Article Per Year (5 Year)
p-Index From 2020 - 2025
0.444
P-Index
This Author published in this journals
All Journal Jurnal Computer Science and Information Technology (CoSciTech) UNIVERSUM : Jurnal KeIslaman dan Kebudayaan
Muhammad Maulana Akbar
Unknown Affiliation
Religion Humanities Computer Science & IT Education

Published : 2 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search

 1 
HOAKS DALAM PERSPEKTIF HADIST: (Studi Kasus LPM Dinamika UIN SU) Muhammad Maulana Akbar; Rambe, Uqbatul Khoir
Universum Vol. 18 No. 2 (2024): December 2024
Publisher : LPPM IAIN Kediri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30762/universum.v18i2.2805

Abstract

Perkembangan teknologi pada awalnya dirancang untuk memudahkan manusia dalam melakukan segala hal. Namun, belakangan ini sejumlah kasus berita bohong telah muncul dari beberapa platform media sosial. Penelitian ini bertujuan untuk mengidentifikasi hoaks dalam perspektif hadis di LPM Dinamika UIN SU. Penelitian ini menggunakan jenis penelitian kualitatif melalui studi pustaka melibatkan pengumpulan data dari berbagai sumber seperti buku, kitab suci, jurnal, dan catatan lain yang relevan untuk mendukung temuan penelitian. Hasil dan pembahasan dari penelitian ini dapat disimpulkan bahwa LPM Dinamika UIN SU dapat mencegah hoaks yang terjadi dalam pemberitaan dengan menggunakan alur keredaksian. Selain alur keredaksian di atas, dalam pemberitaan LPM Dinamika UIN SU juga menggunakan al-Qur’an dan hadis sebagai landasan berpikir.
Optimasi Metode Certainty Factor Menggunakan Rank Order Centroid Pada Sistem Pakar Pendeteksi Turnover Intention Berbasis WEB Muhammad Maulana Akbar; Moh. Dasuki; Miftahur Rahman
Computer Science and Information Technology Vol 6 No 2 (2025): Jurnal Computer Science and Information Technology (CoSciTech)
Publisher : Universitas Muhammadiyah Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37859/coscitech.v6i2.9869

Abstract

Turnover intention, or the tendency of employees to resign, poses a significant challenge for companies—especially when dealing with Generation Z, who tend to have lower job commitment and are more likely to switch jobs. This study aims to develop a web-based expert system to detect the level of employee turnover intention by integrating the Certainty Factor (CF) and Rank Order Centroid (ROC) methods. The CF method is used to handle uncertainty in questionnaire assessments, while ROC is implemented to optimize the weights among aspects, namely Thinking of Quitting, Intention to Search for Alternatives, and Intention to Quit. The system is built based on 36 questionnaire statements and tested on 34 respondents. The results show that the system provides more proportional and realistic interpretations compared to the non-optimized approach. Accuracy testing indicates that 27 out of 34 system results match manual assessments, yielding an accuracy rate of 79.41%. These findings suggest that the system performs reliably and can serve as a practical tool for the early detection of turnover intention in the workplace.
Co-Authors Miftahur Rahman Moh. Dasuki Rambe, Uqbatul Khoir