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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 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) 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 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 Limits: Journal of Mathematics and Its Applications

Nur Chamidah

Departemen Matematika, Fakultas Sains Dan Teknologi, Universitas Airlangga

Author-ID : 2458378

Agriculture, Biological Sciences & Forestry Arts Humanities 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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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

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.

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

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (461.161 KB)

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.

Improving of classification accuracy of cyst and tumor using local polynomial estimator Nur Chamidah; Kinanti Hanugera Gusti; Eko Tjahjono; Budi Lestari
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 17, No 3: June 2019
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v17i3.12240

Cyst and tumor in oral cavity are seriously noticed by health experts along with increasing death cases of oral cancer in developing country. Early detection of cyst and tumor using dental panoramic image is needed since its initial growth does not cause any complaints. Image processing is done by mean for distinguishing the classification of cyst and tumor. The results in previous studies about classification of cyst and tumor were done by using a mathematical computation approach namely supports vector machine method that have still not satisfied and have not been validated. Therefore, in this study we propose a method, i.e., nonparametric regression model based on local polynomial estimator that can be improve the classification accuracy of cyst and tumor on human dental panoramic image. By using the proposed method, we get the classification accuracy of cyst and tumor, i.e., 90.91% which is greater than those by using the support vector machine method, i.e., 76.67%. Also, in validation process we obtain that the nonparametric regression model approach gives a significant Press’s Q statistical testing value. So, we conclude that the nonparametric regression model approach improves the classification accuracy and gives better outcome to classify cyst and tumor using dental panoramic image than the support vector machine method.

Identification the number of Mycobacterium tuberculosis based on sputum image using local linear estimator Nur Chamidah; Yolanda Swastika Yonani; Elly Ana; Budi Lestari
Bulletin of Electrical Engineering and Informatics Vol 9, No 5: October 2020
Publisher : Institute of Advanced Engineering and Science

Infectious disease caused by infection of Mycobacterium tuberculosis is called tuberculosis (TB). A common method in detecting TB is by identifying number of mycobacterium TB in sputum manually. Unfortunately, manually calculation by pathologists take a relatively long time. Previous researches on TB bacteria were still limited to detect the absence or presence of mycobacterium TB in images of sputum. This research aims are identifying number of mycobacterium TB and determining accuracy of classification TB severity by approaching nonparametric Poisson regression model and applying an estimator namely local linear. Steps include processing of image, reducing of dimension by applying partial least square and discrete wavelet transformation, and then identifying the number of mycobacterium TB by using the proposed model approach. In this research, we get deviance values of 28.410 for nonparametric and 93.029 for parametric approaches and the average of classification accuracy values for 4 iterations of 92.75% for nonparametric and 85.5% for parametric approaches. Thus, for identifying many of mycobacterium TB met in images of sputum and classifying of TB severity, the proposed identifying method gives higher accuracy and shorter time in identifying number of mycobacterium TB than parametric linear regression method.

KONSTRUKSI UJI KESESUAIAN MODEL GEOGRAPHICALLY WEIGHTED POLYNOMIAL REGRESSION Nur Chamidah
Jurnal Matematika, Statistika dan Komputasi Vol. 15 No. 2 (2019): JMSK Vol. 15, No. 2, January 2019
Publisher : Department of Mathematics, Hasanuddin University

AbstractGeographically Weighted Polynomial Regression (GWPolR) is a generalization of Geographically Weighted Regression (GWR) model. By using the generalization, GWPolR has parameters much more than GWR model. In general, excess of the number of parameter will have a higher appropriate value. However, the model which has less parameter will have the excess for easing in application and its interpretation. Nevertheless, when the model has more the parameters, then the model will be better significantly to be used. Therefore, the aim of this paper is to construct the conformity between hypothesis test with respect to the GWPolR model. Keywords: Geographically weighted polynomial regression, Geographically Weighted Regression, uji kesesuaian model AbstrakGeographically Weighted Polynomial Regression (GWPolR) merupakan perumuman dari model Geographically Weighted Regression (GWR). Dengan perumuman tersebut, model GWPolR memiliki jumlah parameter yang lebih banyak daripada model GWR. Umumnya, kelebihan model dengan jumlah parameter lebih banyak adalah memiliki nilai kesesuaian lebih tinggi. Sebaliknya, model dengan jumlah parameter yang sedikit memiliki kelebihan berupa kemudahan dalam aplikasi dan interpretasinya. Namun demikian, jika model dengan jumlah parameter yang lebih banyak ternyata secara signifikan lebih baik maka sudah seharusnya model tersebut dipilih untuk digunakan. Oleh karena itu, tujuan paper ini adalah mengkonstruksi uji hipotesis kesesuaian model GWPolR. Kata Kunci: Geographically weighted polynomial regression, Geographically Weighted Regression, uji kesesuaian model

Analisis Pengaruh BI 7-Days Repo Rate Terhadap Indeks Harga Saham Gabungan Menggunakan Pendekatan Regresi Nonparametrik Berdasarkan Estimator Least Square Spline Christopher Andreas; Feevrinna Yohannes Harianto; Elfhira Juli Safitri; Nur Chamidah
Jurnal Matematika, Statistika dan Komputasi Vol. 17 No. 3 (2021): May, 2021
Publisher : Department of Mathematics, Hasanuddin University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20956/j.v17i3.13101

During the Covid-19 pandemic, the Indonesia stock market was under great pressure, so that the value of the Jakarta Composite Index (JCI) fluctuated greatly. To maintain economic stability, Bank Indonesia has regulated monetary policy such as setting the BI 7-Days Repo Rate. Analysis of this effect is important to formulate the right policy. This study aims to design the best model in describing the relationship between JCI value and BI 7-Days Repo Rate. The analysis was carried out by using parametric regression approach based on the ordinary least square method and nonparametric regression approach based on least square spline estimator. The results showed that the parametric regression models failed to meet the classical assumptions. Meanwhile, nonparametric regression can produce an optimal model with high accurate prediction, with an overall mean absolute percentage error value of 3.16%. Furthermore, mean square error, coefficient of determination, and mean absolute deviation also show good results. Thus, the effect of the BI 7-Days Repo Rate on the JCI value forms a quadratic pattern, in which a positive relationship is formed when the BI 7-Days Repo Rate is set at more than 4.25% and vice versa for a negative relationship.

PEMODELAN RISIKO KEJADIAN DIABETES MELLITUS DAN HIPERTENSI BERDASARKAN REGRESI LOGISTIK BIRESPON Marisa Rifada; Nur Chamidah; Sely Novika Norrachma
Jurnal Statistika Universitas Muhammadiyah Semarang Vol 5, No 2 (2017): Jurnal Statistika Universitas Muhammadiyah Semarang
Publisher : Department Statistics, Faculty Mathematics and Natural Science, UNIMUS

Diabetes dan hipertensi merupakan penyakit yang berhubungan erat. Mereka seringterjadi bersama-sama sehingga dianggap sebagai “komorbiditas” (penyakit yangmungkin ada pada pasien yang sama). Penderita hipertensi dapat mempunyai risikoterkena diabetes. Demikian pula sebaliknya, risiko hipertensi juga dapat dialami oleh penderita diabetes. Untuk melihat seberapa besar pengaruh faktor-faktor yang secara signifikan mempengaruhi peluang kejadian sesorang terkena suatu penyakit akan lebih bermanfaat apabila dirumuskan dalam bentuk matematis. Salah satu analisis statistik yang dapat menggambarkan kejadian tersebut adalah analisis regresi logistik birespon yang merupakan pengembangan dari regresi logistik jika terdapat dua variabel respon biner dengan asumsi ada hubungan yang signifikan antar variabel respon. Berdasarkan analisis data secara deskriptif, diabetes dan hipertensi lebih banyak terjadi pada laki-laki dibandingkan perempuan, serta paling banyak terjadi pada usia 55-64 tahun. Responden yang memiliki Body Mass Index (BMI) > 30 Kg/ m2 cenderung terkena Diabetes.Sedangkan responden yang terkena Hipertensi memiliki BMI antara 25 – 30 Kg/ m. Dalam penelitian ini diperoleh nilai odds ratio () sebesar 1.1454 yang artinyaterdapat dependensi antara kejadian Diabetes dengan Hipertensi. Kata Kunci: Diabetes Mellitus, Hipertensi, Regresi Logistik Biner, Odds ratio.

PEMODELAN JUMLAH PENDERITA KONJUNGTIVITIS DI LAMONGAN BERDASARKAN PENDEKATAN MODEL REGRESI GENERALIZED POISSON Zahrotul Azizah; Umi Tri Ruhana; Nur Chamidah
Jurnal Statistika Universitas Muhammadiyah Semarang Vol 3, No 1 (2015): Jurnal Statistika Universitas Muhammadiyah Semarang
Publisher : Department Statistics, Faculty Mathematics and Natural Science, UNIMUS

Konjungtivitis adalah penyakit mata berbahaya yang disebabkan kandungan debu yang tinggi pada udara di daerah tertentu dan mikroorganisme seperti bakteri, alergi, viral, dan sika. Hal lain yang diindikasikan menyebabkan konjungtivitis adalah tingkat kesadaran masyarakat terhadap Perilaku Hidup Bersih dan Sehat (PHBS), jarak tempat tinggal dengan pegunungan kapur, kepadatan penduduk, jumlah pabrik di daerah tempat tinggal, tingkat pendidikan masyarakat, dan letak tempat tinggal dengan jalan raya. Lamongan merupakan daerah yang mempunyai volume debu yang cukup tinggi, terutama daerah sekitar pegunungan kapur dan pabrik sehingga banyak penduduknya yang terjangkit penyakit konjungtivitis dan menghasilkan rasio satu banding dua penderita dari total permasalahan konjungtiva dalam selang waktu tertentu. Jumlah penderita konjungtivitis memiliki ciri percobaan poisson. Pada distribusi poisson, diharuskan memenuhi asumsi equal dispersion (mean sama dengan variansi), padahal pada realita jarang ditemui kasus yang memenuhi equal dispersion. Dalam kasus tersebut, dapat diatasi dengan model regresi Generalized Poisson (GP) yang bisa mengatasi over dispersion atau under dispersion. Berdasarkan analisis model regresi GP pada penelitian ini, dihasilkan bahwa setiap kenaikan kepadatan penduduk sebesar 100 jiwa/Km2menyebabkan bertambahnya penderita konjungtivitis sebesar 3,78 kali,setiap kenaikan jumlah pabrik sebanyak sepuluh pabrik menyebabkan kenaikanpenderita konjungtivitis sebesar 1,135 kali, dan setiap kenaikan satu jumlah penduduk yang berpendidikan terakhir SMP menyebabkan kenaikan jumlah penderita mata konjungtivitis sebesar 2,724. Hasil uji goodness of fit untuk model regresi GP lebih baik dibandingkan jika menggunakan regresi Poisson karena memiliki nilai AIC lebih kecil.Kata Kunci : Konjugtivitis, Lamongan, Generalized Poisson, AIC

Comparison Support Vector Machine and Naive Bayes Methods for Classifying Cyberbullying in Twitter Nur Chamidah; Reiza Sahawaly
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol 7, No 2 (2021): August
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v7i2.21175

Twitter users in Indonesia in 2019 were recorded at 6.43 million. The high level of Twitter users makes it allows for free opinion to anyone, it can cause cyberbullying. Victims of cyberbullying experienced higher levels of depression than other verbal acts of violence. The forms of cyberbullying that occurs on Twitter are Flamming, Denigration, and Body Shaming. The research contribution is able to make social media developers and users more aware of the type of cyberbullying that social media users sometimes do without realizing it. Social media developers can prevent cyberbullying by using policies such as word detection and filtering features that indicate cyberbullying more accurately by classifying it by type and using the most accurate method. To classify cyberbullying forms in twitter, in this study we use the NaÃ¯ve Bayes method and Support Vector Machine (SVM) and compare them based on classification accuracy. This research will also identify words that are characteristic of each category of cyberbullying so that each category is easy to identify by social media users and makes it easier to avoid cyberbullying. The results of this study are the classification accuracy of NaÃ¯ve Bayes of 97.99% and the classification accuracy of SVM of 99.60%. It means that SVM is better than NaÃ¯ve Bayes for classifying the forms of cyberbullying in Twitter.

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

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

Co-Authors A Meylin Abidin, Qumadha Zaenal Afriani Agus Satmoko Adi Aisharezka, Mutiara Akbar, Aditya Syarifudin Al Farizi, Muhammad Fikry Al Hasri, Ilham Maulana Alexandra, Victoria Anggia Alfinda Novi Kristanti Amadea Fitri Syaharani Amanda, Yulia Aminuyati Aminy, Aisyah Ana, Elly Ananda Dwi Andini Putri Mediani Andini Sa'idah 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 Ayu Widyawati 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 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 Fitri, Marfa Audilla Fitri, Marfa Audilla Gaos Tipki Alpandi Gaos Tipki Alpandi 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 Lensa Rosdiana Safitri Lilik Hidayati, Lilik Listyaningsih Listyaningsih M. Fariz Fadillah Mardianto Mahadesyawardani, Arinda Mahadesyawardani, Arinda Marbun, Barnabas Anthony Philbert Marisa Rifada Maula, Sugha Faiz Al Maulidya, Utsna Rosalin MAYA MUSTIKA KARTIKA SARI, MAYA Mediani, Andini Putri Mediani, Andini Putri Melati Tegarina Mochammad Firmansyah Mohamad David Hermawan Muhammad Falah El Fahmi Mutiara Aisharezka N. A. Aprilianti Nadia Murbarani Naufal Ramadhan Al Akhwal Siregar Nia Saurina Nitasari, Alfi Nur 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 Ramadhina, Fidela Sahda Ilona 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 Salsabylla Nada Apsariny Sa’idah, Andini Sediono, Sediono Sely Novika Norrachma Septia Sari, Ni Wayan Widya Setyawan, Muhammad Daffa Bintang Setyowati, Raden Roro Nanik Shafira Salsabilla Siagian, Kimberly Maserati Siburian, Cynthia Anggelyn Siregar, Naufal Ramadhan Al Akhwal Soewignjo, Steven Sofia Fajrina Subiyanto, Marcel Laverda Sufyan Ats Tsauri Suliyanto Suryono, Alda Fuadiyah Suryono, Alda Fuadiyah Suwarno Suwarno Syavrilia Alfiatur Rakhma 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 Wieldyanisa, Ezha Easyfa Wulandari, Nuryuliana Yolanda Swastika Yolanda Swastika Yonani Zahrotul Azizah Zidni Ilmatun Nurrohmah

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