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Abstrak :
Summary: It's been ten years since Nicolette Farrell left her rural hometown after her best friend, Corinne, disappeared from Cooley Ridge without a trace. Back again to tie up loose ends and care for her ailing father, Nic is soon plunged into a shocking drama that reawakens Corinne's case and breaks open old wounds long since stitched. The decade-old investigation focused on Nic, her brother Daniel, boyfriend Tyler, and Corinne's boyfriend Jackson. Since then, only Nic has left Cooley Ridge. Daniel and his wife, Laura, are expecting a baby; Jackson works at the town bar; and Tyler is dating Annaleise Carter, Nic's younger neighbor and the group's alibi the night Corinne disappeared. Then, within days of Nic's return, Annaleise goes missing. Told backwards Day 15 to Day 1 from the time Annaleise goes missing, Nic works to unravel the truth about her younger neighbour's disappearance, revealing shocking truths about her friends, her family, and what "really "happened to Corinne that night ten years ago.

Abstrak :
Summary: In the fading town of Stain'd-by-the-Sea, young apprentice Lemony Snicket has a new case to solve when he and his chaperone are hired to find a missing girl. Is the girl a runaway? Or was she kidnapped? Was she seen last at the grocery store? Or could she have stopped at the diner? Is it really any of your business

Abstrak :
Summary: It begins with a glimpse or a passing thought. It ends in obsession. One day a renowned author discovers that his wife, a war correspondent, has disappeared leaving no trace. Though time brings more success and new love, he remains mystified--and increasingly fascinated--by her absence. Was she kidnapped, blackmailed, or simply bored with their marriage? The unrest she causes is as strong as the attraction she exerts. His search for her--and for the truth of his own life--takes him from France to Spain, Croatia and, eventually, the bleakly beautiful landscape of Central Asia. More than that, it takes him from the safety of his world to a totally unknown path, searching for a new understanding of the nature of love and the power of destiny. With The Zahir, Paulo Coelho demonstrates not just his powerful and captivating storytelling, but also his extraordinary insight into what it is to be a human being in a world full of possibility. In Arabic Zahir means 'visible, present and unable to go unnoticed.' It refers to something or someone that ends up being the only thing we can think of. This state of 'possession' can be understood as saintliness or insanity, with a fine line between the two.

Abstrak :
This book offers an easy-to-read introduction to the theoretical underpinnings of analysis of missing data, provides clear, step-by-step instructions for performing state-of-the-art multiple imputation analyses, and offers practical advice, based on over 20 years' experience, for avoiding and troubleshooting problems. For more advanced readers, unique discussions of attrition, non-Monte-Carlo techniques for simulations involving missing data, evaluation of the benefits of auxiliary variables, and highly cost-effective planned missing data designs are provided. Most analysis described in the book are conducted using the well-known statistical software packages SAS and SPSS, supplemented by Norm 2.03 and associated Java-based automation utilities.

Abstrak :
Pada setiap analisis statistik memungkinkan berhadapan dengan missing values atau missing data karena pada saat survei kemungkinan ada responden yang tidak dapat menjawab pertanyaan atau tidak ingin menjawab pertanyaan pada saat wawancara survei. Missing values tidak dapat langsung dilakukan analisis menggunakan analisis data lengkap, oleh karena itu missing values telah menjadi masalah yang sering dihadapi oleh para peneliti. Dataset survei biasanya terdiri dari sejumlah besar variabel kontinu salah satunya berdistribusi multivariat normal. Salah satu cara untuk menangani missing values dapat dilakukan dengan imputasi, yaitu proses pengisian atau penggantian missing values pada dataset dengan nilai-nilai yang mungkin berdasarkan informasi yang didapatkan pada dataset tersebut. Penelitian ini akan menerapkan metode sequence regression multivariate imputation (SRMI) untuk imputasi missing values pada data multivariat normal. SRMI merupakan metode imputasi ganda yang nilai imputasinya didapatkan dari model sequence of regression yaitu setiap variabel yang mengandung missing values diregresikan terhadap semua variabel lain yang tidak mengandung missing values sebagai variabel prediktor. Cara mendapatkan nilai imputasi digunakan pendekatan iterasi untuk menarik nilai dari distribusi posterior prediktif pada missing values di bawah masing-masing model regresi secara beruntun. Penelitian ini menggunakan data multivariat normal yang telah dibangkitkan sebanyak 500 observasi dengan menggunakan lima nilai imputasi ganda dan hasil evaluasi kualitas imputasi menggunakan Root Mean Square Error (RMSE). Hasil evaluasi kualitas imputasi dapat dikatakan baik jika nilai RMSE semakin kecil, maka eror semakin kecil atau nilai estimasi mendekati nilai sebenarnya (Chai & Draxler, 2014) dan hasil yang didapatkan nilai RMSE kecil sehingga SRMI dapat diterapkan untuk melakukan imputasi terhadap data multivariat normal. ......Missing values are the absence of data items for an observation or more observations that can result in the loss of certain information. During surveys, there are often missing values or missing data because there are likely respondents who cannot answer the question or do not want to answer the question. That is a problem for researchers because, with missing values, the results of observation cannot be analyzed properly. Survey datasets usually consist of continuous variables, one of which is a normal multivariate distribution. One way to deal with missing values ​​can be done by imputation, which is the process of filling or replacing missing values ​​in a dataset with possible values ​​based on the information obtained in the dataset. This study will apply the sequence regression multivariate imputation (SRMI) method for missing values ​​imputation in normal multivariate data. SRMI is a multiple imputation method whose implication value is obtained from the sequence of regression model, that is, every variable containing missing values ​​is regressed on all other variables that do not contain missing values ​​as predictor variables. The method of obtaining imputation values ​​is used by the iterative approach to drawing values ​​from the predictive posterior distribution in the missing values ​​below each successive regression model. This study uses multivariate normal data that has been generated a total of 500 observations using five multiple imputation values ​​and the evaluation results using Root Mean Square Error (RMSE) which have little value in applying to normal multivariate data so SRMI can be applied to impute normal multivariate data.

Abstrak :
Metode machine learning sangat banyak digunakan dalam membantu pekerjaan manusia. Tidak semua data seperti yang diharapkan. Kebanyakan data memiliki missing value. Data yang memiliki missing value harus ditangani dulu pada tahap pra pengolahan, salah satunya adalah dengan cara imputasi missing value. Pada penelitian ini, dilakukan analisis kinerja One-Dimensional Naïve Bayes sebagai metode imputasi data masalah asuransi mobil dan keselamatan berkendara. Berdasarkan hasil simulasi menggunakan SVM didapatkan hasil yang sama untuk imputasi menggunakan modus dan One-Dimensional Naïve Bayes pada data Car Insurance yaitu 1,00. Setelah itu dilakukan telaah lebih lanjut ternyata imputasi setiap missing value dengan modus dan prediksi imputasi dengan One-Dimensional Naïve Bayes persis sama. Pada data Safe Driver, imputasi dengan modus menghasilkan akurasi 0,86 sedangkan imputasi dengan One-Dimensional Naïve Bayes menghasilkan akurasi 0,85. Hasil ini menunjukkan bahwa metode imputasi missing value dengan modus masih sangat direkomendasikan untuk tahap pra pengolahan data pada machine learning. ......Machine learning methods are very widely used in helping human work. Not all data is as expected. Most data have missing values. Data which has a missing value must be handled first at the pre-processing stage, one of which is by imputation of the missing value. In this study, a One-Dimensional Naïve Bayes performance analysis was performed as a data imputation method for car insurance and safe driver problems. Based on simulation results by using SVM obtained the same results for imputation using mode and One-Dimensional NaA ve Bayes on Car Insurance data that is 1,00. After that, a further study is carried out, apparently the imputation of each missing value by mode and the prediction of imputation with One-Dimensional NaAve Bayes are the same. In Safe Driver data, imputation with mode produces 0.86 accuracy while imputation with One-Dimensional NaAve Bayes produces accuracy of 0.85. These results indicate that the method of missing value imputation with mode is still highly recommended for the pre-processing data stage in machine learning.

Abstrak :
Kementrian Kesehatan menyatakan Indonesia sudah memasuki kondisi ageing population, dimana kondisi tersebut ditandai dengan kenaikan persentase penduduk lanjut usia (lansia). Kondisi tersebut tentunya memerlukan perhatian khusus dari pemerintah. Demensia adalah istilah medis untuk menggambarkan gejala penurunan memori dan fungsi kognitif pada tubuh manusia. Indonesia termasuk sepuluh negara dengan jumlah penderita orang dengan demensia (ODD) tertinggi di dunia, dan pada tahun 2050 jumlahnya diprediksi mencapai empat juta jiwa. Prediksi tersebut dibuat berdasarkan perbandingan jumlah lansia di Indonesia dengan jumlah ODD di seluruh dunia. Penelitian bertujuan untuk mengetahui peran dari masing-masing uji klinis untuk mengidentifikasi penderita ODD dan mereduksi komponen uji klinis yang memiliki peran kontribusi rendah. Data yang digunakan adalah data uji klinis NIFD (Neuroimaging in Frontotemporal Dementia). Metode yang akan digunakan adalah Principal Component Analysis (PCA), dimana metode ini bertujuan untuk melihat komponen uji klinis yang memberikan peran kontribusi dalam mengidentifikasi penderita ODD. Selain itu, akan dilakukan proses imputasi missing value dengan menggunakan algoritma pengembangan dari PCA, yaitu SVD-Impute dan PPCA. Setelah dilakukan tiga kali iterasi, pengujian menunjukan bahwa metode PPCA lebih baik dalam melakukan imputasi missing value dibandingkan dengan metode SVDImpute berdasarkan nilai NRMSE dan koefisien korelasi Pearson. ......The Ministry of Health stated that Indonesia had entered a condition of an aging population, where an increase in the proportion of older people marks this condition. This condition certainly requires special attention from the government. Dementia is a medical term to describe symptoms of decreased memory and cognitive function in the human body. Indonesia is one of the ten countries with the highest number of people with dementia in the world, and by 2050 it is predicted to reach four million people. This prediction was based on comparing the number of older people in Indonesia with those with dementia worldwide. The research aims to determine each clinical trial's role in identifying people with dementia and reducing the components of clinical trials with a low role contribution. The data used is NIFD (Neuroimaging in Frontotemporal Dementia) clinical trial data. The method used is Principal Component Analysis (PCA), which aims to see clinical component tests that contribute to identifying people with dementia. In addition, the missing value imputation process will be carried out using the development algorithm from PCA, SVD-Impute and PPCA. After three iterations, the test showed that the PPCA method was better at imputing missing values than the SVDImpute method based on the NRMSE value and Pearson's correlation coefficient.

Abstrak :
Abstrak. Imputasi missing values berperan penting dalam pre-processing data untuk menghasilkan data yang lengkap dan berkualitas. Dalam penelitian ini, dilakukan kombinasi dari Decision Tree dan algoritma Expectation Maximization (EM) sebagai metode imputasi dalam mengestimasi missing values. Namun, terdapat kekurangan pada metode ini, yaitu algoritma Expectation Maximization (EM) cenderung memberikan hasil imputasi yang lebih akurat jika terdapat banyak kemiripan antar atribut. Hal ini dapat diatasi dengan mempartisi segmen horisontal menggunakan algoritma Decision Tree. EM diterapkan pada berbagai segmen horisontal dari data set agar diperoleh banyak kemiripan antar atribut. Metode yang diusulkan ini kemudian dievaluasi dengan membandingkan kinerjanya dengan imputasi menggunakan mean dalam mengestimasi missing values data numerik dan dipilih Decision Tree sebagai classifier. Data yang digunakan untuk simulasi dalam penelitian ini yaitu data set PPOK-OSA. Penelitian ini menunjukkan bahwa metode imputasi missing values yang diusulkan menghasilkan akurasi yang lebih tinggi jika dibandingkan menggunakan nilai mean dalam mengestimasi missing values. Hasil akurasi tertinggi dari metode ini yaitu83.3%, diperoleh pada saat persentase data training 10% dan rata-rata akurasi pada berbagai persentase data training sebesar 70.3%, sedangkan hasil akurasi tertinggi menggunakan nilai mean yaitu 58.3%, diperoleh pada saat persentase data training 20% dengan rata-rata akurasi pada berbagai persentase data training sebesar 50.5%.

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Abstract. Missing values imputation plays a vital role in data pre-processing for ensuring good quality of data. In this study, we present a combination of Decision Tree and Expectation Maximization (EM) algorithm as imputation method to estimate missing values. However, there are shortcomings of method, where the Expectation Maximization algorithm tends to give more accurate imputation results if there are many similarities among attributes. Therefore, this can be overcome by partitioning the horizontal segments using the Decision Tree algorithm. We applied EM on various horizontal segments of a data set where there are many similarities among attributes. Besides, we evaluate our proposed method by comparing its performance with mean values to impute missing values, and we choose the Decision Tree as a classifier. Data used in this research is COPD-OSA data set. In this study shows that ourproposed method leads to higher accuracy than mean imputation. The highest accuracy results obtained from our proposed method is 83.3% when the percentage of training data is 10% and average accuracy in various percentage of training data is 70.3%, while the highest accuracy results using the mean value is 58.3% when the training data percentage is 20% and the average accuracy in various percentage of training data is 50.5%.

Abstrak :
Salah satu metode yang populer untuk mengatasi missing value dalam sebuah survei adalah metode imputasi. Imputasi adalah solusi untuk mengganti suatu missing value dengan suatu nilai pengganti yang didapatkan dari teknik khusus tertentu, misalnya imputasi menggunakan nilai mean, nilai median, dan lain sebagainya. Pada skripsi dibahas suatu teknik imputasi yang menggabungankan dua macam teknik imputasi lain, yakni imputasi fractional dan imputasi hot deck. Imputasi fractional adalah imputasi yang punya kelebihan dalam meminimumkan suatu variansi dalam suatu data dikarenakan nilai imputasi yang dihasilkan berasal dari data set dalam survei itu sendiri, namun kekurangannya adalah bahwa nilai imputasi dari teknik fractional akan membuat jumlah observasi menjadi mengembang. Oleh karena masalah pengembangan data set yang dihasilkan teknik fractional tersebut, imputasi hot deck menjadi solusi untuk membatasi masalah jumlah observasi yang dihasilkan dengan membatasi calon nilai imputasi (donor) untuk suatu nilai hilang. Imputasi yang menggabungkan teknik imputasi fractional dan hot deck akan dikenal dengan nama imputasi fractional hot deck dengan sifat yang mirip dengan imputasi fractional , namun observasi yang dihasilkan lebih sedikit.

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One of the most popular solution of missing value is imputation in a survey is imputation. Imputation is a solution to replace missing value with imputed value from a particular technique, such as mean value, median value, etc. This Thesis specifically discuss about technique that fuse fractional imputation technique and hot deck imputation technique. Fractional imputation is popular because this imputation tends to produce less variance compare to other methods. Unfortunately this method will extend the number of observations. Because fractional imputation tends to extend the number of observations, sampling becomes a solution to produce less observation. Sampling limits the numbers of imputed values (donor) in the observations that adopts hot deck imputation nature. The imputation that fuse fractional imputation and hot deck imputation is known as fractional hot deck, and produce a data set that have similar property to fractional imputation, but less observations.

Abstrak :
Bioinformatika merupakan ilmu yang ditujukan untuk menganalisis informasi biologis. Dalam perkembangan penelitian bioinformatika, data diperoleh salah satunya dengan menggunakan teknologi microarray. Teknologi microarray digunakan oleh lingkup biologi molekuler dalam melihat perbedaan tingkat ekspresi gen dengan cara mengonversi gambar monokromik yang berisi ratusan bahkan ribuan gen dari sampel sel dan menghasilkan data ekspresi gen. Teknologi microarray sering kali menghasilkan data ekspresi gen yang hilang atau tidak terdeteksi akibat adanya kesalahan teknis. Oleh karena itu, diperlukannya suatu metode imputasi pada data untuk mengatasi missing values. Pada penelitian ini, akan dikembangkan suatu metode imputasi yang disebut Biclustering Terurut berbasis k-Nearest Neighbor, Mean Squared Residual, dan Jarak Euclidean. Metode ini merupakan metode imputasi berbasis biclustering dimana bicluster dibentuk berdasarkan suatu kriteria yang melibatkan skor Mean Squared Residue dan Jarak Euclidean. Penggunakan k-Nearest Neighbor sebagai metode pra-imputasi didasarkan pada data ekspresi gen yang sering kali memiliki pola kompleks dan sulit terdeteksi, sehingga perlu pendekatan yang dapat memetakan struktur korelasi pada data. k-Nearest Neighbor mempertimbangkan korelasi pada data microarray dengan menyeleksi kumpulan gen yang memiliki profil ekspresi mirip dengan gen yang ingin diimputasi (gen target). Pada penelitian ini, metode SBi-kNN-MSREimpute diterapkan pada data ekspresi gen pasien penderita COVID-19 yang dilakukan tes rapid harian. Evaluasi kinerja metode SBi-kNN-MSREimpute dilakukan dengan menggunakan NRMSE, dimana hasilnya dibandingkan dengan metode SBi-MSREimpute. Berdasarkan penelitian yang dilakukan, metode SBi-kNN-MSREimpute dinilai lebih baik dibandingkan dengan SBi-MSREimpute untuk setiap missing rate pada tingkatan c berbeda. Nilai c optimal untuk imputasi missing values pada data COVID-19 adalah c = 10% untuk missing rate 25%, 30%, 40% dan c = 15% untuk missing rate 5%, 10%, 15%, 20%, dan 50%. Hasil akhir juga menunjukkan bahwa nilai NRMSE untuk SBi-kNN-MSREimpute relatif stabil bahkan untuk data dengan missing rate tinggi hingga 50%. ......Bioinformatics is a study designed to analyze biological information. In the development of bioinformatics research, data was obtained using microarray technology. Microarray technology is used by the scope of molecular biology in transposing hundreds and even thousands of genes from cellular samples simultaneously and producing a gene expression data. Microarray technology often produces data that is lost or undetected as a result of technical error. Therefore, an imputation method is needed to address the missing values. In this study, a new imputation method called Sequential Biclustering based k-Nearest Neighbor, Mean Squared Residual, and Euclidean Distance (SBi-kNN-MSRE) will be developed. This method is a biclustering-based imputation method where the bicluster is formed based on a criterion involving Mean Squared Residue and Euclidean Distance. The use of k-Nearest Neighbor as a pre-imputation method is based on data on gene expression that often has a complex and difficult pattern of detection, so it requires an approach that can map correlation structures on data. K-nearest neighbor considers a correlation on a microarray data by selecting groups of genes that have an expression profile similar to a gene that wants to be imputed (the target gene). In this study, the SBi-kNN-MSRE method was applied to the data on the genes of patients with covid-19 that daily rapid tests were performed. The performance evaluation of the SBi-kNN-MSRE method is done using NRMSE, where the results are compared to the SBi-MSRE method. According to the result, the SBi-kNN-MSRE method performed better than SBi-kNN-MSRE for each missing rate on different c levels. The optimal c value on the covid-19 data is c = 10% for missing rate 25%, 30%, 40% and c = 15% for missing rate 5%, 10%, 15%, 20% and 50%. The results also showed that NRMSE scores