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"Cluster analysis finds groups in data automatically. Most methods have been heuristic and leave open such central questions as: how many clusters are there? Which method should I use? How should I handle outliers? Classification assigns new observations to groups given previously classified observations, and also has open questions about parameter tuning, robustness and uncertainty assessment. This book frames cluster analysis and classification in terms of statistical models, thus yielding principled estimation, testing and prediction methods, and sound answers to the central questions. It builds the basic ideas in an accessible but rigorous way, with extensive data examples and R code; describes modern approaches to high-dimensional data and networks; and explains such recent advances as Bayesian regularization, non-Gaussian model-based clustering, cluster merging, variable selection, semi-supervised and robust classification, clustering of functional data, text and images, and co-clustering. Written for advanced undergraduates in data science, as well as researchers and practitioners, it assumes basic knowledge of multivariate calculus, linear algebra, probability and statistics."

"Computational intelligence skills, which embrace the family of neural networks,fuzzy systems, and evolutionary computing in addition to other fields withinmachine learning, are effective in identifying, visualizing, classifying, and analysingdata to support business decisions. Developed theories of computational intelligencehave been applied in many fields of engineering, data analysis, forecasting,healthcare, and other. This text brings these skills together to address data science problems."

"The definitive guide to successfully integrating social, mobile, Big-Data analytics, cloud and IoT principles and technologies The main goal of this book is to spur the development of effective big-data computing operations on smart clouds that are fully supported by IoT sensing, machine learning and analytics systems"

"Data merupakan unsur terpenting dalam setiap penelitian dan pendekatan ilmiah. Metodologi sains data digunakan untuk memilah, memilih dan mempersiapkan sejumlah data untuk diproses dan dianalisis. Teknologi big data mampu mengumpulkan data dengan sangat banyak dari berbagai sumber dengan tujuan untuk mendapatkan informasi dengan visualisasi tren atau menyingkapkan pengetahuan dari suatu peristiwa yang terjadi baik dimasa lalu, sekarang, maupun akan datang dengan kecepatan pemrosesan data sangat tinggi. Analisis prediktif memberikan wawasan analisis lebih dalam dan kemunculan machine learning membawa analisis data ke tingkat yang lebih tinggi dengan bantuan teknologi kecerdasan buatan dalam tahap pemrosesan data mentah. Analisis prediktif dan machine learning menghasilkan laporan berbentuk visual untuk pengambil keputusan dan pemangku kepentingan. Berkenaan dengan keamanan siber, big data menjanjikan kesempatan dalam rangka untuk mencegah dan mendeteksi setiap serangan canggih siber dengan memanfaatkan data keamanan internal dan eksternal."

"Data are the prominent elements in scientific researches and approaches. Data Science methodology is used to select and to prepare enormous numbers of data for further processing and analysing. Big Data technology collects vast amount of data from many sources in order to exploit the information and to visualise trend or to discover a certain phenomenon in the past, present, or in the future at high speed processing capability. Predictive analytics provides in-depth analytical insights and the emerging of machine learning brings the data analytics to a higher level by processing raw data with artificial intelligence technology. Predictive analytics and machine learning produce visual reports for decision makers and stake-holders. Regarding cyberspace security, big data promises the opportunities in order to prevent and to detect any advanced cyber-attacks by using internal and external security data."

"Katarak merupakan salah satu jenis kelainan mata yang menyebabkan lensa mata menjadi berselaput dengan pandangan berawan, sehingga memungkinkan untuk mengalami kebutaan total. Penderita katarak dapat disembuhkan dengan operasi setelah sebelumnya dilakukan computed tomography (CT) scan dan magnetic resonance imaging (MRI) sebagai metode untuk mendapatkan citra digital mata. Namun, penggunaan metode ini tidak selalu memungkinkan, terutama untuk fasilitas kesehatan di negara berkembang, karena kurangnya rumah sakit atau klinik mata yang menyediakan fasilitas berteknologi lengkap. Penelitian ini bertujuan untuk membantu proses analisis citra mata agar lebih cepat dan akurat dengan menggunakan model deep learning untuk memprediksi mata katarak menggunakan arsitektur CNN dengan terlebih dahulu menganalisis performa model dan membandingkan akurasi/loss model dengan penelitian sebelumnya. Metode perancangan model deep learning ini dilakukan dimulai dari preprocessing, membangun arsitektur model, proses training, dan diakhiri dnegan evaluasi hasil model dengan mengguakan confusion matrix dan classification report. Dari perancangan ini, didapatkan hasil validasi akurasi model sebesar 92.97% dan hasil validasi loss 0.1539. Dari model yang penulis buat dihasilkan model deep learning dengan nilai evaluasi pendeteksian mata katarak dengan presisi 94.30%, recall 97.47%, dan f-1 score 95.85%. Hasil dari penelitian ini menunjukkan bahwa model yang penulis rancang telah dapat memprediksi gambar penyakit katarak dengan akurasi diatas 80 % dengan loss dibawah 30 % dengan hasil presisi, recall, dan f-1 score >90% dan menunjukkan tingkat overfitting yang minimal.

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Cataract is an eye condition in which the lens of the eye becomes webbed and cloudy, resulting in total blindness. Cataract patients can be cured through surgery after undergoing computed tomography (CT) scans and magnetic resonance imaging (MRI) to obtain digital images of the eyes. However, due to a lack of hospitals or eye clinics that provide complete technology facilities, this method is not always feasible, particularly for health facilities in developing countries, particularly in Indonesia. By first examining the model's performance and comparing the model's accuracy/loss with prior research, this study intends to make the eye image analysis process faster and more accurate by employing a deep learning model to predict cataracts using the CNN architecture. Starting with preprocessing, designing the model architecture, training, and finally evaluating the model outcomes using a confusion matrix and classification report, this deep learning model design technique is followed. The model accuracy validation results from this design are 92.97 % and the loss validation results are 0.1539. A deep learning model with an evaluation value of cataract eye detection with a precision of 94.30 %, recall of 97.47 %, and an f-1 score of 95.85 % was produced from the author's model. According to the findings of this study, the author's model can predict cataract images with an accuracy of more than 80%, a loss of less than 30%, precision, recall, and f-1 score greater than 90%, and minimal overfitting.

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Di antara sebagian besar sektor industri lainnya, industri kimia sedang mengalami pergolakan signifikan yang didorong oleh konsep yang secara kolektif dikenal sebagai Industri 4.0. Data sains adalah komponen penting dari Industri 4.0 karena memungkinkan ekstraksi informasi kontekstual dari berbagai sumber data. Ketika sistem menjadi lebih kompleks, kebutuhan para insinyur untuk mengekstrak sinyal dari data dengan tepat berkembang secara dramatis, menuntut literasi data dan keahlian analitik pada generasi berikutnya dari lulusan teknik kimia. Salah satu dari banyak kasus di mana data sains dan machine learning dapat diterapkan adalah untuk prediksi. Prediksi berbasis machine learning dapat diterapkan pada banyak aspek teknik kimia contohnya pada Chemical Engineering Plant Cost Index (CEPCI). CEPCI sangat penting untuk perhitungan desain pabrik dan dipengaruhi oleh banyak variabel. Pendekatan machine learning diperlukan untuk memperhitungkan semua variabel tersebut dan mendapatkan hasil yang tepat untuk variabel yang ditargetkan. Dengan demikian, tujuan dari tugas akhir ini adalah merancang program yang mampu memprediksi CEPCI. Alhasil, model regresi yang telah dibuat mampu memprediksi Composite CE Index dengan error rata-rata 3.75% dari index aslinya.

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Among most other industrial sectors, the chemical industry is undergoing a significant upheaval driven by concepts known collectively as Industry 4.0. Data science is an important component of Industry 4.0 since it enables the extraction of contextualized information from a variety of data sources. As systems become more complex, the necessity for engineers to appropriately extract signal from data develops dramatically, demanding data literacy and analytics expertise in the next generation of chemical engineering graduates. One of the many cases where data science and machine learning can be applied to is for prediction. Machine Learning based prediction can be applied to many chemical engineering aspects, in this case the Chemical Engineering Plant Cost Index (CEPCI). CEPCI is essential for plant design calculations and is greatly affected by numerous variables. Machine learning approach is needed to account for all said variables and obtain valid result for target variables. Thus, the purpose of this thesis is to design programs that are able to predict CEPCI. As a result, the regression model created was able to predict the Composite CE Index with average error of 3.75% from the real index.

 

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"Credit scoring merupakan bentuk penilaian untuk menentukan kelayakan peminjam. Tidak ada kesepakatan kapan metode ini mulai berkembang. Namun, kesubjektivitasan dan ketidakmampuan manusia untuk memproses permohonan pinjaman dalam jumlah besar setiap harinya adalah alasan penggunaan credit scoring dengan machine learning menjadi sangat dibutuhkan. Untuk mendeteksi dini potensi peminjam yang bermasalah, credit scoring pada tugas akhir ini diprediksi status pinjaman menjadi tiga kelas: default, fully paid, dan late. Berdasarkan permasalahan tersebut, pada tugas akhir ini digunakan model untuk memprediksi status pinjaman pada kasus klasifikasi multikelas credit scoring dengan machine learning menggunakan metode CatBoost. Penggunaan metode CatBoost dimaksudkan untuk mengatasi kasus klasifikasi multikelas pada data yang heterogen dan tidak seimbang (imbalanced data). Data yang digunakan adalah data pinjaman online peer-to-peer (P2P) LendingClub yang memuat tiga jenis informasi yaitu informasi pinjaman, informasi peminjam, dan informasi riwayat pinjaman peminjam. Data pinjaman P2P LendingClub memiliki imbalanced data dan overlapping class. Terdapat tiga skenario sampling strategy SMOTE-NC dilakukan untuk melihat efek imbalanced data dan overlapping class pada permasalahan klasifikasi multikelas tersebut sehingga didapatkan tiga model. Kinerja model CatBoost dievaluasi berdasarkan precision, recall, f1-score serta accuracy dan AUC one-vs-all. Hasil implementasi CatBoost sudah baik pada kelas 1 (fully paid) dikarenakan f1-score ketiga skenario lebih dari 0,75. Namun, pada kelas 0 (default) dan kelas 2 (late) hasil implementasinya masih tidak baik mengingat f1-score pada kelas 0 (default) tertinggi hanyalah 0,15 sementara f1-score kelas 2 (late) bernilai sama yaitu 0,04 pada ketiga skenario model yang dibuat. Efek dari imbalanced data dan overlapping class pada metrik evaluasi model precision, recall, f1-score serta accuracy dan AUC one-vs-all beragam bergantung dengan kelasnya.

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Credit scoring is a form of assessment used to determine the creditworthiness of borrowers. There is no agreement on when this method started to develop. However, subjectivity and the inability of humans to process large volumes of loan applications every day are the reasons why credit scoring with machine learning is highly needed. In order to detect potential problem borrowers early on, this final project predicts the loan status into three classes: default, fully paid, and late. Based on this problem, a model is employed in this final project to predict the loan status in a multi-class classification of credit scoring by using machine learning, specifically using the CatBoost method. The use of CatBoost is intended to address multi-class classification cases with heterogeneous and imbalanced data. The data used in this research is online peer-to-peer (P2P) lending data from LendingClub, which includes three types of information: loan information, borrower information, and borrower's loan history information. The P2P LendingClub loan data has imbalanced data and overlapping classes. Three sampling strategy scenarios of SMOTE-NC are performed to observe the effects of imbalanced data and overlapping classes on this multi-class classification problem, resulting in having three models. The performance of the CatBoost model is evaluated based on precision, recall, f1-score, as well as accuracy and AUC one-vs-all. The implementation of CatBoost yields good results for class 1 (fully paid) as the f1-scores in all three scenarios are above 0.75. However, the implementation results for class 0 (default) and class 2 (late) are still unsatisfactory, considering that the highest f1-score for class 0 (default) is only 0.15, while the f1-score for class 2 (late) has the same value, i.e., 0.04, in all three model scenarios. The effects of imbalanced data and overlapping classes on the evaluation metrics of precision, recall, f1-score, as well as accuracy and AUC one-vs-all vary depending on the class."