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All Journal Jurnal sistem informasi, Teknologi informasi dan komputer Jurnal Pilar Nusa Mandiri Blueprint Journal
Ghofar Taufiq, Ghofar
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Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Library & Information Science Mathematics

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TEKNIK PENGAMANAN DATA AT REST MENGGUNAKAN BITLOCKER DAN VERACRYPT Ghazi, Akhmad Nur; Taufiq, Ghofar
Jurnal Sistem Informasi, Teknologi Informatika dan Komputer Volume 14 No 2, Januari Tahun 2024
Publisher : Universitas Muhammadiyah Jakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24853/justit.14.2.121-127

Abstract

Belum lama ini terjadi banyak kasus kebocoran data seperti source code Twitter yang disebar di github, AI milik Meta yang bocor di forum internet hingga data pelanggan indihome yang diduga bocor. Hal ini dapat terjadi karena adanya celah keamanan dalam penyimpanan data. Oleh karenanya pengamanan data menjadi salah satu usaha yang penting untuk menjaga data pribadi / perusahaan sehingga tidak dapat diakses oleh pihak yang tak berwenang. Penelitian ini bertujuan untuk memberikan informasi mengenai cara melakukan pengamanan data at rest dengan menggunakan Bitlocker dan Veracrypt. Hasil penelitian menunjukkan bahwa BitLocker dan VeraCrypt masing-masing memiliki kelebihan dan kekurangan. BitLocker menawarkan integrasi yang baik dengan sistem operasi Windows dan memiliki dukungan resmi, sementara VeraCrypt adalah perangkat lunak sumber terbuka yang memberikan fleksibilitas dan keandalan. Namun, BitLocker memiliki beberapa keterbatasan dalam hal dukungan platform dan kontrol pengguna, sedangkan VeraCrypt memiliki antarmuka yang lebih kompleks. Temuan penelitian ini dapat memberikan wawasan bagi pengguna dan organisasi dalam memilih solusi enkripsi yang sesuai dengan kebutuhan keamanan data mereka.
IMPLEMENTASI LOGIKA FUZZY TAHANI UNTUK MODEL SISTEM PENDUKUNG KEPUTUSAN EVALUASI KINERJA KARYAWAN Taufiq, Ghofar
Jurnal Pilar Nusa Mandiri Vol 12 No 1 (2016): Pilar Nusa Mandiri : Journal of Computing and Information System Periode Maret 2
Publisher : LPPM Universitas Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1088.168 KB) | DOI: 10.33480/pilar.v12i1.254

Abstract

The main purpose of employee performance evaluation is to monitor and determine the performance of an employee in a company, whether it is working optimally or not to conduct an assessment of the criteria for the performance of an employee . As for the criteria regarding the evaluation of staff performance will be evaluated in this study was the presence, quality of work , creativity , technical skills , communication skills and attitude . These criteria still have data that is ambiguous ( vague ) . By Tahani fuzzy , ambiguous data that can be processed to remove ambiguity data. The aim of this study is to apply fuzzy logic with Tahani method for evaluating employee performance and yield ranking of employee performance evaluation results . While the outcome of this research is a model of a decision support system for employee performance evaluation with fuzzy logic approach Tahani methods that provide information about the results of the performance evaluation of employees .
Rw Segmentation Analysis for the Climate Village Program as a Basis for Planning in South Jakarta Using K-Means Clustering Marni Berek, Maria Susey; Taufiq, Ghofar; Chrisnawati, Giantika
Blueprint Journal Vol 1 No 2 (2025): Agustus: Blueprint Journal
Publisher : PT Yupin Felicitas Utama

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Abstract

Climate change is a global issue that has multidimensional impacts on human life, including in Indonesia. In response to this challenge, the government developed the Climate Village Program (PROKLIM) which prioritizes community empowerment through a community-based approach. This program aims to strengthen climate change adaptation and mitigation efforts through participatory local resource management. This study uses the K-Means clustering method to group areas based on environmental characteristics at the Neighborhood Association (RW) level, in order to identify patterns and support decision making in effective environmental management. This study proves that the K-Means Clustering method is effective in grouping RWs in South Jakarta based on indicators relevant to the Climate Village Program (ProKlim). The latest report from the World Meteorological Organization (2024) states that 2023 was the hottest year in history, with an anomaly (Hasbullah & Assyahri, 2025) of global temperatures reaching 1.45°C above the average temperature in the pre-industrial era. Furthermore, the last nine years (2015–2023) were recorded as the period with the hottest consecutive temperatures in the history of climate records. The segmentation results show clear differences between groups in terms of levels of vulnerability to climate change, community engagement, and environmental preparedness. This grouping provides a strong, data-driven analytical basis, allowing the South Jakarta Environmental Agency (DLH) to use it as a strategic reference for more targeted and targeted planning and implementation of ProKlim. 
Analysis of Reading Interests of Visitors to the Library of State Junior High School 01 Salem Using the K-Means Clustering Algorithm Nurhotimah, Ica; Taufiq, Ghofar; Chrisnawati, Giantika
Blueprint Journal Vol 1 No 2 (2025): Agustus: Blueprint Journal
Publisher : PT Yupin Felicitas Utama

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Abstract

Advances in information and communication technology have made information an important element in people's lives, including education and literacy. The high demand for information has encouraged the application of data mining techniques that combine statistics, mathematics, artificial intelligence, and machine learning to extract useful information from big data. In the context of libraries, data mining can be used to analyze borrowing and visit data to understand user needs patterns. In Indonesia, low reading interest remains a serious issue. Data from PISA (OECD) and UNESCO reports indicate that Indonesia's literacy skills and reading interest levels are below global standards. Many students are not accustomed to accessing reading materials outside of textbooks and rarely visit school libraries, which should serve as centers for literacy. The grouping was based on features such as student names, class, book titles along with publishers and authors, and the date and time of visits to the library. This data was categorized into three groups: high, moderate, and low reading interest. The clustering results using the K-Means Clustering algorithm at SMP Negeri 01 Salem show that the majority of students (118) fall into the low reading interest category, 13 students into the moderate category, and only 1 student (Dian) into the high reading interest category. Evaluating the quality of the clusters using the Davies–Bouldin Index (DBI) yielded a value of 0.2966, indicating very good cluster quality—a low DBI value indicates compact and clearly separated clusters. These results prove that the K-Means algorithm is effective in grouping students based on reading behavior. With this segmentation, schools can develop data-driven literacy strategies: tailoring book collections to each cluster's preferences, conducting special programs for students with low reading interest, and involving students with high reading interest as literacy ambassadors. This approach is expected to increase student engagement and strengthen the reading culture at school.
Co-Authors Chrisnawati, Giantika Ghazi, Akhmad Nur Marni Berek, Maria Susey Nurhotimah, Ica