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All Journal Media Statistika JURNAL MATEMATIKA STATISTIKA DAN KOMPUTASI Perfecting a Video Game with Game Metrics Bulletin of Electrical Engineering and Informatics Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) Jurnal Ketahanan Nasional Journal of Information Systems Engineering and Business Intelligence Syntax Literate : Jurnal Ilmiah Indonesia Indonesian Journal of Statistics and Its Applications Zeta - Math Journal J Statistika: Jurnal Ilmiah Teori dan Aplikasi Statistika JCRS (Journal of Community Research and Service) Contemporary Mathematics and Applications (ConMathA)

Nur Chamidah

Departement Of Mathematics, Faculty Of Science And Technology, Airlangga University

Author-ID : 2458378

Humanities Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Environmental Science Law, Crime, Criminology & Criminal Justice Materials Science & Nanotechnology Mathematics Social Sciences Other

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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.

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

MODELING OF THE PERCENTAGE OF AIDS SUFFERERS IN EAST JAVA PROVINCE WITH NONPARAMETRIC REGRESSION APPROACH BASED ON SPLINE TRUNCATED ESTIMATOR Nadia Murbarani; Yolanda Swastika; Ananda Dwi; Baktiar Aris; Nur Chamidah
Indonesian Journal of Statistics and Applications Vol 3 No 2 (2019)
Publisher : Departemen Statistika, IPB University dengan Forum Perguruan Tinggi Statistika (FORSTAT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29244/ijsa.v3i2.209

Acquired Immune Deficiency Syndrome (AIDS) is a set of symptoms and infection or a syndrome that arise due to damage to the human immune system. AIDS is a health problem that often occurs in developing countries, including in Indonesia. East Java Province was ranked first in the highest number of AIDS sufferers in Indonesia ever reported from 1987-2016 as many as 16,911 people out of a total of 86,780 people. In order to overcome AIDS cases, it is necessary to know the factors that influence it. Data on the percentage of AIDS sufferers and their predictor variables have irregular data patterns or do not match in certain patterns, then the method that can solve these problems is by using the nonparametric regression based on spline truncated estimator. A spline truncated estimator is a segmented polynomial function that has better flexibility because there are knot points indicating changes in data behaviour patterns. The data that used in this study is a secondary data in 2016 obtained from the East Java Provincial Health Office. The results showed that the determination coefficient (R2) based on the best model of 93.84%. This shows that the variables of health facilities, blood donors, health workers, condom users, and residents of 25-29 years are able to explain 93.84% of the percentage of AIDS sufferers in East Java Province in 2016.

ANALISIS PENGARUH ANGKA KEMATIAN BAYI TERHADAP ANGKA HARAPAN HIDUP DI PROVINSI JAWA TIMUR BERDASARKAN ESTIMATOR LEAST SQUARE SPINE Anies Yulinda W; Trias Novia L.; Melati Tegarina; Nur Chamidah
Contemporary Mathematics and Applications (ConMathA) Vol. 1 No. 1 (2019)
Publisher : Universitas Airlangga

Life expectancy can be used to evaluate the government's performance for improving the welfare of the population in the health sector. Life expectancy is closely related to infant mortality rate. Theoretically, decreasing of infant mortality rate will cause increasing of life expectancy. A statistical method that can be used to model life expectancy is nonparametric regression model based on least square spline estimator. This method provides high flexibility to accommodate pattern of data by using smoothing technique. The best estimated model is order one spline model with one knot based on minimum generalized cross validation (GCV) value of 0.607. Each increasing of one infant mortality rate unit will cause decreasing of life expectancy of 0.2314 for infant mortality rate less than 27, and of 0.0666 for infant mortality rate more than and equals to 27. In addition, based on mean square error (MSE) of 0.492 and R2value of 76.59% for nonparametric model approach compared with MSE of 0.634 and R2 value of 71.8% for parametric model approach, we conclude that the use of nonparametric model approach based on least square spline estimator is better than that of parametric model approach.

Modeling of Incident Status Dengue Fever in East Nusa Tenggara Using Geographically Weighted Logistic Regression Approach A Meylin; N. A. Aprilianti; D Lestari; Nur Chamidah
Contemporary Mathematics and Applications (ConMathA) Vol. 2 No. 2 (2020)
Publisher : Universitas Airlangga

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20473/conmatha.v2i2.23856

Dengue fever is a disease caused by one of the four dengue viruses and this disease is an infectious disease that is spread through the bite of the Aedes Aegypti mosquito. When compared with the number of dengue cases in previous years, East Nusa Tenggara (NTT) was one of the provinces that experienced an increase in the number of dengue cases in the last three years. Previous research states that the transmission of dengue fever is caused by several factors, one of which is environmental factors of geographical location so that spatial aspects need to be involved in this study. A the statistical method that can be used to analyze spatial data in the form of a logistic regression equation that has a binary response variable is the Geographically Weighted Logistic Regression (GWLR) method. This study aims to analyze the factors that influence the high number of dengue fever cases in NTT in 2018 using GWLR approach by weighted the Gaussian kernel function. Based on the results of GWLR analysis, the number of rainy days and the number of health workers partially significantly influence the status of dengue fever events in each regency/city in NTT Province in 2018. Based on the calculation of Press’s Q value, the prediction in this study was accurate with the accuracy of classification was 0.8636 or 86.36%.

Sentiment Analysis of User Reviews Based On Naïve Bayes Classifier Algorithm with Hyperparameter Optimization: A Case Study On Application "Kredit Pintar" Salsabylla Nada Apsariny; Sediono Sediono; Nur Chamidah; Elly Ana; Ardi Kurniawan
Syntax Literate Jurnal Ilmiah Indonesia
Publisher : CV. Ridwan Publisher

The development of information and communication technology makes it easy for people to take advantage of their sophistication in various sectors, especially industry and the economy. However, due to the Covid-19 pandemic in Indonesia, it has an impact on the socio-economic community. To meet their needs, most people use online loan platforms because they feel they have easy requirements. One of the most trusted online loan platforms and protected by the OJK is "Kredit Pintar", which is the number one platform that is most widely used by the public to borrow money in 2021. From the facts, the purpose of this research is to find out the reviews of the community of online loan application users “Kredit Pintar” through sentiment analysis based on text data in the form of public comments taken from the Google Play Store site application of “Kredit Pintar”. The data used are 1374 reviews with positive and negative class classifications. This research was conducted by using an analytical text mining method using the Naïve Bayes Classifier algorithm and applying Hyperparameter Optimization by comparing two models of GaussianNB and MultinomialNB functions by Phyton programming. The results of the classification of training and testing data show that based on performance evaluation, the best model uses the MultinomialNB function with an accuracy of 97.08% for training data and 90.54% for testing data.

ESTIMASI SELANG KEPERCAYAAN NILAI UJIAN NASIONAL BERBASIS KOMPETENSI BERDASARKAN MODEL REGRESI SEMIPARAMETRIK MULTIRESPON TRUNCATED SPLINE Lilik Hidayati; Nur Chamidah; I Nyoman Budiantara
MEDIA STATISTIKA Vol 13, No 1 (2020): Media Statistika
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Confidence interval estimation is important in statistical inference for the parameters of the regression model, but the theory of confidence interval estimation for multi-response semiparametric regression model parameters based on the truncated spline estimator has not been examined. In this study, we estimate the confidence interval of the multi-response semiparametric regression model based on the truncated spline estimator by using pivotal quantity method with the central limit theorem approach. This confidence interval theory is applied to data of competency-based national exam (UNBK) scores in West Nusa Tenggara Province where its UNBK in the lowest position among other provinces in Indonesia. The method used for estimating parameters is weighted least square. The best model is determined based on the Generalized Cross Validation (GCV) minimum value. Based on the estimated 95% confidence interval of parameters of the multi-response truncated spline semiparametric regression model, the results showed that the insignificant factors affecting the UNBK scores were gender and parental education duration while the report card of scores and USBK scores had a positive effect on the UNBK scores but only the UNBK scores of mathematics that report card of scores factor has a negative effect on it.

Pemodelan Persentase Kepesertaan BPJS Non Penerima Bantuan Iuran Dengan Pendekatan Regresi Data Panel Dhyana Venosia; Suliyanto; Sediono; Nur Chamidah
J STATISTIKA: Jurnal Imiah Teori dan Aplikasi Statistika Vol 15 No 1 (2022): Jurnal Ilmiah Teori dan Aplikasi Statistika
Publisher : Faculty of Science and Technology, Univ. PGRI Adi Buana Surabaya

Indonesia merupakan salah satu negara yang mengembangkan konsep Universal Health Coverage (UHC) pada sektor kesehatan yang diterapkan pada Sistem Jaminan Sosial Nasional (SJSN) melalui program Jaminan Kesehatan Nasional (JKN) yang dikelola Badan Penyelenggara Jaminan Sosial (BPJS) Kesehatan dengan tujuan sebagaimana tertuang pada Undang-Undang Republik Indonesia Nomor 40 Tahun 2004. Peserta JKN terbagi menjadi Penerima Bantuan Iuran (PBI) dan Non Penerima Bantuan Iuran (Non PBI). Penelitian ini, untuk menganalisis faktor yang mempengaruhi persentase kepesertaan BPJS Non PBI yang diharapkan dapat memberikan prediksi pengoptimalan. Pengoptimalan diperlukan karena, realitanya persentase kepesertaan BPJS Non PBI masih jauh dari target pemerintah, khususnya Provinsi Jawa Timur pada tahun 2017 hingga 2020. Walaupun mengalami peningkatan, di setiap Kabupaten/Kota Provinsi Jawa Timur terindikasi mengalami fluktuasi. Maka, dalam mengestimasi fenomena tersebut digunakan metode regresi data panel melalui pendekatan Fixed Eeffect Model (FEM) dengan alpha sebesar 5 persen. Maka, secara statistik diperoleh kesimpulan bahwa yang berpengaruh signifikan adalah persentase penduduk miskin dan Tingkat Pengangguran Terbuka (TPT).

Co-Authors A Meylin Achmad Yazid Busthomi Rohim Aditya Syarifudin Akbar Agnes Happy Julianto Ana, Elly Ananda Dwi Andini Sa’idah Anies Yulinda W Anisa Laila Azhar Ardi Kurniawan Ardi Kurniawan Baktiar Aris Belindha Ayu Ardhani Budi Lestari Budi Lestari D Lestari Dhyana Venosia Diah Puspita Ningrum Dita Amelia Eko Tjahjono Elly Ana Fiyadika Amalia Nurizah Azzen I Nyoman Budiantara Kinanti Hanugera Gusti Lilik Hidayati Melati Tegarina Mochamad Rasyid Aditya Putra N. A. Aprilianti Nabila Syahfitri Rahmatika Nadia Murbarani Pressylia Aluisina Putri Widyangga Reiza Sahawaly Salsabylla Nada Apsariny Sediono Sediono Suliyanto Toha Saifudin Trias Novia L. Yasmin Kamila Yolanda Swastika Yolanda Swastika Yonani

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