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All Journal Jurnal Sains Informatika Terapan (JSIT)
Siti Nurviatika
Universitas Nusa Putra
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering

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Perancangan Dan Implementasi Website Healthdoc Sebagai Produk Digital Di PT Nugraha Kreasi Digital Siti Nurviatika; Setiawati
Jurnal Sains Informatika Terapan Vol. 5 No. 1 (2026): Jurnal Sains Informatika Terapan (Februari, 2026)
Publisher : Riset Sinergi Indonesia (RISINDO)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62357/jsit.v5i1.912

Abstract

The development of digital technology has driven the emergence of web-based health information platforms that are faster, more efficient, and easily accessible. This study aims to design and implement the HealthDoc website as a digital product that provides healthcare service information through a modern, responsive, and user-friendly interface. The benefits of this research include offering a web-based health information solution for companies and serving as a reference for frontend development using a UI/UX approach. The dataset used consists of company information content, healthcare service data, design assets from the UI/UX team, and visual prototypes created using Figma. The research employs a descriptive qualitative method with the Waterfall development model, covering stages such as requirements analysis, UI/UX design, implementation using HTML, Tailwind CSS, and JavaScript, as well as responsiveness and functionality testing using DevTools and the black-box method. The development process includes design slicing, structuring layouts using Tailwind CSS utility-first classes, adding interactivity with JavaScript, and deploying the website via Vercel to ensure stable online access. The results show that the HealthDoc website operates optimally across various devices, with all features functioning as intended and the interface aligning with the original design. Therefore, the HealthDoc website is effective as both a digital health information platform and a professional company profile
Pengembangan Sistem Klasifikasi Citra Daging Sapi Dan Daging Babi Berbasis Web Menggunakan DENSENET-121 Siti Nurviatika; Ivana Lucia Kharisma; Nugraha
Jurnal Sains Informatika Terapan Vol. 5 No. 2 (2026): Jurnal Sains Informatika Terapan (Juni, 2026)
Publisher : Riset Sinergi Indonesia (RISINDO)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62357/jsit.v5i2.1176

Abstract

The circulation of beef and pork products that are difficult to distinguish visually can create challenges for consumers, making an automated meat identification system necessary. This study aims to develop an image classification model for beef and pork using the Convolutional Neural Network (CNN) method with the DenseNet-121 architecture and to implement it in a Streamlit-based web application. The dataset used in this study consists of 6,000 images, comprising 3,000 beef images and 3,000 pork images collected from two different dataset sources. The dataset underwent several preprocessing stages, including resizing, contrast enhancement, normalization, and data augmentation, and was subsequently divided into training, validation, and testing sets with a ratio of 70:15:15. The results show that the DenseNet-121 model is capable of classifying beef and pork images with excellent performance. Based on the evaluation using a confusion matrix and classification report, the model achieved an accuracy of 97.89%, with high precision, recall, and F1-score values for both classes. The trained model was then deployed in a web application that allows users to perform classification through image uploads or direct image capture using a camera. Based on these findings, it can be concluded that the DenseNet-121 architecture is capable of classifying beef and pork images with high accuracy and has the potential to be utilized as a practical tool for meat type identification.
Perancangan Dan Implementasi Website Healthdoc Sebagai Produk Digital Di PT Nugraha Kreasi Digital Siti Nurviatika; Setiawati
Jurnal Sains Informatika Terapan Vol. 5 No. 1 (2026): Jurnal Sains Informatika Terapan (Februari, 2026)
Publisher : Riset Sinergi Indonesia (RISINDO)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62357/jsit.v5i1.912

Abstract

The development of digital technology has driven the emergence of web-based health information platforms that are faster, more efficient, and easily accessible. This study aims to design and implement the HealthDoc website as a digital product that provides healthcare service information through a modern, responsive, and user-friendly interface. The benefits of this research include offering a web-based health information solution for companies and serving as a reference for frontend development using a UI/UX approach. The dataset used consists of company information content, healthcare service data, design assets from the UI/UX team, and visual prototypes created using Figma. The research employs a descriptive qualitative method with the Waterfall development model, covering stages such as requirements analysis, UI/UX design, implementation using HTML, Tailwind CSS, and JavaScript, as well as responsiveness and functionality testing using DevTools and the black-box method. The development process includes design slicing, structuring layouts using Tailwind CSS utility-first classes, adding interactivity with JavaScript, and deploying the website via Vercel to ensure stable online access. The results show that the HealthDoc website operates optimally across various devices, with all features functioning as intended and the interface aligning with the original design. Therefore, the HealthDoc website is effective as both a digital health information platform and a professional company profile
Pengembangan Sistem Klasifikasi Citra Daging Sapi Dan Daging Babi Berbasis Web Menggunakan DENSENET-121 Siti Nurviatika; Ivana Lucia Kharisma; Nugraha
Jurnal Sains Informatika Terapan Vol. 5 No. 2 (2026): Jurnal Sains Informatika Terapan (Juni, 2026)
Publisher : Riset Sinergi Indonesia (RISINDO)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62357/jsit.v5i2.1176

Abstract

The circulation of beef and pork products that are difficult to distinguish visually can create challenges for consumers, making an automated meat identification system necessary. This study aims to develop an image classification model for beef and pork using the Convolutional Neural Network (CNN) method with the DenseNet-121 architecture and to implement it in a Streamlit-based web application. The dataset used in this study consists of 6,000 images, comprising 3,000 beef images and 3,000 pork images collected from two different dataset sources. The dataset underwent several preprocessing stages, including resizing, contrast enhancement, normalization, and data augmentation, and was subsequently divided into training, validation, and testing sets with a ratio of 70:15:15. The results show that the DenseNet-121 model is capable of classifying beef and pork images with excellent performance. Based on the evaluation using a confusion matrix and classification report, the model achieved an accuracy of 97.89%, with high precision, recall, and F1-score values for both classes. The trained model was then deployed in a web application that allows users to perform classification through image uploads or direct image capture using a camera. Based on these findings, it can be concluded that the DenseNet-121 architecture is capable of classifying beef and pork images with high accuracy and has the potential to be utilized as a practical tool for meat type identification.
Co-Authors IVANA LUCIA KHARISMA Nugraha Setiawati