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Journal : Building of Informatics, Technology and Science

Implementasi Transfer Learning Pada Algoritma Convolutional Neural Network untuk Mengklasifikasikan Image Objek Wisata Mira, Mira; Sembiring, Irwan; Purnomo, Hindriyanto Dwi
Building of Informatics, Technology and Science (BITS) Vol 4 No 1 (2022): June 2022
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (676.196 KB) | DOI: 10.47065/bits.v4i1.1764

Abstract

This study classifies the image of a tourist attraction with 9 labels sky, tree, mountain, water, street, temple, garden, stone and ricefield. The results of multi-label labeling can be used to see the frequency and recommendations of tourist attractions in Central Java, and build a transfer learning model to determine the accuracy value. Classification with multi-label images has its own complexity in the labeling process and few people use it. Testing and evaluating the model uses the equation of accuracy and f-1 score. Several previous researchers also stated that the higher the amount of training data and the number of epochs per step, the higher the accuracy produced. Based on the results of training and evaluation of the four training processes, that 210 data using bs 8, lr 1e-3 and epoch 50 showed an accuracy of 0.8598 with a loss of 0.3245, while 290 data with bs 16, lr 1e-3 and epoch 50 showed an accuracy of 0.8685. with a loss of 0.2903. Then 594 data with bs 32, lr 1e-3 and epoch 50 showed an accuracy of 0.8852 with a loss of 0.2756, and 1000 data with bs 46, lr 1e-3 and epoch 50 showed an accuracy of 0.8833 with a loss of 0.2863. This can answer the statement that the greater the number of datas, the higher the accuracy produced, so that the transfer learning model on the ResNet-50 architecture with multi-label image datas can be applied by showing accuracy results close to the accuracy value on ResNet-50 in the imagenet project. In addition, the contribution of this research is to provide recommendations for potential tourist objects in Central Java, namely tourism objects with the theme of nature, then tourism processed by human hands such as historical places, cultural heritage and family recreation areas.
Sistem Pengukuran pH, Suhu, dan Kelembaban Tanah Pada Tanaman Jagung Menggunakan Metode Proportional-Integral-Derivative Berbasis Internet of things Mira, Mira; Kusnanto, Kusnanto; Oscarito, Oscarito
Building of Informatics, Technology and Science (BITS) Vol 6 No 3 (2024): December 2024
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v6i3.5993

Abstract

West Kalimantan Province experienced a decrease in the harvested area and productivity of corn with an area of 16,371.14 in 2022 with a productivity of 43.81 and a production of 71,717.14 tons. While in 2023 with an area of 15,625.22 with a productivity of 43.54 and a production of 68,028.76 tons. The decline in corn production and the increasing consumption needs every year are challenges for the government and corn farming businesses to answer and meet the availability of corn. Regarding this problem, it is necessary to manage corn plantation land, in order to increase the value of corn production to meet consumption and animal feed needs in West Kalimantan. Measuring pH, temperature and soil moisture is very important to understand the right soil conditions for various types of plants. This study uses a DS18B20 sensor to measure soil temperature, capaltive soil moisture to measure air content in the soil, and a pH sensor to determine the acidity level. The ESP32 microcontroller is used as the main controller connected to the third sensor with IoT technology and the PID method as a control system. The PID constant value was determined using the Ziegler-Nichols method, with a Kp value of 4.31, Ki 0.85, and Kd 4.33 with a setpoint of ph 6.0, a temperature of 26°C and a humidity of 70%. The results showed that the pH value was in the range of 3.03-3.45, falling into the acidic category. Furthermore, the temperature value was at 24.69-28.66, falling into the medium to high category. While the humidity value was between 43-80, falling into the low to high category. The test results showed that the system was able to measure temperature, humidity, and soil pH in real-time with a good level of accuracy. This is supported by the accuracy value of the soil pH sensor of 60.6%, the temperature sensor of 98.65%, and the soil moisture sensor of 98.57%. The accuracy value is obtained from 100% minus the average error value for each measurement.
Co-Authors Adziem, Faidul Afwah, Nur Aisyah Viedar, Listya Akbar Akbar Akila , Nur ali, Rita Aliya, Kesya Shafa Amalia, Triana Amraini, Amraini Anaguna, Nursyam Anastasya, Resky Angguna Aprillia, Hanura Apsari, Rosvita Maulida Ardani, Cindy Ardiyanto, Rasyifa Adila Ariani, Mahrida Dwi Arman, Andi Asih, Finda Fiji Asih, Rinda Asmarani Nur Astuti, Wiwid Badollahi, Ismail Budi Setiawan Cahyaningtyas, Christian Charley, Charley Fadli, M. Wahyuniar Faidul Adzim, Faidul Fiji Asih, Finda Firman syah Fitriani Fitriani Gudiato, Candra Gustina, Indah Hakiki, Ismi Della Hanura Aprilia, Hanura Haria Saputri Heriani, Novia Heryadi, Yadi Hidayati, Dinda Gustri Hidayatullah, Kikin Hindriyanto Dwi Purnomo Indrayani , Syarthini Indrayani, Syarthini Indriana Indriana, Indriana Irwan Sembiring Ishak, Khardianti Alviani Ismail Badollahi Izma Daud, Izma Julianto Julianto Karsidi Karsidi, Karsidi Kristianto, Aloysius Hari Kunaenih, Kunaenih Kusnanto Kusnanto Mahmud Mahmud Marta, Eni Masniah Masniah Masrullah, Masrullah Mastoah, Imas Maulidan, Refly Zidni Maya Sari Mayasari, Nur Melani, Rena Melda, Melda Miftahul Jannah Mitrayati, Mitrayati Muchriana Muchran Muh. Idris Muh. Rusdi Muhaimin Muhaimin Muhammad Khaedar Sahib Muhammad Yamin Mulyadi, Nur Amalia Agus Mursalim, Nur Ainun Muryani Arsal Muttiarni, Muttiarni Nadiva, Nadiva Nanang Sobarna Ningsih, Suci Wahyu Nofriandy, M. Annas Noor Khalilati, Noor Norhalipah, Andi Nur Fadillah Safitri Nurcahyo, Azriel Christian Nurdin K, Nurdin Nurfadillah, Asti Nurlatipah, Yeti Nurlela Nurlela Nurlina Nurlina Nurmagfirah, Nurmagfirah Nursamsi, Nursamsi Nurtaqiya, Nurtaqiya NURUL AZIZAH Odjan, Ana Mariana Kalbu Oktavia, Nor Afni Oscarito, Oscarito P, Noviyanti P, Sudirman Pitri, Rischa Arselya Dwi Prasetya, Yusuf Dimas Prayusniar, Fadya Prisilia, Ni Kadek Neviska Purnamasari, A. Wirta Putri, Lidya Oktavia Putri, Lulu Nurrahmawati Putri, Shintia Novariani Putri, Trie Akra Rachmadaniyah, Rachmadaniyah Rahmadayoni, Nurul Rahmi Rahmi Ramadhan, Mohamad Angga Bahrul Rizki Nur Ramadhani, Novia Putri Ratih Rahmawati Reski, Seri Mulia Rita Rita, Rita Royani, Sumi Rustan, Fajar Eko Purnomo Rusyda, Nurazizah Ruwaida, Laiya Sabilah, Adina Sahriani, Sisi Salam Salam Salam, Salam Salama, Nuzula Elfa sarda, Sultan Sari, Clara Chintya Sari, Linda Sevitri, Shella Shafa Aliya, Kesya Silvester, Silvester Sinar Perbawani Abrina Anggraini Siregar, Pariang Sonang Sopiah, Sopiah Sopiah, Sopiah Sri Adila Nurainiwati Sri Maryati Sri Wahyuni SRI WARDANI Sulaeman Masnan, Sulaeman Susetiadi, Sigit Suwandewi, Alit Syafriafdi, Non Syahraeni, A.Tenri Syahriani, A. Tenri Syihabudin, Syihabudin Tahir, Ayani Dinasti Azira Takdir Takdir, Takdir Taufik Iskandar Tul Jannah, Shovia Viedar, Listya Aisyah Wa Ode Rayyani Wahyudi, Fiqi Wahyudi, Firma Windari, Sulis Wulan, Diah Retno Yayan M.A Nurbayan Yolanda, Feby Yuhansyah Yuhansyah Yuhansyah, Yuhansyah Yuliana Yuliana Zidni Maulidan, Refly