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All Journal JURNAL ILMIAH PENDIDIKAN FISIKA AL BIRUNI Edufisika: Jurnal Pendidikan Fisika Science and Technology Indonesia Jurnal Ilmu Fisika Sainteks: Jurnal Sain dan Teknik JIPFRI (Jurnal Inovasi Pendidikan Fisika dan Riset Ilmiah) Dinamika Kerajinan dan Batik: Majalah Ilmiah
Putra, Valentinus Galih Vidia
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Arts Astronomy Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Decision Sciences, Operations Research & Management Earth & Planetary Sciences Education Electrical & Electronics Engineering Energy Engineering Environmental Science Industrial & Manufacturing Engineering Materials Science & Nanotechnology Physics Social Sciences

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Comparative RSM and ANN Modeling for Predicting Cotton Yarn Unevenness in Ring Spinning: Effect of Spacer Size and Cradle Pressure: Pemodelan RSM dan ANN Komparatif untuk Memprediksi Ketidakrataan Benang Katun pada Ring Spinning: Pengaruh Ukuran Spacer dan Tekanan Cradle Sahroni, Roni; Rival Firdaus, Muhammad; Putra, Valentinus Galih Vidia
Dinamika Kerajinan dan Batik: Majalah Ilmiah Vol. 43 No. 1 (2026): DINAMIKA KERAJINAN DAN BATIK : MAJALAH ILMIAH
Publisher : Balai Besar Standardisasi dan Pelayanan Jasa Industri Kerajinan dan Batik

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Abstract

This study developed and compared response surface methodology (RSM) and artificial neural network (ANN) models for predicting cotton yarn unevenness (U%) as a function of spacer size and cradle pressure in ring spinning. A 3×3 full-factorial experimental design was employed, yielding nine treatment combinations with 10 replicates each (90 samples total). Due to the limited dataset size (9 averaged data points), both models were fitted and assessed on the same data, with model performance interpreted accordingly. The RSM second-order polynomial model achieved R² = 0.98 (SSE = 0.0055), with sensitivity analysis identifying an optimal U% of 9.056% at a spacer size of 3.3 mm and cradle pressure of 1.53 N within the experimental domain [3.3–4.4 mm] × [1–2 N]. Notably, the unconstrained RSM optimum (x₁ = 2.638 mm) lies outside this domain, suggesting the experimental range may not fully bracket the true minimum. The ANN model, featuring a single hidden layer with four neurons trained via backpropagation over 10,000 epochs, achieved R² = 0.96 (MSE = 0.001942). Both models captured nonlinear input–output relationships, with RSM offering interpretable polynomial coefficients and ANN providing flexible data-driven mapping. A comparative analysis of predictive performance metrics (R², SSE, MSE) is provided, along with a discussion of model limitations related to dataset size and generalizability. These findings offer practical guidance for data-driven parameter optimization in ring spinning processes.
Co-Authors Bambang Soegijono Budhy Kurniawan Cahaya Rosyidan Dewi, Siska Astari Fadil Abdullah Ferry Budhi Susetyo Haryanto, Agung Hermansyah Hermansyah Ihsan Ardiansyah, Muhammad Iskandar, Syarif Kusrianto, Adi Maulani, Mustamina Mohamad, Juliany Ningsih mulyani, wiwiek eka Munandar, Taufik Nababan, Frederik Gresia Ngadiono, Ngadiono Ngadiyono Ngadiyono, Ngadiyono Purnomosari, Endah Rival Firdaus, Muhammad Sahroni, Roni Samura, Lisa Wijayono, Andrian Yusril Yusuf