Judul: Perbandingan Metode Diskretisasi Data  Partisi Intuitif dan K-Means Clustering  terhadap Pembuatan Pohon Keputusan
Abstrak: Data sektor pertanian yang dimiliki Departemen Pertanian selalu bertambah setiap tahun sehingga menghasilkan sekumpulan data berukuran besar. Data tesebut mengandung informasi yang masih harus ditambang. Dalam penelitian ini, dibandingkan hasil pembentukan pohon keputusan menggunakan algoritme ID3 dengan metode diskretisasi Partisi Intuitif dan algoritme K-Means Clustering dengan jumlah cluster 4 dan 5 cluster Pohon keputusan dibentuk menggunakan algoritme ID3 yang sudah tersedia dalam perangkat lunak WEKA (Waikato Environment Knowledge Analysis). Berdasarkan keluaran dari WEKA, aturan yang dihasilkan oleh metode Partisi Intuitif berjumlah 63 dengan akurasi sebesar 84,54%. Aturan yang dihasilkan oleh algoritme K-Means clustering dengan 4 cluster berjumlah 49 dengan akurasi sebesar 87.40% dan aturan yang dihasilkan menggunakan algoritme K-Means clustering dengan jumlah cluster 5 berjumlah 59 dengan akurasi sebesar 76.87%. Nilai Root Means Square Error (RMSE) yang dihasilkan oleh masing masing metode yaitu Partisi Intuitif sebesar 0.12, algoritme K-Means clustering dengan jumlah cluster 4 sebesar 0.2 dan algoritme KMeans clustering dengan jumlah cluster 5 sebesar 0.24. Nilai RMSE dari hasil percobaan menunjukkan bahwa hasil terbaik terdapat pada pohon keputusan yang dibentuk dengan metode diskretisasi data partisi intuitif.
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Judul: Perbandingan Hasil Penggerombolan Metode K-Means, Fuzzy K-Means, dan Two Step Cluster
Abstrak: The main principle of cluster analysis is to classify objects into clusters based on similarity measures. K-means and fuzzy k-means can be classified as popular clustering methods, which are suitable for large data with continuous variables. However, a new method has been developed to be used for large data, that is the two step cluster method. This method allows processing data with different types of variables, which in this case are continuous and categorical. The aim of this research is to compare the clustering results of k-means, fuzzy kmeans, and two step cluster method, in order to determine the ideal number of clusters for each method. This research uses hypotetical data taken from SPSS software, which fit the purpose to compare several methods. The results of this study show that in the case of two clusters, k-means and fuzzy k-means methods have more similarities with respect to the number objects in clusters, whereas the two step method gives unequal number of objects in clusters. All methods show that 2 clusters is an ideal number. It is influenced by the ratio between mean squares within clusters, which is smaller than the ratio in the case of 3 and 4 clusters.
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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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