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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Teknotan: Jurnal Industri Teknologi Pertanian TELKOMNIKA (Telecommunication Computing Electronics and Control) Jurnal Mahasiswa TEUB Journal of Environmental Engineering and Sustainable Technology Jurnal Keteknikan Pertanian Tropis dan Biosistem Jurnal Teknologi Pertanian agriTECH Jurnal Ilmiah Rekayasa Pertanian dan Biosistem Industria: Jurnal Teknologi dan Manajemen Agroindustri Advances in Food Science, Sustainable Agriculture and Agroindustrial Engineering (AFSSAAE) Journal of Robotics and Control (JRC) Insyst : Journal of Intelligent System and Computation Jurnal Abdi Masyarakat Indonesia J-Icon : Jurnal Komputer dan Informatika
Dimas Firmanda Al Riza
University of Brawijaya
Aerospace Engineering Agriculture, Biological Sciences & Forestry Humanities Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Immunology & microbiology Industrial & Manufacturing Engineering Materials Science & Nanotechnology Mechanical Engineering Medicine & Pharmacology Public Health Other

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Teknologi Tepat Guna Pengolahan Sampah pada Kelompok Masyarakat Sekar Mayang Purwosekar Kabupaten Malang Dimas Firmanda Al Riza; Yusuf Hendrawan; Retno Damayanti; Hurriyatul Fitriyah
Jurnal Abdi Masyarakat Indonesia Vol 3 No 4 (2023): JAMSI - Juli 2023
Publisher : CV Firmos

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54082/jamsi.856

Abstract

Sampah merupakan salah satu permasalahan yang perlu dikelola, terutama jika terdapat berbagai usaha kecil menengah pada komunitas tersebut. Pengelolaan sampah di daerah Purwosekar, Kabupaten Malang, hanya mengangkut sampah dari rumah ke TPS dan diangkut ke TPA tanpa adanya pengolahan. Hal ini menyebabkan penumpukan sampah dan menganggu lingkungan sekitar. Kelompok masyarakat Sekar Mayang Desa Purwosekar, Kecamatan Tajinan, Kabupaten Malang berinisiatif untuk mengurangi masalah sampah dengan mengolah sampah organik menjadi kompos. Dalam penerapan komposting terdapat beberapa permasalahan pengetahuan dalam pembuatan kompos, belum tersedianya peralatan-peralatan tepat guna yang dapat membantu pra-pengolahan dan pemrosesan sampah dalam proses komposting, serta manajemen pemasaran produk kompos. Oleh karenanya dalam kegiatan Doktor Mengabdi ini kami akan memberikan solusi dengan penerapan teknologi tepat guna untuk membantu pengelolaan sampah dan proses komposting di daerah Purwosekar ini. Program Dokter Mengabdi dilakukan selama Bulan Juni hingga September 2021 melalui implementasi teknologi tepat guna, pelatihan pengolahan kompos, sosialisasi serta pendampingan pemasaran dalam proses pembuatan kompos kepada masyarakat. Kegiatan pengabdian masyarakat dalam menerapkan teknologi tepat guna ini memberikan manfaat dan mempermudah masyarakat Desa Purwosekar dalam pengolahan sampah organik untuk komposting.
Development of tomato maturity level prediction model based on portable visible spectrometer and machine learning Dimas Firmanda Al Riza; Nughi Arie Nugraha; Darmanto Darmanto
Advances in Food Science, Sustainable Agriculture and Agroindustrial Engineering (AFSSAAE) Vol 6, No 3 (2023)
Publisher : Advances in Food Science, Sustainable Agriculture and Agroindustrial Engineering (AFSSAAE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21776/ub.afssaae.2023.006.03.4

Abstract

A tomato is classified as a fruit, which level of maturity is determined by its color. Upon distribution, tomatoes require sorting based on their ripeness level. Generally making improvements done conventionally with the human eye. This method has the disadvantage that the results are subjective. One way that can be used to measure the ripeness level of tomatoes is using a spectroscopic sensor. Spectroscopic sensors can predict the level of ripeness and its contents automatically. This study uses machine learning to create a model to classify ripeness level and predict firmness, total dissolved solids (TDS), and total acid in tomatoes. This study used tomatoes with 3 categories of maturity. Tomatoes were tested non-destructively, namely measuring firmness, total dissolved solids content, and total acid. The data obtained were processed using the Partial Least Square Regression method to predict firmness, TDS, and total acid, while the maturity level used the Naive Bayes method. The data processing results to predict the level of maturity using Naive Bayes obtained a success rate of 100%. While for the predictions of firmness, TDS, and total acid had R2 training and R2 testing, namely 0.685 and 0.678, 0.534 and 0.521, and 0.352 and 0.349, respectively.
The Classification of Aflatoxin Contamination Level in Cocoa Beans using Fluorescence Imaging and Deep learning Sadimantara, Muhammad Syukri; Argo, Bambang Dwi; Sucipto, Sucipto; Al Riza, Dimas Firmanda; Hendrawan, Yusuf
Journal of Robotics and Control (JRC) Vol 5, No 1 (2024)
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jrc.v5i1.19081

Abstract

Aflatoxin contamination in cacao is a significant problem in terms of trade losses and health effects. This calls for the need for a non-invasive, precise, and effective detection strategy. This research contribution is to determine the best deep-learning model to classify the aflatoxin contamination level in cocoa beans based on fluorescence images and deep learning to improve performance in the classification. The process involved inoculating and incubating Aspergillus flavus (6mL/100g) to obtain aflatoxin-contaminated cocoa beans for 7 days during the incubation period. Liquid Mass Chromatography (LCMS) was used to quantify the aflatoxin in order to categorize the images into different levels including “free of aflatoxin”, “contaminated below the limit”, and “contaminated above the limit”.  300 images were acquired through a mini studio equipped with UV lamps.  The aflatoxin level was classified using several pre-trained CNN approaches which has high accuracy such as GoogLeNet, SqueezeNet, AlexNet, and ResNet50. The sensitivity analysis showed that the highest classification accuracy was found in the GoogLeNet model with optimizer: Adam and learning rate: 0.0001 by 96.42%. The model was tested using a testing dataset and obtain accuracy of 96% based on the confusion matrix. The findings indicate that combining CNN with fluorescence images improved the ability to classify the amount of aflatoxin contamination in cacao beans. This method has the potential to be more accurate and economical than the current approach, which could be adapted to reduce aflatoxin's negative effects on food safety and cacao trade losses.
Hitung Cepat Buah Jeruk Berbeda Kultivar pada Pohon berbasis Citra Smartphone dan Kecerdasan Buatan Al Riza, Dimas Firmanda; Maharsih, Inggit Kresna; Huda, Surya
Jurnal Ilmiah Rekayasa Pertanian dan Biosistem Vol 12 No 2 (2024): Jurnal Ilmiah Rekayasa Pertanian dan Biosistem
Publisher : Fakultas Teknologi Pangan & Agroindustri (Fatepa) Universitas Mataram dan Perhimpunan Teknik Pertanian (PERTETA)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/jrpb.v12i2.628

Abstract

Currently, predictions of orange fruit yield in an orchard are still done manually, namely by sampling manually to count the number of oranges on the tree. This method is not effective and the accuracy of predictions cannot be guaranteed. Automation in the process of counting citrus fruit on trees to predict yield can be done with computer vision using artificial intelligence models for object detection. One of the proposed model solutions that can be used for object detection is by using You Only Look Once (YOLO) architecture. However, the performance of the YOLO model for different varieties of orange trees in Indonesia is not yet known. Therefore, in this research, the development of the YOLOv5 model was carried out to quickly count orange fruit on trees of different varieties including the stages of image capture, image resizing, segmentation, model training with hyperparameters such as batch size and epoch, as well as model evaluation. In this study, the primary image dataset taken consisted of images of orange trees with two different cultivars, namely Pontianak Siamese oranges and Terigas Tangerines which have different characteristics. Then the YOLOv5 model is trained using labeled image data. The YOLOv5 model is trained with variations of hyperparameters and then the results are compared. The best model results in Siam Pontianak have a single label configuration in batch size 4 with parameters Mean Average Precision (mAP50), accuracy, precision, recall, and F1-score which produces a value of 0.88; 0.712; 0.853; 0.822; and 0.8372. Meanwhile, the best model results in Keprok Terigas have a single label configuration in batch size 10 with parameters Mean Average Precision (mAP50), accuracy, precision, recall, and F1-score which produces a value of 0.933; 0.75; 0.913; 0.878; and 0.8951.
Studi Miniatur Uv/Vis/Nir Spektrometer untuk Proses Kuantifikasi Mutu Biji Kopi dengan Protokol Cupping Test Iqbal, Zaqlul; Al Riza, Dimas Firmanda; Sutan, Sandra Malin; Nauri, Afid Rahman; Rhamadan, Ilham; Fausi, Ria Risti; Himawan, Harki
Teknotan: Jurnal Industri Teknologi Pertanian Vol 18, No 1 (2024): TEKNOTAN, April 2024
Publisher : Fakultas Teknologi Industri Pertanian

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24198/jt.vol18n1.1

Abstract

Penelitian ini menitikberatkan pada eksplorasi kemampuan spektroskopi UV/Vis/NIR untuk memprediksi parameter cupping test kualitas kopi sangrai. Sampel kopi Arabika disangrai pada suhu 198°C selama 6 menit (Light to Medium), 10 menit (Medium) dan 14 menit (Medium to Dark). Sebanyak 1 kg biji kopi disiapkan untuk tingkat waktu sangrai yang kemudian menghasilkan 20 kelompok sampel untuk menit ke-6 dan masing-masing 25 kelompok sampel pada menit ke-10 dan ke-14. Selanjutnya, dilakukan evaluasi cupping test pada kelompok sampel. Secara simultan pada kelompok sampel yang sama, dilakukan akuisisi data spektra menggunakan instrumen portable Vernier Go Direct SpectroVis Plus dan sensor MEMS (micro-electromechanical system) C12880MA. Dari hasil tersebut, menghasilkan 70 total data cupping test dan spektra yang kemudian digunakan sebagai input pembentukan model kalibrasi (prediksi). Partial Least Square Regression (PLSR) digunakan untuk membentuk model dengan Venetian blinds cross-validation 10-folds sebagai validasi internal. Hasil menunjukkan Vernier Go Direct SpectroVis Plus memiliki sensitifitas lebih baik dalam menangkap informasi yang ada pada biji kopi sangrai dan mampu memprediksi beberapa parameter cupping test yaitu Body (R2 C = 0.726, R2 CV = 0.613), Balance (R2 C = 0.738, R2 CV = 0.603) dan Overall (R2 C = 0.755, R2 CV = 0.628). Sedangkan untuk sensor MEMS C12880MA, nilai prediksi tertinggi didapat pada parameter Acidity dengan nilai R2 C dan R2 CV sebesar 0.546 dan 0.500. Berdasarkan nilai VIP Score, kontribusi terbesar dalam pembentukan model berada di rentang 760-780nm, 808-830 nm dan 843-873 nm untuk Vernier Go Direct SpectroVis Plus serta 565-637 nm dan 705-737 nm untuk MEMS C12880MA.
External Defects and Soil Deposits Identification on Potato Tubers using 2CCD Camera and Principal Component Images Al Riza, Dimas Firmanda; Suzuki, Tetsuhito; Ogawa, Yuichi; Kondo, Naoshi
Industria: Jurnal Teknologi dan Manajemen Agroindustri Vol 12, No 2 (2023)
Publisher : Department of Agro-industrial Technology, University of Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21776/ub.industria.2023.012.02.4

Abstract

AbstractPrecise recognition of potato external defects and the ability to identify defects and non-defect areas are in demand. Common scab represents a significant issue that requires detection, yet identifying the extent of common scab infection remains challenging when using a standard RGB camera. In this research, a 2CCD camera system that could obtain a set of RGB and near-infrared images, which could enhance defect detection, has been used. Image segmentation strategies based on a single principal component image and the principal component pseudo-colored image have been proposed to identify external potato defects while excluding soil deposits on the potato surface, often recognized as defects by the normal color machine vision system. Performance metrics calculation results show relatively good results, with segmentation true accuracy around 64% for both methods. Principal component pseudo-colored images were able to discriminate defects area and soil deposits in a single image. The methods presented in this paper could be used as the basis to develop further classification and grading algorithms.Keywords: image processing, multispectral, PCA, surface defects AbstrakPengenalan yang tepat terhadap cacat eksternal kentang dan kemampuan untuk mengidentifikasi area cacat dan non-cacat sangat dibutuhkan. Keropeng yang umum merupakan masalah signifikan yang memerlukan deteksi, namun mengidentifikasi tingkat infeksi keropeng yang umum tetap menjadi tantangan saat menggunakan kamera RGB standar. Penelitian ini menggunakan sistem kamera 2CCD yang dapat memperoleh serangkaian gambar RGB dan inframerah dekat yang dapat meningkatkan deteksi cacat. Strategi segmentasi gambar berdasarkan gambar komponen utama tunggal dan gambar berwarna semu komponen utama diusulkan untuk mengidentifikasi cacat eksternal kentang dengan mengecualikan endapan tanah pada permukaan kentang yang sering dikenali sebagai cacat oleh sistem penglihatan mesin warna normal. Hasil penghitungan metrik kinerja menunjukkan hasil yang relatif baik, dengan akurasi segmentasi sebenarnya sekitar 64% untuk kedua metode. Komponen utama gambar berwarna semu mampu membedakan area cacat dan endapan tanah dalam satu gambar. Metode yang disajikan dalam penelitian ini dapat digunakan sebagai dasar untuk mengembangkan algoritma klasifikasi dan penilaian lebih lanjut.Kata Kunci: cacat permukaan, multispektral, PCA, pengolahan citra 
Prediction of Physico-Chemical Characteristics in Batu Tangerine 55 Based on Reflectance-Fluorescence Computer Vision Ariani, Safitri Diah Ayu; Maharsih, Inggit Kresna; Al Riza, Dimas Firmanda
Intelligent System and Computation Vol 6 No 1 (2024): INSYST: Journal of Intelligent System and Computation
Publisher : Institut Sains dan Teknologi Terpadu Surabaya (d/h Sekolah Tinggi Teknik Surabaya)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52985/insyst.v6i1.363

Abstract

Oranges (Citrus sp.) are one of the most abundant agricultural commodities in Indonesia. One of the popular local citruses is Batu Tangerine 55. Harvesting tangerines begins 252 days after the flowers bloom. Conventionally, we still determine the level of maturity by observing the color, shape, and hardness. The results of manual grouping tend to be subjective and less accurate. Destructive testing could be carried out and provide objective results; however, it would require sampling and damaging the fruits. Computer vision could be used to evaluate the maturity level of the fruit non-destructively. Dual imaging computer vision, i.e., reflectance-fluorescence mode, could be used to enhance the accuracy of the prediction. This study aims to develop a classification model and predict the physico-chemical characteristics of Batu Tangerine 55. Destructive testing is still being carried out to determine the value of TPT, the degree of acidity, and the firmness of the fruit. Non-destructive testing was carried out to obtain reflectance and fluorescence images. Once we obtain the destructive and non-destructive data, we will incorporate them into the classification and prediction models. The machine learning method for maturity classification uses three models, namely KNN, SVM, and Random Forest. The best results on the reflectance data (RGB) SVM model resulted in an accuracy of 1 for training data and 0.97 for testing data. The maturity parameter prediction method uses the PLS method. The best results for the predicted Brix/Acidity ratio R2 parameter are 0.81 and RMSE 3.4.
Comparison of faster region-based convolutional network for algorithms for grape leaves classification Sarosa, Moechammad; Ma'rifah, Puteri Nurul; Kusumawardani, Mila; Al Riza, Dimas Firmanda
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 1: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i1.pp222-230

Abstract

The shapes of leaves distinguish the Indonesian grape variants. The grape leaves might look the same at first glance, but there are differences in leaf shapes and characteristics when observed closely. This research uses a deep learning method combined with the faster region-based convolutional neural network (R-CNN) algorithm with the Inception network architecture, ResNet V2, ResNet-152, ResNet-101, and ResNet-50, and uses COCO weights trained to classify five grape varieties through leaf images. The study collected 500 images to be used as an independent dataset. The results show that network improvements can effectively improve operating efficiency. There are also limitations to training scores because the F1 score value tends to stabilize or decrease at a certain point. In the Inception ResNet V2 architecture, with the highest average F1 score of 92%, the average computing time for training and testing is longer than other network architectures. This suggests that the algorithm can classify types of grapes based on their leaves.
Model Deteksi Mikroalga Spirulina platensis dan Chlorella vulgaris Berbasis Convolutional Neural Network YOLOv8 Ramadhani, Zahra Cahya; Al Riza, Dimas Firmanda
J-Icon : Jurnal Komputer dan Informatika Vol 12 No 2 (2024): Oktober 2024
Publisher : Universitas Nusa Cendana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35508/jicon.v12i2.15375

Abstract

Microalgae are unicellular microscopic organisms that live in various water. Microalgae such as Spirulina platensis and Chlorella vulgaris are grown due to their potential as bioenergy source. During cultivation, typically, hemocytometers are used to manually count the cells and that is time-consuming and prone to human error. This research aims to develop microalgae detection model based on microscopic images and Convolutional Neural Network using YOLOv8 architecture. The methodology includes sample preparation (dilution and optical density measurement), best density determination, image acquisition, annotation, creation of datasets, YOLOv8 model training, and model performance evaluation. Best density determines good microscopic images. Image acquisition was done using binocular microscope and acquired 560 images which were then annotated. The YOLOv8n, YOLOv8s, and YOLOv8m models were trained using default hyperparameters on Google Collaboratory to determine the augmentation effect on model accuracy. Model performance evaluation was done on selected YOLOv8 models. The results showed the augmentation (crop, brightness, blur) get the highest mAP train and test on YOLOv8m model, which are 0.945 and 0.913. The YOLOv8m model was retrained with various hyperparameters and it was found that the best configuration was SGD optimizer, epoch 50, and learning rate 0.01 with mAP train and test are 0.934 and 0.925. However, 29 epochs yielding a better model with accuracy of 0.8535, minimising overfitting and resource wastage. This research can facilitate the more efficient and automatic counting for microalgae-related research and industry.
PERANCANGAN SISTEM PEMANTAUANMIKROALGA BERBASIS CITRAMIKROSKOP DAN DEEP LEARNINGMENGGUNAKAN YOLOV8 INSTANCESEGMENTATION Wahyudi, Naufal Hilmiy Nizar; Nurussa’adah, n/a; Al Riza, Dimas Firmanda
Jurnal Mahasiswa TEUB Vol. 13 No. 3 (2025)
Publisher : Jurnal Mahasiswa TEUB

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Menurut data terbaru, tingkat kecukupankonsumsi protein masyarakat Indonesia masih dibawah 80%. Mikroalga Spirulina dan Chlorellaberpotensi sebagai alternatif sumber protein dengankandungan gizi tinggi dan kemudahan budidaya.Namun, kultivasi mikroalga memerlukan pemantauankonsentrasi yang akurat untuk pemanenan optimal.Metode manual seringkali lambat dan rentan humanerror, sementara teknologi otomatis terdahulu memilikiketerbatasan dalam deteksi jenis dan kepadatan.Penelitian ini merancang dan mengimplementasikansistem pemantauan mikroalga berbasis citramikroskop dan deep learning menggunakan YOLOv8instance segmentation secara portabel. Sistem yangdibangun mengintegrasikan hardware berupa sisteminstrumen akuisisi dan sistem backlighting sertaaplikasi Android untuk menampilkan hasil analisissistem deep learning. Hasil pengujian menunjukkanpeningkatan performa model seiring epoch, mencapaiprecision 0.793, recall 0.777, dan mAP 0.794 pada epochke-20. Sistem ini mampu beradaptasi dengan berbagaiintensitas cahaya eksternal berkat sistem backlightingyang dapat beroperasi selama 10 jam dengan sisa daya40%. Sistem aplikasi Android menyediakan UI/UXintuitif dengan fitur lengkap dan stabil sebagaiplatform pemantauan. Penelitian ini diharapkan dapatberkontribusi pada kultivasi mikroalga dan menjadidasar pengembangan sistem pemantauan mikroalga dimasa depan.Kata kunci: Mikroalga, Chlorella, Spirulina, Deep Learning,YOLOv8.
Co-Authors Agus Supriatna Somantri Angky Wahyu Putranto Anugerah Dany Priyanto Anung Nugroho Jayalaksono Ariani, Safitri Diah Ayu Arrahmadiana Estuwilujeng Bambang Dwi Argo Bambang Susilo Candika Pambayun Chatarina Umbul Wahyuni Darmanto Darmanto Dewi Maya Maharani Fausi, Ria Risti Ferina Tiara Safitri Fitriyah, Hurriyatul Hamzah, Ilham Fathurrahman Himawan, Harki Huda, Surya Izza, Ni'matul Joko Prasetyo Kondo, Naoshi Kusumawardani, Mila M Bagus Hermanto Ma'rifah, Puteri Nurul Maharsih, Inggit Kresna Makoto Kuramoto Mochamad Bagus Hermanto Moechammad Sarosa Muhammad Lutfi Almer Hasan, Muhammad Lutfi Almer Muharfiza Muharfiza Mutiara Nisa' Amri Naoshi Kondo Nauri, Afid Rahman Nazhif Ubaidillah Nie Sen Nughi Arie Nugraha Nurul Istiqomah Khoirunnisa Nurussa’adah, n/a Ogawa, Yuichi Ramadhani, Zahra Cahya Retno Damayanti Rhamadan, Ilham Sadimantara, Muhammad Syukri Sandra Sandra Sandra Sandra Simping Yuliatun, Simping Sucipto Sucipto Supriyanto Supriyanto Suzuki, Tetsuhito Syed Ihtsham Ul Haq Gilani Tetsuhito Suzuki Titon Elang Perkasa Wahyu Dwi Ristianingrum Wahyudi, Naufal Hilmiy Nizar Yasushi Kohno Yuichi Ogawa Yusuf Hendrawan Zaqlul Iqbal