Article Per Year (5 Year)
p-Index From 2020 - 2025
2.177
P-Index
This Author published in this journals
All Journal IJCCS (Indonesian Journal of Computing and Cybernetics Systems) JUITA : Jurnal Informatika Jurnal Informatika Universitas Pamulang Abdimas Umtas : Jurnal Pengabdian kepada Masyarakat Jurnal Teknik Informatika (JUTIF) Journal of Applied Data Sciences IT-Explore: Jurnal Penerapan Teknologi Informasi dan Komunikasi Jurnal Pendidikan dan Pengabdian Masyarakat Jurnal Informatika: Jurnal Pengembangan IT
Theresia Ratih Dewi Saputri
Universitas Ciputra
Religion Agriculture, Biological Sciences & Forestry Arts Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Social Sciences Other

Published : 12 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 12 Documents
Search

 1   2 
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.
RANCANG BANGUN APLIKASI POLA MAKAN DASH BAGI PENDERITA HIPERTENSI Bryan; Saputri, Theresia Ratih Dewi
IT Explore: Jurnal Penerapan Teknologi Informasi dan Komunikasi Vol 3 No 2 (2024): IT-Explore Juni 2024
Publisher : Fakultas Teknologi Informasi, Universitas Kristen Satya Wacana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24246/itexplore.v3i2.2024.pp177-193

Abstract

Hipertensi merupakan salah satu penyakit tidak menular yang umum terjadi di dunia. Salah satu penyebab umum hipertensi adalah pola makan yang tidak sehat. Dalam menanggapi hal tersebut, Dietary Approach to Stop Hypertension (DASH) disarankan sebagai pola makan yang efektif untuk mengurangi tekanan darah. Meskipun telah terbukti efektif, penderita hipertensi masih belum dapat menerapkan pola makan tersebut secara konsisten. Salah satu faktor penghambat penerapan pola makan DASH adalah ketidaktahuan penderita hipertensi dalam memodifikasi pola makan sesuai anjuran yang ada. Berdasarkan permasalahan tersebut, penelitian ini bertujuan untuk merancang pembuatan aplikasi pola makan DASH bagi penderita hipertensi berbasis mobile yang dapat memberikan rencana makan sesuai pola makan DASH. Rencana makan dalam jangka waktu tujuh hari yang dibuat setiap harinya meliputi sarapan, makan siang, dan makan malam. Menu makanan akan dirancang dan disesuaikan dengan jumlah kebutuhan kalori harian. Penderita juga dapat memantau asupan natrium sesuai dengan makanan yang telah dikonsumsi. Sebelum mengembangkan aplikasi, usability testing dilakukan menggunakan metode heuristic evaluation untuk mengevaluasi desain pada prototype. Pengembangan aplikasi dilakukan menggunakan metode waterfall model dengan arsitektur BLoC. Aplikasi mobile dikembangkan menggunakan bahasa pemrograman Dart, framework Flutter, Bloc, Firebase, dan Edamam untuk sistem operasi Android. Pengujian black box kemudian dilakukan dengan metode functional testing. Setiap fitur yang diujikan berhasil berjalan sesuai yang diharapkan dan dinyatakan lolos pengujian. Hasil penelitian menunjukkan bahwa aplikasi Milda dapat digunakan oleh penderita hipertensi untuk menjalankan pola makan sehat dengan memberikan rencana makan sesuai dengan pola makan DASH. Rencana makan yang diberikan mendorong penderita hipertensi untuk memulai atau mencoba kembali pola makan DASH. Selain itu, variasi menu makanan yang menarik juga mendorong penderita hipertensi untuk lebih konsisten dalam menjalankan pola makan DASH.
Online Measuring Feature for Batik Size Prediction using Mobile Device: A Potential Application for a Novelty Technology Wiradinata, Trianggoro; Saputri, Theresia Ratih Dewi; Sutanto, Richard Evan; Soekamto, Yosua Setyawan
Journal of Applied Data Sciences Vol 4, No 3: SEPTEMBER 2023
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v4i3.121

Abstract

The garment industry, particularly the batik sector, has experienced significant growth in Indonesia, coinciding with a rise in the number of online consumers who purchase batik products through e-Marketplaces. The observed upward trend in growth has seemingly presented certain obstacles, particularly in relation to product alignment and product information dissemination. Typically, batik entrepreneurs originate from micro, small, and medium enterprises (MSMEs) that have not adhered to global norms. Consequently, the sizes of batik products offered for sale sometimes exhibit inconsistencies. The issue of size discrepancies poses challenges for online consumers seeking to purchase batik products through e-commerce platforms. An effective approach to address this issue involves employing a smartphone camera to anticipate body proportions, specifically the length and width of those engaged in online shopping. Subsequently, by the utilization of machine learning techniques, the optimal batik size can be determined. The UKURIN feature was created as a response to a specific requirement. However, it is essential to establish a methodology for investigating the elements that impact the intention to use this feature. This will enable developers to prioritize their feature development strategies more effectively. A total of 179 participants completed an online questionnaire, and subsequent analysis was conducted utilizing the Extended Technology Acceptance Model framework. The findings indicate that Perceived Usefulness emerged as the most influential factor. Consequently, when designing and developing the novelty feature of UKURIN, it is imperative for designers and application developers to prioritize the benefits aspect.
COMPARATIVE STUDY OF CNN-BASED ARCHITECTURES ON EYE DISEASES CLASSIFICATION USING FUNDUS IMAGES TO AID OPHTHALMOLOGIST Yaurentius, Evelyn Callista; Saputri, Theresia Ratih Dewi; Tanuwijaya, Evan; Sutanto, Richard Evan
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 1 (2025): JUTIF Volume 6, Number 1, February 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2025.6.1.3699

Abstract

Eye health has a significant impact on quality of life, with more than 2.2 billion people experiencing vision problems. Many of these cases can be prevented or treated. The use of AI for eye disease classification helps healthcare professionals provide optimal care. However, the complexity of fundus images challenges classification performance. This study examines various Convolutional Neural Network (CNN) architectures using Transfer Learning and Adam optimization. Fundus images are processed using CLAHE (clip limit and grid size) and the Wiener filter (size) to enhance contrast and reduce noise. Afterward, ResNet-152, EfficientNet, MobileNetV1, and DenseNet-121 are tested to identify the most effective model. The study aims to determine the optimal CNN architecture for eye disease classification, assisting ophthalmologists in diagnosing eye diseases through fundus images. The best CNN model, ResNet-152, achieved an accuracy of 94.82%, outperforming other models by 3.95 - 8.29%.
Nature-based Hyperparameter Tuning of a Multilayer Perceptron Algorithm in Task Classification: A Case Study on Fear of Failure in Entrepreneurship Saputri, Theresia Ratih Dewi; Kurniawan, Edwin; Lestari, Caecilia Citra; Antonio, Tony
Journal of Applied Data Sciences Vol 6, No 2: MAY 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v6i2.539

Abstract

Entrepreneurship plays a key role in generating economic growth, encouraging innovation, and creating job opportunities. Understanding which demographic, psychological, and socio-economic factors contribute to fear of failure in entrepreneurship is essential to developing proper standards in entrepreneurship education and policy. However, it remains challenging to accurately classify these factors, especially when balancing model performance with model complexity in a multilayer perceptron algorithm. An effective model requires the correct parameter setting via a hyperparameter tuning process. Adjusting each hyperparameter by hand requires significant effort and knowledge, as there are frequently multiple combinations to consider. Furthermore, manual tuning is prone to human error and may overlook optimal configurations, resulting in inferior model performance and prediction accuracy. This study evaluates nature-inspired optimization techniques, including particle swarm optimization (PSO), genetic algorithm (GA), and grey wolf optimization (GWO). Several parameters are tuned in the present multilayer perceptron model, including the number of hidden layers and the number of nodes in each hidden layer, learning rate, and activation functions. The used dataset which consists of 39 features from 333 samples captured individual fears, loss score, and computational efficiency as the required amount of time for finding the best parameter combination. Model accuracy performance scores are 45.16%, 53.76%, and 58.61% for GA, PSO, and GWO, respectively. Meanwhile their execution time are 10 minutes, 27 minutes, and 23 minutes, for GA, PSO, and GWO, respectively. Experiment results further reveal that each optimization algorithm has distinct advantages: GA excels at speedy convergence, PSO provides a robust exploration of hyperparameter space, and GWO offers remarkable adaptability to complicated parameter interdependencies. This study provides empirical evidence for the efficacy of nature-inspired hyperparameter modification in improving multilayer perceptron performance for fear of failure categorization tasks.
Detecting YouTube Clickbait with Transformer Models: A Comparative Study Samuel, Bryan; Saputri, Theresia Ratih Dewi
IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 19, No 4 (2025): October
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijccs.111977

Abstract

Clickbait remains a common strategy on YouTube, where video titles are often crafted to maximize viewer engagement. Although transformer-based machine learning technologies have advanced rapidly, studies that specifically investigate clickbait in YouTube video titles are still rare, even though such titles have unique linguistic characteristics that are shorter, more informal, and more ambiguous than news headlines or other social media texts. This study compares three Transformer models, namely BERT, RoBERTa, and XLNet, for the task of clickbait detection using two benchmark datasets. Each model was fine-tuned and evaluated using standard classification metrics, with additional analyses on training and inference efficiency. The results show that all three models achieved accuracy above 95 percent. RoBERTa achieved the best performance on the Chaudhary dataset (99.84 percent), while BERT cased performed best on the Vierti dataset (96.91 percent). In contrast, XLNet lagged in both accuracy and computational efficiency, with inference times exceeding six seconds per batch. This study demonstrates a 1.31 percent improvement in accuracy compared to previous SVM-based methods and provides a comprehensive evaluation of three Transformer architectures in the YouTube context, offering empirical guidance for more effective clickbait detection.
Machine Learning Approaches for Predicting Seasonal Stock Trends Gunawan, Jason Miracle; Andreas, Christopher; Saputri, Theresia Ratih Dewi
IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 19, No 4 (2025): October
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijccs.112504

Abstract

The financial market is vital for economic growth yet it often experiences volatility, particularly in Indonesia’s transportation sector where stock prices are strongly affected by seasonal fluctuations. Conventional forecasting methods often neglect these recurring patterns, lowering predictive accuracy. This study assesses the capability of Machine Learning algorithms to capture seasonality in stock price prediction, using PT Garuda Indonesia (Persero) Tbk (GIAA.JK)’s monthly data from August 2019 to May 2025, retrieved from Yahoo Finance. Four models–Linear Regression, Extreme Gradient Boosting (XGBoost), Gated Recurrent Unit (GRU), and Long Short-Term Memory (LSTM)–were trained and tested, with performance evaluated using Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE). Hyperparameter tuning was applied to XGBoost, LSTM, and GRU, while statistical validation employed the Kruskal-Wallis test. Results showed that the tuned GRU outperformed other models, achieving MAE of 5.90, RMSE of 7.33, and MAPE of 9.67%, demonstrating ‘excellent’ accuracy in modelling both short-term and seasonal dynamics. These findings highlight the superiority of GRU in modelling both short-term fluctuations and long-term seasonal dependencies in stock prices. The results contribute practical insights for investors and emphasize the importance of integrating seasonality in predictive models for volatile sectors
Co-Authors Andreas, Christopher Aprillio, Daniel Atmadjaja, Anna Bella Bryan Edwin Kurniawan Evan Tanuwijaya Felicia Graciella Gunawan, Jason Miracle Laura Mahendratta Tjahjono Lestari, Caecilia Citra Maximus Aurelius Wiranata Michelle Chandra Mychael Maoeretz Engel Rinabi Tanamal Salmon Charles Siahaan Samuel, Bryan Sutanto, Richard Evan Tony Antonio Trianggoro Wiradinata Wijaya, Mychael Wiryonoputro, Tinara Nathania Yaurentius, Evelyn Callista Yosua Setyawan Soekamto Yuwono Marta Dinata