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All Journal JUITA : Jurnal Informatika Jurnal Informatika Universitas Pamulang Abdimas Umtas : Jurnal Pengabdian kepada Masyarakat Jurnal Pendidikan dan Pengabdian Masyarakat
Theresia Ratih Dewi Saputri
Universitas Ciputra Surabaya
Religion Agriculture, Biological Sciences & Forestry Arts Computer Science & IT Education Social Sciences Other

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Training of Trainers (TOT) Data Science for Teaching Doctors Rinabi Tanamal; Felicia Graciella; Michelle Chandra; Trianggoro Wiradinata; Yosua Soekamto; Theresia Ratih Dewi Saputri
ABDIMAS: Jurnal Pengabdian Masyarakat Vol. 5 No. 2 (2022): ABDIMAS UMTAS: Jurnal Pengabdian Kepada Masyarakat
Publisher : LPPM Universitas Muhammadiyah Tasikmalaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (726.932 KB) | DOI: 10.35568/abdimas.v5i2.2488

Abstract

Due to the rapid development of technology, technology has become very attached to various fields in Indonesia. The health sector is no exception. If more and more data in the health sector is used properly, it will provide good benefits for the world of health and patients. Therefore, a Data Science Training of Trainers Activity was held for Doctors by the Profession of Doctors at Universitas Ciputra Surabaya. Activities are carried out by providing materials and working on questions by the doctors involved. Thus, it is hoped that the doctors participating in this activity can provide medical literacy knowledge to prospective doctor students at Universitas Ciputra. From the results of this TOT evaluation, it has a positive impact on doctors regarding alternative ways to process the data needed for medical research.
What do Indonesians talk when they talk about COVID-19 Vaccine: A Topic Modeling Approach with LDA Theresia Ratih Dewi Saputri; Caecilia Citra Lestari; Salmon Charles Siahaan
JUITA : Jurnal Informatika JUITA Vol. 10 No. 2, November 2022
Publisher : Department of Informatics Engineering, Universitas Muhammadiyah Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (955.666 KB) | DOI: 10.30595/juita.v10i2.13500

Abstract

To end the COVID-19 pandemics, the government attempted to accelerate the vaccination through various programs and collaboration. Unfortunately, the number is still relatively small compared to the number of populations in Indonesia. There are some reasons attributed to this challenge, one of them being the reluctance of citizens to accept the COVID-19 vaccine due to various factors. Knowing this factor to increase public compliance, the vaccination program can be speed-up. Unfortunately, traditionally acquiring the knowledge related to COVID-19 vaccine rejection can be challenging.  One of the ways to capture the knowledge is by conducting a survey or interview related to COVID-19 vaccine acceptance. This method can be inefficient in terms of cost and resources. To address those problem, we propose a novel method for analyzing the topics related to the COVID-19 Indonesians’ opinions on Twitter by implementing topic modeling algorithm called Latent Dirichlet Allocation. We gathered more than 22000 tweets related to the COVID-19 vaccine. By applying the algorithm to the collected dataset, we can capture the what is general opinion and topic when people discuss about COVID-19 vaccine. The result was validated using the labeled dataset that have been gathered in the previous research. Once we have the important term, the strategy based on can be determined by the medical professional who are responsible to administer the COVID-19 vaccine. 
MINI SERI COMPUTATIONAL THINGKING UNTUK GURU SEKOLAH YAYASAN CIPUTRA PENDIDIKAN Yuwono Marta Dinata; Laura Mahendratta Tjahjono; Mychael Maoeretz Engel; Theresia Ratih Dewi Saputri; Evan Tanuwijaya
Jurnal Pendidikan dan Pengabdian Masyarakat Vol. 4 No. 2 (2021): Mei
Publisher : FKIP Universitas Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (215.357 KB) | DOI: 10.29303/jppm.v4i2.2659

Abstract

Kegiatan pengabdian kepada masyarakat (abdimas) ini bertujuan memperluas wawasan mengenai computational thinking (CT) bagi guru Sekolah. Program mini seri ini dilakukan secara berkala dan berkesinambungan. Program ini diprioritaskan kepada Guru di berbagai sekolah. Pada kesempatan kali ini diperuntukkan kepada Guru-guru di Yayasan Ciputra Pendidikan. CT sendiri merupakan salah satu problem solving yang perlu diajarkan sejak dini. Dengan melihat perkembangan teknologi dalam bidang ilmu komputer yang berkembang pesat maka pendekatan CT ini sangat diperlukan. Universitas Ciputra Surabaya khususnya Fakultas Teknologi Informasi bekerjasama dengan Komunitas Bebras untuk memperluas penggunaan CT bagi siswa/i seluruh Indonesia. Mini seri ini dilakukan dengan memberikan wawasan CT kepada guru sekolah, sehingga guru tersebut dapat menyampaikan dan melatih para siswanya untuk dapat terbiasa menggunakan CT ini dalam kehidupan mereka sehari-hari. Dalam melakukan pengabdian masyarakat ini dilakukan dengan terlebih dahulu berkoordinasi dan berdiskusi dengan koordinator pusat Yayasan, membuat materi computational thinking berupa materi power point maupun website yang siap diakses peserta. Pokok pembahasan dibagi menjadi empat sesi yaitu pendahuluan tentang computational thinking, computational thinking in everyday life, Developing Computational Thinking task dan menerapkan CT dalam pembelajaran di kelas. Kegiatan ini menyasar pada 134 guru Yayasan Sekolah Ciputra dengan durasi pelaksanaan kurang lebih tiga bulan dari persiapan, penyiapan materi, koordinasi, penyuluhan, serta penyusunan laporan dan luaran.
Comparative Study on Regression Algorithms for Predicting Price of Online Course: Udemy Case Study Maximus Aurelius Wiranata; Theresia Ratih Dewi Saputri
Jurnal Informatika Universitas Pamulang Vol 8, No 2 (2023): JURNAL INFORMATIKA UNIVERSITAS PAMULANG
Publisher : Teknik Informatika Universitas Pamulang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32493/informatika.v8i2.30562

Abstract

Talent in the field of information technology is much needed. However, studying in the field of information technology requires a sizable fee. Online courses are a cost-effective option for learning. Online course sites like Udemy provide and sell hundreds of thousands of courses and have thousands of trusted instructors. With so many Udemy instructors, prices vary widely because the course pricing system is completely set by the teaching instructor. This means that the selling price of the course is not affected by the quality of the course, so not all courses are recommended to be purchased. To overcome this problem, a system is needed that can predict course prices so that it can advise instructors in determining selling prices. To compare the best algorithms used to create this system, three algorithms are used in this study: multiple linear regression, polynomial regression, and K-Nearest Neighbors Regression. The researcher uses 1200 data sample from web scraping results from the Udemy site, with one test for each algorithm. As a result, the K-Nearest Neighbors Regression got the best evaluation results with a root mean squared error value of 231659.49, a mean absolute percentage error of 0.43, and a coefficient of determination of 0.18.
Co-Authors Evan Tanuwijaya Felicia Graciella Laura Mahendratta Tjahjono Lestari, Caecilia Citra Maximus Aurelius Wiranata Michelle Chandra Mychael Maoeretz Engel Rinabi Tanamal Salmon Charles Siahaan Trianggoro Wiradinata Yosua Soekamto Yuwono Marta Dinata