Judul: Identifying Factors Affecting Advanced Referral Services Using Group LASSO for Logistic Regression
Abstrak: Kesehatan merupakan kebutuhan yang sangat penting dalam kehidupan dan merupakan hak asasi setiap manusia untuk dapat hidup layak dan produktif, serta sejahtera. Melihat begitu pentingnya kesehatan dalam kehidupan sehari-hari maka diperlukan penyelenggara kesehatan yang baik sebagaimana tercantum dalam Undang-Undang Dasar Republik Indonesia Tahun 1945 Pasal 28 H ayat 1 dan 3. Pemerintah membuat program yang dijalankan oleh BPJS Kesehatan yakni Jaminan Kesehatan Nasional yang bertujuan untuk meningkatkan akses dan mutu pelayanan kesehatan agar tercapai derajat kesejahteraan yang optimal secara efektif dan efisien. Dengan adanya Jaminan Kesehatan Nasional, pelayanan kesehatan diharapkan untuk tidak lagi terpusat di Rumah Sakit atau FKRTL tetapi dilakukan secara berjenjang sesuai kebutuhan medis. Tingginya rasio rujukan diakibatkan oleh pelaksanaan rujukan yang tidak sesuai dengan alur jenjangnya akan menyebabkan penumpukan pasien di salah satu rumah sakit yang pada akhirnya berdampak pada penurunan kualitas pelayanan. Tingginya rasio angka rujukan menjadi permasalahan dalam pelaksanaan sistem rujukan berjenjang sehingga diperlukan untuk mencari tahu faktor-faktor apa saja yang memengaruhi status rujukan pasien FKTP ke FKRTL. Peubah respon yang digunakan dalam penelitian ini adalah statusrujukan pasien Fasilitas Kesehatan Tingkat Pertama (FKTP)apakah termasuk rujuk lanjut atau tidak ke Fasilitas Kesehatan Rujukan Tingkat Lanjut (FKRTL). Bila peubah respon bukan lagi peubah kuantitatif, melainkan berupa peubah kategorik yang hanya terdiri dari beberapa nilai, maka regresi lineartidak dapat digunakan. Pada data berdimensi tinggi, peubah respon memilikiketerkaitan dengan sejumlah kecil peubah penjelas di antara sejumlah besar peubah yangg mungkin. Sehingga, seleksi peubah penting untuk dilakukan dalam mengidentifikasi peubah yang relevan pada data berdimensi tinggi. Salah satu metode alternatif cara untuk menangani kasus ini, dikembangkan metode regresi logistik terpenalti. Penduga koefisien dalam regresi logistik terpenalti dilakukan dengan memaksimumkan log fungsi kemungkinan dengan menambahkan penalti pada fungsi tersebut yang mengakibatkan penduga koefisien menjadi berbias, tetapi dengan mengurangi ragam penduga koefisien akan meningkatkan akurasi prediksi model. Oleh karena itu, regresi logistik terpenalti sering disebut sebagai metode penciutan atau regularisasi. Pada penelitian ini digunakan metode regularisasi Group LASSO karena peubah penjelas pada data penelitian ini berbentuk kelompok. Group LASSO merupakan pengembangan dari metode LASSO dengan menambahkan penalti kelompok ke dalam metode LASSO. dst .., 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
Keyword: group LASSO, healthcare utilization, variable selection

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: Codiaeum Malesia
Abstrak: The Malesian genus Codiaeum has been revised based on the morphological character using available herbarium collections in Herbarium Bogor iense and loan specimens from Rijksherbarium Leiden. The study is based on the observation of 350 specimens. Eleven species have been recognized, namely Codiaeum variegatum, C. luzonicum, C. palawanense, C. trichocalyx, C. megalanthum, C. cilliatum, C. affine, C. tenerifolium dan C. hirsutum, C. macgregorii, C. ludovicianum. number of species is found in the Philippines The highest (8 species), Two species (C. tenerifolium and C. ludovicianum) endemic in Lousiade Archipelago of the New Guinea and C. affine endemic in Banguey Island. C. variegatum is widespread by nature or in cultivation almost in all of Malesian region. C. cillia tum is a species in the earliest level among the other species according to 20 selected characters.
Keyword: Codiaeum, Malesia