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All Journal Teknotan: Jurnal Industri Teknologi Pertanian TELKOMNIKA (Telecommunication Computing Electronics and Control) Pelita Perkebunan Journal of Innovation and Applied Technology Indonesian Green Technology Journal Jurnal Keteknikan Pertanian Tropis dan Biosistem Jurnal Teknologi Pertanian Jurnal Edukasi dan Penelitian Informatika (JEPIN) agriTECH Indonesian Journal of Science and Technology Proceeding of the Electrical Engineering Computer Science and Informatics Jurnal Ilmiah Rekayasa Pertanian dan Biosistem Jurnal Teknik Pertanian Lampung (Journal of Agricultural Engineering) Jurnal Teknologi Pertanian Andalas Advances in Food Science, Sustainable Agriculture and Agroindustrial Engineering (AFSSAAE) Journal of Robotics and Control (JRC) Jurnal Mnemonic Indonesian Journal of Electrical Engineering and Computer Science International Journal of Artificial Intelligence and Robotics (IJAIR) El-Mujtama: Jurnal Pengabdian Masyarakat Jurnal Abdi Masyarakat Indonesia Jurnal Ilmiah Teknologi Pertanian Agrotechno
Yusuf Hendrawan
Universitas Brawijaya
Religion Aerospace Engineering Agriculture, Biological Sciences & Forestry Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Immunology & microbiology Industrial & Manufacturing Engineering Mechanical Engineering Public Health Social Sciences Other

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Journal : TELKOMNIKA (Telecommunication Computing Electronics and Control)

 1 
A fuzzy micro-climate controller for small indoor aeroponics systems Bambang Dwi Argo; Yusuf Hendrawan; Ubaidillah Ubaidillah
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 17, No 6: December 2019
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v17i6.12214

Abstract

The Indonesian agricultural sector faces challenges producing affordably priced food using sustainable practices. A soilless cultural practice, such as indoor aeroponics, is a compelling alternative to conventional agriculture. The objective of the present study was to develop a system for micro-climate management in a pilot-scale indoor aeroponics system. For this purpose, three fuzzy logic controllers were developed and evaluated to maintain plant chamber parameters (temperature, relative humidity, and light intensity) at desired set points controlled by embedded system controls designed using BASCOM-AVR software. The results showed that the fuzzy controllers provided excellent responses and experienced relatively low errors in all controlled parameters. All parameters changes followed the set point very smoothly and responded accordingly.The averaged percent of working times in which temperature, relative humidity, and light intensity were maintained within less than ±1°C, ±5%, and ±30 lux from the set points were found to be 88.43%, 95.91%, and 85.51%, respectively.
Computer vision for purity, phenol, and pH detection of Luwak Coffee Green Bean Yusuf Hendrawan; Shinta Widyaningtyas; Sucipto Sucipto
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 17, No 6: December 2019
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v17i6.12689

Abstract

Computer vision as a non-invasive bio-sensing method provided opportunity to detect purity, total phenol, and pH in Luwak coffee green bean. This study aimed to obtain the best Artificial Neural Network (ANN) model to detect the percentage of purity, total phenol, and pH on Luwak coffee green bean by using color features (red-green-blue, gray, hue-saturation-value, hue-saturation-lightness, L*a*b*), and Haralick textural features with color co-occurrence matrix including entropy, energy, contrast, homogeneity, sum mean, variance, correlation, maximum probability, inverse difference moment, and cluster tendency. The best ANN structure was (5 inputs; 30 nodes in hidden layer 1; 40 nodes in hidden layer 2; and 3 outputs) which had training mean square error (MSE) of 0.0085 and validation MSE of 0.0442.
Image Analysis using Color Co-occurrence Matrix Textural Features for Predicting Nitrogen Content in Spinach Yusuf Hendrawan; Indah Mustika Sakti; Yusuf Wibisono; Muchnuria Rachmawati; Sandra Malin Sutan
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 16, No 6: December 2018
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v16i6.10326

Abstract

This study aimed to determine the nitrogen content of spinach leaves by using computer imaging technology. The application of Color Co-occurrence Matrix (CCM) texture analysis was used to recognize the pattern of nitrogen content in spinach leaves. The texture analysis consisted of 40 CCM textural features constructed from RGB and grey colors. From the 40 textural features, the best features-subset was selected by using features selection method. Features selection method can increase the accuracy of image analysis using ANN model to predict nitrogen content of spinach leaves. The combination of ANN with Ant Colony Optimization resulted in the most optimal modelling with mean square error validation value of 0.0000083 and the R2 testing-set data = 0.99 by using 10 CCM textural features as the input of ANN. The computer vision method using ANN model which has been developed can be used as non-invasive sensing device to predict nitrogen content of spinach and for guiding farmers in the accurate application of their nitrogen fertilization strategies using low cost computer imaging technology.
Plant acoustic frequency technology control system to increase vegetative growth in red-lettuce Yusuf Hendrawan; Adamsyah Harika Putra; Sumardi Hadi Sumarlan; Gunomo Djoyowasito
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 18, No 4: August 2020
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v18i4.14158

Abstract

The application of plant acoustic frequency technology (PAFT) can spur plant growth, increase productivity, immune system, quality, and extend the shelf life of agricultural products after harvest. The application of sound waves in plants can stimulate the opening of the stomata to optimally absorb nutrients and water. This study aims to determine the effect of frequency and time of PAFT exposure by utilizing Javanese gamelan traditional music on the vegetative growth of red-lettuce (Lactuca sativa var.). Javanese gamelan music used was titled Puspawarna with variations in the frequency of 3-5, 7-9, and 11-13 kHz. The variation of exposure time of sound waves was 1, 2, and 3 hours. PAFT exposure was given routinely in the morning and evening. The results of this study indicated that the best treatment was at 3-5 kHz with an exposure time of 2 hours. This treatment gave a significantly better effect when compared to plants without PAFT. The best combination of frequency and time of PAFT exposure produced 10 leaves, plant height of 9.4 cm, wet weight of 4 g, dry weight of 0.22 g, leaf area of 27.19 cm2, leaf red mean of 63, and stomata opening width of 145.44~206.59 µm.
A rapid classification of wheat flour protein content using artificial neural network model based on bioelectrical properties Sucipto Sucipto; Maffudhotul Anna; Muhammad Arwani; Yusuf Hendrawan
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 17, No 2: April 2019
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v17i2.9450

Abstract

A conventional technique of protein analysis is laborious and costly. One rapid method used to estimate protein content is near infrared spectroscopy (NIRS), but the cost is relatively expensive. Therefore, it is necessary to find a cheaper alternative measurement such as measuring the bioelectrical properties. This preliminary study is a new rapid method for classified modeling of wheat flour protein content based on the bioelectrical properties. A backpropagation artificial neural network (ANN) was developed to classify the protein content of wheat flour. ANN input were bioelectrical properties, namely capacitance, and resistance and output was a type of the flour, namely hard, medium and soft flour. The result showed that the ANN model could classify the various type of flour. The best ANN model produces a mean square error (MSE) and regression correlation (R) of 0.0399 and 0.9774 respectively. This ANN model could classify the protein content of wheat flour based on the bioelectrical properties and have the potential to be used as a basic instrument to estimate the protein content.
Classification of water stress in cultured Sunagoke moss using deep learning Yusuf Hendrawan; Retno Damayanti; Dimas Firmanda Al Riza; Mochamad Bagus Hermanto
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 19, No 5: October 2021
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v19i5.20063

Abstract

Water stress greatly determines plant yield as it affects plant metabolism, photosynthesis rate, chlorophyll content index, number of leaves, physiological, biochemical compound, and vegetative growth. The research aimed to detect and classify water stress of cultured Sunagoke moss into several categories i.e. dry, semi-dry, wet, and soak by using a low-cost commercial visible light camera combined with a deep learning model. Cultured Sunagoke moss is a commercial product which has the potential use as rooftop-greening and wall-greening material. This research compared the performance of four convolutional neural network models, such as SqueezeNet, GoogLeNet, ResNet50, and AlexNet. The best convolutional neural network model according to the training and validation result was ResNet50 with RMSProp optimizer, 30 epoch, and 128 mini-batch size; this also gained an accuracy rate at 87.50%. However, the best result of the convolutional neural network model on data testing using confusion matrices on different data sample was ResNet50 with Adam optimizer, 30 epoch, 128 mini-batch size, and average testing accuracy of 94.15%. It can be concluded that based on the overall results, convolutional neural network model seems promising as a smart irrigation system that real-time, non-destructive, rapid, and precise method when controlling water stress of plants.
Co-Authors A.A. Ketut Agung Cahyawan W AA Sudharmawan, AA Adamsyah Harika Putra Adi Rahmanto Wibowo adiansyah, muhammad Agil Adham Reka Agus Somantri Ahmad Diyanal Arifin Ahmad Muhlisin Al-Riza, Dimas Firmanda Ali Djamhuri Alifah Maulidiyah Alvianto, Dikianur Anang Lastriyanto Anggraini, Firdha Dwi Anggraini, Sabrina Angky Wahyu Putranto Anisah Cahyani Anninatul Fuadah Anung Nugroho Jayalaksono Apriansyah, Reza Aprilia Nur Komariyah Ariefandie Febrianto, Noor Arif Hidayat Arif Wicaksono R.P Arifiandika, Rizal Aris Fanani Ary Musthofa Ahmad Ary Mustofa Ary Mustofa Ahmad Asy Syukri, Khoirul Anam Aulia Meidiati Aziz Fathur R Bagas Rohmatulloh Bambang Dwi Argo Bambang Dwi Argo BAMBANG SUSILO Bambang Susilo Cahyanto, Darmawan Dwi Chatarina Umbul Wahyuni Choirul Umam Christiana Wahyu Citra Puspita Rani Cory Vidiati Dalas Gumelar Dana Marsetya Utama Deivy Amaliya Tipdani Dendy Satyabima Dewi Maya Maharani Dian Aris Setiawan Dimas F Al Riza Dimas Firmanda Al Riza Dimas Firmanda Al Riza Dimas Firmanda Al Riza Dina Wahyu Indriani Dina Wahyu Indriani Djoko Wahyudi Dwi Setiawan Elok Zubaidah Elwin Elwin F Al Riza, Dimas Fatma Ridha Nurlaili Fauzy, Muchammad Fenti Siregar Fiqi Ibrahim Saqroth Firdaus Kurnia Putra Firdha Dwi Anggraini Fitriyah, Hurriyatul Gultom, David Parningotan Gunomo Djojowasito Gunomo Djoyowasito Gunomo Djoyowasito hardiansyah, bagus Hartono, Elvianto Dwi Hendrias Hendrias Hendy Firmanto Herdiansyah, Deni Hilmi, Miftahul Hismarto Bahua Imam Santoso Indah Mustika Sakti Indah Royani Izza, Sylvia Ni’matul Joko Prasetyo Khoirul Anam Asy Syukri Kreative Y.R, Rizka Kristianingsih, Wahyu Kusuma Faisal M La Choviya Hawa Liana, Verianti Litapuspita Rizka Perdana Madaniyyah Mustika Islami, Madaniyyah Mustika Maffudhotul Anna Malin Sutan, Sandra Mardhotillah Mardhotillah Meilani Eka Marantika Merisa Yunita Miftahudin Nawawi Miftahul Hilmi Misnawi Jati Mochamad Bagus Hermanto Moh. Risal Siregar Muchammad Fauzy Muchammad Zakaria, Muchammad Muchnuria Rachmawati Muhamad Amar Nadhif muhamad nur afidin Muhammad Arwani Muhammad Fadhil Muhammad Husain Kamaluddin Muhammad Iqbal Musthofa Lutfi Musthofa Lutfi Mustofa, Ary Mutiara Nisa' Amri Nabila Az-Zalikhah Ilham Nafi’ah, Riris Waladatun Niken Dieni Pramesi Niken Lila Widyawati Nugroho, Hermawan Nur Ida Winni Yosika Nurkholis Hamidi Oktaria Eka Y Omah Rochmah Pratama, Kanda Bagus Puguh Sudarsono Putra, Reza Rienaldy Rachmawati, Muchnuria Rahmatullah, Bagus Retno Damayanti Retno Damayanti Soejoedono Reza Rienaldy Putra Riana, Eki Rico Santoso Rini Yulianingsih Rini Yulianingsih Rochima Nisaa’IL-Firdaus Rohmatulloh, Bagas Ronald Nelson Krakuko Ryan Maulana Abdul Hakim Sadimantara, Muhammad Syukri Saiful Imron Sandra Malin Sutan Sandra Sandra Sandra Sandra Sandra Sandra Shinta Rosalia Dewi Shinta Widaningtyas Shinta Widyaningtyas Simping Yuliatun Siti Mariyah Ulfa Siti Nurhayati Somantri, Agus Sucipto, Sucipto Sukses Agustin Nahmudiyah Sumardi H. S. Sumardi Hadi Sumarlan Sumardi Hadi Sumarlan Sumardi Hadi Sumarlan Sumardi Hadi Sumarlan Supriyanto Supriyanto Supriyanto, Supriyanto Syahriyah, Yayah Titon Elang Perkasa Tunjung Mahatmanto Ubaidillah Ubaidillah Vita Noeravila Putri Wachid Rahmanjaya Wahyu Dhiki Saputro Wahyunanto Agung Nugroho Widaningtyas, Shinta Widyaningtyas, Shinta Wignyanto Wignyanto Wike A. P. Dania Yosua Yosua Yudha Firdaus Baharsyah Yuliatun, Simping Yuni Oktopiyani Yusron Sugiarto Yusuf Wibisono Yuyun Wahyuni Zahrok, Isna Arofatuz