Judul: Cluster analysis and biplot analysis of pressure plant pest organisms in West Java Province
Abstrak: Jawa Barat merupakan provinsi dengan produksi tanaman pangan padi terbesar kedua di Indonesia. Adanya organisme pengganggu tanaman (OPT) pada tanaman padi menyebabkan produksi tanaman padi dapat menurun termasuk di provinsi Jawa Barat. Analisis gerombol dapat digunakan untuk melihat jumlah gerombol kabupaten di Jawa Barat yang terbentuk berdasarkan persentase luas lahan yang terserang OPT, informasi tersebut dapat digunakan untuk memperoleh kriteria tingkat serangan OPT. Analisis biplot dapat mengidentifikasi karakteristik antara persentase luas lahan yang terserang OPT dengan kabupaten di Jawa Barat. Hasil analisis gerombol menunjukkan terdapat tipe penggerombolan untuk masing-masing OPT yang berbeda per musimnya, dengan gerombol yang terbentuk antara 2 sampai 4 gerombol. Hasil analisis biplot menunjukkan terdapat kabupaten-kabupaten tertentu yang terserang oleh OPT tertentu yang berbeda tiap musimnya., West Java is the province with the production of food crops in Indonesia's second largest rice. The presence of plant pests (OPT) in rice plants led to decreased production of rice plants can include in the province of West Java. Analysis of clusters can be used to see the number of districts in West Java clusters are formed based on the percentage of land area affected pest, such information can be used to obtain the level of pest attack criteria. Biplot analysis can identify characterize about the percentage of land area affected by the pest districts in West Java. The results of the analysis showed that there were clusters penggerombolan type for each different pest per season, with clusters formed between 2 to 4 clusters. Biplot analysis results showed that there were certain districts were affected by the particular pest that is different each season. Keywords: Biplot Analysis, Cluster Analysis, OPT
Keyword: Analisis Biplot, Analisis Gerombol, OPT

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: 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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