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All Journal JUITA : Jurnal Informatika Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi)
Imam Riadi
Department of Information System, Universitas Ahmad Dahlan
Computer Science & IT Engineering

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Digital Forensic on Secure Digital High Capacity using DFRWS Method Anton Yudhana; Imam Riadi; Budi Putra
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 6 No 6 (2022): Desember 2022
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v6i6.4615

Abstract

As evidenced in the trial, between 2015 and the second quarter of 2022, there were 54 cases involving secure digital high capacity (SDHC) storage hardware as evidenced in trials. In 2021 there will be an increase in cases involving SDHC. The three cases with the highest number are corruption cases, special crimes, and ITE. SDHC is an advanced technology development of Secure Digital (SD) card hardware which functions as storage. SD Card only has a capacity of up to 2 gigabytes, while the largest SDHC capacity is 32 gigabytes. As a storage device that is small, thin, and has a fairly large capacity. this research needs to be done because of the increasingly widespread increase in cases involving SDHC. This study aims to perform digital forensic analysis on SDHC evidence using forensic applications that run on Linux, namely foremost and DC3DD. This study uses the DFRWS method to retrieve valid evidence in court. Based on the research conducted, it was found that the number of files that can be restored at the examination stage using foremost is 77%, and the accuracy of recovered files is 50% with string file hash validation. From this research, it can be concluded that the processing results of DC3DD and Foremost can be used as valid evidence.
A Comparative Study of K-Means and KNN Imputation for Handling Missing Data in Scholarship Applicant Datasets Muhammad Muhammad; Tole Sutikno; Imam Riadi
JUITA: Jurnal Informatika JUITA Vol. 13 Issue 3, November 2025
Publisher : Department of Informatics Engineering, Universitas Muhammadiyah Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30595/juita.v13i3.26502

Abstract

Handling missing values is a key issue in data processing, especially in financial records of prospective scholarship recipients where precision is vital for effective decision making. This research aims to analyze the effectiveness of two commonly used imputation methods, namely K-Nearest Neighbors (KNN) and K-Means, in filling missing values across key attributes such as Semester, Grade Point Average (GPA), number of dependents, number of credits, and parental income. Performance evaluation was conducted using Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE). The results indicate that KNN generally provides more stable and accurate imputations, particularly for attributes with homogeneous distributions such as Semester and GPA, while K-Means demonstrates competitive performance on attributes with higher variability, provided that the number of clusters is optimally defined. Nonetheless, K-Means tends to be more sensitive to increasing proportions of missing data. These findings underscore the importance of selecting imputation methods that align with attribute distribution characteristics and the extent of missing data in order to develop reliable predictive models, as observed in scenarios with 15% and 25% missing data. The findings can also serve as a reference for developing more accurate scholarship selection processes in the presence of incomplete financial data.
Co-Authors Anton Yudhana budi putra Eko Handoyo Imam Mahfudl Nasrulloh Muhammad Muhammad Rusydi Umar Sunardi, Sunardi Tole Sutikno