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All Journal Jurnal Simetris Bulletin of Electrical Engineering and Informatics SITEKIN: Jurnal Sains, Teknologi dan Industri Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Dinamisia: Jurnal Pengabdian Kepada Masyarakat Jurnal Sisfokom (Sistem Informasi dan Komputer) ILKOM Jurnal Ilmiah Informatik : Jurnal Ilmu Komputer Jurnal Telematika Jurnal Mantik SULUH: Jurnal Abdimas Computer Science and Information Technologies Jurnal Abdimas Bina Bangsa Jurnal Informatika: Jurnal Pengembangan IT ABDIKOM
Ridwan Raafi'udin
Universitas Pembangunan Nasional Veteran Jakarta
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Library & Information Science Mathematics Social Sciences Other

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Journal : Jurnal Simetris

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Analisis Nilai Sensor untuk Penilaian Kualitas Aroma Kopi Kolombia Hananto, Bayu; Raafi’udin, Ridwan; Widiyanto, Didit
Simetris: Jurnal Teknik Mesin, Elektro dan Ilmu Komputer Vol 16, No 1 (2025): JURNAL SIMETRIS VOLUME 16 NO 1 TAHUN 2025
Publisher : Fakultas Teknik Universitas Muria Kudus

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24176/simet.v16i1.13989

Abstract

Penilaian kualitas aroma kopi merupakan aspek krusial dalam industri kopi, terutama untuk produk-produk premium seperti kopi Kolombia. Studi ini bertujuan untuk analisis nilai sensor menggunakan electronic nose dalam mengkategorikan kualitas aroma kopi Kolombia berdasarkan tiga kategori: kualitas tinggi (HQ_Coffee), kualitas sedang (AQ_Coffee), dan kualitas rendah (LQ_Coffee). Metode yang digunakan melibatkan pengambilan data aroma kopi menggunakan electronic nose dengan berbagai jenis sensor, termasuk SP-12A, SP-31, TGS-813, TGS-842, SP-AQ3, TGS-823, ST-31, dan TGS-800, yang masing-masing menunjukkan karakteristik respons yang berbeda. Hasil studi menunjukkan bahwa sensor SP-31 memiliki sensitivitas tertinggi terhadap aroma kopi di semua kategori, menjadikannya sensor yang paling andal untuk deteksi kualitas aroma. Sensor TGS-842 menunjukkan fleksibilitas dengan rentang respons yang luas, sementara sensor SP-AQ3 memiliki sensitivitas terendah, yang mungkin membatasi efektivitasnya dalam mendeteksi variasi aroma yang kompleks. Kesimpulannya, penggunaan kombinasi sensor dalam electronic nose dapat menghasilkan penilaian kualitas aroma kopi yang lebih cepat, konsisten, dan objektif dibandingkan dengan metode manual.
Effects of Semi-Automated Preprocessing in The Beef Freshness Prediction based on Near Infrared Spectroscopy Raafi'udin, Ridwan; Purwanto, Yohanes Aris; Sitanggang, Imas Sukaesih; Astuti, Dewi Apri
Simetris: Jurnal Teknik Mesin, Elektro dan Ilmu Komputer Vol. 16 No. 2 (2025): JURNAL SIMETRIS VOLUME 16 NO 2 TAHUN 2025
Publisher : Fakultas Teknik Universitas Muria Kudus

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24176/simet.v16i2.15142

Abstract

This study investigates the application of near-infrared spectroscopy (NIR) within the wavelength range of 1350–2550 nm to predict key quality parameters of beef, specifically focusing on tenderloin cuts. The quality indicators assessed include drip loss, color, pH, moisture content, storage duration, and total plate count (TPC) as a measure of microbial load. Predictive modeling was conducted using three machine learning algorithms: Partial Least Squares (PLS), Support Vector Regression (SVR), and Random Forest Regressor (RFR). To enhance model accuracy, a semi-automated preprocessing pipeline was employed utilizing the Nippy library. This library integrates several spectral preprocessing techniques including Savitzky-Golay filtering, Standard Normal Variate (SNV), Robust Normal Variate (RNV), Local Standard Normal Variate (LSNV), as well as clipping, resampling, baseline correction, and smoothing.  Among the models developed using raw spectral data, the RFR model exhibited the highest performance, achieving coefficient of determination (R²) values of 0.82 for drip loss, 0.65 for color, 0.67 for pH, 0.61 for moisture content, 0.81 for storage duration, and 0.76 for TPC. Post preprocessing, the predictive accuracy improved significantly with R² values increasing to 0.89, 0.82, 0.87, 0.85, 0.91, and 0.90 respectively for the same parameters. These findings underscore the potential of combining advanced machine learning techniques with robust preprocessing methods to enhance the non-destructive, rapid assessment of beef quality parameters. This approach offers a promising tool for quality control in the meat processing industry, facilitating more efficient and accurate monitoring of product standards.
Co-Authors Ali Khumaidi ali khumaidi, ali Catur Nugrahaeni Puspita Dewi DEWI APRI ASTUTI Didit Widiyanto Edyana, Fajar Fajar Edyana Hananto, Bayu Henki Bayu Seta Imas Sukaesih Sitanggang Indra Permana Solihin Intan Hesti Indriana Jayanta Jayanta Mohamad Bayu Wibisono Muhammad Adrezo Musthofa Galih Pradana Neny Rosmawarni Nugrahaeni Puspita Dewi, Catur Nurul Afifah Arifuddin Prananto, Muhammad Teguh Ria Maria Theresa Tri Rahayu Triwahyono, Bambang Wirawan, Rio