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All Journal Jurnal Ilmiah Merpati (Menara Penelitian Akademika Teknologi Informasi) JURNAL SISTEM INFORMASI BISNIS Jupiter Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) KLIK (Kumpulan jurnaL Ilmu Komputer) (e-Journal) Jurnal ELTIKOM : Jurnal Teknik Elektro, Teknologi Informasi dan Komputer Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) JURNAL MEDIA INFORMATIKA BUDIDARMA Jurnal Komputasi Jiko (Jurnal Informatika dan komputer) Journal of Computer Science and Informatics Engineering (J-Cosine) JURIKOM (Jurnal Riset Komputer) Journal of Electronics, Electromedical Engineering, and Medical Informatics Jurnal Informatika dan Rekayasa Elektronik Madaniya JUSTIN (Jurnal Sistem dan Teknologi Informasi) Journal of Data Science and Software Engineering
Irwan Budiman
Universitas Lambung Mangkurat
Religion Aerospace Engineering Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science

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Journal : Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi)

 1 
Using Social Media Data to Monitor Natural Disaster: A Multi Dimension Convolutional Neural Network Approach with Word Embedding Mohammad Reza Faisal; Irwan Budiman; Friska Abadi; Muhammad Haekal; Mera Kartika Delimayanti; Dodon Turianto Nugrahadi
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 6 No 6 (2022): Desember 2022
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v6i6.4525

Abstract

Social media has a significant role in natural disaster management, namely as an early warning and monitoring when natural disasters occur. Artificial intelligence can maximize the use of natural disaster social media messages for natural disaster management. The artificial intelligence system will classify social media message texts into three categories: eyewitness, non-eyewitness and don't-know. Messages with the eyewitness category are essential because they can provide the time and location of natural disasters. A common problem in text classification research is that feature extraction techniques ignore word meanings, omit word order information and produce high-dimensional data. In this study, a feature extraction technique can maintain word order information and meaning by using three-word embedding techniques, namely word2vec, fastText, and Glove. The result is data with 1D, 2D, and 3D dimensions. This study also proposes a data formation technique with new features by combining data from all word embedding techniques. The classification model is made using three Convolutional Neural Network (CNN) techniques, namely 1D CNN, 2D CNN and 3D CNN. The best accuracy results in this study were in the case of earthquakes 78.33%, forest fires 81.97%, and floods 78.33%. The calculation of the average accuracy shows that the 2D and 3D v1 data formation techniques work better than other techniques. Other results show that the proposed technique produces better average accuracy.
A Cost-Effective Vital Sign Monitoring System Harnessing Smartwatch for Home Care Patients Dodon Turianto Nugrahadi; Rudy Herteno; Mohammad Reza Faisal; Nursyifa Azizah; Friska Abadi; Irwan Budiman; Muhammad Itqan Mazdadi
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 7 No 6 (2023): December 2023
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v7i6.5126

Abstract

Pap smear is a digital image generated from the recording of cervical cancer cell preparation. Images generated are susceptible to errors due to relatively small cell sizes and overlapping cell nuclei. Therefore, an accurate analysis of the Pap smear image is essential to obtain the right information. This research compares nucleus segmentation and detection using gray-level cooccurrence matrix (GLCM) features in two methods: Otsu and polynomial. The data tested consisted of 400 images sourced from RepoMedUNM, a publicly accessible repository containing 2,346 images. Both methods were compared and evaluated to obtain the most accurate characteristics. The research results showed that the average distance of the Otsu method was 6.6457, which was superior to the polynomial method with a value of 6.6215. Distance refers to the distance between the nucleus detected by the Otsu and the Polynomial method. Distance is an important measure to assess how closely the detection results align with the actual nucleus positions. It indicates that the polynomial method produces nucleus detections that are on average closer to the actual nucleus positions compared to the Otsu method. Consequently, this research can serve as a reference for future studies in developing new methods to enhance identification accuracy.
Co-Authors A.A. Ketut Agung Cahyawan W Abdul Gafur Achmad Zainudin Nur Ahmad Faris Asy'arie Ahmad Faris Asy’arie Ahmad Rusadi Arrahimi Ahmad Rusadi Arrahimi - Universitas Lambung Mangkurat) Ahmad Rusadi Arrahimi - Universitas Lambung Mangkurat) Ahmad Shofi Khairian Aji Triwerdaya Ajwa Helisa Akhmad Yusuf Andi Farmadi Andi Farmadi Andi Farmadi Andi Farmandi Antar Sofyan Aris Pratama Artesya Nanda Akhlakulkarimah Dendy Fadhel Adhipratama Dendy Dita Amara Dodon Turianto Nugrahadi Dwi Kartini Dwi Kartini Dwi Kartini Dwi Kartini Dwi Kartini, Dwi Faisal Murtadho Fatma Indriani Fatma Indriani Fitrinadi Friska Abadi Halimah Halimah Halimah Ichwan Dwi Nugraha Kevin Yudhaprawira Halim Lutfi Salisa Setiawati M Kevin Warendra Mera Kartika Delimayanti Muflih Ihza Rifatama Muhammad Adhitya Pratama Muhammad Darmadi Muhammad Haekal Muhammad Halim Muhammad Haris Qamaruzzaman Muhammad I Mazdadi Muhammad Iqbal Muhammad Irfan Saputra Muhammad Itqan Masdadi Muhammad Itqan Mazdadi Muhammad Latief Saputra Muhammad Mada Muhammad Nazar Gunawan Muhammad Reza Faisal, Muhammad Reza Muhammad Ridha Maulidi Muhammad Rizky Adriansyah Muhammad Rusli Muliadi Muliadi Muliadi - Muliadi Aziz Muliadi Muliadi Muliadi Muliadi Muliadi Muliadi muliadi muliadi Mutiara Ayu Banjarsari Nahdhatuzzahra Nahdhatuzzahra Nor Indrani Nursyifa Azizah Oni Soesanto Patrick Ringkuangan Radityo Adi Nugroho Rahman Hadi Rahman Rahmat Hidayat Rahmat Ramadhani Retma Ramadina Riana Riana Riza Susanto Banner Rizki Amelia Rudy Herteno Rudy Herteno Salsabila Anjani Sam'ani Sam'ani Saragih, Triando Hamonangan Septiadi Marwan Annahar Septyan Eka Prastya Setyo Wahyu Saputro Sofyan, Antar Sulastri Norindah Sari Sutami Sutan Takdir Alam Toni Prahasto Tri Mulyani Wahyu Caesarendra Wahyudi Wahyudi Yuli Christyono