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STUDI AWAL SISTEM INFORMASI UJI FORENSIK PROSES KLASTERISASI PROYEKTIL AMUNISI SENJATA API MENGGUNAKAN ALGORITMA GRAY LEVEL CO-OCCURENCE MATRIX DAN K-MEAN CLUSTERING

SUPRIYADI, Didik and Widodo, Catur Edi and Isnanto, Rizal (2024) STUDI AWAL SISTEM INFORMASI UJI FORENSIK PROSES KLASTERISASI PROYEKTIL AMUNISI SENJATA API MENGGUNAKAN ALGORITMA GRAY LEVEL CO-OCCURENCE MATRIX DAN K-MEAN CLUSTERING. Masters thesis, UNIVERSITAS DIPONEGORO.

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Abstract

Pemanfaatan teknologi menjadi solusi saat perkembangan jaman terus meningkat dan berkembang. Tidak terkecuali keterkaitan teknologi untuk bidang keamanan negara. Metode yang mendukung klaterisasi adalah ekstraksi ciri menggunakan Gray Level Co-occurence Matrix (GLCM) yang dilakukan sebelum proses klaterisasi itu sendiri. GLCM sangat cocok digunakan untuk melakukan ekstraksi ciri pada citra yang memiliki pola-pola khusus seperti penelitian pengenalan pola wayang Prosedur penelitian ini merupakan alur dari flowchat untuk membangun sistem informasi untuk uji forensik proses klasterisasi proyektil amunisi senjata api menggunakan algoritma Gray Level Co-occurrene Matrix (GLCM) dan K-Means clustering. Pada Gambar 3.1 berikut merupakan kerangka sistem informasi sebagai penjelas setiap alur input, proses dan output diilustrasikan.Hasil penelitian menunjukkan bahwa penggunaan metode GLCM sebagai ekstraksi fitur dari citra grayscale dan metode K-Means untuk clustering memberikan hasil dan akurasi yang cukup baik. Performa model mencapai 71.14% meski dengan keterbatasan data yang dimiliki. Model tersebut dapat digunakan tidak hanya pada aplikasi console seperti Google Collabs, tetapi juga dapat digunakan pada aplikasi yang memiliki GUI dengan performa aplikasi yang cukup stabil.
Kata Kunci: uji forensik, klasterisasi proyektil amunisi senjata, algoritma gray level co-occurrence, K-Mean clustering

The use of technology is a solution when developments continue to increase and develop. The connection between technology in the field of state security is no exception. The method that supports clusterization is feature extraction using Gray Level Co-occurrence Matrix (GLCM), which is carried out before the clusterization process. GLCM is very suitable for extracting features or characteristics in images that have special patterns, such as puppet pattern recognition research. This research procedure is a flow chart to build an information system for forensic testing of the clustering process for firearm ammunition projectiles using the Gray Level Co algorithm. -occurrence Matrix (GLCM) and K-means clustering. Figure 3.1 below illustrates the information system framework as an explanation of each input flow, process and output. The research results show that using the GLCM method for feature extraction from grayscale images and the K-Means method for clustering provide quite good results and accuracy. The model performance reached 71.14% even with limited data. This model can be used not only in console applications such as Google Collabs but also in applications with a GUI with fairly stable application performance.
Keywords: forensic testing, clustering of weapon ammunition projectiles, gray level co-occurrence algorithm, K-Mean clustering

Item Type: Thesis (Masters)
Uncontrolled Keywords: uji forensik, klasterisasi proyektil amunisi senjata, algoritma gray level co-occurrence, K-Mean clustering
Subjects: Sciences and Mathemathic
Divisions: Postgraduate Program > Master Program in Information System
Depositing User: ekana listianawati
Date Deposited: 09 Sep 2024 04:40
Last Modified: 09 Sep 2024 04:40
URI: https://eprints2.undip.ac.id/id/eprint/26418

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