Penentuan Banyak Kelompok dalam Fuzzy C-Means Cluster Berdasarkan Proporsi Eigen Value Dari Matriks Similarity dan Indeks XB (Xie dan Beni)

Dalam analisis pengelompokkan (cluster), banyak kelompok menjadi suatu masalah yang berarti. Beberapa peneliti memilih banyak kelompok sesuai dengan kebutuhan dalam penelitiannya. Beberapa penelitian dalam analisis cluster lebih menitikberatkan pada struktur dan metode pengelompokkan yang terus berkembang dari waktu ke waktu. Metode terakhir yang sedang diminati adalah Fuzzy C-means Cluster. Fuzzy C-means Cluster melakukan pengelompokkan dengan prinsip meminimumkan fungsi objektif pengelompokkannya dimana salah satu parameternya adalah fungsi keanggotaan dalam fuzzy (sebagai pembobot) yang disebut juga dengan fuzzier (Klawonn dan Höppner, 2001). Makalah ini selain mengkaji metode pengelompokkan dengan Fuzzy C-means Cluster juga akan memilih banyak kelompok ideal dengan menggunakan indeks XB (Xie dan Beni). Untuk jumlah objek yang besar, indeks XB akan dihitung sebanyak objek yang dikelompokkan, maka hal ini tidaklah efektif. Untuk itu dicoba untuk membatasi banyak kelompok dengan menggunakan proporsi eigen value dari matriks kemiripan (similarity). Dengan membatasi banyak kelompok, perhitungan untuk mendapatkan kelompok ideal akan semakin cepat. Hal ini akan sangat berguna untuk efisiensi algoritma perhitungan indeks XB. Kata kunci : analisis pengelompokkan, cluster, fuzzy c-means, indeks XB, proporsi, eigen value, matriks kemiripan, similarity

Analisis Biplot Pada Pengelompokan Kecamatan Di Kabupaten Tasikmalaya Berdasarkan Indikator Kemiskinan

Poverty is a social problem that continues to exist in people's lives according to Nurwati, 2008. Therefore, the problem of poverty is the center of attention of the Tasikmalaya Regency government. In the National Long-Term Development Plan (RPJPN) 2005-2025 the problem of poverty is seen in a multidimensional framework, therefore poverty is not only related to income measurement, but related to several things. This is because poverty is not only related to the size of income but involves several things. In the Tasikmalaya Regency Regional Medium-Term Development Plan (RPJMD), the target for achieving the poverty rate in 2021 is 10.23%. Based on BPS publications, there are 10.75% of the population of Tasikmalaya Regency who are categorized as poor, meaning that the Tasikmalaya Regency government's target has not been achieved. So it is necessary to make efforts to overcome the problem of poverty. This study aims to group sub-districts in Tasikmalaya Regency based on the similarity of poverty indicators owned by each sub-district by using biplot analysis. The data used is poverty indicator data for 39 sub-districts in Tasikmalaya Regency in 2021. From the research results it is known that the amount of variation that can be described is 97%, meaning that the plots formed can best describe actual conditions. data information. In addition, three clusters have the same poverty indicators. Cluster 1 contains sub districts that have an indicator in the form of a high student to school ratio in SMA/SMK/MA. Cluster 2 contains sub districts that have moderate to low indicators on all variables except the ratio of SMP/MTs students and the ratio of SMA/SMK/MA students. Meanwhile, Cluster 3 consists of sub-districts that have an indicator in the form of a high ratio of SMP/MTs students

Analisis Sentimen Ulasan Pengguna Aplikasi E-Samsat Provinsi Jawa Barat Menggunakan Metode BiGRU

Organizing the facilitation of local revenue tasks and public services is one of the main tasks, functions, detailed unit tasks, and work procedures of the West Java Provincial Revenue Agency. One of the public services for the community in improving service to the West Java community is to launch an e-samsat innovation in providing annual Motor Vehicle Tax (PKB) payment services and updating ownership status through an Android-based smartphone application called Samsat Mobile Jawa Barat (SAMBARA) and can be downloaded for free on the Google Play Store. Service satisfaction is an important aspect in service development, therefore research was conducted. This study analyzes the sentiment of the Samsat Mobile Jawa Barat (SAMBARA) application on the Google Play Store by categorizing user reviews into three groups: Positive, Negative, and Neutral. The method chosen is the Bidirectional Gated Recurrent Unit (BiGRU). BiGRU is able to predict user reviews with an accuracy of up to 87.37%, which is considered good and can be used to help the development of service applications in West Java

Data tweet clustering using bidirectional gated recurrent unit and k-prototype for the Indonesian political year

As time passes, social media, which was formerly used as a means of communication between users, is experiencing a transition as a means for broadcasting information, conducting business, advertising, and even political campaigning. In elections, social media is also used to discredit political opponents to reduce the electability of opposing candidate. Spreading hate speech and fake news to undermine the electability of opposing candidate is a common violation of the law committed by supporters of one candidate over another. Considering that the number of social media users increases annually at a very rapid rate, the hazard of social media abuse has the potential to grow. In 2022, Indonesia had 191 million social media users in January 2022. Obviously, this will make the election situation more tumultuous and has the potential to cause societal divisions. The government must have a control system in place to screen social media content that can be considered illegal. In this study, fake news and hate speech are classified using the Bidirectional Gated Recurrent Unit (BiGRU). Lastly, K-Prototype was used to do clustering based on categorization dimensions and probable distribution to identify which clusters had the greatest risk of breaking the law, creating confusion, and dispersing broadly throughout society. It is hoped that the clusters that are created will represent the levels of priority of tweet data that requires prompt attention from the government to prevent it from spreading and inciting social unrest. Based on the results of the analysis, the BiGRU fake news model yields a F1-score of 95%, while the BiGRU hate speech model yields a F1-score of 90%. Clustering data using K-Prototype in this research can reduce the number of tweet data from 13,183 to 1,791 data. These new data are considered as a priority that must be pursued in preventing social media disputes

UNet-VGG16 with transfer learning for MRI-based brain tumor segmentation

A brain tumor is one of a deadly disease that needs high accuracy in its medical surgery. Brain tumor detection can be done through magnetic resonance imaging (MRI). Image segmentation for the MRI brain tumor aims to separate the tumor area (as the region of interest or ROI) with a healthy brain and provide a clear boundary of the tumor. This study classifies the ROI and non-ROI using fully convolutional network with new architecture, namely UNet-VGG16. This model or architecture is a hybrid of U-Net and VGG16 with transfer Learning to simplify the U-Net architecture. This method has a high accuracy of about 96.1% in the learning dataset. The validation is done by calculating the correct classification ratio (CCR) to comparing the segmentation result with the ground truth. The CCR value shows that this UNet-VGG16 could recognize the brain tumor area with a mean of CCR value is about 95.69%

The ensemble distance on model-based clustering for regions clustering based on rainfall: The case of rainfall in West Java Indonesia

Time series data clusters are being researched thoroughly. The distance metric drives the development of the clustering time series. The ARIMA model is one of the models that can be employed in model-based clustering, although differing model selection criteria can lead to uncertainty in the model. In this investigation, we created a technique for ensemble distance-based time series data clustering. To express the distance between two series, five distances based on the five model selection criteria are utilized. The average of the five distances reflects the distance of two time series data. According to the simulation results, the ensemble distance method could boost clustering accuracy by more than 11%. Based on the pattern of rainfall levels, we applied our methods to find clusters of locations in the Province of West Java (Indonesia). The findings indicate that the rainfall pattern in the same cluster is similar. The cluster model is effective and feasible for representing individual models in a cluster

Bayesian Bernoulli Mixture Regression Model for Bidikmisi Scholarship Classification

Bidikmisi scholarship grantees are determined based on criteria related to the socioeconomic conditions of the parent of the scholarship grantee. Decision process of Bidikmisi acceptance is not easy to do, since there are sufficient big data of prospective applicants and variables of varied criteria. Based on these problems, a new approach is proposed to determine Bidikmisi grantees by using the Bayesian Bernoulli mixture regression model. The modeling procedure is performed by compiling the accepted and unaccepted cluster of applicants which are estimated for each cluster by the Bernoulli mixture regression model. The model parameter estimation process is done by building an algorithm based on Bayesian Markov Chain Monte Carlo (MCMC) method. The accuracy of acceptance process through Bayesian Bernoulli mixture regression model is measured by determining acceptance classification percentage of model which is compared with acceptance classification percentage of  the dummy regression model and the polytomous regression model. The comparative results show that Bayesian Bernoulli mixture regression model approach gives higher percentage of acceptance classification accuracy than dummy regression model and polytomous regression mode

Message from the Chair of the Committee

Atas nama panitia penyelenggara, kami merasa terhormat dan senang menyambut seluruh peserta, pembicara utama (keynote speaker), dan invited speaker, serta peserta dalam Seminar Nasional Statistika XI (SNS XI). Acara ini adalah seminar nasional tahunan yang diselenggarakan oleh Departemen Statistika Universitas Padjadjaran, dengan dukungan dari Forstat dan Jurnal Inferensi ITS. Secara khusus, tema dari SNS XI ini adalah "Machine Learning: Statistics and Lifestyle" yang merupakan penelitian mengenai kemajuan statistika di era machine learning dan kecerdasan buatan. Kami berharap acara ini dapat memfasilitasi semua peserta untuk berinteraksi secara intensif guna memperluas jaringan ilmiah di masa depan

Spatially Constrained Neo-Normal Mixture Model (SC-Nenomimo) Dengan Pendekatan Bayesian Pada Segmentasi Citra MRI Tumor Otak

Segmentasi citra MRI dalam dunia medis bertujuan untuk memisahkan Region of Interest (ROI) atau segmen yang dianggap penting secara medis dengan segmen-segmen lainnya dalam citra. Hasil dari segmentasi citra MRI, dapat digunakan oleh dokter dan tenaga medis untuk mendiagnosa letak dan batas, tumor pada ROI guna mengambil keputusan medis.. Beberapa metode untuk segmentasi dikembangkan dalam analisis cluster, salah satunya adalah Model-based Clustering. Beberapa model yang telah dikembangkan masih menggunakan distribusi Normal sebagai distribusi pembangun model mixture-nya. Hal ini kurang cocok dengan citra MRI yang pola datanya tidak selalu simetris. Distribusi yang diusulkan dalam penelitian ini adalah Neo-Normal, yaitu distribusi adaptif yang dapat merelaksasi sifat distribusi Normal, namun dapat pula mengakomodasi pola asimetris, landai maupun runcing. Model yang terbentuk adalah Neo-Normal Mixture Model (Nenomimo). Kelemahan pada model yang menggunakan distribusi Normal adalah bahwa antar pixel dalam citra masih dianggap independen, sehingga model akan lebih peka terhadap noise. Untuk mengatasi noise, model Nenomimo digabungkan dengan metode pengidentifikasi lokasi pixel, yaitu Markov Random Fields, sehingga nama modelnya menjadi Spatially constrained Neo-Normal Mixture Model (Sc-Nenomimo). Pendekatan Bayesian digunakan dalam estimasi parameter model, karena telah terdapat informasi awal distribusi prior. Implementasi dari model digunakan untuk segmentasi citra MRI tumor otak dan hasilnya direstrukturisasi kedalam bentuk cita 3D untuk memberikan visualisasi yang lebih baik. Hasil segmentasi dengan Nenomimo dan Sc-Nenomimo memiliki tingkat akurasi yang baik, hal ini diberikan oleh nilai Misclassification Ratio yang kurang dari 2%. Pola citra MRI dapat didekati dengan baik oleh kedua model dengan nilai Fit Distribution Ratio sekitar 65%. Struktur 3D sangat membantu mengetahui letak dan batas tumor otak. Selain itu dengan struktur 3D dapat diperoleh estimasi volume tumor, yaitu sekitar 1192 s/d 1572 mm3. ======================================================================================================= Medical image segmentation aims to separate the tumor area as the Region of Interest from the other segment in MRI brain tumor image. The segmentation results will provide some information such as the location and also give a clear boundary of the tumor. This will help the doctor to run the safe surgical treatment and minimize the damage potential of the healthy part of the brain. Image segmentation through clustering analysis has been widely developed under several algorithms. This study chooses the model-based clustering in the form of the finite mixture model since it could cluster the observation by considering the data pattern. The previous state of the art uses the Gaussian distribution to construct the model. This model has disadvantage since the symmetrical property of Gaussian is not always fit the MRI data pattern. To solve this problem, this study uses Neo-Normal distribution to replace the Gaussian. The Neo-Normal is a relaxation of Normal distribution which can accommodate both symmetrical and asymmetrical patterns along with the ability to have long and fat tail properties. The proposed model is called Neo-Normal mixture model or Nenomimo. The other disadvantage of Gaussian model is the assumption of pixel independencies. This assumption makes noise would be group in the same cluster as the tumor. To overcome this problem, this study tries to hybrid the Nenomimo with the Markov Random Field. The model then becomes a Spatially constraint Neo-Normal mixture model (Sc-Nenomimo). Bayesian coupled with Markov chain Monte Carlo uses in the optimization since there is prior information in every parameter distribution. This study has succeeded to provide the Nenomimo and Sc-Nenomimo with the application of MRI based brain tumor segmentation. The segmentation results for both models have high accuracy since the Misclassification Ratio is less than 2%. Fit Distribution Ratio claim that both models could represent the MRI pattern of about 65%. In addition, the segmented images constructed in a 3D structure to give better visualization. With this structure, the location and boundary of the tumor area are more clear to see, and we can obtain the tumor volume estimation of about 1192 1572 mm3

A Bayesian Neo-Normal Mixture Model (Nenomimo) for MRI-Based Brain Tumor Segmentation

The detection of a brain tumor through magnetic resonance imaging (MRI) is still challenging when the image is in low quality. Image segmentation could be done to provide a clear brain tumor area as the region of interest. In this study, we propose an improved model-based clustering approach for MRI-based image segmentation. The main contribution is the use of the adaptive neo-normal distributions in the form of a finite mixture model that could handle both symmetrical and asymmetrical patterns in an MRI image. The neo-normal mixture model (Nenomimo) also resolves the limitation of the Gaussian mixture model (GMM) and the generalized GMM (GGMM), which are limited by the short-tailed form of their distributions and their sensitivity against noise. Model estimation is done through an optimization process using the Bayesian method coupled with a Markov chain Monte Carlo (MCMC) approach, and it employs a silhouette coefficient to find the optimum number of clusters. The performance of the Nenomimo was evaluated against the GMM and the GGMM using the misclassification ratio (MCR). Finally, this study discovered that the Nenomimo provides better segmentation results for both simulated and real data sets, with an average MCR for MRI brain tumor image segmentation of less than 3%