Judul: Cluster Ensemble Analysis for Grouping Districts/Cities in Indonesia Based on Education Quality
Abstrak: Clustering merupakan metode yang cukup populer dan telah banyak digunakan di berbagai bidang. Pengelompokan dalam statistik dilakukan dengan menggunakan analisis gerombol. Penelitian ini bertujuan untuk mengelompokkan kabupaten/kota berdasarkan kualitas pendidikan dengan dua tipe data yang berbeda. Cluster ensemble adalah metode yang tepat untuk mengelompokkan data campuran. Metode ini menggabungkan hasil dari dua metode penggerombolan yang berbeda yang kemudian digabungkan untuk memperoleh hasil akhir. Metode untuk data numerik menggunakan metode agglomerative hierarchical clustering dan untuk data kategorik menggunakan algoritma squeezer. Pengelompokan 514 kabupaten/kota di Indonesia berdasarkan 18 peubah menghasilkan 3 gerombol. Jumlah anggota setiap gerombol adalah 446, 51, dan 17. Gerombol 1 merupakan wilayah dengan kualitas pendidikan terbaik berdasarkan indikator yang digunakan. Gerombol 2 dicirikan dengan rataan RLS dan HLS tertinggi serta rataan rasio murid terhadap guru yang paling besar dan sangat jauh dari batas ideal. Sementara itu, gerombol 3 menjadi gerombol dengan kualitas pendidikan yang paling rendah. Gerombol ini dicirikan dengan rataan RLS dan persentase guru terkualifikasi SMK yang sangat kecil., Clustering is a method that is quite popular and has been widely used in various fields. Clustering in statistics is done by using cluster analysis. This research aims to classify regions based on the quality of education with two different data types. Cluster ensemble are an appropriate method for clustering mixed data. This method combines the results of two different clustering methods which are then obtained into the final result. The method for numeric data uses the hierarchical clustering method and for categorical data uses the squeezer algorithm. Clustering of 514 district/cities in Indonesia based on 18 variables resulted in 3 clusters. The number of members of each cluster is 446, 51, and 17. Cluster 1 is the region with the best quality of education based on the indicators used. Cluster 2 is characterized by the highest average RLS and HLS and the highest student to teacher ratio which is very far from the ideal limit. Meanwhile, cluster 3 is the group with the lowest quality of education. This cluster is characterized by a very small average RLS and percentage of SMK qualified teachers.
Keyword: cluster ensemble, data campuran, pendidikan, squeezer

Judul: Ensemble Cluster Analysis with Squeezer Algorithm for Clustering Education Quality Base On Indicators of Sustainable Development Goals (Case Study: Regencies/Cities in West Sumatera)
Abstrak: Kesenjangan kualitas pendidikan di Sumatera Barat masih terjadi antara daerah perkotaan dengan daerah pedesaan, sehingga perlu dilakukan pengelompokan untuk membantu pemerintah dalam menyusun kebijakan agar tepat sasaran. Analisis yang umum digunakan dalam pengelompokan yaitu analisis gerombol. Namun demikian, pada penelitian ini menggunakan data bertipe campuran (kategorik dan numerik), sehingga pengelompokan dilakukan menggunakan analisis gerombol ensemble dengan algoritme squeezer untuk mengelompokkan data bertipe kategorik, serta pautan lengkap dan pautan ward untuk mengelompokkan data bertipe numerik. Data yang digunakan yaitu data pendidikan dari laman resmi Badan Pusat Statistik (BPS) dan Kementerian Pendidikan dan Kebudayaan (Kemendikbud) Provinsi Sumatera Barat tahun 2019. Banyaknya gerombol akhir yang dihasilkan sebanyak dua gerombol. Gerombol 1 merupakan gerombol terbesar dengan anggota 13 kabupaten/kota, sedangkan Gerombol 2 hanya beranggotakan 6 kabupaten/kota yang terdiri atas Kabupaten Lima Puluh Kota, Kabupaten Pasaman, Kota Padang, Kota Padang Panjang, Kota Bukittinggi, dan Kota Payakumbuh. Gerombol 2 memiliki kualitas pendidikan yang lebih baik dibandingkan Gerombol 1. Hal ini ditunjukkan dengan sebanyak 50 persen kabupaten/kota pada Gerombol 2 sudah memiliki perguruan tinggi. Selain itu, Angka Partisipasi Murni (APM) SLTP, Angka Partisipasi Murni (APM) SLTA, banyaknya murid yang berhasil menamatkan SD, persentase guru SD berkualifikasi >= D4/ S1, dan pendapatan orang tua murid pada Gerombol 2 lebih tinggi dibandingkan Gerombol 1. Kata kunci: analisis gerombol ensemble, data campuran, pendidikan, Sustainable Development Goals (SDGs), The gap in the quality of education between urban areas and rural areas in West Sumatera is still happening. There for, it need to cluster the areas to assist the Goverment in formulating policies so right on target. Cluster analysis is one of the methode that can uses to solve this problem. However, this study uses categorical data and numerical data (mixed data), so that the analysis can be used is ensemble cluster analysis, a squeezer algorithm to group categorical data, and complate lingkage and ward lingkage to group numerical data. This study uses data of education form The Central Bureau of Statistics (BPS) and The Ministry of Education and Culture (Kemendikbud) of West Sumatera Province in 2019. The final cluster shaped is two clusters. Cluster 1 is the largest cluster that consists of 13 regencies/cities members, meanwhile cluster 2 only consists of 6 regencies/cities members, that are Lima Puluh Kota Regency, Pasaman Regency, Padang City, Padang Panjang City, Bukittinggi City, and Payakumbuh City. Cluster 2 has a better quality of education than cluster 1. This is indicated by as many as 50 percent of regencies/cities in cluster 2 already have a college. In addition, The Net Enrollment Rate (APM) for junior high school, Net Enrollment Rate (APM) for senior high school, the number of students who successfully completed elementry school, the percentage of elementry school’s teacher with an undergraduate qualification, and the income of parents in cluster 2 were higher than in cluster 1. Keywords: cluster ensemble analysis, education, mixed-type data, Sustainable Development Goals (SDGs)
Keyword: cluster ensemble analysis, education, mixed-type data, sustainable development goals (SDGs)

Judul: Hydroxyapatite-Chitosan Composite Coating on Stainless Steel 316 to Improve Corrosion Resistance
Abstrak: Metals that were used in bone implantation should have good corrosion resistance that were not easily corroded when implanted into the body. Metal corrosion resistance of stainless steel 316 can be enhanced through the metal coating by hydroxyapatite-chitosan composite with composition ratio of hydroxyapatite (gram) and chitosan 3% (mL) 0.2:0, 0.2:0.1, 0.2:0.5, 0.2:1.0, and 0.2:1.5. Besides improving the corrosion resistance of metal, the presence of chitosan also maintain the position of hydroxyapatite on metal surfaces. Corrosion resistance increases with increasing amount of chitosan added and the best composition of hydroxyapatite-chitosan composite was 0.2:1.0.
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