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All Journal TEKNIK INFORMATIKA Seminar Nasional Aplikasi Teknologi Informasi (SNATI) Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) Indonesian Journal on Computing (Indo-JC) Proceeding of the Electrical Engineering Computer Science and Informatics JURNAL MEDIA INFORMATIKA BUDIDARMA JTP - Jurnal Teknologi Pendidikan Building of Informatics, Technology and Science Jurnal Locus Penelitian dan Pengabdian eProceedings of Engineering Advance Sustainable Science, Engineering and Technology (ASSET)
Dade Nurjanah
Universitas Telkom
Aerospace Engineering Automotive Engineering Chemistry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Mathematics

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Journal : Building of Informatics, Technology and Science

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Cyberbullying Detection on Twitter using Support Vector Machine Classification Method Putri Waisnawa, Ni Luh Putu Mawar Silveria; Nurjanah, Dade; Nurrahmi, Hani
Building of Informatics, Technology and Science (BITS) Vol 3 No 4 (2022): March 2022
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (398.518 KB) | DOI: 10.47065/bits.v3i4.1435

Abstract

Bullying is when someone or a group of individuals is continuously attacked. Because of the advancement of the internet, it has become very easy for society to engage in harmful acts of bullying by attacking a person or group of people who can hurt the victim, this is known as cyberbullying. Twitter is a social media platform that may be used by the society to share information and can also be used to perpetrate cyberbullying actions by sending messages (tweets) that addressed to the victims. This final project was developing a system to detect cyberbullying on Twitter. The system uses the Support Vector Machine method to classify whether the tweets that are shared include cyberbullying or not. In addition, this research also uses Term Frequency-Inverse Document Frequency (TF-IDF) and N-gram feature extraction for data that has gone through the pre-processing stage. In collecting data, the author crawled tweets based on the keywords 'jelek', 'bodoh', 'goblok', 'brengsek', 'bangsat', 'memalukan', 'laknat', 'bacot' and 'pelacur'. The best performance results of the research is 76.2% accuracy, 73.2% precision, 78.2% recall and 75.6% F1-Score generated by the RBF kernel with a total of n=1
Recommendation System from Microsoft News Data using TF-IDF and Cosine Similarity Methods Yunanda, Gisela; Nurjanah, Dade; Meliana, Selly
Building of Informatics, Technology and Science (BITS) Vol 4 No 1 (2022): June 2022
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (406.563 KB) | DOI: 10.47065/bits.v4i1.1670

Abstract

The rapidly growing information causes information overload, so news portals publish information massively. Readers need time to search and read more news, but the time relevance of news wears off quickly. A recommendation system is needed that can recommend news according to the preferences of readers. This study recommends news using the TF-IDF method. TF-IDF gives weight to each word in the news title, and then looks for similarity between stories using cosine similarity. To prove the accuracy of whether the system recommendation results were actually clicked by the reader, the recommendation results were matched with the reader's news history on the online news portal Microsoft News using a hit-rate. The hit-rate result in this study was 80.77%.
Hate Speech Detection on Twitter through Natural Language Processing using LSTM Model Arbaatun, Cepthari Ningtyas; Nurjanah, Dade; Nurrahmi, Hani
Building of Informatics, Technology and Science (BITS) Vol 4 No 3 (2022): December 2022
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v4i3.2718

Abstract

Currently, social media is a place to express opinions. This opinion can be positive or negative. However, lately, the opinion that often appears is a negative opinion, such as hate speech. Hate speech is often found on social media, such as malicious comments intended to insult individuals or groups. Based on WeAreSocial data in 2021, one of the most used social media platforms in Indonesia is Twitter, with 63.6% of users. According to the Indonesia National Police, hate speech cases were more dominant during the period from April 2020 to July 2021. Therefore, efforts are needed to identify hate speech on the Twitter platform. One way to detect hate speech is by using deep learning. In this research, we use a deep learning model of Long Short-Term Memory (LSTM) with word embedding. FastText and Global Vector (GloVe) is the word embeddings that we use as input for word representation and classification. FastText embeddings make use of subword information to create word embeddings and GloVe embeddings using an unsupervised learning method trained on a corpus to generate distributional feature vectors. From the evaluation results on the experimental model, LSTM-FastText using random oversampling has an advantage with an F1-score of 89.91% compared to LSTM-GloVe to obtain an F1-score of 82.14%.
Co-Authors Ade Romadhony Aditia Rafif Khoerulloh Aditiyan Iswahyudin Adiyaka Niastya Ihsan Maulana Agung Toto Wibowo Arbaatun, Cepthari Ningtyas Ardiyanto, Fajar Widhi Ayu, Ravika Berliana Shafa Wardani Dhuki Dwi Rachman Diffo Elza Pratama Fahmi, Fitra Zul Fakhri Fauzan Fikri Bahiransyah Fitra Zul Fahmi Florita Diana Sari Hani Nurrahmi Henri Tantyoko Henri Tantyoko Hilmi Eko Arianto Irfansyah, Muhammad Rafi Ismail, Asep Maulana Jayaperwira, Iklil Johan NMP Simatupang Josua Deston Girsang Julia Julia Maman Abdurohman Mohammad Fathurrahman Muhammad Rafi Irfansyah Muttaqien, Rifqi Fauzia Odia Pratama Putri Waisnawa, Ni Luh Putu Mawar Silveria R. Hatim Muhammad Amin Rahmawati Sitti Azizah Razaq, Muhammad Thaariq Rifqi Fauzia Muttaqien Riki Nur Afifuddin Rita Rismala Rusmawati, Yanti sa'adah, siti Sari Rahmawati Selly Meliana Setyadi Putra Rahmandya Shendy Murty Siti Sa'adah Wahyu Setiawan Wardani, Berliana Shafa Yanti Rusmawati Yunanda, Gisela Zaki Mudzakir Hidayatullah