Judul: Perbandingan Performa Regresi Logistik Ordinal dengan Support Vector Machine pada Pengklasifikasian Indeks Pembangunan Manusia
Abstrak: Klasifikasi adalah pendekatan untuk memprediksi sebuah data termasuk pada suatu kategori tertentu. Salah satu permasalahan dalam klasifikasi yaitu banyaknya data yang tidak seimbang antar kategori yang berbeda. Penanganan data tidak seimbang perlu dilakukan agar akurasi yang dihasilkan tidak bias. Indeks Pembangunan Manusia (IPM) kabupaten/kota di Indonesia memiliki empat kategori sehingga dapat dilakukan klasifikasi menggunakan metode regresi logistik ordinal atau Support Vector Machine (SVM). Penelitian ini bertujuan membandingkan performa regresi logistik ordinal dengan metode SVM pada data seimbang hasil Synthetic Minority Oversampling Technique (SMOTE) dalam pengklasifikasian IPM kabupaten/kota di Indonesia tahun 2021. Data yang digunakan pada penelitian ini adalah IPM kabupaten/kota di Indonesia dari BPS tahun 2021 dengan enam peubah penjelas. Metode terbaik adalah metode yang memiliki nilai akurasi, sensitivitas, dan spesifisitas yang tinggi. Hasil penelitian menunjukkan bahwa perbedaan nilai akurasi, sensitivitas, dan spesifisitas pada hasil klasifikasi dengan metode regresi logistik ordinal dan SVM tidak terlalu signifikan., Classification is a technique to predict the data into a certain category. One of the problems in classification is the amount of imbalanced data in different categories. The imbalanced data needs to be handled so that the accuracy is not biased. Human Development Index (HDI) of the districts/cities in Indonesia has four categories so that it can be classified using the ordinal logistic regression or Support Vector Machine (SVM) methods. This research aims to compare the performance of the classification methods using ordinal logistic regression and SVM on balanced data with Synthetic Minority Oversampling Technique (SMOTE) for classify HDI of the districts/cities in 2021. The data used in this research is the HDI of the districts/cities in Indonesia from the Central Bureau of Statistics’ 2021 with six explanatory variables. The best method is the method that has the highest accuracy, sensitivity, and specificity. The result of the research showed that the differences in accuracy, sensitivity, and specificity in classification results using the ordinal logistic regression and SVM are not very significant.
Keyword: classification, Human Development Index, ordinal logistic regression, Support Vector Machine

Judul: Pemodelan Indeks Pembangunan Manusia Kabupaten dan Kota di Pulau Jawa Tahun 2022 Menggunakan Geographically Weighted Regression
Abstrak: Indeks Pembangunan Manusia (IPM) sangat penting untuk melihat capaian pembangunan manusia di suatu wilayah. Pulau Jawa memiliki ketimpangan nilai IPM baik antarprovinsi maupun antarkabupaten/kota yang memungkinkan adanya heterogenitas spasial. Heterogenitas spasial dapat menimbulkan adanya hubungan yang berbeda antara peubah respons dengan peubah penjelas di setiap lokasinya yang disebut nonstasioneritas spasial. Oleh karena itu, metode yang dapat digunakan adalah Geographically Weighted Regression (GWR). Tujuan dari penelitian ini adalah menentukan fungsi pembobot kernel terbaik karena sangat memengaruhi model GWR dan menentukan peubah-peubah yang berpengaruh signifikan terhadap IPM kabupaten/kota di Pulau Jawa tahun 2022. Penelitian ini menggunakan data IPM yang disediakan oleh Badan Pusat Statistik dan Kementerian Pendidikan, Kebudayaan, Riset, dan Teknologi pada tahun 2022. Hasil penelitian menunjukkan bahwa fungsi pembobot kernel terbaik adalah adaptive Gaussian dengan AICc sebesar 601,422. Terdapat tiga kelompok kabupaten/kota berdasarkan peubah penjelas yang signifikan. Persentase penduduk miskin, persentase rumah tangga dengan akses terhadap sanitasi layak, produk domestik regional bruto per kapita, upah minimum, banyaknya rumah sakit umum, dan angka partisipasi kasar SMA/sederajat berpengaruh signifikan terhadap IPM pada semua kabupaten/kota di Pulau Jawa, sedangkan tingkat partisipasi angkatan kerja tidak berpengaruh signifikan. Peubah yang memiliki pengaruh paling besar pada setiap kabupaten/kota di Pulau Jawa adalah persentase penduduk miskin., Human Development Index (HDI) is very important to see the achievements of human development in a region. Java Island has inequality in HDI values both between provinces and districts/cities, which allows spatial heterogeneity. Spatial heterogeneity can have a different relationship between dependent variable and independent variables at each location, which is called spatial nonstationerity. Therefore, a method that can be used is Geographically Weighted regression (GWR). The purpose of this research is to determine the best kernel weighting function because it greatly affects the GWR model and determine the variables that have a significant effect on the HDI of districts/cities in Java Island in 2022. This research used HDI data provided by Statistics Indonesia and the Ministry of Education, Culture, Research, and Technology in 2022. The results showed that the best kernel weighting function was adaptive Gaussian with AICc of 601,422. There were three groups of districts/cities based on significant independent variables. The percentage of poor people, percentage of households with access to proper sanitation, gross regional domestic product per capita, minimum wage, number of public hospitals, and gross enrollment rate of senior high school/equivalent had a significant effect on HDI in all districts/ cities in Java Island, while the labor force participation rate had no significant effect. The variable that has the largest effect in each district/city in Java Island is the percentage of poor people.
Keyword: regresi, regression, spatial heterogeneity, geographically weighted regression, heterogenitas spasial, indeks pembangunan manusia, pulau jawa, human development index, java island

Judul: Synthesis of Apatite - Chitosan Composite Using Duck Eggshell and Chitosan with Temperature and pH Control
Abstrak: Composite of apatite-chitosan was prepared by using chitosan as polymer matrix. Calcium phosphate was prepared from CaO and (NH4)2HPO4, which the ratio Ca : P had to be 1.67. Chitosan is produced by deacetilase from biopolymer chitin. Duck eggshell (95% CaCO3) as starting material was converted to be calcium oxide (CaO) through calcinations with temperature 1000oC for 5 hours. In-situ and ex-situ methods were prepared from apatite and chitosan (dissolved with acetate acid 2%). Calcium phosphate prepared for bone implantation, but it is too fragile and brittle to be implant into the body. So addition of chitosan can decrease crystalinity and increase mechanic characteristic (tougher). Composite apatite-chitosan was resulted by fast precipitation. Fast precipitation was held in a breaker glass with nitrogen atmosphere, under controlled temperature at 37oC and pH 7, in order to increase amount of hydroxyapatite. Precipitation with CaO which was dropped by (NH4)2HPO4. Hydroxyapatite and chitosan were appearing at samples with in-situ and ex-situ methods which indicating by peak matching of XRD (closed lattice parameter, crystal size), overlapping at FTIR spectra and supporting by SEM picture (homogenous and agglomerated particle). However, it still needs further research to examine biocompatibility osteoconductive degree of sample by in-vivo and in-vitro test.
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