THE USE OF RICE POLISHING MACHINE TOSHI KINHE MPGV 50 IN INCREASING RICE YIELD IN RMU AT SUBAK BENGKEL, TABANAN
Abstract
Rice (Oryza sativa L.) is a major commodity that determines national food security, with rice yield as an important indicator of post-harvest success. Rice yield in Subak Bengkel, Tabanan is still low (45–50%) due to the use of conventional Rice Milling Units (RMUs) that are less efficient. This study aims to analyze the adoption of the Toshi Kinhe MPGV 50 Rice Polishing Machine innovation and its effect on increasing rice yield. The research method uses SEM-PLS to analyze innovation adoption factors and multiple linear regression to examine the effect of technology on yield. The results show that innovation adoption is significantly influenced by relative advantage (0.42), observability (0.40), compatibility (0.36), and trialability (0.31), while complexity (-0.28) has a negative effect. The regression model with R² = 0.71 shows that machine adoption, usage intensity, grain moisture content, operator experience, and RMU capacity explain 71% of the variation in rice yield. The Toshi Kinhe MPGV 50 machine has been proven to increase yield by more than 5% and improve the physical quality of rice. Consequently, machine adoption needs to be supported by operator training, grain moisture management, and field demonstration programs to expand its application and improve farmer welfare.
References
Alfred, R., Obit, J.H., Chin, C.P.Y., Haviluddin, H. and Lim, Y. (2021), “Towards paddy rice smart farming: A review on big data, machine learning, and rice production tasks”, IEEE Access, Institute of Electrical and Electronics Engineers Inc., doi: 10.1109/ACCESS.2021.3069449.
Chiang, S.H. and Ton, M.B. (2025), “Mapping Rice Phenology Using MODIS Products in An Giang Province, Mekong River Delta, Vietnam”, Remote Sensing, Multidisciplinary Digital Publishing Institute (MDPI), Vol. 17 No. 9, doi: 10.3390/rs17091583.
Cinar, I. and Koklu, M. (2022), “Identification of Rice Varieties Using Machine Learning Algorithms”, Tarim Bilimleri Dergisi, Ankara University, Vol. 28 No. 2, pp. 307–325, doi: 10.15832/ankutbd.862482.
Granić, A. (2022), “Publisher Correction: Educational Technology Adoption: A systematic review”, Education and Information Technologies, Springer, Vol. 27 No. 8, p. 11971, doi: 10.1007/s10639-022-11053-0.
Hair, J.F., Sarstedt, M., Ringle, C.M., Sharma, P.N. and Liengaard, B.D. (2024), “Going beyond the untold facts in PLS–SEM and moving forward”, European Journal of Marketing, Emerald Publishing, Vol. 58 No. 13, pp. 81–106, doi: 10.1108/EJM-08-2023-0645.
Henseler, J. and Schuberth, F. (2025), “Should PLS become factor-based or should CB-SEM become composite-based? Both!”, European Journal of Information Systems, Taylor and Francis Ltd., doi: 10.1080/0960085X.2024.2357123.
Jannah, R.D. (2025), “ANALISIS FLYPAPER EFFECT DAN PENGARUHNYA TERHADAP BELANJA DAERAH”, Jurnal Ilmu Dan Riset Akuntansi, Vol. 14 No. 8.
Kashem, A., Karim, R., Das, P., Datta, S.D. and Alharthai, M. (2024), “Compressive strength prediction of sustainable concrete incorporating rice husk ash (RHA) using hybrid machine learning algorithms and parametric analyses”, Case Studies in Construction Materials, Elsevier Ltd, Vol. 20, doi: 10.1016/j.cscm.2024.e03030.
Paramarta, P.M.A.A. (2024), “Analisis Pengaruh Saluran Komuikasi dan Keadaan Penyuluh Terhadap Adopsi Inovasi Combine Harvester di Kabupaten Tabanan Pande Made Ari Ananta Paramarta”, Jurnal Cendekia Ilmiah, Vol. 3 No. 6, pp. 7627–7631.
Satpathi, A., Setiya, P., Das, B., Nain, A.S., Jha, P.K., Singh, S. and Singh, S. (2023), “Comparative Analysis of Statistical and Machine Learning Techniques for Rice Yield Forecasting for Chhattisgarh, India”, Sustainability (Switzerland), MDPI, Vol. 15 No. 3, doi: 10.3390/su15032786.
Sheng, R.T.C., Huang, Y.H., Chan, P.C., Bhat, S.A., Wu, Y.C. and Huang, N.F. (2022), “Rice Growth Stage Classification via RF-Based Machine Learning and Image Processing”, Agriculture (Switzerland), MDPI, Vol. 12 No. 12, doi: 10.3390/agriculture12122137.
Silva, M., Fiueiredo, R., Pazianotto, R. and Zuccari, M. (2025), “Influence of land use on benthic macroinvertebrate assemblages in headwater streams of the Jaguari River Basin, Brazil”, Revista Ambiente e Agua, Institute for Environmental Research in Hydrographic Basins (IPABHi), Vol. 20 No. e3044, pp. 445–458, doi: 10.4136/1980-993X.
Tseng, H.H., Yang, M. Der, Saminathan, R., Hsu, Y.C., Yang, C.Y. and Wu, D.H. (2022), “Rice Seedling Detection in UAV Images Using Transfer Learning and Machine Learning”, Remote Sensing, MDPI, Vol. 14 No. 12, doi: 10.3390/rs14122837.
Yusuf, K.A. and Oladipo, A. (2025), Effects of Rice Polishing Duration and Tomato Maturity on Their Colorimetric Characteristics.
