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All Journal Techno.Com: Jurnal Teknologi Informasi JOIV : International Journal on Informatics Visualization International Journal of Artificial Intelligence Research Jurnal Sisfokom (Sistem Informasi dan Komputer) Jurnal Ilmiah Media Sisfo JOURNAL OF SCIENCE AND SOCIAL RESEARCH JOISIE (Journal Of Information Systems And Informatics Engineering) INTI Nusa Mandiri Jurnal Ekonomi Manajemen Sistem Informasi Jurnal Teknologi Dan Sistem Informasi Bisnis JATI (Jurnal Mahasiswa Teknik Informatika) Indonesian Journal of Electrical Engineering and Computer Science Jurnal Pendidikan Guru (JPG) Bulletin of Computer Science Research JITSI : Jurnal Ilmiah Teknologi Sistem Informasi Journal of Education Research Algoritme Jurnal Mahasiswa Teknik Informatika Jurnal Pustaka AI : Pusat Akses Kajian Teknologi Artificial Intelligence Jurnal Hasi Penelitian Dan Pengkajian Ilmiah Eksakta - JPPIE Jurnal Ekonomika Dan Bisnis Jurnal Informatika Teknologi dan Sains (Jinteks) Jurnal Sains dan Teknologi Jurnal Komtekinfo Indonesian Journal Computer Science (ijcs) Journal of Information System and Education Development Kesatria : Jurnal Penerapan Sistem Informasi (Komputer dan Manajemen) The Indonesian Journal of Computer Science CSRID
Ramadhanu, Agung
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Penerapan Image Processing untuk Identifikasi RAM, SSD, dan Webcam Menggunakan Metode K-Means Clustering Hikmi, Zakiya; Ramadhanu, Agung
Jurnal Teknologi Dan Sistem Informasi Bisnis Vol 8 No 1 (2026): Januari 2026
Publisher : Prodi Sistem Informasi Universitas Dharma Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47233/jteksis.v8i1.2266

Abstract

The development of computer hardware requires appropriate automatic identification methods to assist in inventory, maintenance, and learning processes. Manual identification methods for hardware such as RAM, SSD, and webcams are often ineffective due to the difficulty of distinguishing their visual forms, especially for those who are unfamiliar with them. This study aims to apply image processing techniques using the K-Means clustering method to identify these three types of devices. The system was created using MATLAB with a graphical user interface (GUI) for ease of use. The process begins by capturing images in RGB format, which are then converted to Lab* color space. Segmentation is performed using the K-Means clustering method, which divides objects from the background into two clusters. The segmentation results are then refined using morphological operations. Next, shape features and texture features are extracted using Gray Level Co-occurrence Matrix (GLCM), which includes contrast, correlation, energy, and homogeneity. The features obtained are compared with the database using Euclidean distance to determine the type of hardware. The test results show that the system is able to accurately distinguish between RAM, SSD, and webcams. In conclusion, the use of K-Means clustering, GLCM, and distance-based classification can be an effective solution in identifying computer hardware through images.
Implementasi Metode K-Means Clustering untuk Mengklasterikasikan Perangkat Elektronik dengan Teknik Pengolahan Citra Firmansyah, Ryan; Ramadhanu, Agung
Jurnal Penelitian Dan Pengkajian Ilmiah Eksakta Vol 5 No 1 (2026): Jurnal Hasi Penelitian Dan Pengkajian Ilmiah Eksakta - JPPIE
Publisher : LPPM Universitas Dharma Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47233/jppie.v5i1.2270

Abstract

Grouping electronic devices such as computers, laptops, and smartphones will be very useful in situations where there are a large number of devices to manage, for example in companies, schools, or service centers. This study uses the k-means clustering method with image processing techniques through the Matlab application. The test data used was taken from the internet, consisting of 30 samples comprising 10 computers, 10 laptops, and 10 smartphones. In accordance with the existing dataset, clustering will be performed on three types of electronic devices, namely computers, laptops, and smartphones. After conducting various tests and model designs, the overall accuracy of the model is 100%. This research can cluster 30 samples consisting of 10 computer images, 10 laptop images, and 10 smartphone images. All samples used were taken from the internet.
Co-Authors Afriadi Afriadi Afriadi, A Agsera, Nilam Agus Salim, David Agusty, Dhia Fadhila Ahmad Syarif ahmad yani Akbar, Syifa Chairunnissa Deliva Al-arrafi, Muhammad Ikhsan Angga Angga Anggara Putra, Febri Antoni Antoni atiqah, sri Bayuputra, Ramdani Chairunnissa Deliva Akbar, Syifa Chan, Fajri Rinaldi Delvi, Syerlin Aprilia Desi Permata Sari Devita, Retno Dicky Imansyah, Muhammad Dila, Rahmah Dinantia, Triend Enggari, Sofika Erlanda, Hadrian Fajri Saputra, Charisman Firmansyah, Ryan Gafari, Abuzar Gunadi Widi Nurcahyo Hadi Syahputra Halifia Hendri Harnaranda, Jefri Hendri, Hallifia Hidayati, Dzil Hidayattullah, Hafis Hikmi, Zakiya Honestya, Gabriela Ilmawan, Fachrul Irsyad, As'Ary Sahlul Kareem, Shahab Wahhab Karseno, Doni Khomsi, Ahmad Maharani, Filsha Rifi Majid, Mazlina Abdul Mardison Mardison Masri, Taufik Muhammad Idris Muhammad Yusuf Nadia, Nadia Aini Hafizhah Negoro, Wahyu Saptha Ningsih, Neni Sri Wayuni Nurdiansyah, Ali Nurjannah, Farah Permata, Edo Pertiwi, Yuliana Pratama, Dede Putra, Kharisma Utama putri, kamila amaliah Rahmad Rahmad Rahmad, R Rianti, Eva Riati, Itin Riyan Saputra, Riyan Rosa, Imelda Sajida, Mayang salim, alfajri Saputra, Randy Sarjon Defit Selvia, Dina Silfia Andini, Silfia Sovia, Rini Sumijan, S Sutri, Ridwan Syafri Arlis Syafril Syafril Syafril, S Syalsabilla, Adinda Tesa Vausia Sandiva Utama Putra, Kharisma Vidyanti, Angela Citra Wiratama, Aditya Wirdawati, Wira Yanti, Rahma Yasmin, Nabila Yasmin, Nabilla Yemi, Leonardo Yesi Betriana Roza, yesibetriana_18 Yolanda Yolanda, Yolanda Yosfand, Windra Yuhandri Yulihartati, Sandra Zubaidah, Rima Puti