Identifikasi Faktor Kenaikan Harga Komoditas Pasar Melalui Agregasi Berita Online (Studi Kasus: Jawa Timur)
Abstract
Abstrak
Penelitian ini bertujuan untuk membuat sistem monitoring harga komoditas yang mampu mengidentifikasi permasalahan penyebab kenaikan harga komoditas melalui agregasi berita online. Sistem ini memiliki keluaran utama, yaitu monitoring fluktuasi harga pasar dan identifikasi penyebab kasus kenaikan harga komoditas menggunakan data harga komoditas harian yang dikumpulkan dari seluruh pasar induk di Jawa Timur pada Desember 2018 - Mei 2019 yang disediakan oleh SISKAPERBAPO. Untuk mendapatkan hasil identifikasi, sistem melakukan beberapa tahap. Pertama, sistem melakukan labelling terhadap harga pasar yang mengalami kenaikan tidak normal. Selanjutnya, sistem mencari berita terkait kenaikan harga tersebut dan melakukan information retrieval berdasarkan klasifikasi teks terhadap 5W1H pada label why. Hasil dari penelitian ini yaitu, eksperimen pada identifikasi faktor kenaikan harga komoditas menunjukkan bahwa sebesar 1.91% berita memiliki kesesuaian berdasarkan tanggal berita, akurasi berdasarkan relevansi berita dan kasus sebesar 70.49%, dan ekstraksi informasi untuk identifikasi faktor menghasilkan sebesar 39.87% berita relevan menunjukkan hasil identifikasi faktor kenaikan harga komoditas.
Kata kunci: Monitoring System, Tim Pengendalian Inflasi Daerah (TPID), Information Retreival, Klasifikasi 5W1H
Abstract
This research aims to create a commodity price monitoring system that is able to identify the problems causing the increase in commodity prices through online news aggregation.. This system has main outputs, namely monitoring market price fluctuations and identifying causes of cases of rising commodity prices from online news aggregations, which used daily commodity price data collected from all wholesale markets in East Java in December 2018 - May 2019 provided by SISKAPERBAPO. To get the results of identification, the system performs several stages of processing. First, the system of labeling market prices has increased. Furthermore, the system looks for news related to the price increases and conducts information retrieval based on the classification of text against 5W1H on the label why. The results of this research, namely, the experiment on identifying commodity price increase factors shows that 1.91% of news has conformity based on news date, accuracy based on news and case relevance of 70.49%, and information extraction to identify generating factors of 39.87% relevant news shows results identification of factors in rising prices.
Keywords: Monitoring System, Tim Pengendalian Inflasi Daerah (TPID), Information Retreival, 5W1H Classification
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References
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