Garuda - Garba Rujukan Digital

Siti Nurajizah

Universitas Bina Sarana Informatika

Author-ID : 2058572

Religion Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Electrical & Electronics Engineering Engineering Health Professions Languange, Linguistic, Communication & Media Library & Information Science Mechanical Engineering Social Sciences Other

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Rancang Bangun Animasi Interaktif Berbasis Multimedia Pada Pengenalan Dasar Bahasa Jepang Safitri Linawati; Siti Nurajizah
Computer Science (CO-SCIENCE) Vol. 1 No. 2 (2021): Juli 2021
Publisher : LPPM Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/coscience.v1i2.427

The use of language as a medium of communication is currently important. Language is a way to communicate and interact with other people. Selain bahasa inggris yang menjadi bahasa internasional, terdapat beberapa bahasa negara lain yang juga tidak kalah diminati untuk dipelajari, salah satunya adalah bahasa jepang. Japanese language has a unique writing and pronunciation structure, so it takes persistence in learning it. However, the use of letters, sentence structure, and grammar structures that are different from Indonesian grammar makes it difficult for beginners to learn Japanese. The author tries to create an application that connects the multimedia functions on the computer with the knowledge of basic Japanese language introduction. In this study, using the waterfall software development method in making animation of basic Japanese language introduction. The result of this research is an animation that discusses the introduction of the country of Japan, the letters used in Japanese, greetings that are often used in everyday life, and how to introduce oneself in Japanese which is presented in the form of interactive animation so that it can help users. to understand basic Japanese.

Analisis Performa Model ResNet-50 Pada Diagnosis Pneumonia Balita Berdasarkan Citra Radiografi Thorax Rahmawati, Ami; Yulianti, Ita; Nurajizah, Siti; Hidayatulloh, Taufik; Sari, Ani Oktarini
Computer Science (CO-SCIENCE) Vol. 5 No. 1 (2025): Januari 2025
Publisher : LPPM Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/coscience.v5i1.7618

One of the most serious complications of ARI is pneumonia, where this disease causes sufferers to experience pain when breathing and limited oxygen intake. According to the World Health Organization (WHO), pneumonia is classified as a life-threatening disease due to the high mortality rate caused. To be able to diagnose this disease, patients usually undergo various medical examination methods, one of which is through chest radiography. However, the challenge in diagnosing pneumonia generally lies in the complexity and uncertainty in interpreting the results of these methods. Therefore, this study was conducted with the aim of building an image classification model based on the Chest radiography dataset from toddler patients using the ResNet-50 architecture, which is a variant of the Convolutional Neural Networks (CNN) algorithm. The combination of the two methods is applied to analyze and process images and obtain pattern recognition with high accuracy. The research methods used include the application of data augmentation, CNN architecture design, model training, and performance evaluation. The evaluation results show that the model has quite good performance with an accuracy of 85%, which indicates the model's ability to classify images with a fairly high level of accuracy, and has the potential to help the pneumonia diagnosis process more efficiently and accurately.

Analisis Performa Model ResNet-50 Pada Diagnosis Pneumonia Balita Berdasarkan Citra Radiografi Thorax Rahmawati, Ami; Yulianti, Ita; Nurajizah, Siti; Hidayatulloh, Taufik; Sari, Ani Oktarini
Computer Science (CO-SCIENCE) Vol. 5 No. 1 (2025): Januari 2025
Publisher : LPPM Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/coscience.v5i1.7618

One of the most serious complications of ARI is pneumonia, where this disease causes sufferers to experience pain when breathing and limited oxygen intake. According to the World Health Organization (WHO), pneumonia is classified as a life-threatening disease due to the high mortality rate caused. To be able to diagnose this disease, patients usually undergo various medical examination methods, one of which is through chest radiography. However, the challenge in diagnosing pneumonia generally lies in the complexity and uncertainty in interpreting the results of these methods. Therefore, this study was conducted with the aim of building an image classification model based on the Chest radiography dataset from toddler patients using the ResNet-50 architecture, which is a variant of the Convolutional Neural Networks (CNN) algorithm. The combination of the two methods is applied to analyze and process images and obtain pattern recognition with high accuracy. The research methods used include the application of data augmentation, CNN architecture design, model training, and performance evaluation. The evaluation results show that the model has quite good performance with an accuracy of 85%, which indicates the model's ability to classify images with a fairly high level of accuracy, and has the potential to help the pneumonia diagnosis process more efficiently and accurately.

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