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All Journal Jurnal Teknik ITS IPTEK The Journal of Engineering IPTEK Journal of Proceedings Series Sewagati
Detak Yan Pratama
Departemen Teknik Fisika, Institut Teknologi Sepuluh Nopember, Kampus ITS, Keputih, Sukolilo, Surabaya 60111
Arts Humanities Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Education Electrical & Electronics Engineering Engineering Languange, Linguistic, Communication & Media Mechanical Engineering Public Health Social Sciences Other

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DESIGN OF SOLAR TRACKER SYSTEM USING PHOTOTRANSISTOR Ahmad Fauzan Adziimaa; Detak Yan Pratama; Nabila Widad Dira Sinatrya; Dewi Sugihartati; Wahyu Nur Handayani
IPTEK The Journal of Engineering Vol. 7 No. 1 (2021)
Publisher : Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j23378557.v7i1.a7247

Abstract

Intensity of the sun be an important parameter to determine the potential energy in any region. Nowdays, measurement of solar intensity as a panel solar tracker use many variety of sensors. Phototransistor is one of the light sensors that has a greater light sensitivity compared to other light sensors. The design of the system uses four phototransistor which are placed each of two sensors in the east and west of the panel. Accuracy testing calculated by comparing the measurement results on sensor circuit with Lux Meter as validator. The results have shown that the average of phototransistor error is 1.24% and 1.27% on the east side phototransistor and 0.47% and 0.93% on the west side phototransistor.
Rice Identification Using Convolutional Neural Network with YOLOv7 algorithm and VGG16 Assad Resi Alfurqan; Detak Yan Pratama; Andi Rahmadiansah; Dyah Sawitri; Lizda Johar Mawarani
IPTEK The Journal of Engineering Vol. 10 No. 3 (2024)
Publisher : Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j23378557.v10i3.a20143

Abstract

Rice is the most widely consumed food worldwide. The many types of rice cause various difficulties in the process of classifying rice varieties. The process of manually classifying rice varieties that rely on human power has drawbacks including the subjectivity of assessment between observers, limited physical capabilities, and longer observation times. In this research a rice variety classification system has been developed using the Convolutional Neural Network with the YOLOv7 and VGG16 algorithms. The rice varieties classified are basmati, IR64, and rojolele varieties. The model with the YOLOv7 algorithm is trained for object segmentation of rice grains and is used to create rice grain image datasets. The model with the VGG16 algorithm was trained by transfer learning and used for classifying rice grain varieties. The model with a learning rate hyperparameter of 0,000061, the ReLU activation function, the number of neurons 256 in the second classification layer, with the fine-tuning training method, has the best performance with an accuracy value of 100%. The best VGG16 model weight is used in application implementation. Identification of the type of rice with the application can be done on the image of a batch of homogeneous and heterogeneous rice grains with various arrangements.
Co-Authors Achmadi Achmadi Agus M Hatta Agus Muhammad Hatta Ahmad Fauzan Adziimaa Andi Rahmadiansah Andi Rahmadiansyah Andri Haris Setyawan Apriani Kusumawardhani Assad Resi Alfurqan Aulia M.T. Nasution Aulia Muhammad Taufiq Nasution Aulia Siti Aisjah Bambang Lelono Widjiantoro Bangkit Dwijo Saputro Dewi Sugihartati Dewi Sugihartati Doty Dewi Risanti Dyah Sawitri Endah Laksmi Nugraha Gunawan Nugroho Hatta, Agus Muhamad Hery Widijanto Ika Puspita Iwan Cony Setiadi, Iwan Cony Katherin Indriawati Lizda Johar Mawarani Luthfansyah Mohammad M Kamalul Wafi Muhammad Khamim Asyari Nabila Widad Dira Sinatrya Nabila Widad Dira Sinatrya Ontoseno Penangsang Purwadi Agus Darwito Ridho Hantoro Rizky Nanda Puspitasari Ruri Agung Wahyuono Sekartedjo Sekartedjo Septyaningrum, Erna Suyanto, Suyanto Syamsul Arifin Wahyu Nur Handayani Wahyu Nur Handayani Zulkifli Zulkifli Zulkifli Zulkifli