Judul: Application of the DBSCAN Algorithm for Clustering City/Regency in West Java Based on Food Insecurity Indicators
Abstrak: Terdapat beberapa permasalahan umum ketika melakukan analisis gerombol, diantaranya adalah adanya “curse of dimensionality” di ruang dimensi tinggi dan adanya bentuk gerombol yang tidak beraturan (arbitrary) serta kehadiran outlier. Permasalahan tersebut dapat diatasi dengan melakukan reduksi dimensi terlebih dahulu dan menggunakan analisis gerombol yang berbasis kepadatan. Kerawanan pangan merupakan salah satu permasalahan utama yang menjadi isu global bagi sebagian besar negara. Di Indonesia terdapat FSVA (Food Security and Vulnerability Atlas) yang merupakan peta tematik visualisasi geografis wilayah rentan terhadap kerawanan pangan. Penentuan status rawan pangan suatu daerah pada FSVA diukur dengan analisis yang relatif sederhana yaitu metode pembobotan pada sembilan indikator kerawanan pangan kronis. Pada penelitian kali ini, penggerombolan wilayah yang rentan terhadap kerawanan pangan untuk kota/kabupaten di Jawa Barat dilakukan dengan metode machine learning dan indikator yang digunakan lebih beragam (25 peubah) untuk menghasilkan gerombol yang lebih baik. Penggerombolan dilakukan dengan metode DBSCAN (DensityBased Spatial Clustering of Applications with Noise) pada data yang telah direduksi dimensi terlebih dahulu menggunakan metode t-SNE (t-Distributed Stochastic Neighbor Embedding). Data yang digunakan dalam penelitian ini adalah data Susenas (Survei Sosial Ekonomi Nasional) tahun 2020 untuk provinsi Jawa Barat. Hasil penggerombolan terbaik ditentukan berdasarkan parameter optimal yang dievaluasi menggunakan koefisien Silhouette dan indeks DBCV (Density Based Clustering Validation). Parameter optimal yang menghasilkan koefisien Silhouette dan indeks DBCV tertinggi diperoleh pada perplexity = 1, epsilon = 31, dan minPts = 2. Penggerombolan terbaik menghasilkan lima gerombol dengan tidak ada outlier., There are several common problems when performing cluster analysis, including the existence of a “curse of dimensionality” in high-dimensional space and the presence of arbitrary cluster shapes, and also the presence of outliers. These problems can be solved by reducing the dimensions first and using density-based cluster analysis. Food insecurity is one of the main problems that become a global issue for most countries. In Indonesia, there is an FSVA (Food Security and Vulnerability Atlas) which is a thematic map visualizing geographical areas vulnerable to food insecurity. Determination of the food insecurity status of an area on the FSVA is measured by relatively simple analysis, namely the weighting method on nine indicators of chronic food insecurity. In this study, clustering of areas that are vulnerable to food insecurity for cities/districts of West Java is carried out using machine learning methods and the indicators used are more diverse (25 variables) to produce better clusters. Clustering is performed using the DBSCAN (Density-Based Spatial Clustering of Applications with Noise) method on data that has been reduced in dimensions first using t-SNE (t-Distributed Stochastic Neighbor Embedding). The data used in this study is the 2020 Susenas (Survei Sosial Ekonomi Nasional) data for the province of West Java. The best clustering result is determined based on the optimal parameters evaluated using the Silhouette coefficient and the DBCV (Density-Based Clustering Validation) index. Optimal parameters that produce the highest Silhouette coefficient and DBCV index are obtained at perplexity = 1, epsilon = 31, and minPts = 2. The best clustering results in five clusters with no outliers.
Keyword: cluster analysis, DBSCAN, dimensionality reduction, food insecurity, t-SNE

Judul: Modeling the Diversity of Food Security Index in Indonesia with Spatial Approach, Efficiency Analysis, and Clustering
Abstrak: Ketahanan pangan yang digambarkan oleh ukuran Indeks Ketahanan Pangan (IKP) umumnya memiliki variasi permasalahan regional antar wilayahnya dan cenderung berfluktuasi antar tahunnya, sehingga diperlukan pendekatan penanganan yang disesuaikan dengan kondisi masing-masing wilayah. Penelitian ini menggunakan pendekatan geographically temporally weighted regression yang memungkinkan untuk memodelkan keragaman spasial dan temporal dalam data, dan sesuai dengan sifat data ketahanan pangan yaitu cenderung bervariasi antar wilayah dan tahun. Selanjutnya output analisis tersebut diperdalam dengan data envelopment analysis untuk menilai efisiensi pemanfaatan sumber daya terhadap kondisi ketahanan pangan antar wilayah dan menggunakan penggerombolan k-medoid untuk melakukan perbandingan kondisi pangan antar kelompok wilayah. Seluruh wilayah di Indonesia menjadi objek penelitian ini, yaitu sebanyak 514 kabupaten/kota selama tahun 2020-2022. Hasil dari penelitian menunjukkan bahwa peubah angka harapan hidup, laju pertumbuhan penduduk, persentase penduduk miskin, produksi tanaman pangan, dan pengeluaran per kapita berpengaruh pada taraf nyata 5% terhadap nilai IKP di sebagian besar wilayah, dengan nilai Pseudo R^2 sebesar 91.2%. Hasil analisis DEA menunjukkan terdapat dua puluh tujuh daerah di Indonesia yang selalu konsisten dengan tingkat efisiensi ketahanan pangan maksimum. Namun secara keseluruhan, kemampuan efisiensi mengalami penurunan yang cukup signifikan pada tahun 2022. Hal ini diperkuat dengan hasil analisis gerombol k-medoid bahwa dari tiga gerombol yang terbentuk (gerombol dengan IKP cenderung tinggi, sedang, dan rendah), gerombol dengan tingkat ketahanan pangan rendah mengalami peningkatan sebesar 9% dari tahun 2020 ke tahun 2022 yang perlu diperhatikan., Food security, which is represented by the Food Security Index (FSI) measure, generally has regional variations between regions and tends to fluctuate between years, so a handling approach that is tailored to the conditions of each region is needed. This study uses a geographically temporally weighted regression approach that allows to model spatial and temporal variability in the data, which is in accordance with the nature of food security data, which tends to vary between regions and years. Then the output of the analysis is deepened with data envelopment analysis to assess the efficiency of resource utilization towards food security conditions between regions and using k-medoid clustering to compare food conditions between regional groups. All regions in Indonesia became the object of this research, namely 514 districts/cities during 2020-2022. The results of the study indicated that the variables of life expectancy, population growth rate, percentage of poor people, food crop production, and per capita expenditure have a significant effect on the FSI value in most regions, with a Pseudo R^2 value of 91.2%. The results of DEA analysis showed that there are twenty seven regions in Indonesia that are always consistent with the maximum level of food security efficiency. But overall, the efficiency ability has decreased significantly in 2022. This is reinforced by the results of the k-medoid clustering analysis that of the three clusters formed (clusters with IKP tending to be high, medium, and low), the cluster with a low level of food security has increased by 9% from 2020 to 2022, which needs more attention.
Keyword: data envelopment analysis, food security index, geographically temporally weighted regression, k-medoid clustering

Judul: Hydrothermal Synthesis of Biphasic Calcium Posphate
Abstrak: Biphasio calsium phosphate (BCP), which was a mixture of hydroxypatite (HA) and tricalcium phosphate (TCP), was synthesized by hydrothermal method followed by sintering process. Hydroxyapatite was produced by using natural calcium source CaO from hen's eggshell and (NH4)2HPO4 as phosphate souce, other hydroxyapatite sample was produce by using synthetic calcium source Ca(OH) and H3PO4 as phosphate source.
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