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)