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: Comparison Study of Distance Measures in Nonlinear Panel Data Clustering with K-Means Method
Abstrak: Analisis penggerombolan digunakan untuk mengelompokkan objek berdasarkan kesamaan karakteristik antar objek. Analisis gerombol biasanya diterapakan pada data cross section akan tetapi pada penelitian ini analisis gerombol diterapkan pada data panel nonlinier dengan menggunakan metode k-means. Pemilihan metode jarak sangat berpengaruh terhadap optimasi penggerombolan. Data yang digunakan dalam penelitian ini ada dua yaitu data simulasi dan data riil. Sehingga, tahap pertama dilakukan simulasi data untuk mendapatkan metode jarak terbaik pada data panel nonlinier. Metode jarak yang digunakan adalah Euclidean, Manhattan, Maximum, Frechet, dan Dynamic Time Warping (DTW). Berdasarkan hasil evaluasi seluruh skenario data simulasi dapat ditarik kesimpulan bahwa jika objek datanya tidak tumpang tindih dan memiliki rentang waktu yang panjang maka sebaiknya menggunakan metode jarak maksimum. Akan tetapi, jika objek datanya tumpang tindih dan memiliki rentang waktu yang pendek maka sebaiknya menggunakan metode jarak dtw atau frechet. Selanjutnya, implementasinya pada data Indonesian Coronavirus Disease (COVID-19) dengan tujuan untuk mengelompokkan Provinsi berdasarkan jumlah kasus positif aktif. Hasilnya menunjukkan bahwa jumlah cluster yang optimal ketika dibentuk tiga gerombol dengan nilai kriteria Calinski Harabatz (CH) sebesar 143.459. Gerombol A beranggotakan 30 anggota Provinsi, Gerombol B tiga anggota Provinsi, sedangkan Gerombol C satu anggota yaitu Provinsi DKI Jakarta., Cluster analysis is used to group objects based on the similarity of characteristics between objects. Cluster analysis is usually applied to cross-sectional data, but in this study, cluster analysis was applied to nonlinear panel data using the K-Means method. The selection of the right distance measure affects the optimization of clustering. The data used in this study are simulation data and real data, so the first stage of the research was carried out by simulating data to obtain the best distance measure on nonlinear panel data. The distance measure used is Euclidean, Manhattan, Maximum, Fréchet, and Dynamic Time Warping (DTW). Based on the evaluation results of all simulation data scenarios, it can be concluded that if the data objects do not overlap and have a long time span, it is better to use the maximum distance measure. If the data objects overlap and have a short time span, then we recommend using the DTW or Fréchet spacing method. Furthermore, the implementation is carried out on Indonesian Coronavirus Disease (COVID-19) data with the aim of grouping Provinces based on the number of active positive cases. The results show that the number of clusters is optimal when three clusters are formed, with the value of the Calinski Harabatz (CH) criteria of 143,459. Cluster A consists of 30 provinces, Cluster B consists of three provinces, while Cluster C consists of one province, DKI Jakarta Province. Keywords: Coronavirus Disease; Calinski-
Keyword: Coronavirus Disease, Calinski-Harabatz, Dynamic Time Warping, Frechet, Maximum, K-Means

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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