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All Journal Journal of Students‘ Research in Computer Science (JSRCS) Journal of Computer Science and Informatics Engineering
Rahmadiyah, Shafira Nur
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management

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Analisis Keamanan Web Samsat Menggunakan Metode OWASP Zulfan, Zarifah Aina; Rahmadiyah, Shafira Nur; Manullang, Setti
Journal of Computer Science and Informatics Engineering Vol 4 No 1 (2025): Januari
Publisher : Ali Institute of Research and Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55537/cosie.v4i1.987

Abstract

There are websites where people can communicate and exchange information between the government and the general public. A website is a type of media that contains information that can be accessed from anywhere on the internet and is accessible from anywhere in the world. Pengujian sistem keamanan aplikasi berbasis website is a crucial aspect in the era of web-based application development that involves pesat. This study was conducted on the Samsat website. The Samsat website's maintenance is an issue for the administrator. These problems are always resolved, and they can be resolved when a bencana occurs. Without an effective management system, any information technology will be able to support a particular organization or institution on its own. Based on this background, an analysis of the Samsat website is needed to find out whether the website is safe. The research method uses OWASP (Open Web Application Security Project).
Implementasi K-Means Clustering pada Citra Digital Tomat untuk Identifikasi Kondisi Segar dan Busuk Aznawi, Nasrul Mahruf; Setiadi, Muhammad Irham; Aina, Zarifah; Manullang, Setti; Rahmadiyah, Shafira Nur
Journal of Students‘ Research in Computer Science Vol. 6 No. 1 (2025): Mei 2025
Publisher : Program Studi Informatika Fakultas Ilmu Komputer Universitas Bhayangkara Jakarta Raya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31599/srtqmw49

Abstract

The manual identification of fresh and rotten tomatoes has still relied on human visual observation, which tends to be inconsistent and time-consuming. This study aimed to develop a tomato image classification system using the K-Means Clustering method based on color, shape, and texture features to automatically identify fresh and rotten conditions. The Dataset consisted of 500 tomato images for training and 60 tomato images for testing, equally representing fresh and rotten conditions. The process involved converting the images into L*a*b and grayscale formats, performing segmentation using K-Means, and extracting shape and texture features for the classification process. The testing results showed that the system successfully classified fresh and rotten tomatoes with an accuracy rate of 95%, with both precision and recall exceeding 93% for each class. These findings indicated that the K-Means method could be effectively applied in tomato image processing to support the sorting process of agricultural products. This research contributed to the development of a digital image-based classification system that could be integrated into smart agriculture systems.
Co-Authors Aina, Zarifah Aznawi, Nasrul Mahruf Manullang, Setti Setiadi, Muhammad Irham Zulfan, Zarifah Aina