Judul: Peramalan Level Gerombol pada Data Deret Waktu Harga Saham Perbankan Indonesia dengan Metode Gated Recurrent Unit
Abstrak: Banyaknya emiten saham subsektor perbankan yang terdaftar di BEI menawarkan berbagai pilihan investasi sehingga dapat diterapkan metode penggerombolan untuk membantu menyederhanakan informasi. Namun, fluktuasi harganya yang tinggi memperbesar risiko adanya outlier sehingga perlu metode penggerombolan yang robust. Selain itu, dalam mengantisipasi perubahan harga di masa depan dapat diterapkan metode peramalan yang mampu menangkap pola kompleks harga saham dan ketergantungan jangka panjang. Penelitian ini menerapkan metode penggerombolan k-medoid dengan ukuran jarak DTW dan metode peramalan GRU. Sebanyak 41 emiten subsektor perbankan digerombolkan dengan metode k-medoid yang menghasilkan 5 gerombol optimum (koefisien silhouette = 0,524). Setiap gerombol memiliki kemiripan dalam pola pergerakan harga saham dan karakteristik emiten di dalamnya. Pemodelan GRU dilakukan pada setiap prototype gerombol dengan hypeparameter tuning menggunakan metode grid search dan skema k-fold time series cross-validation untuk menghasilkan model terbaik dari serangkaian kombinasi hyperparameter yang diberikan. Evaluasi pada data uji setiap prototype menunjukkan nilai yang lebih baik daripada data latih dengan nilai MAPE dan RMSE relatif < 5%. Hasil peramalan selama delapan minggu menunjukkan hanya prototype gerombol pertama, kedua, dan ketiga yang memiliki pola peningkatan harga saham, sementara prototype gerombol keempat dan kelima menunjukkan penurunan harga yang diakibatkan tantangan atau rendahnya ekspektasi jangka pendek terhadap emiten-emiten gerombol tersebut., The many banking issuers listed on the IDX provided diverse investment options, necessitating a clustering method to simplify information. However, high share price fluctuations increased the risk of outliers, so a robust clustering method was needed. Additionally, forecasting methods that could capture complex stock price patterns and long-term dependencies were applied to anticipate future price changes. This research applied the k-medoid clustering with the DTW distance and the GRU forecasting. Forty-one issuers in the banking subsector were clustered using the k-medoid method, which produced 5 optimal clusters (silhouette coefficient = 0,524). Each cluster had similarities in the stock price movement pattern and the characteristics of the issuers in it. GRU modeling was carried out on each cluster prototype with hyperparameter tuning using the grid search and k-fold time series cross-validation to produce the best model from a given series of hyperparameter combinations. Evaluation of the test data for each prototype showed better values than the training data with MAPE and relative RMSE values < 5%. Eight-week forecasts showed that the first, second, and third cluster prototypes had increasing stock prices, while the fourth and fifth clusters showed declines due to challenges or low short-term expectations for issuers.
Keyword: bank stock prices, dynamic time warping, gated recurrent unit, k-medoid, swarm level forecasting

Judul: Pengelompokan Emiten Berdasarkan Fluktuasi Harga Saham Syariah Menggunakan Algoritma Agglomerative Hierarchical Clustering
Abstrak: Kebijakan pemerintah untuk memutus rantai penularan Covid-19 memberikan dampak pada sektor ekonomi di Indonesia. Banyak pekerja yang harus mencari sumber pendapatan lain karena kehilangan pekerjaannya di masa pandemi. Salah satu usaha yang dilakukannya yaitu berinvestasi saham. Indonesia sebagai negara dengan mayoritas penduduk muslim membuat pasar modal syariah khususnya saham syariah semakin diminati. Namun demikian, saham syariah yang memiliki tingkat pengembalian tinggi juga memiliki risiko yang tinggi. Salah satu faktor yang membuat risiko saham tinggi adalah harga saham yang bersifat fluktuatif dan stokastik. Oleh karena itu, perlu adanya sebuah analisis tentang penggerombolan emiten berdasarkan fluktuasi harga saham syariah. Berdasarkan karakteristik data harga saham syariah yang merupakan data deret waktu, algoritma penggerombolan yang akan diterapkan adalah agglomerative hierarchical clustering dengan metode jarak dynamic time warping. Parameter jarak terpilih adalah average linkage karena memiliki koefisien korelasi cophenetic tertinggi yaitu 0,643. Jumlah gerombol yang optimal berdasarkan kriteria koefisien silhouette dan rasio keragaman adalah lima gerombol. Setelah dilakukan uji chow breakpoint pada setiap emiten di setiap gerombol, terdapat tiga emiten di gerombol satu, tiga emiten di gerombol dua, satu emiten di gerombol tiga, dan satu emiten di gerombol lima yang tidak signifikan terjadi perubahan struktural pada periode sebelum dan sesudah munculnya pandemi Covid-19., The government policies to break the chain of Covid-19 transmission have an impact on the economic sector in Indonesia. Many workers lost their job and have to find other ways to survive. One of them is by investing in stocks. Indonesia, as a country with a predominantly Islamic population, the sharia capital market, especially sharia stocks is high in demand. However, sharia stocks that have a high return rate also have high risks. One of the factors that make stock risk is high is due to the stock price that is volatile and stochastic. Therefore, it is necessary to cluster the stock issuers based on price pattern. Based on the characteristics of sharia stock price which is time series data, the clustering algorithm that were applied is agglomerative hierarchical clustering with dynamic time warping distance. The distance parameter that were chosen is the average linkage because it has the highest cophenetic correlation coefficient, 0.643. The optimal number of clusters based on the criteria of the silhouette coefficient and diversity ratio is five clusters. According to the chow breakpoint test there were three issuers in cluster one, three issuers in cluster two, one issuer in cluster three, and one issuer in cluster five with no significant structural changes the period before and after the emergence of Covid-19. The test was performed independently on each issuer in all cluster.
Keyword: chow breakpoint test, dynamic time warping, sharia stock, time series clustering

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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