Articles
<![CDATA[Intelligent System for Fall Prediction Based on Accelerometer and Gyroscope of Fatal Injury in Geriatric ]]>
<![CDATA[Amiroh, Khodijah]]>;
<![CDATA[Rahmawati, Dewi]]>;
<![CDATA[Wicaksono, Ardian Yusuf]]>
<![CDATA[ JURNAL NASIONAL TEKNIK ELEKTRO Vol 10, No 3: November 2021 ]]>
Publisher : <![CDATA[Jurusan Teknik Elektro Universitas Andalas ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
Full PDF (373.135 KB)
|
DOI: 10.25077/jnte.v10n3.936.2021
<![CDATA[Methods of prevention and equipment to reduce the risk of falls based on accelerometer and gyroscope sensor have developed rapidly because its operations are cheaper than video cameras. Improved accuracy of detection and fall prediction based on accelerometer and gyroscope sensor is carried out by utilizing Artificial Intelligence (AI) to predict falling patterns. However, the existing fall prediction system is less responsive and also has a low level of accuracy, sensitivity and specificity. The current system does not have a notification system to care givers or doctors in the hospital. To overcome the above problems, this study proposes the development of smart fall prediction system based on accelerometer and gyroscope for the prevention of fractures in geriatric populations (JaPiGi) which are accurate and have high sensitivity and specificity. This study uses Fuzzy Mamdani to recognize movements falling forward, falling sideways, sitting, sleeping, squatting and praying. The total data tested was 100 data from 10 participants. The introduction of this movement is based on 6 input variables from data of accelerometer and gyroscope sensor. To calculate the accuracy, precision, sensitivity and specificity in this study using the equation Receiver Operating Characteristic (ROC). Motion recognition is carried out 3 times with an average accuracy of 90%.]]>
<![CDATA[PELATIHAN KETERAMPILAN WEB DESIGN BAGI SISWA SMK NEGERI 1 SURABAYA ]]>
<![CDATA[Titus Kristanto]]>;
<![CDATA[Dewi Rahmawati]]>;
<![CDATA[Arliyanti Nurdin]]>;
<![CDATA[Fidi Wincoko Putro]]>;
<![CDATA[Ardian Yusuf Wicaksono]]>;
<![CDATA[Mohammad Sholik]]>
<![CDATA[ Randang Tana - Jurnal Pengabdian Masyarakat Vol 2 No 2 (2019): Randang Tana - Jurnal Pengabdian Masyarakat ]]>
Publisher : <![CDATA[Unika Santu Paulus Ruteng ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
DOI: 10.36928/jrt.v2i2.393
<![CDATA[SMK Negeri 1 Surabaya merupakan sekolah kejuruan negeri favorit di Surabaya. Lokasi SMK Negeri 1 Surabaya berdekatan dengan RSI Ahmad Yani Surabaya. Kegiatan Pengabdian kepada Masyarakat (PkM) ini dilandasi pemikiran bahwa pada era milenial ini, SMK Negeri 1 Surabaya harus dapat bersaing dengan sekolah kejuruan lain, baik sekolah negeri maupun swasta, entah di kota Surabaya sendiri ataupun di kota-kota lain. Solusi yang ditawarkan dari Institut Teknologi Telkom Surabaya dalam mengikuti perkembangan zaman adalah pelatihan keterampilan web design yang ditujukan bagi para siswa SMK Negeri 1 Surabaya Jurusan Multimedia dan Jurusan Rekayasa Perangkat Lunak. Tujuan kegiatan pelatihan keterampilan ini adalah untuk meningkatkan kemampuan siswa SMK Negeri 1 Surabaya dalam membuat dan mengelola web secara mandiri. Pelatihan keterampilan web design ini melibatkan dosen dan mahasiswa dari program studi Rekayasa Perangkat Institut Teknologi Telkom Surabaya. Hasil kegiatan pelatihan keterampilan web design adalah para siswa mampu merancang dan mengelola web secara mandiri, mulai dari tingkat dasar sampai tingkat dinamis.]]>
<![CDATA[Intelligent System for Fall Prediction Based on Accelerometer and Gyroscope of Fatal Injury in Geriatric ]]>
<![CDATA[Khodijah Amiroh]]>;
<![CDATA[Dewi Rahmawati]]>;
<![CDATA[Ardian Yusuf Wicaksono]]>
<![CDATA[ JURNAL NASIONAL TEKNIK ELEKTRO Vol 10, No 3: November 2021 ]]>
Publisher : <![CDATA[Jurusan Teknik Elektro Universitas Andalas ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
Full PDF (373.135 KB)
|
DOI: 10.25077/jnte.v10n3.936.2021
<![CDATA[Methods of prevention and equipment to reduce the risk of falls based on accelerometer and gyroscope sensor have developed rapidly because its operations are cheaper than video cameras. Improved accuracy of detection and fall prediction based on accelerometer and gyroscope sensor is carried out by utilizing Artificial Intelligence (AI) to predict falling patterns. However, the existing fall prediction system is less responsive and also has a low level of accuracy, sensitivity and specificity. The current system does not have a notification system to care givers or doctors in the hospital. To overcome the above problems, this study proposes the development of smart fall prediction system based on accelerometer and gyroscope for the prevention of fractures in geriatric populations (JaPiGi) which are accurate and have high sensitivity and specificity. This study uses Fuzzy Mamdani to recognize movements falling forward, falling sideways, sitting, sleeping, squatting and praying. The total data tested was 100 data from 10 participants. The introduction of this movement is based on 6 input variables from data of accelerometer and gyroscope sensor. To calculate the accuracy, precision, sensitivity and specificity in this study using the equation Receiver Operating Characteristic (ROC). Motion recognition is carried out 3 times with an average accuracy of 90%.]]>
<![CDATA[Modified Convolutional Neural Network Architecture for Batik Motif Image Classification ]]>
<![CDATA[Ardian Yusuf Wicaksono]]>;
<![CDATA[Nanik Suciati]]>;
<![CDATA[Chastine Fatichah]]>;
<![CDATA[Keiichi Uchimura]]>;
<![CDATA[Gou Koutaki]]>
<![CDATA[ IPTEK Journal of Science Vol 2, No 2 (2017) ]]>
Publisher : <![CDATA[Lembaga Penelitian dan Pengabdian kepada Masyarakat ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
Full PDF (655.679 KB)
|
DOI: 10.12962/j23378530.v2i2.a2846
<![CDATA[Batik is one of the cultural heritages of Indonesia that have many different motifs in each region as well as in its usage. However, the Indonesians sometimes not knowing the batik motif that they’re wearing every day, and sometimes they have a batik image without knowing batik information contained in their batik image. With the growing number of images of batik and batik motifs, a classification method that can classify various motifs of batik is required to automatically detect the motif from the batik image. Image processing using the Deep Learning especially for image classification is widely used recently because it has good results. The most popular method in deep learning is Convolutional Neural Network (CNN) which has been proved robust in natural images. This study offers a batik motif image classification system using CNN method with new network architecture developed by combining GoogLeNet and Residual Networks named IncRes. IncRes merges the Inception Module with Residual Network structure. With the 70.84% accuracy, the system can be used to classify the batik image motif accurately.]]>
<![CDATA[Kernel Comparison on Support Vector Machine for Detecting Stairs Descent ]]>
<![CDATA[Ahmad Wali Satria Bahari Johan]]>;
<![CDATA[Ardian Yusuf Wicaksono]]>;
<![CDATA[Muhammad Dzulfikar Fauzi]]>;
<![CDATA[Rizky Fenaldo Maulana]]>;
<![CDATA[Kharisma Monika Dian Pertiwi]]>
<![CDATA[ CESS (Journal of Computer Engineering, System and Science) Vol 7, No 2 (2022): July 2022 ]]>
Publisher : <![CDATA[Universitas Negeri Medan ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
DOI: 10.24114/cess.v7i2.33477
<![CDATA[Terdapat 4 kernel yang dapat digunakan dalam klasifikasi Support Vector Machine dalam membuat hyperplane. Keempat kernel tersebut adalah linear, polynomial, gaussian dan sigmoid. Setiap kernel dapat menghasilkan akurasi yang berbeda-beda. Hal ini dikarenakan pengaruh sebaran data yang diklasifikasikan. Terdapat 2 kelas yang diklasifikasikan, yaitu lantai dan tangga turun. Dilakukan proses ekstraksi fitur tekstur terhadap citra lantai dan tangga turun menggunakan metode Gray Level Co-occurence Matrix. Terdapat 7 fitur dari GLCM yang dihasilkan pada proses ekstraksi fitur. Selanjutnya dilakukan klasifikasi menggunakan Support Vector Machine dengan mencoba setiap kernelnya. Dari hasil pengujian didapatkan kernel linear menghasilkan akurasi yang paling tinggi, yaitu 89%. Kernel sigmoid mendapatkan akurasi 84%. Kernel Gaussian mendapatkan akurasi sebesar 85%. Sedangkan kernel polynomial mendapatkan akurasi yang paling rendah yaitu 78%.]]>
<![CDATA[Rancang Bangun Aplikasi Pemeliharaan Alat Menggunakan QR-Code (Studi Kasus Telkom Property Surabaya Utara) ]]>
<![CDATA[Dhimas Bintang Bagaskara]]>;
<![CDATA[Bagus Kurniawan]]>;
<![CDATA[Mohammad Sholik]]>;
<![CDATA[Fidi Wincoko Putro]]>;
<![CDATA[Ardian Yusuf Wicaksono]]>;
<![CDATA[Titus Kristanto]]>;
<![CDATA[Amirah Diandra]]>
<![CDATA[ Journal of Computer System and Informatics (JoSYC) Vol 3 No 4 (2022): August 2022 ]]>
Publisher : <![CDATA[Forum Kerjasama Pendidikan Tinggi (FKPT) ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
DOI: 10.47065/josyc.v3i4.2153
<![CDATA[Telkom Property North Surabaya has a responsibility in terms of maintaining and maintaining equipment for all assets of the Telkom Indonesia company in the North Surabaya and Madura areas. Previously, tool maintenance records were written manually, but the Mei-V application has an impact because now tool maintenance recording can be done through an android application by scanning a QR-Code. The Mei-V application was created with the aim of providing a solution so that equipment maintenance records can be carried out through an application that is connected to a direct database. The application uses the Spiral Model method so that it can carry out continuous development in the form of adding functions or changes to suit existing needs. The result of implementing the Spiral Model that can be felt is the flexibility of application development because it can always be monitored and improved at any time. The Mei-V application can provide various useful information for maintenance activities, including detailed information on equipment maintenance, maintenance reports that can be downloaded by supervisors. The Mei-V application testing was carried out with two test methods. Functionality testing is carried out with details of 4 test scenarios and shows a 100% success percentage. While the Usage Test conducted on 5 respondents showed positive results with details of the average score scale of 3.8 out of 5 points.]]>
<![CDATA[Rancang Bangun Sistem Otentikasi Terpusat Berbasiskan Model Single Sign On di IT Telkom Surabaya ]]>
<![CDATA[Kharisma Monika Dian Pertiwi]]>;
<![CDATA[Rizky Fenaldo Maulana]]>;
<![CDATA[Ardian Yusuf Wicaksono]]>
<![CDATA[ Jurnal Ilmu Komputer Vol 15 No 2 (2022): Jurnal Ilmu Komputer ]]>
Publisher : <![CDATA[Informatics Department, Faculty of Mathematics and Natural Sciences, Udayana University ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
<![CDATA[IT Telkom Surabaya is one of the campuses located in Surabaya City, East Java. Currently IT Telkom Surabaya has thousands of students and hundreds of employees consisting of staff and lecturers. Along with the development of information technology, the IT Telkom Surabaya campus has many information systems that support its business processes. However, the information system has not used a single authentication system. So a user must remember the account used in each of these information systems. Therefore, in this study, researchers developed a centralized authentication system based on the Single Sign On model in the IT Telkom Surabaya campus. The purpose of this study is to integrate the authentication and authorization processes of several information systems in the IT Telkom Surabaya Campus. This Centralized Authentication System was built using SDLC (System Development Life Cycle) development with the waterfall method and was built based on web applications using HTML, CSS, Javascript, PHP, and using MySQL as a DBMS (Database management system). Testing on this system uses the black-box testing method which is used to test the functionality of the system by trying the functions and menus provided. The results of system functionality testing show that all functionality can run according to the flow.]]>
<![CDATA[PENINGKATAN KEMAMPUAN ONLINE STREAMING BAGI PENGURUS LANGGAR WAKAF AL QODIR ]]>
<![CDATA[Pangestu Widodo]]>;
<![CDATA[Ardian Yusuf Wicaksono]]>
<![CDATA[ BUDIMAS : JURNAL PENGABDIAN MASYARAKAT Vol 4, No 2 (2022): BUDIMAS : VOL. 04 NO. 02, 2022 ]]>
Publisher : <![CDATA[LPPM ITB AAS Indonesia Surakarta ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
DOI: 10.29040/budimas.v4i2.6699
<![CDATA[Langgar Wakaf Al Qodir adalah sebuah musholla di daerah Jemursari, Surabaya. Di musholla Al Qodir sering diselenggarakan pengajian, namun masih dalam bentuk offline. Hal ini menyebabkan kecepatan penyebaran informasi yang relatif lambat serta jangkauan penyebaran informasi yang terbatas. Untuk mengatasi masalah-masalah tersebut maka melalui kegiatan pengabdian kepada masyarakat ini diadakan pelatihan penyelenggaraan online streaming bagi pengurus Langgar Wakaf Al Qodir. Evaluasi kegiatan pengabdian dilakukan dengan pengamatan aktivitas mitra pengabdian pasca pelatihan. Berdasarkan pengamatan selama sekitar tujuh bulan pada channel Youtube yang dibuat oleh mitra pengabdian pasca pelatihan, maka dapat disimpulkan bahwa pengabdian ini efektif dan memberikan dampak yang cukup signifikan terhadap aktivitas mitra pengabdian.]]>
<![CDATA[Self-Health Examination Pavilion (APKM) for Health Consultation ]]>
<![CDATA[Johan, Ahmad Wali Satria Bahari]]>;
<![CDATA[Montolalu , Billy]]>;
<![CDATA[T. Rasmana , Susijanto]]>;
<![CDATA[Yusuf Wicaksono , Ardian]]>;
<![CDATA[Dzulfikar Fauzi , Muhammad]]>
<![CDATA[ Journal of Advances in Information and Industrial Technology Vol. 5 No. 2 (2023): Nov ]]>
Publisher : <![CDATA[LPPM Telkom University Surabaya ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
DOI: 10.52435/jaiit.v5i2.366
<![CDATA[The Self-Health Examination Platform (APKM) aims to help medical personnel reduce the usual initial examination procedures so that further examinations or consultations can be carried out immediately. The usual initial examinations that are carried out include measuring body weight, height, body temperature, blood pressure, oxygen levels in the blood, number of heart beats per minute, respiratory rate per minute, and an electrocardiogram to record heart activity. All measurements are carried out in one bridge and are equipped with an audio-visual guide. Measurement devices use equipment with a high level of precision and work automatically so that they can be used by ordinary people. However, the results of this examination need to be consulted with a doctor. In this research, it is proposed to develop APKM for telemedicine so that patients who have carried out independent examinations can consult directly with a doctor regarding the results of their examination.]]>
<![CDATA[Aplikasi Android untuk Rekomendasi Pemilihan Buah Anggur Hijau Menggunakan VGG16 ]]>
<![CDATA[Setyawan, Nathanael Ferdian Putra]]>;
<![CDATA[Nusyura, Fauzan]]>;
<![CDATA[Wicaksono, Ardian Yusuf]]>;
<![CDATA[Rahmanti, Farah Zakiyah]]>
<![CDATA[ Jurnal JTIK (Jurnal Teknologi Informasi dan Komunikasi) Vol 9 No 1 (2025): JANUARI-MARET 2025 ]]>
Publisher : <![CDATA[Lembaga Otonom Lembaga Informasi dan Riset Indonesia (KITA INFO dan RISET) ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
DOI: 10.35870/jtik.v9i1.3152
<![CDATA[This study focuses on developing an Android-based recommender system using convolutional neural networks (CNNs) to select high-quality grapes. The main objective of this study is to compare the performance of two popular CNN architectures, VGG16 and ResNet18, in classifying the quality of sour grapes. The subjective and time-consuming nature of conventional methods prompted us to search for a more efficient solution.The dataset used consists of 282 images of green grapes. The evaluation results show that the VGG16 model achieves 93% accuracy in classifying grape quality, outperforming the ResNet18 model with only 82% accuracy. These results indicate that the VGG16 architecture is more suitable for this classification task. The development of this system is expected to contribute to smart agricultural automation to improve efficiency and support the food industry.]]>
