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All Journal IJCCS (Indonesian Journal of Computing and Cybernetics Systems) JUITA : Jurnal Informatika Sinkron : Jurnal dan Penelitian Teknik 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

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Penerapan Metode Image-to-Speech melalui Kamera dalam Aplikasi berbasis Kecerdasan Buatan untuk Orang dengan Disleksia Aprillio, Daniel; Atmadjaja, Anna Bella; Bryan; Wijaya, Mychael; Saputri, Theresia Ratih Dewi
Jurnal Informatika Universitas Pamulang Vol 9 No 1 (2024): JURNAL INFORMATIKA UNIVERSITAS PAMULANG
Publisher : Teknik Informatika Universitas Pamulang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32493/informatika.v9i1.39173

Abstract

Dyslexia occurs worldwide despite the culture or language. Dyslexia affects about 9% - 12% of the population, with 2% - 4% of the population experiencing significant reading impairments. This research aims to develop an artificial intelligence-based application using the Image-to-Speech method that can convert digital text into audible sound for individuals with dyslexia without requiring their brain to process the writing. This method can assist people with dyslexia in daily life challenges such as reading traffic signs, books, or documents. Results from 10 experiments on the implementation of the proposed method indicate that individuals with dyslexia can scan the text they want to read using a camera from a smartphone or laptop. The expirements also shows that the application can convert text in image form into sound comprehensible to those with dyslexia, thus facilitating their recognition of digital writing with 90% accuracy. The application also demonstrates efficiency in terms of data processing time. The average time required for image to audio conversion is 0.22 seconds, with an average memory usage of 163.2 MiB.
Rancang Bangun Aplikasi Diet untuk Ibu Menyusui Pasca Persalinan dengan Algoritma Mifflin-St Jeor Wiryonoputro, Tinara Nathania; Saputri, Theresia Ratih Dewi
Jurnal Informatika: Jurnal Pengembangan IT Vol 8, No 3 (2023)
Publisher : Politeknik Harapan Bersama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30591/jpit.v8i3.5733

Abstract

Pregnancy is a significant and transformative period for women, both physically and emotionally. During this time, it is crucial for expectant mothers to prioritize their own health and well-being to create a healthy environment for their growing baby. One of the physical changes that many breastfeeding mothers experience after childbirth is weight gain. Factors contributing to this include increased caloric needs, lack of sleep, reduced physical activity, and feelings of stress and fatigue due to caring for a newborn. Maintaining a healthy weight is vital to reduce the risk of various health issues and ensure the quality of breast milk for the baby. However, it is important to note that mothers should not engage in strict dieting during the postpartum period, or the puerperium, which lasts up to 40 days after delivery. During this time, mothers should gradually resume normal activities and movement. To support breastfeeding mothers in maintaining their health after childbirth, a structured and monitored approach that provides tailored information according to each stage of development is necessary. The Laav application, available for iOS, is designed to calculate and record the caloric intake of breastfeeding mothers, helping them achieve proper nutrition while maintaining an ideal weight. The application is built using the User-Centered Design (UCD) methodology and uses the Mifflin-St Jeor algorithm to calculate calories. The application is programmed in SwiftUI, a language optimized for the iOS platform
Comparative Study on Machine Learning Algorithms for Code Smell Detection U, Hayya; Saputri, Theresia Ratih Dewi
Sinkron : jurnal dan penelitian teknik informatika Vol. 10 No. 1 (2026): Article Research January 2026
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v10i1.15439

Abstract

Detecting code smells is crucial for maintaining software quality, but rule-based methods are often not very adaptive. On the other side, existing machine learning studies often lack large-scale comparisons on modern datasets. The goal of this research is to comprehensively compare the performance of various machine learning algorithms for multi-label code smells classification in terms of effectiveness and efficiency. The dataset used in this research is SmellyCode++, containing more than 100,000 samples. Seven models: Logistic Regression, Linear SVM, Naive Bayes, Random Forest, Extra Trees, XGBoost, and LightGBM combined with Binary Relevance were trained on data balanced using random undersampling and multi-label synthetic minority over-sampling. The performance of each model was evaluated using the F1-Macro, Hamming Loss, and Jaccard Score metrics. A non-parametric statistical analysis was also conducted to validate the findings. The experiment found that ensemble-based models statically significantly outperformed the linear and probabilistic models. The performance among the top ensemble models was found to be statistically equivalent. With this statistical equivalence in accuracy, computational efficiency measured with training time became the critical tiebreaker. BR_RandomForest, BR_XGBoost, and BR_ExtraTrees proved highly efficient, while BR_LightGBM was significantly slower. This study concludes that BR_RandomForest offers the best overall trade-off in providing top tier accuracy combined with excellent computational efficiency, making it a robust choice for practical applications.
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 U, Hayya Wijaya, Mychael Wiryonoputro, Tinara Nathania Yaurentius, Evelyn Callista Yosua Setyawan Soekamto Yuwono Marta Dinata