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All Journal Indonesian Journal of Electronics and Instrumentation Systems Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) Jurnal Teknologi Khazanah Informatika: Jurnal Ilmu Komputer dan Informatika
Raden Sumiharto
Departemen Ilmu Komputer Dan Elektronika, FMIPA UGM, Yogyakarta
Computer Science & IT Electrical & Electronics Engineering Industrial & Manufacturing Engineering Mechanical Engineering

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Sistem Pemantauan Pertumbuhan Anggrek Berdasarkan Pengolahan Citra Digital Magnolia Gina Ro'fataka Satriorini; Raden Sumiharto; Roghib Muhammad Hujja
IJEIS (Indonesian Journal of Electronics and Instrumentation Systems) Vol 12, No 2 (2022): Oktober
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijeis.77893

Abstract

Growth monitoring and plant protectionis the major aspect of horticulture because its productivity depends on the health of the plants. Manual direct measurement methods tend to be destructive  towards  observed  plants. In  this  research,  a  smart  non-contact  growth  monitoring system was implemented on a chamber with orchid plants asthe objectsobserved. The images of  the  orchids  were  taken  and  became  the  input  of  the  system  to  be  processed  to  estimate  the height of the plants.The contour of the orchid plant as the object was obtained and the height was calculated based on the highest and the lowest contour.The result shows that the developed system is proven to be capable of measuring orchid’s height in real-time  with  accuracy  more than  95,7%.  Thus,  this  system will  effectively  help  farmers  to improve  the  quality  and  the quantity of the plant’s productivity.
Klasifikasi Suara Untuk Memonitori Hutan Berbasis Convolutional Neural Network Rizqi Fathin Fadhillah; Raden Sumiharto
IJEIS (Indonesian Journal of Electronics and Instrumentation Systems) Vol 13, No 1 (2023): April
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijeis.79536

Abstract

Forest has an important role on earth. The need to monitor forest from illegal activities and the types of animals in there is needed to keep the forest in good condition. However, the condition of the vast forest and limited resource make direct forest monitoring by officer (human) is limited. In this case, sound with digital signal processing can be used as a tool for forest monitoring. In this study, a system was implemented to classify sound on the Raspberry Pi 3B+ using mel-spectrogram. Sounds that classified are the sound of chainsaw, gunshot, and the sound of 8 species of bird. This study also compared pretrained VGG-16 and MobileNetV3 as feature extractor, and several classification methods, namely Random Forest, SVM, KNN, and MLP. To vary and increase the number of training data, we used several types of data augmentation, namely add noise, time stretch, time shift, and pitch shift. Based on the result of this study, it was found that the MobileNetV3-Small + MLP model with combined training data from time stretch and time shift augmentation provide the best performance to be implemented in this system, with an inference duration of 0.8 seconds; 93.96% accuracy; and 94.1% precision.
Contact Lens Detection Using Domain Specific BSIF and Discrete Wavelet Transform Muhamad Ilham Aji Vachroni; Raden Sumiharto; Dyah Aruming Tyas
Khazanah Informatika Vol. 9 No. 2 October 2023
Publisher : Department of Informatics, Universitas Muhammadiyah Surakarta, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23917/khif.v9i2.20084

Abstract

Iris is one of the reliable biometrics because it has a texture that rich properties and the texture is not changeable lifetime. Iris recognition has drawbacks in the matching process when using contact lenses. Contact lens can changes in the texture of the iris, which can reduce the accuracy of recognition. Therefore, a system is needed to detect contact lenses while someone is detected using contact lens, the system can reject the registration or authentication process. Methods used to detect contact lenses are Domain Specific Binarized Statistical Image Feature (BSIF) and Discrete Wavelet Transform (DWT) for feature extraction. Both methods are fused and modeled using the Support Vector Machine (SVM). Based on the test results, the most optimal kernel is 5x5 12bit. Using the kernel, the accuracy and f1 score obtained 99.1%. In the experiments conducted, this research applies Principal Component Analysis (PCA) to reduce features. However, the role of PCA does not affect the performance of the model. The best model tested with real life data, the Pocophone f1 smartphone and CCTV were used to take pictures of the eyes. The Result 6 experiments wich are 4 without contact lenses and 2 wearing contact lenses, there are only 2 detected correctly. This is because the ability of the images taken from the Poco F1 and CCTV have low resolution.
Implementasi Kontrol Nutrisi Dan pH Pada Hidroponik Cerdas Berbasis Arduino Dan JST Muhammad Naufal Zul Hazmi; Raden Sumiharto
IJEIS (Indonesian Journal of Electronics and Instrumentation Systems) Vol 13, No 2 (2023): October
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijeis.86268

Abstract

This research aims to implement an automated nutrition and pH control system in NFT hydroponic system based on ANN control. NFT hydroponics involves growing plants without soil as a medium. In hydroponics, it is essential to continuously control the nutrient levels and pH of the solution. However, manual control performed by humans continuously is inefficient and time-consuming.The ANN method is used to model and predict the output actuators based on sensor input in the NFT hydroponic system. This ANN architecture consists of several layers with the following number of neurons: input layer 2, first hidden layer 128, second hidden layer 64, and output layer 3, representing multipleoutputs. The ANN training process involves classifying the data samples using various hyperparameters.The research findings demonstrate the ANN classification model successfully applied to control pH and nutrient levels through the predicted output actuators. The pump actuators are activated according to input received from the TDS and pH sensors. Through the variation of hyperparameters, the classification model with a test_size: 0.3, epoch: 400, batch_size: 32, and random_state: 42 provided the best performance in prediction. This ANN classification model achieved the best results in model testing with an accuracy rate:  97.96% from 49 data.
Co-Authors Abdul Majid Abdul Rokhman As Syukur Adrianus Prima Manggala Agus Harjoko Agus Harjoko Ali Akbar Farghani Ardya Yunita Putri Bagaskara Ilham Abadi Bakhtiar Alldino Ardi Sumbodo Dien Rahmawati Dwindawan Holandrio Dwitiya Bagus Widyantara Dyah Aruming Tyas Dyah Aruming Tyas Farida Dewanti Fredy Aga Nugroho Hujja, Roghib Muhammad Ika Candradewi Intan Nur Fadliilah Khaula Nurul Hakim Kholid Mukhtar Lukman Awaludin Lutfi Ardiyanto Magnolia Gina Ro'fataka Satriorini Mahendra Budi Nugraha Muhamad Ilham Aji Vachroni Muhammad Miftahul Amri Muhammad Naufal Zul Hazmi Muhammad Shahid Ardi Phisca Aditya Rosyady Praja Sapta Ardiantara Rangga Kurniawan Ladioktaviagusdi Rif’Atunnisa Rif’Atunnisa Rindi Kusumawardani Rizqi Fathin Fadhillah Roghib Muhammad Hujja Rosyidatul Ilma Satrio Firmansyah Satrio Sani Sadewo Setyawan Bekti Wibowo Tri Kuntoro Priyambodo Unggul Wahyu Tri Purnomo Putro Zaki Hamizan