Article Per Year (5 Year)
p-Index From 2021 - 2026
0.444
P-Index
This Author published in this journals
All Journal Jurnal Ilmu Komputer dan Teknologi (IKOMTI) Jurnal Kolaborasi Akademika
Indah Chairun Nisa
Unknown Affiliation
Humanities Computer Science & IT Economics, Econometrics & Finance Environmental Science

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

Found 2 Documents
Search

 1 
Technological Innovation in Arabic Language Education as a Tool for Community Empowerment Toward Social, Economic, and Environmental Sustainability Mahdir Muhammad; Indah Chairun Nisa; Rizki Julia Utama
JKA Vol. 2 No. 2 (2025): JKA
Publisher : Bansigom Na Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26811/ztrw2g22

Abstract

This paper examines the transformative role of technological innovation in Arabic language education as a strategic means of community empowerment. It investigates how integrating educational technologies within Arabic teaching contributes to broader sustainable development goals (SDGs), particularly in fostering social inclusion, economic opportunity, and environmental consciousness. Employing a qualitative literature-based methodology, the study synthesizes recent advancements in mobile learning, AI-assisted platforms, and paperless educational tools. Findings suggest that when adapted with cultural sensitivity and sustainability objectives, Arabic language technologies can drive inclusive development and support marginalized communities. Recommendations are offered for educators, policymakers, and developers aiming to enhance the impact of Arabic language education in the digital age.
Perbandingan Algoritma Regresi Logistik, Support Vector Machine, dan Gradient Boosting Pada Analisis Sentimen Data Komentar Siswa Muntiari, Novita Ranti; Kharis Hudaiby Hanif; Indah Chairun Nisa
Jurnal IT UHB Vol 4 No 2 (2023): Jurnal Ilmu Komputer dan Teknologi
Publisher : Universitas Harapan Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35960/ikomti.v4i2.1286

Abstract

Evaluasi pengajaran dilakukan menggunakan aplikasi Digital Teacher Assessment (DITA) belum melibatkan klasifikasi pengelompokkan. Data komentar yang terkumpul dikelompokkan menjadi tiga kategori yaitu komentar positif, negatif, dan netral. Berdasarkan kategori komentar membutuhkan analisis sentimen dalam mengelompokkan komentar tersebut. Analisis sentimen menggunakan lexicon based. Selanjutnya data komentar tersebut diberi bobot menggunakan TF-IDF sebelum diklasifikasikan dan dievaluasi. Dalam penelitian ini menggunakan algoritma regresi logistik, support vector machine (SVM), dan gradient boosting. Hasil penelitian menunjukkan perbandingan akurasi dari algoritma regresi logistik, support vector machine (SVM), dan gradient boosting dengan algoritma gradient boosting memiliki tingkat akurasi yang paling tinggi yaitu 97,5%. Dari hasil penelitian dapat disimpulkan bahwa algoritma gradient boosting memiliki tingkat akurasi lebih baik dalam mengklasifikasi data analisis sentimen komentar siswa.
Co-Authors Hanif, Kharis Hudaiby Mahdir Muhammad Muntiari, Novita Ranti Rizki Julia Utama