Article Per Year (5 Year)
p-Index From 2021 - 2026
0.882
P-Index
This Author published in this journals
All Journal Infotech Journal Jurnal Sistem Informasi Dan E-Bisnis Jurnal Sistem Informasi dan Teknologi (SINTEK) Sintek Kuwera
Dewi Sahara Nasution
Unknown Affiliation
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering

Published : 5 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search
Journal : Infotech Journal

 1 
PENENTUAN PENYUSUTAN ASET TETAP PADA MANAJEMEN ASET MENGGUNAKAN ALGORITMA BINARY SEARCH Purnomo, Dwi; Fitri Anggraeni; Dewi Sahara Nasution; Satria Putra Utama; cahyadi
INFOTECH journal Vol. 10 No. 2 (2024)
Publisher : Universitas Majalengka

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31949/infotech.v10i2.10891

Abstract

Assets are objects that have value and must be recorded, also commonly referred to as wealth. Assets can be capital, objects, land and buildings. The increasing number of assets will continue to grow and get bigger, problems arise when maintaining and monitoring the value of assets that have dynamic value over time. This research aims to group asset data using the Binary Search algorithm. So it will be easy to identify and know the final asset value which will be used by company or organizational decision makers in terms of asset renewal or asset replacement.
PENILAIAN ESAI MATA KULIAH BAHASA INGGRIS BERBASIS MACHINE LEARNING MENGGUNAKAN ALGORITMA REGRESI LINIER Cahyadi; Purnomo, Dwi; Dewi Sahara Nasution; fitri anggraini
INFOTECH journal Vol. 11 No. 1 (2025)
Publisher : Universitas Majalengka

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31949/infotech.v11i1.13014

Abstract

Manual essay assessment is time-consuming and subjective. This study proposes an automated evaluation system using a linear regression algorithm to improve efficiency and objectivity. The model analyzes linguistic and structural features such as word count, sentence length, word complexity, and grammatical patterns. The dataset consists of student essay scored by tutors  as training references. Natural Language Processing (NLP) techniques are applied to extract linguistic features and map reference scores using linear regression. The system helps instructors provide more consistent and efficient feedback while reducing subjectivity in grading. Additionally, this approach enhances assessment scalability, making it applicable to large numbers of essays. However, the model has limitations, particularly in understanding deep contextual meaning, creativity, and argument coherence. Future improvements may integrate advanced NLP models to enhance comprehension. Despite its limitations, this system presents a valuable step toward automated essay assessment in education
Co-Authors Cahyadi Dwi Purnomo Fitri Anggraeni Fitri Anggraini Hidayat, Irdianto Loevita Yulian Sari Mochammad Arief Sutisna Nur Kumala Dewi Raden Mohamad Herdian Bhakti Sutar Warno