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All Journal Jurnal Edukasi dan Penelitian Informatika (JEPIN) Scientific Journal of Informatics Register: Jurnal Ilmiah Teknologi Sistem Informasi Techno Nusa Mandiri : Journal of Computing and Information Technology Journal of Big Data Analytic and Artificial Intelligence
Fitriyanto, Rachmad
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Computer Science & IT Education

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Manajemen jpeg/exif file fingerprint dengan algoritma Brute Force string matching dan Hash Function SHA256 Fitriyanto, Rachmad; Yudhana, Anton; Sunardi, Sunardi
Register: Jurnal Ilmiah Teknologi Sistem Informasi Vol 5, No 2 (2019): July-December
Publisher : Information Systems - Universitas Pesantren Tinggi Darul Ulum

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (2069.12 KB) | DOI: 10.26594/register.v5i2.1707

Abstract

Management of jpeg/exif file fingerprint with Brute Force string matching algorithm and Hash Function SHA256Metode pengamanan berkas gambar jpeg/exif saat ini hanya mencakup aspek pencegahan, belum pada aspek deteksi integritas data. Digital Signature Algorithm (DSA) adalah metode kriptografi yang digunakan untuk memverifikasi integritas data menggunakan hash value. SHA256 merupakan hash function yang menghasilkan 256-bit hash value yang berfungsi sebagai file fingerprint. Penelitian ini bertujuan untuk menyusun file fingerprint dari berkas jpeg/exif menggunakan SHA256 dan algoritma Brute Force string matching untuk verifikasi integritas berkas jpeg/exif. Penelitian dilakukan dalam lima tahap. Tahap pertama adalah identifikasi struktur berkas jpeg/exif. Tahap kedua adalah akuisisi konten segmen. Tahap ketiga penghitungan hash value. Tahap keempat adalah eksperimen modifikasi berkas jpeg/exif. Tahap kelima adalah pemilihan elemen dan penyusunan file fingerprint. Hasil penelitian menunjukkan sebuah jpeg/exif file fingerprint tersusun atas tiga hash value. SOI (Start of Image) segment hash value digunakan untuk mendeteksi terjadinya modifikasi berkas dalam bentuk perubahan tipe berkas dan penambahan objek pada konten gambar. Hash value segmen APP1 digunakan untuk mendeteksi modifikasi pada metadata berkas. Hash value segmen SOF0 digunakan untuk mendeteksi gambar yang dimodifikasi dengan teknik recoloring, resizing, dan cropping. The method of securing jpeg/exif image files currently has covered only the prevention aspect instead of the data integrity detection aspect. Digital Signature Algorithm is a cryptographic method used to verify the data integrity using hash value. SHA256 is a hash function that produces a 256-bit hash value functioning as a fingerprint file. This study aimed at compiling fingerprint files from jpeg/exif files using SHA256 and Brute Force string matching algorithm to verify the integrity of jpeg/exif files. The research was conducted in five steps. The first step was identifying the jpeg/exif file structure. The second step was the acquisition of the segment content. The third step was calculating the hash value. The fourth step was the jpeg/exif file modification experiment. The fifth step was the selection of elements and compilation of fingerprint files. The obtained results showed a jpeg/exif fingerprint file which was compiled in three hash values. The hash value of SOI segment was used to detect the occurrence of file modification in the form of file type changing and object addition on the image content. The hash value of APP1 segment was used to detect the metadata file modification. The hash value of SOF0 segment was used to detect the images modified by recoloring, resizing, and cropping techniques.
Penyusunan File Fingerprint untuk Berkas Jpeg/exif dengan Hash Function SHA512 dan Algoritma Boyer-Moore String Matching Fitriyanto, Rachmad; Yudhana, Anton; Sunardi, Sunardi
JEPIN (Jurnal Edukasi dan Penelitian Informatika) Vol 6, No 1 (2020): Volume 6 No 1
Publisher : Program Studi Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26418/jp.v6i1.33119

Abstract

Pengamanan berkas jpeg/exif dalam komunikasi digital umumnya bersifat untuk pencegahan, belum dapat digunakan untuk pendeteksian keutuhan data. File Fingerprint merupakan konsep sidik jari yang disusun berdasarkan konten dari dokumen digital. Penelitian ini bertujuan untuk menyusun file fingerprint dari berkas jpeg/exif yang dapat digunakan untuk mendeteksi terjadinya modifikasi pada berkas. Penyusunan file fingerprint dilakukan dalam lima tahap. Tahap pertama adalah identifikasi struktur berkas jpeg/exif menggunakan algoritma Boyer-Moore string matching untuk menentukan indeks lokasi segmen jpeg/exif. Tahap kedua adalah akuisisi konten segmen. Tahap ketiga adalah penghitungan hash value menggunakan hash function SHA512. Tahap keempat adalah pengujian modifikasi berkas jpeg/exif. Tahap kelima adalah penyusunan file fingerprint. Hasil yang diperoleh menunjukkan file fingerprint dari berkas jpeg/exif berasal dari tiga segmen, SOI, APP1 dan SOF0. Hash value dari segmen SOI digunakan untuk mendeteksi modifikasi dalam bentuk konversi tipe berkas dan penambahan objek pada gambar. Hash value dari segmen APP1 untuk mendeteksi modifikasi pada metadata. Hash value dari segmen SOF0 untuk mendeteksi modifikasi dalam bentuk resizing, recoloring dan cropping.  Berkas file fingerprint yang dihasilkan memiliki ukuran rata-rata 0,017% dari ukuran berkas gambar dari smartphone Asus Z00UD dan 0,015% dari ukuran berkas gambar dari smartphone Samsung Galaxy A5.
RETRACTED: Boyer-Moore String Matching Algorithm and SHA512 Implementation for Jpeg/exif File Fingerprint Compilation in DSA Fitriyanto, Rachmad; Yudhana, Anton; Sunardi, Sunardi
Scientific Journal of Informatics Vol 7, No 1 (2020): May 2020
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v7i1.19059

Abstract

This paper is retracted by editor due to publication ethics missconducted by author (simultaneously publication in other journal).Similar article has appeared in http://jurnalnasional.ump.ac.id/index.php/JUITA/article/view/4413
RETRACTED: Boyer-Moore String Matching Algorithm and SHA512 Implementation for Jpeg/exif File Fingerprint Compilation in DSA Fitriyanto, Rachmad; Yudhana, Anton; Sunardi, Sunardi
Scientific Journal of Informatics Vol 7, No 1 (2020): May 2020
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v7i1.19059

Abstract

This paper is retracted by editor due to publication ethics missconducted by author (simultaneously publication in other journal).Similar article has appeared in http://jurnalnasional.ump.ac.id/index.php/JUITA/article/view/4413
Manajemen jpeg/exif file fingerprint dengan algoritma Brute Force string matching dan Hash Function SHA256 Fitriyanto, Rachmad; Yudhana, Anton; Sunardi, Sunardi
Register: Jurnal Ilmiah Teknologi Sistem Informasi Vol 5, No 2 (2019): July
Publisher : Information Systems - Universitas Pesantren Tinggi Darul Ulum

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26594/register.v5i2.1707

Abstract

Management of jpeg/exif file fingerprint with Brute Force string matching algorithm and Hash Function SHA256Metode pengamanan berkas gambar jpeg/exif saat ini hanya mencakup aspek pencegahan, belum pada aspek deteksi integritas data. Digital Signature Algorithm (DSA) adalah metode kriptografi yang digunakan untuk memverifikasi integritas data menggunakan hash value. SHA256 merupakan hash function yang menghasilkan 256-bit hash value yang berfungsi sebagai file fingerprint. Penelitian ini bertujuan untuk menyusun file fingerprint dari berkas jpeg/exif menggunakan SHA256 dan algoritma Brute Force string matching untuk verifikasi integritas berkas jpeg/exif. Penelitian dilakukan dalam lima tahap. Tahap pertama adalah identifikasi struktur berkas jpeg/exif. Tahap kedua adalah akuisisi konten segmen. Tahap ketiga penghitungan hash value. Tahap keempat adalah eksperimen modifikasi berkas jpeg/exif. Tahap kelima adalah pemilihan elemen dan penyusunan file fingerprint. Hasil penelitian menunjukkan sebuah jpeg/exif file fingerprint tersusun atas tiga hash value. SOI (Start of Image) segment hash value digunakan untuk mendeteksi terjadinya modifikasi berkas dalam bentuk perubahan tipe berkas dan penambahan objek pada konten gambar. Hash value segmen APP1 digunakan untuk mendeteksi modifikasi pada metadata berkas. Hash value segmen SOF0 digunakan untuk mendeteksi gambar yang dimodifikasi dengan teknik recoloring, resizing, dan cropping. The method of securing jpeg/exif image files currently has covered only the prevention aspect instead of the data integrity detection aspect. Digital Signature Algorithm is a cryptographic method used to verify the data integrity using hash value. SHA256 is a hash function that produces a 256-bit hash value functioning as a fingerprint file. This study aimed at compiling fingerprint files from jpeg/exif files using SHA256 and Brute Force string matching algorithm to verify the integrity of jpeg/exif files. The research was conducted in five steps. The first step was identifying the jpeg/exif file structure. The second step was the acquisition of the segment content. The third step was calculating the hash value. The fourth step was the jpeg/exif file modification experiment. The fifth step was the selection of elements and compilation of fingerprint files. The obtained results showed a jpeg/exif fingerprint file which was compiled in three hash values. The hash value of SOI segment was used to detect the occurrence of file modification in the form of file type changing and object addition on the image content. The hash value of APP1 segment was used to detect the metadata file modification. The hash value of SOF0 segment was used to detect the images modified by recoloring, resizing, and cropping techniques.
MULTILEVEL MODAL VALUE ANALYSIS FOR INTERPRETING CATEGORICAL K-MEDOIDS CLUSTERS DATA Fitriyanto, Rachmad; Syafiqoh, Ummi
Jurnal Techno Nusa Mandiri Vol. 21 No. 2 (2024): Techno Nusa Mandiri : Journal of Computing and Information Technology Period o
Publisher : Lembaga Penelitian dan Pengabdian Pada Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/techno.v21i2.5796

Abstract

Consumer segmentation plays a crucial role for business owners in developing their enterprises. K-Medoid is commonly used for segmentation functions due to its low computational complexity. However, K-Medoid has limitations, such as the variability in cluster sizes across different iterations and the challenge of determining the optimal number of clusters. The Davies-Bouldin Index (DBI) is a metric used to evaluate the number of clusters by calculating the ratio between the within-cluster distance and the between-cluster distance. Most segmentation studies typically stop at the formation of clusters without further interpretation, particularly when dealing with categorical data. This study aims to modify the use of K-Medoid and propose a method for interpreting clusters with categorical data. The research began with questionnaire design and the data collecting from 100 respondents, which was normalized in the second stage. Clustering used K-Medoid with variations K values from K=2 to K=10, with each K value tested 10 times. The clustering results were evaluated using the DBI to select the optimal clusters. Data interpretation conducted using modal values, calculated as the ratio of the number of times a specific attribute variable was selected by respondents to the total number of data points in the cluster. Utilization and hierarchical visualization of modal values proposed in this study offer insights into the dominant variables within an attribute and also depict the relationships between attributes based on the ranking of modal values. These advantages facilitate business analysts in labeling clusters for developing consumer-driven business strategies.
FEATURE SELECTION COMPARATIVE PERFORMANCE FOR UNSUPERVISED LEARNING ON CATEGORICAL DATASET Fitriyanto, Rachmad; Mohamad Ardi
Jurnal Techno Nusa Mandiri Vol. 22 No. 1 (2025): Techno Nusa Mandiri : Journal of Computing and Information Technology Period o
Publisher : Lembaga Penelitian dan Pengabdian Pada Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/techno.v22i1.6512

Abstract

In the era of big data, Knowledge Discovery in Databases (KDD) is vital for extracting insights from extensive datasets. This study investigates feature selection for clustering categorical data in an unsupervised learning context. Given that an insufficient number of features can impede the extraction of meaningful patterns, we evaluate two techniques—Chi-Square and Mutual Information—to refine a dataset derived from questionnaires on college library visitor characteristics. The original dataset, containing 24 items, was preprocessed and partitioned into five subsets: one via Chi-Square and four via Mutual Information using different dependency thresholds (a low-mid-high scheme and dynamic quartile thresholds: Q1toMax, Q2toMax, and Q3toMax). K-Means clustering was applied across nine variations of K (ranging from 2 to 10), with clustering performance assessed using the silhouette score and Davies-Bouldin Index (DBI). Results reveal that while the Mutual Information approach with a Q3toMax threshold achieves an optimal silhouette score at K=7, it retains only 4 features—insufficient for comprehensive analysis based on domain requirements. Conversely, the Chi-Square method retains 18 features and yields the best DBI at K=9, better capturing the intrinsic characteristics of the data. These findings underscore the importance of aligning feature selection techniques with both clustering quality and domain knowledge, and highlight the need for further research on optimal dependency threshold determination in Mutual Information.
Optimalisasi Pengelompokkan Konsumen dengan Multi Internal Metric Validation dan Boxplot Analysis Fitriyanto, Rachmad; Nurindah, Nurindah
Journal of Big Data Analytic and Artificial Intelligence Vol 8 No 1 (2025): JBIDAI Juni 2025
Publisher : STMIK PPKIA Tarakanita Rahmawati

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.71302/jbidai.v8i1.67

Abstract

The simultaneous use of multiple internal validation metrics to determine the optimal number of clusters in K-Means Clustering often results in differing K values, which can confuse data practitioners when extracting insights, such as identifying customer characteristics. This study aims to develop an evaluation framework to address the ambiguity arising from varying K values produced by different internal validation metrics. The proposed K evaluation framework consists of two stages. In the first stage, five internal validation metrics—Davies-Bouldin Index (DBI), Silhouette Score, Elbow Method, Dunn Index, and Calinski-Harabasz Index—are used as filters to generate up to five top K candidates. The second stage involves boxplot analysis, interquartile range (IQR), and elbow visualization to explore the cohesiveness and stability of the resulting clusters. The first-stage evaluation yielded four potential cluster counts: K = 2, 5, 7, and 10. In the second stage, based on the elbow graph of the average interquartile range, K = 5 was identified as the most optimal number of clusters compared to the other candidates. These results indicate that using a larger number of internal validation metrics may increase the likelihood of producing multiple K values. However, a higher number of clusters does not necessarily guarantee better quality. The implications of this research highlight the importance of a layered evaluation approach in determining the optimal number of clusters, especially when employing multiple internal validation metrics. The proposed framework can assist data practitioners in making more informed decisions and reducing ambiguity in the clustering process. In the future, this framework can be extended by incorporating external validation metrics or adapted to other clustering algorithms.
Co-Authors Anton Yudhana Mohamad Ardi NURINDAH NURINDAH Sunardi Sunardi Sunardi, Sunardi Syafiqoh, Ummi