AND ITS Nur Chamidah, ; Toha Saifudin,
Matematika dan Sains Vol 16, No 1 (2009)
Publisher : Matematika dan Sains

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

Abstract

Suppose that n observations follow multiplicative nonparametric regression models with errors which are lognormally distributed. The assumption on causes the values of ln would be normally distributed . So, by taking natural logarithm of the model, we have an additive nonparametric regression model. In this paper, we estimate regression function of the model by using nonparametric regression approach, i.e, cubic spline estimator. Next, we give an applying illustration of the model on Gmelina Arborea Roxb data.
MODEL KALIBRASI DENGAN PENDEKATAN WAVELET DAN PARTIAL LEAST SQUARE SERTA PENERAPANNYA DENGAN OSS-R Ana, Elly; chamnidah, Nur; Saifudin, Toha; Atiqi, Aniq; Erfiana, Erfiana
MATEMATIKA Vol 14, No 2 (2011): JURNAL MATEMATIKA
Publisher : MATEMATIKA

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (13.646 KB)

Abstract

Determination process of active compound concentration which contained by a drug plant quantitatively and qualitative can know by HPLC (High Performance Liquid Chromatography) and FTIR (Fourier TrasformInfrared)  methods. Calibration aim to find relation between a group of size measure which is relative reached and cheap with a group of other size measure which is difficult and relative costly. Measurement of FTIR was done by nsampel at pwaves number. Based on these information, problems occur because the number of predictor bigger thanthe number of perception, so that require to reduce dimension. Discrete Wavelet Transform (DWT) can reduce dimension become new variables. with . But, result of reduction still have high collinearity between coefficients of wavelet.Partial Least Squares (PLS) can be the good solving for the problems. Combining of DWT and PLS methods on calibration models use OSS-R give a good criterion of model .
Perluasan Geographically Weighted Regression Menggunakan Fungsi Polinomial Toha Saifudin; Fatmawati Fatmawati; Nur Chamidah
Prosiding SI MaNIs (Seminar Nasional Integrasi Matematika dan Nilai-Nilai Islami) Vol 1 No 1 (2017): Prosiding SI MaNIs (Seminar Nasional Integrasi Matematika dan Nilai Islami )
Publisher : Mathematics Department

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Abstract

Geographically weighted regression (GWR) merupakan metode regresi pada data spasial dengan koefisien regresi bervariasi antar pengamatan. Dalam GWR, variabel-variabel bebas dan variabel tak bebas dihubungkan menggunakan fungsi linier. Sementara itu, dalam kondisi riil ada banyak kemungkinan kasus data spasial yang menunjukkan bahwa hubungan antara variabel tak bebas dengan variabel bebas cenderung tidak linier. Pemaksaan dalam menggunakan hubungan linier terhadap kasus tersebut bisa jadi merupakan salah satu faktor penyebab rendahnya kesesuaian model GWR. Oleh karena itu diperlukan perluasan fungsi pada model GWR. Tujuan paper ini adalah membuat model perluasan GWR menggunakan fungsi polinomial. Estimasi parameter model perluasan GWR diuraikan menggunakan prosedur Weighted Least Square (WLS). Hasil-hasil numerik berdasarkan studi kasus menunjukkan bahwa perluasan GWR dengan fungsi polinomial menghasilkan tingkat kesesuaian model yang lebih baik daripada GWR klasik.
Sentiment Analysis Towards Kartu Prakerja Using Text Mining with Support Vector Machine and Radial Basis Function Kernel Belindha Ayu Ardhani; Nur Chamidah; Toha Saifudin
Journal of Information Systems Engineering and Business Intelligence Vol. 7 No. 2 (2021): October
Publisher : Universitas Airlangga

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20473/jisebi.7.2.119-128

Abstract

Background: The introduction of Kartu Prakerja (Pre-employment Card) Programme, henceforth KPP, which was claimed to have launched in order to improve the quality of workforce, spurred controversy among members of the public. The discussion covered the amount of budget, the training materials and the operations brought out various reactions. Opinions could be largely divided into groups: the positive and the negative sentiments.Objective: This research aims to propose an automated sentiment analysis that focuses on KPP. The findings are expected to be useful in evaluating the services and facilities provided.Methods: In the sentiment analysis, Support Vector Machine (SVM) in text mining was used with Radial Basis Function (RBF) kernel. The data consisted of 500 tweets from July to October 2020, which were divided into two sets: 80% data for training and 20% data for testing with five-fold cross validation.Results: The results of descriptive analysis show that from the total 500 tweets, 60% were negative sentiments and 40% were positive sentiments. The classification in the testing data show that the average accuracy, sensitivity, specificity, negative sentiment prediction and positive sentiment prediction values were 85.20%; 91.68%; 75.75%; 85.03%; and 86.04%, respectively.Conclusion: The classification results show that SVM with RBF kernel performs well in the opinion classification. This method can be used to understand similar sentiment analysis in the future. In KPP case, the findings can inform the stakeholders to improve the programmes in the future. Keywords: Kartu Prakerja, Sentiment Analysis, Support Vector Machine, Text Mining, Radial Basis Function 
Applying SMOTE-NC on CART Algorithm to Handle Imbalanced Data in Customer Churn Prediction: A Case Study of Telecommunications Industry Ilma Amira Rahmayanti; Sediono Sediono; Toha Saifudin; Elly Ana
Syntax Literate Jurnal Ilmiah Indonesia
Publisher : CV. Ridwan Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (405.247 KB) | DOI: 10.36418/syntax-literate.v6i2.5166

Abstract

These days, telecommunications is very much needed in all areas of life. This condition has made the competition among the company is extremely tense. One strategic way to protect the company is to retain existing customers. The retention program as a scheme to retain customers must be implemented precisely and efficiently so that the company can maintain as many customers as possible. In this case, customer churn prediction holds an essential role. However, the existence of imbalanced data can increase prediction errors and create problems. Hence, in order to overcome the issue, this study combined the Synthetic Minority Oversampling Technique – Nominal Continuous (SMOTE-NC) with Classification and Regression Trees (CART). SMOTE-NC was applied to balance classes on training data, while CART formed a classification tree from those balanced data. Then, this classification tree created by CART algorithm had become the basis for predicting customer churn. The data used in this study are from https://community.ibm.com/, where the variables are related to customer demographics, customer contracts, usage history, and customer status of one of the telecom companies. Based on the analysis of these data, SMOTE-NC and CART combination succeeded in reducing errors in predicting customer churn, which also led recall value to increase by approximately 19%. Moreover, the accuracy generated from this combination method was still in a pretty good range of over 75%. Therefore, this study proposes an excellent way to improve the performance of churn prediction, especially in the telecommunications industry.
Peningkatan Kompetensi Guru SMA/MA di Kecamatan Bungah dalam Sistem Pembelajaran Daring Menggunakan Learning Management System Menuju Terbentuknya Sekolah Digital Siti Maghfirotul Ulyah; Elly Pusporani; Toha Saifudin; Christopher Andreas; Ayuning Dwis Cahyasari
MUST: Journal of Mathematics Education, Science and Technology Vol 6, No 2 (2021): DECEMBER
Publisher : Universitas Muhammadiyah Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30651/must.v6i2.10667

Abstract

SMA ASSA’ADAH dan MA MA’ARIF NU ASSA’ADAH merupakan sekolah-sekolah yang berlokasi di Kecamatan Bungah, Kabupaten Gresik. Dalam pelaksanaan pembelajaran jarak jauh, sekolah-sekolah tersebut memiliki permasalahan seperti belum efektifnya penggunaan fitur-fitur pada platform pembelajaran online dikarenakan keterbatasan keterampilan guru dan keterbatasan sinyal internet peserta didik dan guru. Oleh karena itu, perlu diberikan pelatihan penggunaan fitur-fitur platform pembelajaran online yang efektif dan interaktif. Tujuan dari penelitian ini adalah untuk mengetahui apakah ada peningkatan kompetensi guru sesudah dilakukan pelatihan tersebut. Penelitian ini dilakukan dengan metode kuantitatif, yaitu dengan mengaplikasikan uji-T berpasangan pada data hasil pre-test dan post-test. Hasil penelitian menunjukkan bahwa rata-rata nilai post-test lebih tinggi daripada nilai pre-test. Hasil pengujian dengan uji-T berpasangan juga menunjukkan bahwa terdapat perbedaan yang signifikan antara kompetensi peserta sebelum dan sesudah pelatihan. Hal ini menunjukan bahwa proses pelatihan efektif meningkatkan kompetensi peserta.
Designing Standard Growth Chart Based on Weight-For-Age Z-Score of Children in East Java Using Least-Square Spline Estimator Nur Chamidah; Ardi Kurniawan; Toha Saifudin
Contemporary Mathematics and Applications (ConMathA) Vol. 4 No. 2 (2022)
Publisher : Universitas Airlangga

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20473/conmatha.v4i2.38917

Abstract

Children would be categorized as children who have underweight nutritional status, if according to index of anthropometric they have a lack of weight. In Indonesia, this anthropometric index is recorded on a Card Toward Health called as KMS. This card follows the WHO-2005 standard which is designed based on samples from Brazil, Ghana, India, Norway, Oman, and USA. Those samples, of course, physically are very different from Indonesian children. Therefore, in this paper we design weight-for-age Z-score standard growth charts of children by using least-square spline estimator and samples of children from East Java province, Indonesia. Next, the proposed children standard growth charts are used to assess East Java children nutritional status. The results show that the proposed standard growth charts have met the goodness of fit criteria namely the average values of coefficient determination for boy and girl are close to one, and values of mean square errors are close to zero. It means that the proposed growth charts are more suitable to be used to assess the nutritional status of East Java children, because they can better explain the real conditions of children in East Java, Indonesia than the WHO-2005 standard growth charts.
Pemodelan Penderita Tuberkulosis di Jawa Timur Berdasarkan Pendekatan Geographically Weighted Regression (GWR) Diah Puspita Ningrum; Toha Saifudin; Suliyanto Suliyanto; Nur Chamidah
Jurnal Matematika, Statistika dan Komputasi Vol. 19 No. 1 (2022): SEPTEMBER, 2022
Publisher : Department of Mathematics, Hasanuddin University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20956/j.v19i1.21262

Abstract

Tuberculosis is the 13th trigger of death causes around the world. Even after Covid-19, tuberculosis ranks 2nd as a contagious killer disease. In 2020, Indonesia ranks 2nd out of 8 countries with the highest contributor to tuberculosis sufferers after India. East Java Province is the region with the largest number of tuberculosis cases in order of 8. Tuberculosis cases in East Java in 2020 have decreased, but when viewed from the success rate of treatment of tuberculosis cases per district/city in East Java, it was found that 53% still did not meet the target of 90%. According to (World Health Organization), gender affects the occurrence of tuberculosis disease, where men are more susceptible than women. In finding treatment for all tuberculosis incidents in East Java, the highest patient was male. This study was conducted to model tuberculosis in men in the East Java area. The results of the study prove that the modeling of male tuberculosis in East Java used linear regression and GWR  (Geographically Weighted Regression) obtained the best model was GWR with Fixed Gaussian Kernel weighting, CV value of 5.68, and R2 86.47%. Variables that have a significant effect on male tuberculosis in East Java are BCG immunization for male infants, public places meeting health requirements, youth who smoke tobacco every day, sex ratio, and households with access to proper sanitation facilities.      
Prediksi Harga Ekspor Non Migas di Indonesia Berdasarkan Metode Estimator Deret Fourier dan Support Vector Regression Chaerobby Fakhri Fauzaan Purwoko; Sediono Sediono; Toha Saifudin; M Fariz Fadillah Mardianto
Inferensi Vol 6, No 1 (2023)
Publisher : Department of Statistics ITS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j27213862.v6i1.15558

Abstract

Economic growth is one of the indicators in the Sustainable Development Goals (SDGs) on increasing economic activity.  One of the activities that supports the running of the economy is trade between countries, such as exports.  In Indonesia, non-oil and gas exports have played an important role in total exports in recent years, including coal exports being the main export.  Therefore, price predictions for Indonesia's non-oil and gas exports are very important as material for evaluating policies to encourage economic growth.  This is the main focus of this research.  In this study, non-oil and gas export price forecasts are made taking into account current issues such as the COVID-19 pandemic and the Russia-Ukraine war.  The accuracy of the model obtained from the Fourier series estimator and Support Vector Regression (SVR) is investigated by comparing the Mean Absolute Percentage Error (MAPE) value to predict Indonesia's non-oil and gas export prices.  The results of the study show that the COVID-19 pandemic and the Russia-Ukraine war have had a significant impact on non-oil and gas export prices. The SVR model with the Radial Basis Function (RBF) kernel shows better accuracy than the Fourier series estimator model of the cos sin function, with MAPE values of 9.29 and 15.26% for each test data, respectively.  Therefore, this study is expected to be the basis for formulating policies related to regulating non-oil and gas export processes to support economic growth in Indonesia.
Comparison Of Kernel Support Vector Machine In Stroke Risk Classification (Case Study:IFLS data) Lensa Rosdiana Safitri; Nur Chamidah; Toha Saifudin; Gaos Tipki Alpandi
Proceedings of The International Conference on Data Science and Official Statistics Vol. 2023 No. 1 (2023): Proceedings of 2023 International Conference on Data Science and Official St
Publisher : Politeknik Statistika STIS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34123/icdsos.v2023i1.381

Abstract

Stroke s a disability main source and main disability source to lost years of disability-adjusted life. Currently the information technology development, especially the field of machine learning has an important role in early warning of various diseases, such as strokes. One of the methods used for stroke classifying is Support Vector Machine (SVM). In this study, we aim to compare several kernel functions in SVM such as linear, radial basis function(RBF), polynomial, and sigmoid for classifying stroke risk. We determine the best kernel based on accuracy, sensitivity, and specificity values. The result of this study shows that linear kernel function gives the best performance in classifying with values of classification accuracy 99.0%, specificity 100.0%, ,and sensitivity 97.0%. Those scores are the highest scores among the other kernel , that means the linear kernel function is the best method for classifying strokes risk.
Co-Authors Abdul Aziz Aditya Syarifudin Akbar 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 Angga Kusuma Bayu Viargo Aniq Atiqi Any Tsalasatul Fitriyah Ardi Kurniawan Ardi Kurniawan Ariani, Fildzah Tri Januar Ariyawan, Jovansha Aulia, Niswa Faizah Auliyah, Nina Ayuning Dwis Cahyasari Azis, Aurelia Islami Azizah, Khansa Belindha Ayu Ardhani 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 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 Khairian, Farhan Aldan Kholidiyah, Azizatul Leni Sartika Panjaitan Lensa Rosdiana Safitri M. Fariz Fadillah Mardianto Maelcardino Christopher Justin Mahadesyawardani, Arinda 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 Purnama, Titania Faisha Puspasari, Laili Rahayu, Rizky Dwi Kurnia 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 Setyawan, Muhammad Daffa Bintang Shalwa Oktavrilia Kusuma Siagian, Kimberly Maserati 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