Analysis of Mill Motor Speed on the Sugar Value in Bagasse Using the Fuzzy Logic Method at the Sugar Factory of PT. Pratama Nusantara Sakti

  • Ricky Rachman Nursa
    Universitas Lampung
  • Helmy Fitriawan
    Universitas Lampung
  • Sri Ratna Sulistiyanti
    Universitas Lampung
DOI: https://doi.org/10.23960/jtepl.v15i3.1130-1142
Keywords Bagasse pol, Fuzzy logic, Motor speed, Sugar industry, Sugarcane milling
Abstract Views (Last 12 Months)
31 Abstract Views
31 Downloads

Abstract

The Indonesian sugar industry faces a serious challenge in the form of low efficiency in sugarcane milling, which is indicated by the high pol value in bagasse. This condition indicates that a considerable amount of sugar remains trapped in the bagasse, resulting in sugar losses and reduced productivity. One of the operational factors contributing to this phenomenon is the rotational speed of the mill motor, as non-optimal speed can affect the level of juice extraction and the amount of sugar remaining in the bagasse. Therefore, this study aims to analyze the effect of mill motor rotational speed on the pol value of bagasse and to optimize this parameter using the fuzzy logic method. The fuzzy system was designed to process machine variables (motor speed and motor load) as well as supporting factors (moisture content, temperature, service life, and harvesting age) through inference rules based on membership functions. Results show that most fuzzy predictions are consistent with the actual data from the quality control division, with a high level of accuracy indicated by an RRMSE of 7.84%, MAE of 0.0603, and MAPE of 3.34%. These findings demonstrate that fuzzy logic is capable of handling uncertainty and the complexity of variables in the milling process, while also providing a practical solution to reduce sugar losses, improve quality, and enhance the productivity of the national sugar industry.

Downloads

Download data is not yet available.

References

Abdussamad, Z. (2021). Metode Penelitian Kualitatif. CV Syakir Media Press, Aceh. ISBN: 978-602-5440-02-1.

Athiyah, U., Handayani, A.P., Aldean, M.Y., Putra, N.P., & Ramadhani, R. (2021). Sistem inferensi fuzzy: Pengertian, penerapan, dan manfaatnya. Journal of Dinda Data Science Information Technology and Data Analytics, 1(2), 73–76. https://doi.org/10.20895/dinda.v1i2.201

Carani, N.M.M., Asmara, R., & Mutisari, R. (2024). Analisis efisiensi teknis penggunaan faktor produksi pada usahatani tebu (Saccharum officinarum L) di Kecamatan Bareng, Kabupaten Jombang, Jawa Timur. Jurnal Ekonomi Pertanian dan Agribisnis, 8(2), 551–559.

Erdoğdu, A., Dayi, F., Yildiz, F., Yanik, A., & Ganji, F. (2025). Combining fuzzy logic and genetic algorithms to optimize cost, time and quality in modern agriculture. Sustainability, 17(7), 2829. https://doi.org/10.3390/su17072829

Göktepe Körpeoğlu, S., Filiz, A., & Göktepe Yıldız, S. (2025). AI-driven predictions of mathematical problem-solving beliefs: Fuzzy logic, adaptive neuro-fuzzy inference systems, and artificial neural networks. Applied Sciences, 15(2), 494. https://doi.org/10.3390/app15020494

Hartadi, F.T., Wicaksana, B.A., Saputro, H., & Priambodo, A.S. (2024). Sistem kendali fuzzy untuk robot mobile: Studi kasus pelacakan objek bergerak menggunakan simulasi Webots. Jurnal Informatika dan Teknik Elektro Terapan, 12(3), 2050-2060. https://doi.org/10.23960/jitet.v12i3.4608

Husna, A., Putri, D.N., & Manshur, H.A. (2023). Performance analysis of sugar production in Ngadirejo Sugar Factory. Jurnal Agroindustri Halal, 9(3), 278–288. https://doi.org/10.30997/jah.v9i3.6513

Imani, A., Sukwika, T., & Febrina, L. (2021). Karbon aktif ampas tebu sebagai adsorben penurun kadar besi dan mangan limbah air asam tambang. Jurnal Teknologi, 13(1), 33–42.

Kementerian Pertanian. (2023). Outlook Tebu 2023. Pusat Data dan Sistem Informasi Pertanian. https://satudata.pertanian.go.id/assets/docs/publikasi/Outlook_Tebu_2023.pdf

Kuspratomo, A.D., Burhan, B., & Fakhry, M. (2012). Pengaruh varietas tebu, potongan, dan penundaan giling terhadap kualitas nira tebu. Agrointek, 6(2), 123-132.

Kusuma, H.S., Permatasari, D., Umar, W.K., & Sharma, S.K. (2024). Sugarcane bagasse as an environmentally friendly composite material to face the sustainable development era. Biomass Conversion and Biorefinery, 14, 26693–26706. https://doi.org/10.1007/s13399-023-03764-2

Kwenda, P.R. (2015). A review of the sugar milling process in South Africa and how it influences the length of the milling season. [Master’s Thesis], University of KwaZulu-Natal, Pietermaritzburg.

Maulana, A.R. (2018). Desain sistem pengendalian kecepatan motor DC pada rancang bangun mini konveyor berbasis fuzzy logic controller. Jurnal Teknik Elektro, 7(3), 225-233.

Notojoewono, A.W. (1984). Tebu Rakyat Intensifikasi dan Koperasi Unit Desa. BP3G, Pasuruan.

Nurmuslimah, S. (2020). Aplikasi metode fuzzy Mamdani untuk pemilihan tebu berkualitas pada produksi gula. Network Engineering Research Operation, 5(1), 5-14.

Pohan, L.A., & Aprilia, R. (2025). Forecasting passport application demand using the Chen average-based FTS method at the Medan immigration office. Desimal: Jurnal Matematika, 8(3), 473-480.

Purnat, H., Supriyono, S., Pratiwi, A.F., & Yusuf, M. (2020). Mekanisme soft starting pada pengaturan kecepatan motor BLDC menggunakan kendali logika fuzzy. E-JOINT, 1(1), 13-19. https://doi.org/10.35970/e-joint.v1i1.208

Rezk, H., & Faraji, H. (2024). Integrating fuzzy modelling and war strategy optimization for identifying optimal operating factors of direct ethanol fuel cell. Results in Engineering, 24, 102983. https://doi.org/10.1016/j.rineng.2024.102983

Ridho, M.A., Sulila, M.S., Hanafi, A., Nabila, P.R., & Safitri, R.L. (2016). Prototype alat pengukur rendemen gula menggunakan sensor ping dan sensor warna TCS3200. Transmisi: Jurnal Ilmiah Teknik Elektro, 18(2), 70–74.

Santoso, N.A., & Setiawati, W. (2023). Penerapan metode logika fuzzy dalam menentukan harga gabah pada petani. REMIK: Riset dan E-Jurnal Manajemen Informatika Komputer, 7(3), 1355–1366.

Srichaipanya, W., & Chuan-Udom, S. (2020). Optimization of milling performance of a sugar mill. E3S Web of Conferences, 187, 01001. https://doi.org/10.1051/e3sconf/202018701001

Sukamto, S. (2019). Pengendalian kecepatan motor induksi menggunakan kontroller logika fuzzy. JEECAE (Journal of Electrical Electronics Control and Automotive Engineering), 4(1), 245–252.

Winata, E.D., & Susanto, W.H. (2014). Pengaruh penambahan antiinversi dan suhu imbibisi terhadap tingkat kesegaran nira tebu. Jurnal Pangan dan Agroindustri, 3(1), 271–280. https://jpa.ub.ac.id/index.php/jpa/article/view/131

Yuliandari, P., Fauzi, A.M., Suprihatin, S., & Suparno, O. (2010). Kajian penerapan produksi bersih di stasiun gilingan pada proses produksi gula. JIEMS (Journal of Industrial Engineering and Management Systems), 3(1), 45-57.

Zadeh, L.A. (1965). Fuzzy sets. Information And Control, 8, 338-353.

Zulfahmi, A., Zoni, M., & Hidayat. (2021). Perancangan pengontrolan kecepatan motor DC pada elevator berbasis FLC (fuzzy logic control). Abstract of Undergraduate Research, Faculty of Industrial Technology, Bung Hatta University, 17(1).

Cover
Published
2026-06-29
How to Cite
Nursa, R. R., Fitriawan, H., & Sulistiyanti, S. R. (2026). Analysis of Mill Motor Speed on the Sugar Value in Bagasse Using the Fuzzy Logic Method at the Sugar Factory of PT. Pratama Nusantara Sakti. Jurnal Teknik Pertanian Lampung (Journal of Agricultural Engineering), 15(3), 1130–1142. https://doi.org/10.23960/jtepl.v15i3.1130-1142