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All Journal Jurnal Teknologi Pendidikan Jurnal Mantik Journal of Computer Science, Information Technology and Telecommunication Engineering (JCoSITTE) MEANS (Media Informasi Analisa dan Sistem) Instal : Jurnal Komputer Jurnal Info Sains : Informatika dan Sains JURNAL TEKNOLOGI INFORMASI & KOMUNIKASI DALAM PENDIDIKAN Innovative: Journal Of Social Science Research
Reni Rahmadani
Pendidikan Teknologi Informatika Dan Komputer, Fakultas Teknik, Universitas Negeri Medan
Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Public Health Other

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Implementasi Algoritma Merkle-Hellman Knapsack dalam Penyandian Record Database Rahmadani, Reni; Hutahaean, Harvei Desmon; Sari, Ressy Dwitias
MEANS (Media Informasi Analisa dan Sistem) Volume 5 Nomor 2
Publisher : LPPM UNIKA Santo Thomas Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (915.713 KB) | DOI: 10.54367/means.v5i2.983

Abstract

A lot of data is misused without the data owner being aware of it. Software developers must ensure the security user data on their system. Due to the size of the market that houses data, the security of record databases must be of great concern. Cryptographic systems or data encryption can be used for data security. The Merkle-Hellman Knapsack algorithm is included in public-key cryptography because it uses different keys for the encryption and decryption processes. This algorithm belongs to the NP-complete algorithm which cannot be solved in polynomial order time. This algorithm has stages of key generation, encryption, and decryption. The results of this study secure database records from theft by storing records in the form of ciphertext/password. Ciphertext generated by algorithmic encryption has a larger size than plaintext.
Penerapan Algoritma Mixed Heuristic dalam Penilaian Ujian Pada Jurusan Pendidikan Teknik Elektro Universitas Negeri Medan Sari, Ressy Dwitias; Rahmadani, Reni
MEANS (Media Informasi Analisa dan Sistem) Volume 5 Nomor 2
Publisher : LPPM UNIKA Santo Thomas Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (684.879 KB) | DOI: 10.54367/means.v5i2.989

Abstract

During the COVID-19 pandemic, teaching and learning activities switch of technology such as e-learning. In the Department of Electrical Engineering Education, Universitas Negeri Medan, 80% of electrical lecturers provide multiple choice exams and short answers. The scoring system cannot be performed automatically by a computer. The algorithm used in this study is a Mixed Heuristic algorithm. This algorithm is used because it is an effective way of working for sentences of more than 1 word. And give the results in the form of ranking in the form of similarity proportions. From the process of using a mixed heuristic algorithm, it can be proven that this algorithm can perform keywords on the answer keys and answers of users or students. The algorithm is also able to provide value to a user or student's answers
Web-Based Simulation System in Formulating Sustainable Replanting Mendoza, Muhammad Dominique; Suwanto, Fevi Rahmawati; Matondang, Ishaq; Hutajulu, Olnes Yosefa; Rahmadani, Reni; Sari, Ressy Dwitias
Innovative: Journal Of Social Science Research Vol. 4 No. 6 (2024): Innovative: Journal Of Social Science Research
Publisher : Universitas Pahlawan Tuanku Tambusai

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/innovative.v4i6.17556

Abstract

Abstrak Simulasi berbasis web harus mencakup kemampuan untuk mendistribusikan dan menggabungkan model dan hasil simulasi dengan mudah, mirip dengan aksesibilitas dan kemampuan penyusunan publikasi web kontemporer. Kehadiran konten web yang dapat dieksekusi, kapasitas untuk portabel secara universal, pemanfaatan teknologi komponen, dan ketersediaan paket standar tingkat tinggi untuk akses database dan pembuatan antarmuka pengguna grafis merupakan faktor penting yang memfasilitasi implementasi simulasi berbasis web. Pemanfaatan perangkat lunak berbasis komponen memungkinkan penciptaan lingkungan simulasi yang sangat modular, memfasilitasi penggunaan ulang komponen perangkat lunak secara ekstensif. Pengaturan simulasi menghadapi tekanan yang semakin meningkat karena sifat simulasi berbasis web yang luas. Penggabungan teknologi komponen dalam lingkungan simulasi berbasis web memungkinkan perlakuan model simulasi sebagai komponen individual, yang dapat dirakit secara fleksibel untuk membangun model. Selain itu, simulasi berbasis web ini memfasilitasi hubungan dinamis antara input dan output simulasi dengan sistem basis data, sehingga menyediakan sarana yang nyaman dan mudah beradaptasi untuk menyimpan hasil simulasi. Kata Kunci: Java, Penanaman Kembali, Berkelanjutan, Simulasi Berbasis Web
Academic Performance Prediction of PTIK Students through Machine Learning Models at Universitas Negeri Medan Tansa Trisna Astono Putri; Reni Rahmadani; Rosma Siregar; Hanapi Hasan
Journal of Computer Science, Information Technology and Telecommunication Engineering Vol 7, No 1 (2026)
Publisher : Universitas Muhammadiyah Sumatera Utara, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30596/jcositte.v7i1.29570

Abstract

This study addressed the need for an effective approach to predicting student academic performance in higher education using data-driven methods. The study aimed to implement machine learning models to predict the academic performance of students in the Information and Communication Technology Education Study Program at Universitas Negeri Medan. A quantitative predictive design was employed using a dataset of 40 student records. Five classification models were tested, namely Logistic Regression, Decision Tree, Random Forest, Support Vector Machine, and Naïve Bayes. The results showed that all models produced strong predictive performance. Decision Tree achieved the highest accuracy at 93.1%, Logistic Regression produced the highest precision at 95.9% and the highest F1-score at 93.2%, while Support Vector Machine obtained the highest recall at 93.2%. These findings indicated that machine learning was feasible for predicting student academic performance in the study program. The study concluded that Logistic Regression provided the most balanced overall performance and had strong potential to support early academic intervention and data-based academic decision making in higher education.
Co-Authors Adilla Ulfhi Azmi Rizky Lubis Azqal Azkia Devi Silvia Panjaitan Eka Dodi Suryanto Fevi Rahmawati Suwanto Fikri Hanif Hamzah, Afif Hanapi Hasan Hervei Desmon Hutahaean Hutahaean, Harvei Desmon Hutajulu, Olnes Yosefa Ishaq Matondang Khofifah Qalbun Salima Marwan Affandi Matondang, Ishaq Maulana, Bagoes Mendoza, Muhammad Dominique Mendoza, Muhammad Dominique Olnes Y. Hutajulu Purba, Julio Aldrin Putri, Tansa Trisna Astono Ressy Dwitias Sari Rosma Siregar SABRINA, ELSA Sari, Ressy Dwitias Sihombing, Gracia Napare Suwanto, Fevi Rahmawati