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All Journal MEKTEK Foristek Techno.Com: Jurnal Teknologi Informasi MEKTRIK CESS (Journal of Computer Engineering, System and Science) JOURNAL OF APPLIED INFORMATICS AND COMPUTING Technomedia Journal Jurnal Sistem Cerdas JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) CCIT (Creative Communication and Innovative Technology) Journal ScientiCO : Computer Science and Informatics Journal ILKOMNIKA: Journal of Computer Science and Applied Informatics IAIC Transactions on Sustainable Digital Innovation (ITSDI) SATIN - Sains dan Teknologi Informasi The Indonesian Journal of Computer Science
Yusuf Anshori
Fakultas Teknik, Universitas Tadulako
Automotive Engineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering Languange, Linguistic, Communication & Media Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering

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Journal : Technomedia Journal

 1 
Serli Discovery Learning Dalam Mendukung Pembelajaran Ilmu Pengetahuan Alam Siswa Berbasis Android: Serli Discovery Learning in Supporting Android-Based Natural Science Learning for Students Putri Febrina, Annisa; Ngemba, Hajra Rasmita; Hendra, Syaiful; Anshori, Yusuf; Azizah, Azizah
Technomedia Journal Vol 9 No 1 Juni (2024): TMJ (Technomedia Journal)
Publisher : Pandawan Incorporation, Alphabet Incubator Universitas Raharja

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33050/tmj.v9i1.2219

Abstract

The development of information technology, have a positive impact on education by providing flexible learning support. The design and presentation of the learning tool significantly affect students' interest in learning. In the current technological era, students and teachers must consider the accessibility and mobility of learning media. The purpose of this research is to develop the SERLI Application, an android-based Natural Science learning module that can be accessed anywhere by students and teachers. The application development model used is the Hannafin & Peck Model, which consists of  needs analysis,  design  stages, and  implementation & development. The method for evaluating user satisfaction uses the End User Computing Satisfaction method. This method considers variables such as content, accuracy, display format, ease of use, and timeliness. The experts tested the SERLI Application, gave a 86.58% success rate. Student users and teachers also evaluated the application and achieved a 85% success rate. These results are very good. The implementation result of this research is an android-based learning application product that contains a science lesson module. In this application, there is an initial display, namely registration into the application, then the main display of the application which contains modules and practice questions, then the display for the teacher.
Implementasi Data Mining untuk Prediksi Status Proses Persalinan pada Ibu Hamil Menggunakan Algoritma Naive Bayes Pusadan, Mohammad Yazdi; Ghifari, Ari; Anshori, Yusuf
Technomedia Journal Vol 8 No 1 Juni (2023): TMJ (Technomedia Journal)
Publisher : Pandawan Incorporation, Alphabet Incubator Universitas Raharja

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33050/tmj.v8i1.1980

Abstract

Childbirth is the process of taking out the fetus after 20 weeks of gestation or more to be able to live outside the uterus through the birth canal or another way, with or without assistance. Maternal Mortality Rate in Indonesia is still quite high based on the White Book of National Health System Reform in March 2022, at 305 for every 100.000 births. Causes of the high Maternal Mortality Rate is the risky of childbirth process for the mother and the baby. Clinical prediction is growing by adopting computer sience and information technology in data processing, accompanied by data mining methods for processing. The problem of pregnant mother can be anticipated by using the system for predicting the status of the childbirth process with the implementation of data mining and Naïve Bayes algorithm, with the purpose for helping to reduce Maternal Mortality Rate, especially caused by risky childbirth process. This study using 600 training data, then tested using the Confusion Matrix method on 100 testing data. Obtained Precision value was 82.4%, Recall value was 94%, F-Measure value was 88.7 and Accuracy value was 92%.
Co-Authors A. Y. Erwin Dodu A. Y. Erwin Dodu A.Y. Erwin Dodu A.Y. Erwin Dodu Amriana Amriana Andi Hendra Anita Ahmad Kasim Arief Pratomo Azizah Azizah Budiman, Wahyu Candriasih, Ni Kadek Chairunnisa Ar. Lamasitudju Deny Wiria Nugraha Dewa Made P. Wedananta Dival Maulana, Muhammad Dodu, A. Y. Erwin Dwi Shinta Angreni Fadli Kurniawan Fajar, Moh Ghifari, Ari Hajra Rasmita Ngemba Ihwan, Abib Raifmuaffah Iskandar Iskandar Jeprianto Rurungan, Jeprianto JULIAN WITJAKSONO Komang, Gusti Laila, Rahma Limbong, Dennis Tandi Luh Putu Ratna Sundari Maulana, Muhammad Syahputra Megawati Purwaningsih Mohammad Yazdi Pusadan Mutaqin, Ghani Nadyanti, Nadyanti Nanda, Rifqi Ningsih, Alief Surya Nouval Trezandy Lapatta Ono, Jumaita Pangara, Takdir S Parenrengi, Andi Fathur Alamsyah A. Putri Febrina, Annisa Rahmah Laila Rinianty, Rinianty Rizka Ardiansyah Rizqa Raaiqa Bintana Ryfial Azhar, Ryfial Santoso, Naufal Tamam Septiano Anggun Pratama Syahrullah Syahrullah Syahrullah Syaiful Hendra Tamaulina Br Sembiring Widyati, Made Ayu Sri Wirdayanti Yudhaswana Joefrie , Yuri Yudhaswana, Yuri Yuri Yudhaswana Joefrie