Judul: Winsorization Approach for Handling Outlier Problems in Clustering Non-Cyclical Consumer Sector Stocks Using K-Means and K-Medoids Methods
Abstrak: Non-cyclical consumer sector stocks are often chosen by investors because the products in this sector are essential products that are always in demand by society. Therefore, the demand tends to be stable and defensive or less affected by economic shocks. However, it does not guarantee that every stock in this sector will perform well, so it is necessary to group stocks based on their fundamental indicators in the form of financial ratios. This research aims to identify the best method by considering outliers and determining the clusters with the best fundamental performance as a recommendation for investors to make the right investment decisions. The data used in this study is secondary data with observations in the form of 50 non-cyclical consumer sector stocks. The variables used are Earning per Share, Return on Equity, Return on Assets, Debt to Equity Ratio, Price to Earnings Ratio, and Price to Book Value. The clustering results indicated that K-Medoids is the best clustering method, both on the data before and after handling extreme outliers with the winsorization approach. However, the optimum number of clusters produced is different, with 3 and 6 clusters. Considering the influence of extreme outliers and to get a more informative clustering result, the clustering result after the winsorization approach was chosen, resulting in 6 clusters. Cluster 1, which consists of AALI, GGRM, INDF, and SGRO can be recommended because it has excellent fundamental performance, especially in terms of Earning per Share in 2022., Saham pada sektor consumer non-cyclical sering dipilih oleh investor karena produk di sektor ini merupakan produk kebutuhan pokok yang selalu dibutuhkan oleh masyarakat. Oleh karena itu, permintaannya cenderung stabil dan defensif atau tidak terlalu dipengaruhi oleh fluktuasi ekonomi. Namun, hal tersebut tidak menjamin bahwa setiap saham pada sektor ini memiliki kinerja yang baik sehingga perlu dilakukan penggerombolan saham berdasarkan indikator fundamentalnya yang berupa rasio keuangan. Penelitian ini bertujuan mengidentifikasi metode terbaik dengan mempertimbangkan pencilan dan menentukan gerombol dengan performa fundamental terbaik sebagai rekomendasi bagi investor untuk pengambilan keputusan investasi yang tepat. Data yang digunakan pada penelitian ini merupakan data sekunder dengan amatan berupa 50 saham sektor consumer non-cylical. Peubah yang digunakan ialah Earning per Share, Return on Equity, Return on Assets, Debt to Equity Ratio, Price to Earnings Ratio, dan Price to Book Value. Hasil penggerombolan menunjukkan bahwa metode K-Medoids merupakan metode terbaik, baik pada data sebelum maupun sesudah penanganan pencilan dengan pendekatan winsorization. Meskipun demikian, jumlah gerombol optimum yang dihasilkan berbeda, yaitu 3 dan 6 gerombol. Dengan mempertimbangkan pengaruh pencilan ekstrem serta untuk mendapatkan hasil penggerombolan yang lebih informatif, dipilihlah hasil penggerombolan setelah penerapan pendekatan winsorization yang menghasilkan 6 gerombol. Gerombol 1 yang terdiri atas saham AALI, GGRM, INDF, dan SGRO diduga dapat direkomendasikan karena memiliki performa fundamental yang sangat baik, terutama dari sisi Earning per Share pada tahun 2022.
Keyword: fundamental, K-Medoids, pencilan, saham, winsorization, fundamentals, stocks, outliers

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: Methane Consumption (CH4) on Upland Rice Cultivation Under Micro Climate Cobdition With N Fertilizer Levels
Abstrak: Methane (CH4) is one of radiatively active trace gases or greenhouse gas, having strong absorbsivity to the infrared outgoing radiation emitted by the earth's surface, hence causing the warming of the atmosphere. Methane concentration in the atmosphere increases with a rate of about 12+1 ppbv/yr, or 36 Tg/yr. The increase of methane was predicted to have a significant impact on the earth's climate. The presence of 1.3 ppmv of methane in the atmosphere causes the globally average surface temperature 1.30C higher than it would be with no methane.
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