Garuda - Garba Rujukan Digital

PERANCANGAN SIMULASI PERGERAKAN AUTOMATIC CEILING SUSPENSION UNTUK ALAT RONTGEN STASIONER Dewa Gde Ardha Putra; Irawadi Buyung; Sri Lestari
JURNAL TEKNOLOGI TECHNOSCIENTIA Technoscientia Vol 9 No 2 Februari 2017
Publisher : Lembaga Penelitian & Pengabdian Kepada Masyarakat (LPPM), IST AKPRIND Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34151/technoscientia.v9i2.132

Ceiling suspension stand is a method of x-ray tube placement on a stationary x- ray apparatus by hanging on the ceiling of the examination room and can be moved horizontally or vertically using a rail as a track. Simulating the movement of Automatic Ceiling Suspension for Stationary X-ray Equipment was made to produce an automatic movement of the ceiling suspension system used on stationary x-ray apparatus. Simula- tions created will facilitate x-ray equipment operator when setting the position of the x-ray tube during the examination. Simulations using a dc motor as the prime mover which is divided into horizontal and vertical movement. The movement of the motor is controlled by a microcontroller as a command center in the simulation. The movement generated by the simulation in the form of transfer of the parking position toward the examination table, the parking position to the vertical bucky, a vertical bucky toward the examining table po- sition, and vertical bucky towards the parking position. The test results of simulation crea- ted is 8 seconds to move from the parking position to the position of the examination ta- ble, 38 seconds to move from the parking position to the vertical bucky.

Implementasi Radio Frequency Identification (RFID) Untuk Kartu Pasien Berbasis Data Digital Evrita Lusiana Utari; Irawadi Buyung; Agus Qomaruddin Munir
Jurnal Teknologi Vol 15 No 1 (2022): Jurnal Teknologi
Publisher : Jurnal Teknologi, Fakultas Teknologi Industri, Institut Sains & Teknologi AKPRIND Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34151/jurtek.v15i1.3744

Today's technological developments have largely replaced conventional ones. One example is the RFID system. Patient Card is an identification in the form of patient data documents in conventional hospitals. Therefore, with this RFID system, it can contain a security control system both in terms of administration and information technology based on a database in the Hospital. The data is stored in the Patient Card based on digital data. Utilization of digital data-based patient cards to speed up patient services in the registration process during treatment, and reduce queue times when the data identification process is carried out by the patient registration department, as well as the efficiency of human resources. The process of designing tools by initializing the use of tool components. The data processing is carried out by Arduino Nano to read RFID and serial communication to communicate with Arduino UNO as a graphic LCD display. The Arduino Uno processor used detects the card. With the validation of the card, the patient can continue the examination process to the desired poly and exit in the form of a printed queue number according to the destination poly.

Conv-Tire: Tire Condition Assessment using Convolutional Neural Networks Latifah Listyalina; Irawadi Buyung; Agus Qomaruddin Munir; Ikhwan Mustiadi; Dhimas Arief Dharmawan
Telematika Vol 19, No 3 (2022): Edisi Oktober 2022
Publisher : Jurusan Teknik Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31315/telematika.v19i3.7697

Purpose: In this study, the authors designed an algorithm based on convolutional neural networks that can automatically assess tire quality.Design/methodology/approach: The proposed algorithm is built through several stages as follows. In the first stage, the tire images, which are the input of the designed algorithm, are acquired. Further, the acquired images are divided into two sets, namely training and testing sets. The training set contains tire images used in the training phase of several convolutional neural networks (CNN) architectures such as ResNet-50, MobileNetV2, Inception V3, and DenseNet-121. The training phase is carried out in a number of epochs, and at each epoch, the cross entropy loss function will be calculated which expresses the performance of the CNN architecture in classifying tire images. For this reason, the training stage requires a label or reference that shows the feasibility of the tires displayed in each image.Findings/result: In the testing phase, trained CNN architectures are used to classify tire images from the test set. Classification performance in the test set is also expressed in terms of cross-entropy loss function value. In addition, the accuracy value has also been calculated which shows the percentage of the number of tire images that are successfully classified correctly to the total number of tire images in the test set, namely the DenseNet-121 model has the best accuracy of 92.62%.Originality/value/state of the art: Given the high accuracy achieved by our algorithm, this work can be used as a reference by other researchers, specifically to benchmark their tire quality classification methods developed in the future.

Identifying Types of Waste as Efforts in Plastic Waste Management Based on Deep Learning Buyung, Irawadi; Munir, Agus Qomaruddin; Wijaya, Nurhadi; Listyalina, Latifah
Telematika Vol 20, No 3 (2023): Edisi Oktober 2023
Publisher : Jurusan Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31315/telematika.v20i3.10804

Purpose: This research aims at designing a computer algorithm for automatic waste sorting.Design/methodology/apprach: This research is quantitative and uses secondary data, specifically images of various types of waste. The images will be classified into organic and inorganic waste types with the assistance of a deep learning model. In this research, we propose the EfficientNet method for Waste Type Identification as an Effort in Plastic Waste Management. Experiments were conducted on a secondary dataset from Kaggle.com, which involved classifying various types of waste into 'Plastic' and 'Non-Plastic' categories, showing the effectiveness of the proposed method.Findings/result: The measurement is performed to compute the accuracy of the designed deep learning model in classifying waste images into the appropriate waste types. Based on the research results, our system achieved the highest accuracy of 97% during testing.Originality/value/state of the art: The designed method can perform fast and automatic waste sorting, which is useful in reducing the increasing amount of waste accumulating each year.

Penerapan Alat Penerangan dengan Memanfaatkan Teknologi Solar Cell di Taman Giwangan, Yogyakarta Listyalina, Latifah; Buyung, Irawadi; Munir, Agus Qomaruddin; Sabdullah, Mursid; Ratnaningsih, Wahyu
Jurnal ETAM Vol. 4 No. 1 (2024): FEBRUARY
Publisher : Politeknik Negeri Samarinda

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46964/etam.v4i1.613

Melaksanakan kegiatan pengabdian masyarakat bagi tenaga pengajar merupakan salah satu upaya untuk melaksanakan tugas sebagai pelaksanaan tridarma perguruan tinggi. Selain itu, dalam bidang teknologi, energi yang terbarukan mempunyai peran yang sangat penting dalam memenuhi kebutuhan energi. Salah satunya ialah energi listrik yang menjadi energi vital dalam kehidupan sehari-hari. Pada saat ini, penggunaan lampu didukung dengan tersedianya berbagai lampu hemat energi di mana terdapat sistem pada lampu tersebut akan mengkonversikan cahaya matahari menjadi tenaga listrik. Dari teknologi di atas, telah dilakukan kegiatan pengabdian di salah satu pemukiman warga di Giwangan, Yogyakarta, yaitu aplikasi lampu penerangan untuk jalan taman di Giwangan Yogyakarta berbasis solar panel. Tujuan pengabdian ini adalah memberikan bekal pengetahuan kepada masyarakat mengenai sistem lampu penerangan menggunakan tenaga surya. Pengabdian ini dilakukan dengan beberapa tahap seperti sosialisasi energi terbarukan dan teknologi penerangan jalan, konsultasi area pemasangan lampu, pemasangan lampu bertenaga surya, dan peninjauan kembali apakah telah bisa digunakan sesuai yang diharapkan. Kegiatan pengabdian aplikasi lampu solar sel telah selesai dilakukan sesuai metode yang dipaparkan

Rubber Leaf Image Classification Using Artificial Intelligence Methods as an Effort to Improve Plantation Production Results Buyung, Irawadi; Utari, Evrita Lusiana; Mustiadi, Ikhwan; Winardi, Sugeng; Ariyanto, Ipan; Listyalina, Latifah
Telematika Vol 21 No 3 (2024): Edisi Oktober 2024
Publisher : Jurusan Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31315/telematika.v21i2.13587

Purpose: Rubber is one of the plantation commodities that contributes positively to the trade surplus in the agricultural sector. Seeing the positive trend in global rubber consumption and production, demand is expected to continue increasing in the future. To enhance rubber productivity, rubber processing technology can be used to make it more efficient, thus increasing the amount of latex extracted from the sap and reducing waste materialDesign/methodology/approach: One technology that can be developed to increase the productivity efficiency of rubber plants is by using Artificial Intelligence. This technology is expected to be implemented in the rubber plantation sector, specifically in the automatic recognition of rubber leaves.Findings/result: The measurement and performance analysis of the rubber leaf image classification algorithm based on Artificial Intelligence has also been evaluated, showing near-perfect accuracy on training data (99.86%) and very good performance on validation data (97.43%), with a very low validation loss (0.0873), indicating that the model has learned well by the last epochOriginality/value/state of the art: The population in this study consists of image data from various tree leaves, including 10 types of rubber leaves and non-rubber leaves

Identifying Types of Waste as Efforts in Plastic Waste Management Based on Deep Learning Buyung, Irawadi; Munir, Agus Qomaruddin; Wijaya, Nurhadi; Listyalina, Latifah
Telematika Vol 20 No 3 (2023): Edisi Oktober 2023
Publisher : Jurusan Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31315/telematika.v20i3.10804

Purpose: This research aims at designing a computer algorithm for automatic waste sorting.Design/methodology/apprach: This research is quantitative and uses secondary data, specifically images of various types of waste. The images will be classified into organic and inorganic waste types with the assistance of a deep learning model. In this research, we propose the EfficientNet method for Waste Type Identification as an Effort in Plastic Waste Management. Experiments were conducted on a secondary dataset from Kaggle.com, which involved classifying various types of waste into 'Plastic' and 'Non-Plastic' categories, showing the effectiveness of the proposed method.Findings/result: The measurement is performed to compute the accuracy of the designed deep learning model in classifying waste images into the appropriate waste types. Based on the research results, our system achieved the highest accuracy of 97% during testing.Originality/value/state of the art: The designed method can perform fast and automatic waste sorting, which is useful in reducing the increasing amount of waste accumulating each year.

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