Judul: Applying Hidden Logit Model with Randomized Response (Case Study
Abstrak: In 1965 Stanley Warner (Fox & Tracy, 1986) pioneered the randomized response technique to reduce bias response caused by respondents who lied, or did not answer the question given by the interviewer to estimate the sensitive characteristics. Warner model directing respondents to answer either of two sensitive opposite questions. Warner model still has weaknesses, then Simmons (Fox & Tracy, 1986) develops a randomized response technique of Warner model by replacing one question with a question that not associated with the sensitive issues at all (unrelated question model). According to Stem & Steinhorst (Fox & Tracy, 1986) unrelated question model still has weaknesses too and then developed into a forced response model. Maddala (1983) pioneered the development of sensitive attributes in the randomized response technique as a binary response variable associated with several explanatory variables. This development adopts logistic regression to estimate its parameters. Since π* is the population proportion that have sensitive attributes that can not be predicted directly, but estimated by π, Corstange (2004) named this prediction method with hidden logit estimation. This research aimed to find factors that affect a student accessing porn sites. This research obtain that proportion estimator of the population that accessing porn sites is 24%. Variables that significantly influence a student to access pornographic websites such as gender, maternal education, ownership of the laptop and how to connect to the internet. This research also needs to be developed again primarily related to response variables that have more than two categories (multinomial) such as issues about the type of drugs consumed, how many times had an abortion in a population of prostitutes and so on.
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Judul: Identifikasi Faktor yang Memengaruhi Layanan Rujuk Lanjutan Menggunakan Regresi Logistik Group LASSO
Abstrak: Health is a very important need in life and is a human right to be able to live a decent, productive, and prosper life. For its importance in everyday life, it is necessary to have good health providers as stated in the 1945 Constitution of the Republic of Indonesia Article 28 H paragraphs 1 and 3. The government has made a program run by BPJS Kesehatan, namely the National Health Insurance which aims to improve access and quality of health services in order to achieve optimal welfare degrees effectively and efficiently. With the existence of the National Health Insurance, it is hoped that health services will no longer be centered in hospitals or FKRTL but will be carried out in stages according to medical needs. The high referral ratio is caused by the implementation of referrals that are not in accordance with the level flow, which will cause an accumulation of patients in one hospital, so that it has an impact on the decline in service quality. The high ratio of referral rates is a problem in implementing a tiered referral system, so it is necessary to find out what factors affecting the referral status of FKTP patients to FKRTL. The response variable used in this study was the patient's referral status of First Level Health Facilities (FKTP) whether or not including further referral to Advanced Referral Health Facilities (FKRTL). If the response variable is no longer a quantitative variable, but a categorical variable consisting of only a few values, then linear regression cannot be used. In high-dimensional data, the response variable is related to a small number of explanatory variables among a large number of possible variables. Thus, the selection of variables is important to do in identifying the relevant variables in high-dimensional data. One alternative method to deal with this case, the penalized logistic regression method was developed. Coefficient estimators in penalized logistic regression are carried out by maximizing the log of the probability function by adding a penalty to the function which causes the coefficient estimator to be biased, but reducing the variance of the coefficient estimator will increase the accuracy of the model prediction (Hastie et al. 2008). Therefore, penalized logistic regression is often referred to as the shrinking or regularization method. In this study, the LASSO Group regularization method was used because the explanatory variables in the research data were in the form of groups. Group LASSO is an extension of the LASSO method by adding group penalties to the LASSO method. To find out what factors affecting the patient's referral status to FKRTL, logistic regression is used, in which the response variable is -1 for outpatient treatment, recovery, internal referral, forced discharge, others; death and 1 for further referral to FKRTL, following the Bernoulli distribution. The covariates included in the process of variables are age, family relationship, gender, marital status, class of treatment, segment of participants, ownership of health facilities, type of health facility, type of health facility, and level of service. Dummy/dummy variables are variables used to quantify qualitative variables such as in this study,
Keyword: group LASSO, healthcare utilization, variable selection

Judul: Uremia pada anjing
Abstrak: Uremia adalah suatu keadaan peningkatan kadar urea didalam plasma darah yang melebihi dari kadar normalnya. Sering juga digunakan sebagai istilah klinik 'clinical uraemia yang berarti menunjukkan gejala kompleks yang terlihat pada kasus kegagalan ginjal baik yang akut maupun kronis, sehingga terjadi retensi nitrogen. Urea merupakan senyawa nitrogen bukan protein yang di- sintesis oleh hati sebagai hasil dari katabolisme protein. Urea tersebut kebanyakan diekskresikan melalui air kemih dan hanya sedikit yang lewat tinja ataupun keringat. Kadar urea darah anjing normal berkisar antara 10-30 mg/100 ml darah. Namun pada kasus uremia yang parah kadar urea bisa meningkat sampai 500 mg/100 ml darah. Uremia ini sering terjadi pada anjing-anjing sudah berumur tua atau anjing yang mengalami gangguan pada ginjalnya. ...
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