Judul: Indonesian Basketball League 2022 Players Clustering Using K-Means and K-Medoid with t-SNE Dimensionality Reduction
Abstrak: Basket modern telah berkembang menjadi olahraga “position-less”. Posisi dan peran pemain tidak lagi ditentukan berdasarkan kriteria antropometri, melainkan karakteristik dan skill set yang dibuktikan oleh catatan statistik di lapangan. Perbedaan karakteristik dan skill set dapat diketahui dengan menggerombolkan pemain berdasarkan antropometri dan statistik pertandingan. Analisis gerombol tradisional, cenderung menghasilkan hasil kurang optimal jika diterapkan langsung pada data berdimensi tinggi karena adanya “curse of dimensionality”. Permasalahan tersebut dapat diatasi dengan reduksi dimensi. Penelitian ini bertujuan menggerombolkan pemain Indonesian Basketball League (IBL) 2022, berdasarkan antropometri dan statistik pertandingan menggunakan k-means dan k-medoid dengan reduksi dimensi t-SNE, serta menentukan metode penggerombolan terbaik. Metode penggerombolan terbaik ditentukan berdasarkan parameter optimal yang dievaluasi menggunakan koefisien Silhouette dan indeks S_Dbw. Berdasarkan hasil evaluasi, metode k-medoid pada t-SNE perplexity = 2 menghasilkan nilai evaluasi optimal, namun kedua hasil evaluasi menghasilkan banyak gerombol (k) optimal berbeda. Berdasarkan Silhouette, k-medoid pada t-SNE perplexity = 2 optimal pada k = 9, sedangkan berdasarkan indeks S_Dbw, k-medoid pada t-SNE perplexity = 2 optimal pada k = 10. Penentuan gerombol terbaik dilakukan secara eksploratif dengan radar chart dan Chernoff faces guna melihat perbedaan hasil optimal. Hasil penggerombolan terbaik yang diperoleh adalah k-medoid pada t-SNE perplexity = 2 dengan 10 gerombol optimal., Modern basketball has evolved into a “position-less” sport. Player positions and roles are no longer determined by anthropometric criteria, but rather based on their characteristics and skill sets, as indicated by statistical field performance. Grouping players based on anthropometry and match statistics allows for the identification of differences in their characteristics and skill sets. Traditional cluster analysis tends to produce less-optimal results when applied directly to high dimensional data because of the "curse of dimensionality". This issue can be addressed through dimensionality reduction techniques. This study aims to cluster Indonesian Basketball League (IBL) 2022 players, based on anthropometry and match statistics using k-means and k-medoid with t-SNE dimensionality reduction, and determine the best clustering method. The best clustering method is determined by evaluating the optimal parameters using Silhouette coefficient and S_Dbw index. Based on the evaluation results, the k-medoid with t-SNE perplexity = 2 yields optimal evaluation values, however the two evaluations indicate different optimal cluster sizes (k). Based on the Silhouette coefficient, k-medoid at t-SNE perplexity = 2 is optimal at k = 9, while based on the S_Dbw index, k-medoid at t-SNE perplexity = 2 is optimal at k = 10. To compare the optimal results, the determination of the best cluster is further explored using radar charts and the Chernoff faces. The best clustering results were obtained using the k-medoid with t-SNE perplexity = 2 and k = 10.
Keyword: chernoff faces, IBL 2022 match statistics, k-means, k-medoid, t-SNE

Judul: Optimization of Provincial Clustering Based on Sustainable Development Goals with Hierarchical Clustering, K-Medoids, and DBSCAN
Abstrak: Analisis gerombol merupakan teknik statistika untuk mengidentifikasi gerombol bermakna dari objek data multivariat berdasarkan ukuran kemiripan. Selain memilih ukuran kemiripan, ketepatan dalam menerapkan metode pembentukan gerombol juga menjadi hal fundamental. Karakteristik data dapat memengaruhi performa analisis gerombol. Data multidimensional dengan jumlah dimensi yang besar memiliki tantangan adanya curse of dimensionality. Salah satu cara mengatasi masalah tersebut adalah dengan melakukan reduksi dimensi. Penelitian ini mengevaluasi performa metode reduksi dimensi analisis komponen utama dan t-SNE, serta membandingkan tiga metode penggerombolan yaitu metode hierarki, metode partisi dengan algoritma K-Medoids, dan metode berbasis kepekatan dengan algoritma Density-Based Spatial Clustering of Applications with Noise (DBSCAN) pada data multidimensi, studi kasus penggerombolan provinsi berdasarkan tujuan pembangunan berkelanjutan (TPB). Evaluasi kinerja hasil penggerombolan didasarkan pada tiga indeks validitas. Hasil penelitian menunjukkan bahwa kombinasi t-SNE dengan algoritma hierarki pautan tunggal atau dengan DBSCAN menghasilkan gerombol paling optimal dengan nilai Silhouette tertinggi (0,56), indeks Davies-Bouldin terendah (0,47), dan indeks Calinski-Harabasz tertinggi (75,04) dibandingkan hasil penggerombolan algoritma lainnya. Penggerombolan optimal tersebut menghasilkan tujuh gerombol provinsi dengan karakteristik pencapaian TPB yang berbeda. Terdapat dua gerombol yang menunjukkan pencapaian TPB rendah, sehingga memerlukan perhatian ekstra sesuai dengan karakteristiknya masing-masing. Gerombol-gerombol tersebut terdiri atas provinsi di wilayah timur Indonesia., Cluster analysis is a statistical technique used to identify meaningful clusters from multivariate data objects based on similarity measures. In addition to choosing the right similarity measures, the accuracy in applying clustering methods is also fundamental. The characteristics of the data can influence the performance of cluster analysis. Multidimensional data with many dimensions face the challenge known as the curse of dimensionality. Dimensionality reduction helps address this issue. This study evaluates the performance of two dimensionality reduction methods, principal component analysis and t-SNE, and compares three clustering methods: hierarchical clustering, K-Medoids, and Density-Based Spatial Clustering of Applications with Noise (DBSCAN). The study focuses on clustering provinces based on sustainable development goals (SDGs). Clustering performance is evaluated using three validity indices. Results indicate that combining t-SNE with the single-linkage hierarchical algorithm or DBSCAN produces the most optimal clusters. These methods achieve the highest Silhouette score (0.56), the lowest Davies-Bouldin index (0.47), and the highest Calinski-Harabasz index (75.04) compared to other algorithms. The optimal clustering results in seven distinct clusters of provinces with different SDGs achievement characteristics. Two clusters, comprising provinces in eastern Indonesia, show low SDGs achievement and require extra attention based on their specific characteristics.
Keyword: cluster analysis, DBSCAN, principal component analysis, sustainable development goals, t-SNE

Judul: Quality improvement of paddy soil and discharged water in drainage channel at acid sulphate soil.
Abstrak: Oxidation of pyrite in acid sulphate soil reduced the quality of soil and discharged water from paddy field. Improvement of soil quality in paddy cultivation could be managed by addition of organic matter together with regulating water to maintain its reductive condition. Water quality improvement in the paddy field was conducted by addition of good quality water (pH=5,0)through utilization of high tide water in dry season and rain water during rainy season while for improvement of discharged water, a system of drainage channel was developed
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