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"Regresi Poisson sering digunakan untuk menganalisis data diskrit count data. Regresi ini memiliki asumsi equidispersi. Namun, dalam banyak kasus sering dijumpai asumsi tersebut tidak terpenuhi karena adanya overdispersi pada data. Salah satu penyebab overdispersi adalah excess zero. Model regresi yang dapat digunakan untuk mengatasi masalah tersebut adalah regresi Zero-Inflated Poisson ZIP . Regresi ZIP menyelesaikan masalah excess zero dengan mengidentifikasi structural zeros di tahap pertama dan model Poisson counts di tahap kedua. Pada penelitian ini, parameter regresi ditaksir menggunakan metode Bayesian. Pada metode Bayesian, unsur ketidakpastian parameter dipertimbangkan model dalam bentuk distribusi prior. Dengan mengombinasikan distribusi prior dan likelihood, diperoleh distribusi posterior dari parameter yang menjadi perhatian dalam penelitian. Teknik komputasional Markov Chain Monte Carlo-Gibbs Sampling MCMC-GS digunakan untuk melakukan sampling nilai-nilai parameter dari distribusi posterior tersebut. Metode ini kemudian diterapkan untuk memodelkan frekuensi komplikasi motorik pada 215 penderita penyakit Parkinson. Diperoleh hasil bahwa total skor MDS-UPDRS Part 2 dan 3 berasosiasi dengan konsumsi atau tidaknya obat-obatan pada pasien. Lebih lanjut, untuk mereka yang mengonsumsi obat, total skor MDS-UPDRS Part 1 berasosiasi dengan frekuensi komplikasi motorik.

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Poisson regression is commonly used for analizing count data. This method requires equidispersion assumption. However, in the case of overdispersion, this assumption is not always fulfilled. Overdispersion may exist when there is excess zeros in the data. One of the regression models which might solve it is Zero Inflated Poisson ZIP regression. ZIP regression solves the excess zero problem by identifying the structural zeros at the first stage, then Poisson counts model at the second stage. In this research, the regression parameters are estimated using Bayesian method. Bayesian method acomodates the uncertainty parameters through prior distribution. Combining the prior distribution and likelihood from the data results in the posterior distribution of the parameters of interest. True parameters are then sampled using Markov Chain Monte Carlo Gibbs Sampling MCMC GS. Therefore, this method is applied to model the frequency of motor complications in 215 Parkinson 39 s disease patients. The result shows that total score of MDS UPDRS Part 2 and 3 associated with those taking the medicines or not. Furthermore, for those taking the medicines, total score of MDS UPDRS Part 1 associated with motor complications frequency."

"ABSTRACTDalam studi tentang kesehatan, salah satu hal yang cukup menarik untuk diteliti adalah rdquo;time-to-event rdquo;. Time-to-event umum digunakan dalam melakukan analisis survival, seperti analisis terhadap penyakit Parkinson. Penyakit Parkinson merupakan salah satu gangguan yang mempengaruhi penghasil dopamin pada daerah otak yang disebut sebagai substantia nigra. Gejala penyakit Parkinson diukur secara khusus melalui suatu tingkatan yang disebut tingkatan Hoehn dan Yahr. Tingkatan ini didistribusikan pada bilangan bulat antara 0 sampai dengan 5. Tingkat 0 merupakan tingkat yang tidaklah memiliki dampak besar dan tingkat 5 merupakan tingkat paling parah. Dalam penelitian ini, akan dikonstruksikan fungsi survival dari waktu pasien yang memiliki tingkatan Hoehn dan Yahr pada tingkat A hingga meningkat menuju tingkat B dengan A < B. Dengan A = 1, 2 dan B = 3, 4, 5 akan dihasikan enam buah grafik fungsi survival secara keseluruhan. Proses pengkonstruksian fungsi survival menggunakan algoritme Metropolis-Hastings dalam Metode Markov Chain Monte Carlo pada Inferensi Bayesian dan hasilnya dibandingkan dengan pendugaan Kaplan-Meier untuk fungsi survival. Hasil yang didapatkan melalui algoritme ini lebih merepresentasikan fungsi survival yang sebenarnya jika dibandingkan dengan penduga Kaplan-Meier, meskipun terdapat banyak sekali data tersensor dalam kumpulan data.

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ABSTRACTIn medicine study, one of the thing that is interesting enough to be studied is rdquo time to event. In general, time to event is used in doing survival analysis, such as analysis of Parkinson disease. Parkinson disease is one of disease which affects dopamine producer in brain area that is called by substantia nigra. The symptom of Parkinson disease is measured specifically by stages that are called by Hoehn and Yahr stages. This stages are distributed on integers between 0 to 5. Stage 0 is stage that does not have big impact and stage 5 is the most severe level. In this study, the survival function will be constructed from the time that the patient has the Hoehn and Yahr stages at A until increase to stage B with A B. With A 1, 2 and B 3, 4, 5, overall it will be generated six graphs of survival function. The process of constructing survival function using the Metropolis Hastings algorithm in Markov Chain Monte Carlo Methods on Bayesian Inference, and the results are compared with Kaplan Meier estimator for survival function. The result that is obtained through this algorithm is more represents the actual survival function if it is compared with Kaplan Meier estimator, although there are so many censored data in the dataset."

"Known as pantun to the Malays in Brunei, Malaysia, Pattani, Riau, Singapore, and Southern Phillipines, it is called peparikan to the Javanese, sesindiran to the Sundanese and many other different names in different ethnic groups in the different parts of the Indo-Malay world, which is made up of Brunei, Indonesia, Malaysia, Singapore, Pattani in southern Thailand, and Mindanao in the southern Philippines. In almost every settlement that sprang up along the major rivers and tributaries in the Indo-Malay world, the pantun blend well with their natural and cultural surroundings. In this article, the geographical extent of the pantun family in the Indo-Malay world is likened to a mighty river that has a complex network of tributaries all over the Indo-Malay world. Within the Indo-Malay world, it is the movement of the peoples help the spread of pantun from one area to the other and makes it an art form of immensely rich and intricate as can be seen from the examples given."

"ABSTRACTAlthough it is common to assess visceral adipose tissue (VAT) by CT and MRI with a single slice at the umbilicus or the fourth and fifth lumbar vertebrae (L4-L5), recent studies reported that this single-slice method for determining an individual's VAT may be inaccurate. Therefore, VAT accumulation should be based on total volume and determined with multiple slices rather than by cross-sectional area. However, obtaining multiple slices is burdensome for both subjects and analysts and lacks versatility despite its accuracy. The purpose of this study was to develop a new equation model for predicting VAT volume while maintaining the measurement accuracy of the multiple-slice method. We analyzed data from 214 Japanese male adults (48.5±9.3 years) and developed multiple, stepwise, linear regressions with VAT volume as a dependent variable and age, BMI, waist circumference and VAT areas (the standard L4-L5 measurement site 0 cm, +5 cm, +10 cm) as independent variables. From these results, we determined the best prediction equation for VAT volume as follows: VAT volume = (30.4×BMI) + (17.9×VAT area at L4-L5+10 cm) - 501.5. The model explained 93.1% of VAT variance and the predicted VAT volume significantly correlated with the measured VAT volume (r=0.97). This study developed a new VAT assessment method with a high level of accuracy. The method is significantly less burdensome in measurement and analysis than the multiple-slice method. Researchers can use this equation when they require an accurate evaluation of VAT accumulation. However, they should bear in mind that this equation was derived from data acquired from middle-aged, overweight and obese male subjects."

"ABSTRACTModel hurdle adalah model alternatif untuk mengatasi penyebaran berlebihan (varians datanya adalah lebih tinggi dari nilai rata-rata) yang disebabkan oleh kelebihan nol. Model rintangan dapat memodelkan secara terpisah variabel respons yang memiliki nilai nol dan positif, melibatkan dua proses yang berbeda. Proses pertama adalah proses biner yang menentukan apakah variabel respon memiliki nilai nol atau nilai positif, dan dapat dimodelkan dengan biner model, menggunakan regresi logistik. Untuk variabel respons positif, kemudian lanjutkan ke proses kedua, yaitu proses yang hanya mengamati jumlah positif. Yang positif count dapat dimodelkan dengan model Zero-Truncated menggunakan regresi Poisson. Rintangan model juga dikenal sebagai model dua bagian. Estimasi parameter menggunakan Bayesian metode. Kombinasi informasi sebelumnya dengan informasi dari data yang diamati membentuk distribusi posterior yang digunakan untuk memperkirakan parameter. Distribusi posterior bentuk yang diperoleh tidak tertutup, sehingga diperlukan teknik komputasi, yaitu Markov Chain Monte Carlo (MCMC) dengan algoritma Gibbs Sampling. Metode ini diterapkanke data Parkinson untuk memodelkan frekuensi komplikasi motorik pada 300 Parkinsonpasien. Data tersebut digunakan dari Parkinson's Progressive Markers Initiative (PPMI, 2018). Hasil yang diperoleh adalah MDS-UPDRS (Movement Disorder Society-Unified Skala Peringkat Penyakit Parkinson) bagian 1, MDS-UPDRS bagian 2, dan MDS-UPDRS bagian 3 terkait secara signifikan MDS-UPDRS bagian 4 di kedua tahap.

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ABSTRACTThe obstacle model is an alternative model for overcoming excessive spread (the data variant is higher than the average value) which is questioned by zero excess. The obstacle model can separately model response variables that have zero and positive values, involving two different processes. The first process is a binary process that determines whether the response variable has a zero value or a positive value, and can be modeled with a binary model, using logistic regression. For positive response variables, then proceed to the second process, which is a process that is only positive. The positive one calculated can be modeled with a Zero-Truncated model using Poisson regression. The Obstacle Model is also known as the two part model. Parameter estimation using the Bayesian method. The combination of previous information with information from data collected collects the distributions used for parameter estimation. The posterior distribution of the obtained form is not closed, computational techniques are needed, namely Markov Chain Monte Carlo (MCMC) with Gibbs Sampling algorithm. This method is applied to Parkinson's data to model the frequency of motor complications in 300 Parkinson's patients. The data is used from Parkinson's Progressive Markers Initiative (PPMI, 2018). The results obtained are MDS-UPDRS (Movement Disorder-Community Parkinson's Disease Assessment Scale) part 1, MDS-UPDRS part 2, and MDS-UPDRS part 3 which significantly related MDS-UPDRS part 4 in both glasses."

"Model Cox merupakan model yang sering digunakan untuk menganalisis time-tovent data, yaitu data yang pengamatannya bergantung pada waktu. Terkadang, Selain informasi tentang waktu, data time-to-event juga dilengkapi dengan informasi tambahan (variabel penjelas). Analisis data waktu ke acara seperti ini dengan menggunakan model Cox akan menghasilkan perkiraan bahaya. Model Cox memiliki dua komponen utama yaitu baseline hazard dan mengandung fungsi eksponensial koefisien regresi. Bahaya didefinisikan sebagai produk antara dua komponen ini. Untuk dapat memperoleh bahaya spesifik, bahaya baseline dan koefisien regresi di model Cox harus diperkirakan. Dalam tesis ini, asumsi konstanta akan didefinisikan sebagai bahaya dasar dari model Cox. Kemudian, konstanta dan koefisien regresi dimasukkan Model ini akan diestimasi dengan menggunakan metode Bayesian dimana sampel diambil Parameter distribusi posterior dilakukan dengan menggunakan metode Markov chain Monte Carlo dengan algoritma pengambilan sampel Gibbs. Untuk metode Bayesian, distribusi sebelumnya untuk Bahaya baseline diasumsikan mengikuti distribusi gamma dan untuk koefisien regresi diasumsikan mengikuti distribusi normal. Data EKG (echocardiogram) yang terdiri dari106 observasi dan enam variabel penjelas digunakan dalam analisis. Mendapatkan hasil bahwa estimasi parameter yang diperoleh konvergen.

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The Cox model is a model that is often used to analyze time-to-event data, namely data whose observations are time dependent. Sometimes, in addition to information about time, time-to-event data is also supplemented with additional information (explanatory variables). Analysis of time-to-event data like this using the Cox model will yield hazard estimates. The Cox model has two main components, namely the baseline hazard and contains an exponential regression coefficient function. Hazard is defined as a product between these two components. In order to obtain a specific hazard, the baseline hazard and regression coefficient in the Cox model must be estimated. In this thesis, the constant assumption will be defined as the basic hazard of the Cox model. Then, the constants and regression coefficients are entered. This model will be estimated using the Bayesian method where the sample is taken. Posterior distribution parameters are carried out using the Markov chain Monte Carlo method with the Gibbs sampling algorithm. For the Bayesian method, the previous distribution for baseline hazard is assumed to follow the gamma distribution and for the regression coefficient it is assumed to follow a normal distribution. EKG (echocardiogram) data which consists of

106 observations and six explanatory variables were used in the analysis. Obtain the result that the parameter estimates obtained are convergent."

"The shipbuilding industry is characterized by high-risk business activities; therefore, caution should be taken in its operational processes. From upstream to downstream, the shipbuilding industry depends on other industries. In this study, a risk assessment was conducted on the construction of new vessels using the Bayesian network approach; accordingly, the risk assessment was carried out using a probabilistic value at risk (VaR). The study was carried out by PT PAL Indonesia in association with the construction of a new tanker ship (building production codes M271 and M272). An analysis was conducted on three main components of new vessel construction—design components, material and production components, and subcomponents of the previous two components. From the study, we could conclude that the probability of delay for new vessel construction caused by design delay is 0.05; the probability of delay caused by material delay is 0.65; and the probability of delay caused by production delay is 0.3. For delays caused by design factors, a yard plan is the sub-component that contributes predominantly to delays (i.e., probability of 0.3). For delays caused by material factors, the sub-component with the greatest impact is hull and machinery outfitting, with a probability of 0.3. For delays caused by production factors, the sub-component with the biggest impact is hull construction, with a probability of 0.39. Thus, we could conclude that a project delay would occur if the material component and the hull construction sub-components were not handled properly."

"Model regresiZero Inflated Poisson (ZIP) digunakan untuk memodelkan count data dengan overdispersi yang disebabkan oleh nilai nol yang berlebih pada pengamatannya(excess zero). Namun, ketika overdispersi berasal dariexcess zerodan datacount, makaZIP tidak lagi cocok. Model regresi Zero Inflated Negative Binomial (ZINB) bertujuan untuk mengetahui variabel apa saja yang berpengaruh secara signifikan terhadap variabelrespon. Data untuk regresi ZINB ini memiliki dua sumber overdispersi. Terdapat dua proses pada variabel respon, yang mendasari pengamatan masuk ke dalam structural zeros atau Negative Binomial (NB) counts. Jadi, regresi ZINB terdiri dari dua model. Pada kedua model tersebut dilakukan penaksiran parameter menggunakan metode Bayesian. Metode ini menganggap parameter-parameter yang digunakan merupakan variabel acakyang memiliki distribusi sebagai informasi prior, dan mengkombinasikannya dengan data yang dimiliki. Kombinasi tersebut selanjutnya disebut sebagai distribusi posterior. Sampling parameter dari distribusi posterior dilakukan dengan simulasi Markov Chain Monte Carlo (MCMC). Sebagai penerapan, digunakan data Parkinson dari Parkinsons Progression Markers Initiative (PPMI). Variabel responnya yaitu frekuensi seberapa sering pasien mengalami komplikasi setelah meminum obat atau tidak, dan variabel prediktornya berupa skor pemeriksaan aspek motorik, non-motorik, dan respon-respon tubuh. Diperoleh hasil bahwa model ZINB cocok untuk memodelkan data tersebut yangditandai dengan hasil simulasi yang konvergen.

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Zero Inflated Poisson (ZIP) regression model is a standard framework for modeling discrete data with over-dispersion caused by excess zero. When over-dispersion has comefrom excess zero and count data, ZIP is no longer matches. A Zero Inflated Negative Binomial (ZINB) regression model aims to analyze the variables affecting data with two sources of over-dispersion. Hence there are two processes at the response variable, which make an observation classified as structural zeros or Negative Binomial (NB) counts. So, ZINB regression consists oftwo models. This paper will use Bayesian method forestimating parameter in both models. The Bayesian method considers parameters to bea random variable that has distribution known as prior distribution, and combine with information of the data. This combination referred as posterior distribution. Sampling parameter from posterior distribution is done using Markov Chain Monte Carlo (MCMC) simulation. As an application, the Parkinsons data is used from Parkinsons Progression Markers Initiative (PPMI). Frequency of how often the patient has complications aftertaking the drug or not is the response, and the predictive variables are motoric aspect, non-motoric aspect, and body responses test scores. The simulation result shows that it isconvergent, indicate that ZINB model is suitable for modeling Parkinsons data."