IMPLEMENTASI GOOGLE LOOKER STUDIO UNTUK ANALISIS TREN DAN VISUALISASI DATA (STUDI KASUS: PRODUKSI PADI PULAU SUMATERA)
Kata Kunci:
Crop Yield, Prediction Model, Data MiningAbstrak
This study analyzes rice production outcomes in Sumatra Island from 1995 to 2020 using data obtained from Kaggle and the Indonesian Agency for Meteorology, Climatology, and Geophysics (BMKG). The dataset includes annual rice production, harvested area, and climate variables such as rainfall, humidity, and average temperature. Analysis was conducted using Google Looker Studio to generate various visualizations facilitating data interpretation. The findings reveal that North Sumatra Province has the highest rice production, followed by South Sumatra and Lampung. While rainfall does not consistently correlate with rice production, a strong relationship was observed between harvested area and rice yields, underscoring the importance of efficient land management and agricultural practices. The study also identifies a decline in rice production across provinces since 2017. Limitations include the restricted data period up to 2020 and the exclusion of variables such as government policies and agricultural technology. Future research should expand data coverage, consider additional variables, and employ advanced predictive model.
Referensi
Alamri, M. H., Rauf, A., & Saleh, Y. (2022). Analisis Faktor-Faktor Produksi Terhadap Produksi Padi Sawah Di Kecamatan Bintauna Kabupaten Bolaang Mongondow Utara. AGRINESIA: Jurnal Ilmiah Agribisnis, 6(3), 240–249.
Alfred, R., Obit, J. H., Chin, C. P.-Y., Haviluddin, H., & Lim, Y. (2021). Towards paddy rice smart farming: a review on big data, machine learning, and rice production tasks. Ieee Access, 9, 50358–50380.
Anh, D. L. T., Anh, N. T., & Chandio, A. A. (2023). Climate change and its impacts on Vietnam agriculture: A macroeconomic perspective. Ecological Informatics, 74, 101960.
Asher, J., & Rachmawati, E. P. (2024). Visualisasi Data Operasi SAR BASARNAS Di Indonesia Menggunakan Google Looker Studio. Indonesian Journal of Computer Science, 13(2).
Attri, I., Awasthi, L. K., Sharma, T. P., & Rathee, P. (2023). A review of deep learning techniques used in agriculture. Ecological Informatics, 102217.
Balasundram, S. K., Shamshiri, R. R., Sridhara, S., & Rizan, N. (2023). The role of digital agriculture in mitigating climate change and ensuring food security: An overview. Sustainability, 15(6), 5325.
Behera, B., Haldar, A., & Sethi, N. (2023). Agriculture, food security, and climate change in South Asia: a new perspective on sustainable development. Environment, Development and Sustainability, 1–26.
Bharadiya, J. P. (2023). A comparative study of business intelligence and artificial intelligence with big data analytics. American Journal of Artificial Intelligence, 7(1), 24.
Bibi, F., & Rahman, A. (2023). An overview of climate change impacts on agriculture and their mitigation strategies. Agriculture, 13(8), 1508.
Br Sinuhaji, R. (2024). Analisis Pendapatan dan Faktor yang mempengaruhi Produksi Petani Padi Tadah Hujan (Studi Kasus: Desa Rampah Kecamatan Kutambaru Kabupaten Langkat). Universitas Medan Area.
Chandio, A. A., Jiang, Y., Ahmad, F., Adhikari, S., & Ain, Q. U. (2021). Assessing the impacts of climatic and technological factors on rice production: Empirical evidence from Nepal. Technology in Society, 66, 101607.
Costa, A. P. R., Motta, C. L. R., & Schneider, D. (n.d.). Designing and Building a Low-Cost IoT Solution for Natural Disaster Monitoring and Mitigation: An Experience Report.
Gahar, R. M., Arfaoui, O., & Hidri, M. S. (2024). Open Research Issues and Tools for Visualization and Big Data Analytics. ArXiv Preprint ArXiv:2404.12505.
Galitan, J. H., Duko, F., & Hatim, F. (2024). Analisis Produksi Padi Di Indonesia: Indonesia. Innovative: Journal Of Social Science Research, 4(2), 7281–7301.
Grigorieva, E., Livenets, A., & Stelmakh, E. (2023). Adaptation of agriculture to climate change: A scoping review. Climate, 11(10), 202.
Hadiyanti, D., & Suparwoto, S. (2023). Adaptasi varietas padi Inpago dan Inpari di lahan rawa lebak dangkal provinsi Sumatera Selatan. Jurnal Pertanian Agros, 25(4), 3439–3446.
Hussain, S., Huang, J., Huang, J., Ahmad, S., Nanda, S., Anwar, S., … Cao, X. (2020). Rice production under climate change: adaptations and mitigating strategies. Environment, Climate, Plant and Vegetation Growth, 659–686.
Karim, R., & Paman, U. (2023). Faktor-Faktor yang Mempengaruhi Produksi Padi Sawah di Nagari Balai Panjang Kecamatan Lareh Sago Halaban Kabupaten Lima Puluh Kota, Provinsi Sumatera Barat. Dinamika Pertanian, 39(1), 105–112.
Khang, A. (2023). Handbook of Research on AI-Equipped IoT Applications in High-Tech Agriculture. IGI Global.
Khanh, P. T., Ngoc, T. T. H., & Pramanik, S. (2023). Future of smart agriculture techniques and applications. In Handbook of Research on AI-Equipped IoT Applications in High-Tech Agriculture (pp. 365–378). IGI Global.
Kharismawati, K. H. D., & Karjati, P. D. (2021). Pengaruh luas lahan dan jumlah tenaga kerja terhadap produksi padi di 10 kabupaten Jawa Timur tahun 2014-2018. Economie: Jurnal Ilmu Ekonomi, 3(1), 50–66.
Mayasari, R., Nugraha, B., Juwita, A. R., & Heryana, N. (2023). Analisis Produktifitas Padi di Pulau Sumatera menggunakan Exploratory Data Analysis (EDA). Jurnal Elektronik Sistem Informasi Unsika, 1(1), 17–24.
Ngukhiew, H., & Smutkupt, U. (2024). Real Time Production Status Monitoring System. Science & Technology Asia, 45–52.
Nofitasari, R. (2024). Strategi Pengembangan Teknologi Pertanian Padi Sawah di Langkat Sumatera Utara. JIA (Jurnal Ilmiah Agribisnis): Jurnal Agribisnis Dan Ilmu Sosial Ekonomi Pertanian, 9(1), 38–48.
Nugroho, M. Y. (2023). Analisis Faktor-Faktor yang Mempengaruhi Produksi Padi di Sumatera Menggunakan Metode Regresi Linier. Prosiding Seminar Nasional Teknologi Informasi Dan Bisnis, 36–40.
Oliveira, R. C. de, & Silva, R. D. de S. e. (2023). Artificial intelligence in agriculture: benefits, challenges, and trends. Applied Sciences, 13(13), 7405.
Pickson, R. B., He, G., & Boateng, E. (2021). Impacts of climate change on rice production: evidence from 30 Chinese provinces. Environment, Development and Sustainability, 1–19.
Putra, A. P., Bachtiar, E. A., Hidayatulloh, R., Ramadhani, A. S., Ummah, K., & Sholihah, W. (2024). Perancangan Sistem Rekomendasi Komoditas Pertanian Berdasarkan Lokasi Geografis Untuk Meningkatkan Produktivitas Petani. Jurnal Informatika Dan Teknik Elektro Terapan, 12(1). https://doi.org/10.23960/jitet.v12i1.3936
Sabrina, S. S. (2024). Visualisasi Data Penyebab Kematian Di Indonesia Rentang Tahun 2000-2022 Dengan Power Bi. Jurnal Informatika Dan Teknik Elektro Terapan, 12(2). https://doi.org/10.23960/jitet.v12i2.4071
Saud, S., Wang, D., Fahad, S., Alharby, H. F., Bamagoos, A. A., Mjrashi, A., … Adnan, M. (2022). Comprehensive impacts of climate change on rice production and adaptive strategies in China. Frontiers in Microbiology, 13, 926059.
Straffelini, E., & Tarolli, P. (2023). Climate change-induced aridity is affecting agriculture in Northeast Italy. Agricultural Systems, 208, 103647.
Suarna, A. (2021). Analisis Faktor-Faktor Yang Mempengaruhi Produksi Padi Di Desa Poto Kecamatan Moyo Hilir Kabupaten Sumbawa.
Sukhdeve, S. R., & Sukhdeve, S. S. (n.d.). Google Cloud Platform for Data Science.
Tang, L., Risalat, H., Cao, R., Hu, Q., Pan, X., Hu, Y., & Zhang, G. (2022). Food security in China: a brief view of rice production in recent 20 years. Foods, 11(21), 3324.
Walis, N. R., Setia, B., & Isyanto, A. Y. (2021). Faktor-faktor yang berpengaruh terhadap produksi padi di Desa Pamotan Kecamatan Kalipucang Kabupaten Pangandaran. Jurnal Ilmiah Mahasiswa Agroinfo Galuh, 8(3), 648–657.
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