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All Journal Journal of Innovation and Future Technology (IFTECH) Indonesian Research Journal on Education Kesatria : Jurnal Penerapan Sistem Informasi (Komputer dan Manajemen) Komputasi : Jurnal Ilmiah Ilmu Komputer dan Matematika
Fitri Safnita
Universitas Bung Hatta
Religion Arts Humanities Chemistry Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Energy Engineering Environmental Science Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Mathematics Physics Social Sciences Other

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Penerapan Algoritma K-Means Dalam Pengklasteran Hasil Evaluasi Akademik Mahasiswa Fitri Safnita; Sarjon Defit; Gunadi Widi Nurcahyo
Kesatria : Jurnal Penerapan Sistem Informasi (Komputer dan Manajemen) Vol 5, No 2 (2024): Edisi April
Publisher : LPPM STIKOM Tunas Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/kesatria.v5i2.360

Abstract

Several institutions that have utilized computer-based information systems for many years certainly have quite large amounts of data. The data generated and stored in a computer system is designed to be fast and accurate in both operation and administration. This data is designed for reporting and analysis that uses that data. It turns out that there is a lot of data available, with so much data we are increasingly faced with the question, "What knowledge can we gain from this data?" The K-Means algorithm is an iterative clustering algorithm that partitions a data set into a number of clusters that are initially determined. The K-Means algorithm is an iterative clustering algorithm that partitions a data set into a number of clusters that are initially determined. The K-Means algorithm is easy to implement and run, relatively fast, easy to adapt, commonly used in practice. The parameter that must be entered when using the K-Means algorithm is the K value. The K value is generally used based on previously known information regarding how many clusters appear in This research aims to group students based on academic evaluation results. The method used to manage student academic data uses the Data Mining method with the K-Means Clustering Algorithm. The dataset processed in this research comes from the Faculty of Engineering, Informatics Engineering Study Program, Islamic University of Riau. The dataset consists of 180 student data starting from semester 1 to semester 4. The results obtained from this research are in the form of grouping students based on the achievement student cluster, there are 104 students with a percentage of 57.72%, the student cluster with potential for achievement is 62 students with a percentage of 34 .41%, the potentially problematic student cluster has 10 students with a percentage of 5.55%, and the problematic student cluster has 4 students with a percentage of 2.22%. Therefore, it is hoped that the results of this research will provide new knowledge that can be used as a source of information and function as a reference model for academic planners to monitor and predict the development of each student's academic performance.
KESIAPAN ETIKA PENGGUNAAN AI GENERATIF PADA TUGAS AKADEMIK: PENGARUH PEMAHAMAN INTEGRITAS AKADEMIK DAN PERSEPSI MANFAAT-RISIKO Eka Ramadhani Putra; Putri Ramadani; Fitri Safnita
Journal of Innovation And Future Technology Vol. 8 No. 1 (2026): Vol 8 No 1 (Februari 2026): Journal of Innovation and Future Technology (IFTECH
Publisher : LPPM Unbaja

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47080/iftech.v8i1.4526

Abstract

Generative AI tools are increasingly used by students to support academic tasks such as drafting, coding, and summarizing. While these tools may improve efficiency and learning, they also introduce ethical risks related to academic integrity, transparency, privacy, and misinformation. This study examines ethical readiness for using generative AI in academic assignments and tests the effects of students' understanding of academic integrity and their perceived benefit-risk appraisal. A cross-sectional survey was administered to undergraduate students in semester 4 (N = 180). Data were analyzed using multiple regression. Key findings (simulated example): integrity understanding positively predicted ethical readiness (beta = 0.348, p <0.001), perceived risk also showed a positive effect (beta = 0.185, p = 0.013), while perceived benefit was not significant (beta = -0.053, p = 0.498).
Penerapan Metode Data Mining Clustering terhadap Penjualan Produk Detergen pada Ritel Mart Menggunakan Algoritma K-Means Nurul Aulya; Fitri Safnita
Indonesian Research Journal on Education Vol. 6 No. 3 (2026): Irje 2026
Publisher : Fakultas Keguruan dan Ilmu Pendidikan, Universitas Pahlawan Tuanku Tambusai

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/irje.v6i2.4171

Abstract

Data mining dapat membantu pengguna mendapatkan informasi yang berguna serta meningkatkan pengetahuan bagi penggunanya. Informasi yang dihasilkan kemudian diolah untuk menghasilkan yang biasanya berbentuk sebuah pola. kemudian pola tersebut diubah menjadi sebuah pengetahuan. salah satu metode data mining adalah Clustering yaitu pengelompokkan data berdasarkan karakteristik data tertentu kedalam karakteristik objek yang sama untuk menghasilkan pengelompokkan objek yang mirip satu sama lain dalam kelompok-kelompok. K-means merupakan salah satu algoritma Clustering yang bertujuan untuk membagi data menjadi beberapa kelompok. Pada penelitian ini, Algorritma K-means akan digunakan untuk pengelompokkan pembelian detergen pada sebuah retail mart yang terletak dikota Payakumbuh. Pengelompokkan ini akan menghasilkan detergen mana yang paling laku atau yang paling banyak dibeli dan juga detergen mana yang kurang diminati pembeli sehingga pemilik toko dapat mencari alternatif lain agar produk detergen yang kurang diminati menjadi laku dan banyak diminati oleh konsumen.
Assessment of Information Security Maturity Using the KAMI Index 5.0 Aligned with ISO/IEC 27001 Fitri Safnita; Putri Ramdani; Maisan Dewi Puspa Khairani; Eka Ramadhani Putra
Komputasi: Jurnal Ilmiah Ilmu Komputer dan Matematika Vol. 23 No. 2 (2026): Komputasi: Jurnal Ilmiah Ilmu Komputer dan Matematika
Publisher : Program Studi Ilmu Komputer, Universitas Pakuan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33751/komputasi.v23i2.110

Abstract

The advancement of digital government initiatives has led to a wider deployment of IT systems for public service delivery. Consequently, public sector agencies must establish robust cyber defenses to safeguard critical information and infrastructure. This research evaluates the information security posture of the Padang City Communication and Informatics Office, focusing on its readiness to sustain digital transformation. The assessment aligns with the ISO/IEC 27001 framework and utilizes the KAMI Index 5.0 as the primary diagnostic tool. Employing a qualitative descriptive methodology, the study collected empirical data through field observations, stakeholder interviews, and comprehensive policy reviews. The diagnostic results revealed a compliance score of 518, demonstrating that the organization has established the baseline requirements of the ISO/IEC 27001 standard. Nonetheless, the overall maturity remains at Level II, showing that while several protective processes are active, they lack formal documentation and optimal coordination. To bridge these gaps, structured action plans are formulated across seven key assessment domains: governance (5 actions), risk mitigation (14 actions), security policy framework (5 actions), asset classification (13 actions), technical infrastructure (6 actions), data privacy (11 actions), and third-party control (6 actions). These actionable steps provide a strategic roadmap to enhance the mature of city's electronic administration security.
Co-Authors Eka Ramadhani Putra Eka Ramadhani Putra Gunadi Widi Nurcahyo Maisan Dewi Puspa Khairani Nurul Aulya Putri Ramadani Putri Ramdani Sarjon Defit