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All Journal Jurnal Pusat Inovasi Masyarakat Indonesian Journal of Statistics and Its Applications
Linda Sakinah
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Computer Science & IT Mathematics Social Sciences Other

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Edukasi Pengelolaan Sampah dan Budidaya Maggot Black Soldier Fly (BSF) di Desa Cihideung Ilir, Kecamatan Ciampea, Bogor Marciano Oscar Maida; Raihan Muhammad Iqbal Hidayatullah; Muhammad Ariq Faishal; Cantika Graviola; Dhikma Yogi Senasta Aji; Ramadhita Adji Mubarrak; Linda Sakinah; Alfan Ahadan; Muhammad Alhas Finaldin; Narni Farmayanti
Jurnal Pusat Inovasi Masyarakat (PIM) Vol. 4 No. 2 (2022): Oktober 2022
Publisher : Lembaga Penelitian dan Pengabdian kepada Masyarakat, Institut Pertanian Bogor

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29244/jpim.4.2.40-50

Abstract

Indonesia dikenal sebagai salah satu negara yang menghasilkan sampah dengan jumlah terbanyak di dunia. Kementerian Lingkungan Hidup dan Kehutanan melaporkan pada tahun 2021 terdapat 21,88 juta ton sampah dihasilkan dengan 35,68% diantaranya sampah tidak terkelola. Berdasarkan Sistem Informasi Pengelolaan Sampah Nasional, komposisi sampah yang dihasilkan berupa 41% jenis sisa makanan dan 40,9% bersumber dari rumah tangga. Besarnya jumlah limbah pangan menjadi masalah yang harus diselesaikan. Pengetahuan masyarakat Desa Cihideung Ilir tentang sampah dan pengelolaannya masih minim. Fasilitas pendukung yang kurang pun turut memperburuk kondisi tersebut. Oleh karena itu, diperlukan edukasi dan sosialisasi akan hal tersebut baik kepada orang dewasa maupun anak-anak agar dapat diterapkan sejak dini. Salah satu pengolahannya dapat dilakukan dengan memanfaatkan media budidaya maggot BSF (Black Soldier Fly). Limbah pangan dapat dimanfaatkan sebagai pakan maggot, karena maggot memiliki kemampuan mendegradasi bahan organik. Maggot yang telah berkembang sempurna dapat dimanfaatkan oleh warga sebagai pengembangan ekonomi kreatif bagi generasi muda. Cara pengelolaan sampah organik disampaikan melalui kegiatan penyuluhan, kerja bakti, serta pelatihan langsung kepada warga Desa Cihideung Ilir. Kegiatan tersebut diharapkan dapat membantu menyelesaikan permasalahan sampah organik di lingkungan warga.
Energy Sector Stock Price Forecasting with Time Series Clustering Approach: Peramalan Harga Saham Sektor Energi dengan Pendekatan Penggerombolan Data Deret Waktu Linda Sakinah; Rahma Anisa; I Made Sumertajaya
Indonesian Journal of Statistics and Applications Vol 8 No 2 (2024)
Publisher : Statistics and Data Science Program Study, IPB University, IPB University, in collaboration with the Forum Pendidikan Tinggi Statistika Indonesia (FORSTAT) and the Ikatan Statistisi Indonesia (ISI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29244/ijsa.v8i2p132-142

Abstract

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 doesn’t 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.
Co-Authors Alfan Ahadan Cantika Graviola Dhikma Yogi Senasta Aji I Made Sumertajaya Marciano Oscar Maida Muhammad Alhas Finaldin Muhammad Ariq Faishal Narni Farmayanti Rahma Anisa Raihan Muhammad Iqbal Hidayatullah Ramadhita Adji Mubarrak