Model Descriptive Analytics Terhadap Data Iklim Menggunakan Sistem Informasi Berbasis Datafikasi dan TAM

Authors

Fasia Meta Sefira
Jap Tji Beng Universitas Tarumanagara
Sri Tiatri
Tiara Zahro
Tasya Mulia Salsabila
Rahmiyana Nurkholiza
Margareta Zheng
Vienchenzia Oeyta Dwitama Dinatha

DOI:

https://doi.org/10.31539/intecoms.v8i3.15692

Abstract

Data iklim seringkali hanya sebagai arsip, belum dimanfaatkan secara optimal. Penelitian ini bertujuan untuk menganalisis data iklim Kabupaten Sleman selama tahun 2024. Data yang dipakai merupakan data sekunder berasal dari BMKG, yang mencakup beberapa variabel seperti, suhu terendah, suhu tertinggi, suhu rata-rata, kelembapan rata-rata, dan curah hujan. Data diolah melalui proses datafikasi, data mentah diubah menjadi informasi yang lebih bermakna. Kemudian dianalisis dengan descriptive analytics, menghasilkan visualisasi tren dalam bentuk grafik. Hasil analisis menunjukkan bahwa suhu rata-rata harian relatif stabil pada kisaran 26 – 30°C, dengan terjadi sedikit penurunan pada pertengahan tahun. Analisis korelasi mengungkapkan hubungan negatif antara suhu dan kelembapan (r = -0,54), dan hasil regresi linier menghasilkan nilai R-squared negatif -0.017. Ini menunjukkan adanya faktor-faktor lain, seperti tekanan udara atau pola angin. Penerapan model Technology Acceptance Model (TAM) sebagai kerangka konseptual untuk melihat seberapa jauh pengguna memahami informasi untuk mendukung pengambilan keputusan.

References

Beng, J. T., Tiatri, S., Wangi, V. H., & Lusiana, F. (2020, December). Learning through KMS model using video conference to optimize the absorptive capacity of vocational school students during COVID-19 pandemic. In The 2nd Tarumanagara International Conference on the Applications of Social Sciences and Humanities (TICASH 2020) (pp. 730-734). Atlantis Press.

Beng, J. T., Tiatri, S., Zheng, M., Nurkholiza, R., Dinatha, V., & Salsabila, T. M. (2025). Development of a Training Model on the Use of Laser Engraving Technology for Vocational High School Female Students in Semi-Urban Areas: Gender Equality in Education. TEM Journal, 14(2), 1860–1866. https://doi.org/10.18421/TEM142-82

Chan, J., Sanders, C., Bennett Moses, L., & Blackmore, H. (2022). Datafication and the practice of intelligence production. Big Data and Society, 9(1). https://doi.org/10.1177/20539517221089310

Davis, F. D. (1989). Perceived Usefulness, Perceived Ease of Use, and User Acceptance of Information Technology. MIS Quarterly, 13(3), 319. https://doi.org/10.2307/249008

Diehl, A., Pelorosso, R., Ruiz, J., Pajarola, R., Gröller, M. E., & Bruckner, S. (2021). Hornero: Thunderstorms Characterization using Visual Analytics (Vol. 40, Issue 3).

Fadholi, A. (2013). Persamaan regresi prediksi curah hujan bulanan menggunakan data suhu dan kelembapan udara di Ternate. Statistika, 13(1).

Gaina, R. S., & Indriyani, A. R. A. (2021). Pengembangan Obyek Wisata pada Agrowisata Salak Pondoh di Bangunkerto Kabupaten Sleman, Yogyakarta. In Surono, U. B., Megaprastio, B. (Eds.), Kontribusi bidang sosial humaniora, pertanian dan teknologi dalam pembangunan berkelanjutan (pp. 9–21). NUTA MEDIA.

García-Martínez, I., Fernández-Batanero, J. M., Fernández-Cerero, J., & León, S. P. (2023). Analysing the Impact of Artificial Intelligence and Computational Sciences on Student Performance: Systematic Review and Meta-analysis. Journal of New Approaches in Educational Research, 12(1), 171–197. https://doi.org/10.7821/naer.2023.1.1240

Gunathilake, M. B., Zamri, M. N. M., Alagiyawanna, T. P., Samarasinghe, J. T., Baddewela, P. K., Babel, M. S., ... & Rathnayake, U. S. (2021). Hydrologic utility of satellite-based and gauge-based gridded precipitation products in the Huai Bang Sai Watershed of Northeastern Thailand. Hydrology, 8(4), 165. DOI: 10.3390/hydrology8040165

Houtmeyers, K. C., Jaspers, A., & Figueiredo, P. (2021). Managing the Training Process in Elite Sports: From Descriptive to Prescriptive Data Analytics. International Journal of Sports Physiology and Performance, 16(11), 1719–1723. https://doi.org/10.1123/ijspp.2020-0958

Hwang, H., An, S., Lee, E., Han, S., & Lee, C. H. (2021). Cross‐societal analysis of climate change awareness and its relation to sdg 13: A knowledge synthesis from text mining. Sustainability (Switzerland), 13(10). https://doi.org/10.3390/su13105596

Jap, T. (2017). The technology acceptance model of online game in Indonesian adolescents. Makara Hubs-Asia, 21(1), 24-31. https://doi.org/10.7454/mssh.v21i1.3497

Jap, T., & Tiatri, S. (2024). Cross-disciplinary curricula in Bachelor of Information Systems education: a case study in Indonesia. In Teaching Information Systems (pp. 68–86). Edward Elgar Publishing. https://doi.org/10.4337/9781802205794.00010

Liu, Z., & Wu, G. (2022). Quantifying the precipitation–temperature relationship in China during 1961–2018. International Journal of Climatology, 42(5), 2656–2669. https://doi.org/10.1002/joc.7384

Mufti, F., Ismail, N., & Umar, M. (2017). TREND ANALYSIS OF EXTREAM RAINFALL FROM 1982 - 2013 AND PROJECTION FROM 2014 - 2050 IN BANDA ACEH AND MEULABOH. Jurnal Natural, 17(2), 122–127. https://doi.org/10.24815/jn.v17i2.7012

Rangwala, I., Moss, W., Wolken, J., Rondeau, R., Newlon, K., Guinotte, J., & Travis, W. R. (2021). Uncertainty, complexity and constraints: how do we robustly assess biological responses under a rapidly changing climate?. Climate, 9(12), 177.

Rawat, A., Kumar, D., & Khati, B. S. (2024). A review on climate change impacts, models, and its consequences on different sectors: a systematic approach. Journal of Water and Climate Change, 15(1), 104–126. https://doi.org/10.2166/wcc.2023.536

Sarvina, Y., June, T., Hadi Sutjahjo, S., Nurmalina, R., & Surmaini, E. (2021). The impacts of climate variability on coffee yield in five indonesian coffee production centers. Coffee Science, 16, 1–9. https://doi.org/10.25186/.v16i.1917

Sebestyén, V., Czvetkó, T., & Abonyi, J. (2021). The Applicability of Big Data in Climate Change Research: The Importance of System of Systems Thinking. In Frontiers in Environmental Science (Vol. 9). Frontiers Media S.A. https://doi.org/10.3389/fenvs.2021.619092

Sekaranom, A. B., Nurjani, E., & Nucifera, F. (2021). Agricultural Climate Change Adaptation in Kebumen, Central Java, Indonesia. Sustainability, 13(13), 7069. https://doi.org/10.3390/su13137069

Silva, A. J., Cortez, P., Pereira, C., & Pilastri, A. (2021). Business analytics in Industry 4.0: A systematic review. Expert Systems, 38(7). https://doi.org/10.1111/exsy.12741

Surmaini, E., Runtunuwu, E., Las, I., Besar, B., Dan, P., Sumberdaya, P., & Pertanian, L. (2008). UPAYA SEKTOR PERTANIAN DALAM MENGHADAPI PERUBAHAN IKLIM. In Jurnal Litbang Pertanian (Vol. 30, Issue 1).

Susilawaty, A., Ekasari, R., Widiastuty, L., Wijaya, D. R., Arranury, Z., & Basri, S. (2021). Climate factors and dengue fever occurrence in Makassar during period of 2011–2017. Gaceta Sanitaria, 35, S408–S412. https://doi.org/10.1016/j.gaceta.2021.10.063

Susilokarti, D., Arif, S. S., Susanto, S., & Sutiarso, L. (2015). IDENTIFIKASI PERUBAHAN IKLIM BERDASARKAN DATA CURAH HUJAN DI WILAYAH SELATAN JATILUHUR KABUPATEN SUBANG, JAWA BARAT (Identification of Climate Change Based on Rainfall Data in Southern Part of Jatiluhur, Subang District, West Jawa). Jurnal Agritech, 35(01), 98. https://doi.org/10.22146/agritech.13038

Venkatesh, V., & Davis, F. D. (2000). A Theoretical Extension of the Technology Acceptance Model: Four Longitudinal Field Studies. Management Science, 46(2), 186–204. https://doi.org/10.1287/mnsc.46.2.186.11926

Wang, G., Zhao, J., Van Kleek, M., & Shadbolt, N. (2022). “Don’t make assumptions about me!”: Understanding Children’s Perception of Datafication Online. Proceedings of the ACM on Human-Computer Interaction, 6(CSCW2), 1–24. https://doi.org/10.1145/3555144

Wolniak. (2023). The concept of descriptive analytics. Scientific Papers of Silesian University of Technology Organization and Management Series, 2023(172). https://doi.org/10.29119/1641-3466.2023.172.42

Xavier, L. L., Honório, N. A., Pessanha, J. F. M., & Peiter, P. C. (2021). Analysis of climate factors and dengue incidence in the metropolitan region of Rio de Janeiro, Brazil. PLOS ONE, 16(5), e0251403. https://doi.org/10.1371/journal.pone.0251403

Yusuf, M., Setyanto, A., & Aryasa, K. (2022). Analisis Prediksi Curah Hujan Bulanan Wilayah Kota Sorong Menggunakan Metode Multiple Regression. In Jurnal Sains Komputer & Informatika (J-SAKTI) (Vol. 6, Issue 1).