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All Journal Teknotan: Jurnal Industri Teknologi Pertanian Agrointek Jurnal Teknologi Pertanian Journal of Engineering and Technological Sciences agriTECH Jurnal Agroindustri Proceeding of the Electrical Engineering Computer Science and Informatics Industria: Jurnal Teknologi dan Manajemen Agroindustri Jurnal Teknologi & Industri Hasil Pertanian Agroindustrial Journal Teknologi dan Industri Pertanian Indonesia Jakadimas
Nafis Khuriyati
Departemen Teknologi Industri Pertanian, Fakultas Teknologi Pertanian, Universitas Gadjah Mada, Jl. Flora No 1, Yogyakarta 55281
Agriculture, Biological Sciences & Forestry Arts Humanities Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Industrial & Manufacturing Engineering Materials Science & Nanotechnology Medicine & Pharmacology Social Sciences Other

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Journal : Agrointek

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Prediksi fisikokimia melon (Cucumis melo I.) secara non-destruktif dengan impuls akustik dan jaringan saraf tiruan Avicenna Nur Kasih; Nafis Khuriyati; affan fajar falah
AGROINTEK Vol 18, No 3 (2024)
Publisher : Agroindustrial Technology, University of Trunojoyo Madura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21107/agrointek.v18i3.21746

Abstract

Melon (Cucumis melo L.) is one of the favorite fruits in Indonesia. The relatively short harvest period and high price of melons make melons a superior business commodity. Quality testing of melon fruit is still widely done by relying on destructive testing, which damages the fruit. Therefore, a non-destructive testing approach is needed to predict the parameter values of the physicochemical properties of melon fruit non-destructively with acoustic impulse technology. This study aims to develop a model to predict the physicochemical properties of melon based on parameters of acoustic properties. A total of 120 Honey Globe melons (Cucumis melo var. inodorus) cultivated in the Greenhouse FRC UGM were used as samples. Each fruit was measured non-destructively using knocking tools to generate data on dominant frequency, magnitude, zero-moment power (Mo), and short-term energy (STE). Destructive testing was subsequently conducted to measure moisture content, total soluble solids, and hardness. The destructive and non-destructive test data obtained were processed using an artificial neural network (ANN) to build a prediction model. The training algorithm used was Backpropagation. The results of the ANN training showed the best network structure was 4-4-1. The best learning rates used are 0.1 and 1. Analysis of the reliability of predictions using artificial neural networks carried out based on the calculation of R2 and Mean Squared Error (MSE) values shows that the prediction model consisting of model I, model II, and model III can fulfill the predictions made with high R2 test values, which are sequentially 0.98875; 0.96716; and 0.9215; and MSE values that are relatively small, which are sequentially 0.0016; 0.5296; and 0.2002.
Klasifikasi asal geografis kopi bubuk liberika tungkal Jambi dan Probolinggo menggunakan near-infrared spectroscopy (NIRS) dan principal component analysis (PCA) Ikhsan Nurdziky; Anggoro Cahyo Sukartiko; Nafis Khuriyati
AGROINTEK Vol 19, No 2 (2025)
Publisher : Agroindustrial Technology, University of Trunojoyo Madura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21107/agrointek.v19i2.25131

Abstract

Regional origin classification of Geographical Indication (GI) products is necessary for their authentication purpose. Among others, coffee is Indonesia's largest GI group, with Liberica being the least studied. This study aims to classify the geographical origin of dry- and honey-processed Liberica Tungkal Jambi ground coffee, which has been certified and dry-processed Liberica Probolinggo, which has the potential to be certified in the future. A total of 12 samples were tested using Near-Infrared Spectroscopy (NIRS). Spectrum data were analyzed using Principal Component Analysis (PCA) with various preprocessing spectral data, including Multiplicative Scatter Correction (MSC), Baseline, and Detrending. The results showed the ability of the combination of NIRS and PCA to classify three groups of coffee samples, namely: dry-processes Liberica Tungkal Jambi ground coffee, honey-processed Liberica Tungkal Jambi ground coffee, and dry-processed Liberica Probolinggo ground coffee, with the success rate ranged between 88 to 99%. Detrending was the most effective preprocessing for visualizing the classification. The result illustrates the great potential of near-infrared (NIR) spectroscopy in classifying the geographical origin of Liberica ground coffee.
Co-Authors Adi Djoko Guritno, Adi Djoko Aditya Nugroho affan fajar falah Anggoro Cahyo Sukartiko Ardhianata Triasmoko Ariesta Cintiadewi Arsyad Sumantika Atris Suyantohadi Atris Suyantohadi Avicenna Nur Kasih Damayanti, Indriani Daryanti, Reishi Ari Didik Purwadi Didik Purwadi Falah, Muhammad Affan Fajar Fibriato, Muh Bayu Guntarti Tatik Mulyati GUSMON SIDIK Haruhiko Murase Ibnu Wahid Fakhrudin Aziz Ikhsan Nurdziky Jumeri (Jumeri) Kuncoro Harto Widodo Makhmudun Ainuri Mirwan Ushada Mohammad Affan Fajar Falah Munawaroh, Solehatun Murase, Haruhiko Muslikah Desti Kurniasari Niha, Aevita Ainun Nugroho, Darmawan Ari Okayama, Tsuyoshi Persia Manggala Pradita, Sintia Putri Putri, Yolanda Reptami Putro, Nur Achmad Sulistyo Rizqi Wahyudi Rudiati Evi Masithoh Saroyo, Pujo Tiara Tannesha Hartono Tsuyoshi Okayama Viona Zulfia Wagiman Wagiman Wagiman, Wagiman Wahyu Supartono Wahyuni, Cici Yohanita Maulina Akbar Yustina, Ita