Article Per Year (5 Year)
p-Index From 2021 - 2026
0.23
P-Index
This Author published in this journals
All Journal Journal of Information Systems Engineering and Business Intelligence JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika)
Anggit Dwi Hartanto
Amikom Yogyakarta University
Computer Science & IT Education

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

Found 2 Documents
Search

 1 
Lexicon-Based Indonesian Local Language Abusive Words Dictionary to Detect Hate Speech in Social Media Mardhiya Hayaty; Sumarni Adi; Anggit Dwi Hartanto
Journal of Information Systems Engineering and Business Intelligence Vol. 6 No. 1 (2020): April
Publisher : Universitas Airlangga

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20473/jisebi.6.1.9-17

Abstract

Background: Hate speech is an expression to someone or a group of people that contain feelings of hate and/or anger at people or groups. On social media users are free to express themselves by writing harsh words and share them with a group of people so that it triggers separations and conflicts between groups. Currently, research has been conducted by several experts to detect hate speech in social media namely machine learning-based and lexicon-based, but the machine learning approach has a weakness namely the manual labelling process by an annotator in separating positive, negative or neutral opinions takes time long and tiringObjective: This study aims to produce a dictionary containing abusive words from local languages in Indonesia. Lexicon-base is very dependent on the language contained in dictionary words. Indonesia has thousands of tribes with 2500 local languages, and 80% of the population of Indonesia use local languages in communication, with the result that a significant challenge to detect hate speech of social media.Methods: Abusive words surveys are conducted by using proportionate stratified random sampling techniques in 4 major tribes on the island of Java, namely Betawi, Sundanese, Javanese, MadureseResults: The experimental results produce 250 abusive words dictionary from 4 major Indonesian tribes to detect hate speech in Indonesian social media by using the lexicon-based approach. Conclusion: A stratified random sampling technique has been conducted in 4 major Indonesian tribes to produce 250 abusive words for hate speech detection using the lexicon-based approach.
ANALISIS PERBANDINGAN KINERJA ALGORITMA STEMMING NAZIEF-ADRIANI DAN IN-IDRIS DALAM PENGOLAHAN TEKS BAHASA INDONESIA Muhammad Iqbal; Ema Utami; Anggit Dwi Hartanto
JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Vol 11, No 1 (2026)
Publisher : STKIP PGRI Tulungagung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29100/jipi.v11i1.7723

Abstract

Stemming merupakan salah satu tahap penting dalam pemrosesan bahasa alami (Natural Language Processing/NLP) untuk mengubah kata berim-buhan menjadi bentuk dasarnya. Penelitian ini membandingkan dua algoritma stemming populer dalam Bahasa Indonesia, yaitu Nazief-Adriani yang berbasis kamus dan In-Idris yang berbasis aturan. Evaluasi dilakukan terhadap tiga jenis dokumen dengan karakteristik gaya bahasa berbeda untuk mengukur kinerja masing-masing algoritma berdasarkan tiga parameter: akurasi, durasi pemrosesan, dan Root Mean Square Er-ror (RMSE). Hasil menunjukkan bahwa algoritma In-Idris memiliki tingkat akurasi lebih tinggi (rata-rata 86%) dan RMSE lebih rendah (0.13) dibandingkan Nazief-Adriani (75,3% dan 0.24), sehingga lebih stabil dan akurat dalam mengidentifikasi bentuk dasar kata. Sementara itu, Nazief-Adriani menunjukkan keunggulan dalam efisiensi waktu pemrosesan. Temuan ini menegaskan pentingnya pemilihan algoritma yang disesuaikan dengan kebutuhan spesifik aplikasi NLP, serta mem-buka peluang pengembangan pendekatan hibrida untuk hasil yang lebih optimal.
Co-Authors Ema Utami Hayaty, Mardhiya Muhammad Iqbal Sumarni Adi