Modeling multiple linear regression analysis in the formation of biogas pressure

Authors

  • Basirun Basirun Program Studi Statistika, Fakultas Matematika dan Ilmu Pengetahuan Alam, Universitas Hamzanwadi
  • Ristu Haiban Hirzi Program Studi Statistika, Fakultas Matematika dan Ilmu Pengetahuan Alam, Universitas Hamzanwadi
  • Muanah Muanah Program Studi Teknik Pertanian, Fakultas Pertanian, Universitas Muhammadiyah Mataram

DOI:

https://doi.org/10.31764/jau.v10i3.16302

Keywords:

biogas pressure, multiple linear regression, temperatur

Abstract

Fossil energy reserves to date are dwindling inversely proportional to the amount of consumption. So to overcome this problem, alternative energy is needed, one of which is biogas which is sourced from organic waste. The biogas production process has so far experienced many obstacles so that the formation of pressure has not been optimal. The aim of the research was to create a model to see the magnitude of the influence of humidity and temperature on the pressure of the biogas produced. The method used is multiple linear regression with the following stages, identifying variables, testing classical assumptions, model building, and model goodness. Based on the results of the analysis, the model Y ̂=17.029-0.042X_1+3.480X_2 is obtained. Simultaneous test results show that simultaneously humidity and temperature have a significant effect because the sig is 0.000<α(0.05). The results of the partial test (T-Test) of each variable also showed significant results on biogas pressure because the sig was 0.000<α(0.05). The coefficient of determination of 0.8180 means that humidity and temperature variables affect the formation of biogas pressure by 81.80% and the rest is influenced by other factors such us pH, C/NRatio, starter, and so on.

References

Cahyono, T. (2015). Buku Statistik Uji Normalitas. In Yayasan Sanitarian Banyumas, Banyumas, Indonesia.

Dhaniswara, T. K., & Fitri, M. A. (2017). Pengaruh Perlakuan Awal Sampah Organik Terhadap produksi Biogas Secara Anaerobic Digestion. Journal of Research and Technology.

Draper, N., & Smith, H. (1981). Applied regression analysis, second edition. In Wiley series in probability and mathematical statistics.

Hasim, H., Laome, L., & ... (2020). Regresi Ridge dalam Mengatasi Multikolinearitas pada Kasus Indeks Pembangunan Manusia. E-Journal ….

Irawan, D., & Khudori, A. (2015). PENGARUH SUHU ANAEROBIK TERHADAP HASIL BIOGAS MENGGUNAKAN BAHAN BAKU LIMBAH KOLAM IKAN GURAME. Turbo : Jurnal Program Studi Teknik Mesin. https://doi.org/10.24127/trb.v4i1.3

Padilah, T. N., & Adam, R. I. (2019). ANALISIS REGRESI LINIER BERGANDA DALAM ESTIMASI PRODUKTIVITAS TANAMAN PADI DI KABUPATEN KARAWANG. FIBONACCI: Jurnal Pendidikan Matematika Dan Matematika. https://doi.org/10.24853/fbc.5.2.117-128

Padmawati, I. R., & Fachrurrozie. (2015). Pengaruh mekanisme good corporate governance dan kualitas audit terhadap tingkat konservatisme akuntansi. Accounting Analysis Journal.

Reza, M., Putra, S., Prasetyo, E., Ekonomi, F., Akuntansi, P., & Kahuripan, U. (2020). Analisis Kualitas Sistem Informasi Akuntansi, Perceived UsefulnessTerhadap Kepuasan Pengguna Pada Tanaya Realtydi Kota Sidoarjo. Jurnal Ekonomi Bisnis.

Rezeki, S., Ivontianti, W. D., & Khairullah, A. (2021). Optimasi Temperatur Pada Produksi Biogas dari Limbah Rumah Makan di Kota Pontianak. Jurnal Engine: Energi, Manufaktur, Dan Material. https://doi.org/10.30588/jeemm.v5i1.850

Rutledge, D. N., & Barros, A. S. (2002). Durbin-Watson statistic as a morphological estimator of information content. Analytica Chimica Acta. https://doi.org/10.1016/S0003-2670(01)01555-0

Tampubolon, B. I., Fauzi, A., & Ekayani, M. (2016). INTERNALISASI BIAYA EKSTERNAL SERTA ANALISIS KEBIJAKAN PENGEMBANGAN ENERGI PANAS BUMI SEBAGAI ENERGI ALTERNATIF. RISALAH KEBIJAKAN PERTANIAN DAN LINGKUNGAN: Rumusan Kajian Strategis Bidang Pertanian Dan Lingkungan. https://doi.org/10.20957/jkebijakan.v2i2.10966

Tri Subhi, K., & Al Azkiya, A. (2022). Comparison of Cochrane-Orcutt and Hildreth-Lu Methods to Overcome Autocorrelation in Time Series Regression (Case Study of Gorontalo Province HDI 2010-2021). Parameter: Journal of Statistics. https://doi.org/10.22487/27765660.2022.v2.i2.15913

Uyanık, G. K., & Güler, N. (2013). A Study on Multiple Linear Regression Analysis. Procedia - Social and Behavioral Sciences. https://doi.org/10.1016/j.sbspro.2013.12.027

Wati, S. E., Sebayang, D., & Sitepu, R. (2013). Perbandingan Metode Fuzzzy Dengan Regresi LInier Berganda Dalam Peramlan Jumlah Produksi. Saintia Matematika.

Wiśniewska, M., Kulig, A., & Lelicińska-Serafin, K. (2020). Odour emissions of municipal waste biogas plants-impact of technological factors, air temperature and humidity. Applied Sciences (Switzerland). https://doi.org/10.3390/app10031093

Yahya, Y., Tamrin, T., & Triyono, S. (2018). PRODUKSI BIOGAS DARI CAMPURAN KOTORAN AYAM, KOTORAN SAPI, DAN RUMPUT GAJAH MINI (Pennisetum Purpureum cv. Mott) DENGAN SISTEM BATCH. Jurnal Teknik Pertanian Lampung (Journal of Agricultural Engineering). https://doi.org/10.23960/jtep-l.v6i3.151-160

Zaki, A., & Santoso, H. A. (2016). Model Fuzzy Tsukamoto untuk Klasifikasi dalam Prediksi Krisis Energi di Indonesia. Creative Information Technology Journal. https://doi.org/10.24076/citec.2016v3i3.76

Published

2023-07-31