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: Peramalan Harga Saham Sektor Energi dengan Pendekatan Penggerombolan Data Deret Waktu
Abstrak: Investasi saham menjanjikan return lebih tinggi namun memiliki risiko tinggi karena fluktuasi harga yang sulit diprediksi. Sektor energi potensial karena memiliki kenaikan indeks sektoral tertinggi tahun 2022. Namun, hal tersebut tidak menandakan kenaikan harga saham terjadi merata pada semua emiten. Oleh karena itu, perlu analisis penggerombolan emiten berdasarkan kemiripan pergerakan harga sahamnya. Hasilnya digunakan untuk peramalan harga saham level gerombol. Penelitian ini bertujuan untuk mengevaluasi kinerja penggerombolan emiten sektor energi menggunakan jarak autocorrelation-based dan dynamic time warping(DTW) serta melakukan peramalan harga saham level gerombol. Data yang digunakan adalah data harga saham penutupan periode mingguan. Proses penggerombolan menggunakan metode berhirarki pautan rataan. Peramalan harga saham setiap gerombol menggunakan model ARIMA level gerombol yang performanya dievaluasi dengan rolling-cross validation. Hasil penelitian menunjukan jarak DTW memiliki kinerja penggerombolan terbaik. Emiten sektor energi dikelompokkan menjadi empat gerombol dengan kategori strong cluster karena koefisien silhouette > 0,71. Model ARIMA setiap gerombol menghasilkan nilai MAPE antara 10-20% sehingga dikategorikan model dengan peramalan baik. Gerombol A dan D direkomendasikan untuk investor karena diduga memiliki potensi keuntungan tertinggi dari capital gain berdasarkan hasil peramalan harga saham. Gerombol A dan D juga berisi perusahaan dengan fundamental dan kebijakan deviden yang baik., Stock investment promises higher returns but carries high risks because unpredictable price fluctuations. Energy sector shows potential due to its highest sectoral index growth in 2022. However, this does not indicate that stock price increases occur evenly among all issuers. Therefore, it’s necessary to analyze clustering of issuers based on similarity of their stock price movements and used for forecasting stock prices at cluster level. This study aims to evaluate performance of clustering energy sector issuers using autocorrelation-based distance and dynamic time warping(DTW), and to forecast stock prices at cluster level. The data used consists weekly closing stock prices. The clustering used hierarchical average linkage method. Stock price forecast for each cluster used ARIMA model and its performance was evaluated using rolling-cross validation. The results showed that DTW distance had the best clustering performance. Energy sector issuers were grouped into four clusters with strong cluster category, indicated by silhouette coefficient > 0.71. ARIMA models for each cluster produced MAPE values between 10-20%, categorizing them as good forecasting models. Clusters A and D were recommended for investors because have highest potential for capital gain based on forecasted stock prices. That clusters also consisted of companies with strong fundamentals and dividend policies.
Keyword: ARIMA, autocorrelation-based, dynamic time warping, stock prices, 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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