Article Per Year (5 Year)
p-Index From 2020 - 2025
0.408
P-Index
This Author published in this journals
All Journal Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Jurnal Nasional Pendidikan Teknik Informatika (JANAPATI)
Amrul Faruq
Unknown Affiliation
Computer Science & IT Education Engineering

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

Found 2 Documents
Search

 1 
Ensembled Machine Learning Methods and Feature Extraction Approaches for Suicide-Related Social Media Merinda Lestandy; Abdurrahim Abdurrahim; Amrul Faruq; M. Irfan; Novendra Setyawan
Jurnal Nasional Pendidikan Teknik Informatika : JANAPATI Vol. 13 No. 2 (2024)
Publisher : Prodi Pendidikan Teknik Informatika Universitas Pendidikan Ganesha

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23887/janapati.v13i2.70016

Abstract

Suicide is a pressing public health concern that affects both young people and adults. The widespread use of mobile devices and social networking has facilitated the gathering of data, allowing academics to assess patterns, concepts, emotions, and opinions expressed on these platforms. This study is to detect suicidal inclinations using Reddit online dataset. It allows for the identification of people who express thoughts of suicide by analyzing their postings. The method addresses and evaluates different machine learning classification models, namely linear SVC, random forest, and ensemble learning, along with feature extraction approaches such as TF-IDF, Bag of Words, and VADER.   This study utilised a voting classifier in our ensemble model, where the projected class output is selected by the class with the highest probability. This approach, typically known as a "voting classifier," employs voting to forecast results. The results collected suggest that employing ensemble learning with the TF-IDF 2-grams approach yields the highest F1-score, specifically 0.9315. The efficacy of TF-IDF 2-grams can be determined to their capacity to capture a greater amount of contextual information and maintain the order of words.
Analyzing Reddit Data: Hybrid Model for Depression Sentiment using FastText Embedding Amrul Faruq; Merinda Lestandy; Adhi Nugraha; Abdurrahim
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 8 No 2 (2024): April 2024
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v8i2.5641

Abstract

Depression, a prevalent mental condition worldwide, exerts a substantial influence on various aspects of human cognition, emotions, and behavior. The alarming increase in deaths attributable to depression in recent years demonstrates the imperative need to address this problem through prevention and treatment interventions. In the era of thriving social media platforms, which have a significant impact on society and psychological aspects, these platforms have become a means for people to express their emotions and experiences openly. Reddit stands out among these platforms as a significant place. The main aim of this study is to examine the feasibility of forecasting individuals' mental states by classifying Reddit articles on depression and non-depression. This work aims to employ deep learning algorithms and word embeddings to analyze the textual and semantic settings of narratives to detect symptoms of depression. The study effectively employed a BiLSTM-BiGRU model that applied FastText word embeddings. The BiLSTM-BiGRU model analyzes information bidirectionally, detecting correlations in sequential data. It is suitable for tasks dependent on input order or for addressing data uncertainties. The Reddit dataset, which contains text concerning depression, achieved an accuracy score of 97.03% and an F1 score of 97.02%.
Co-Authors Adhi Nugraha M. Irfan Merinda Lestandy Novendra Setyawan