Article Per Year (5 Year)
p-Index From 2021 - 2026
0.408
P-Index
This Author published in this journals
All Journal Journal of Artificial Intelligence and Engineering Applications (JAIEA) Journal of Data Analytics, Information, and Computer Science (JDAICS)
Sahrul Bariyah
Universitas Bina Sarana Informatika
Automotive Engineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Mathematics Other

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

Found 2 Documents
Search

 1 
Performance Evaluation of Machine Learning Algorithms in Sentiment Analysis of Spotify Reviews Frizi Olivian; Sahrul Bariyah; Grant Christo Budiyanto; Riski Annisa; Lady Agustin Fitriana; Weiskhy Steven Dharmawan
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 5 No. 3 (2026): June 2026
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59934/jaiea.v5i3.2362

Abstract

The rapid growth of digital music streaming platforms has generated a massive volume of user reviews on the Google Play Store, making manual analysis practically infeasible. This study evaluates and compares the performance of three machine learning algorithms Support Vector Machine (SVM), Neural Network (Multilayer Perceptron), and Random Forest in classifying sentiments from Spotify user reviews written in Indonesian. A total of 10,000 reviews were collected from the Google Play Store using the google-play-scraper library and processed through a text preprocessing pipeline comprising cleaning, case folding, word normalization, tokenization, stopword removal, and stemming using the Sastrawi library. Sentiment labeling was performed automatically using the InSet lexicon, categorizing reviews into three classes: Positive (56.63%), Neutral (30.60%), and Negative (12.76%). Feature extraction was conducted using the TF-IDF method, with an 80:20 train-test split strategy and stratified sampling to maintain class distribution. Model performance was evaluated based on accuracy, precision, recall, and F1-score metrics. The results demonstrate that SVM and Neural Network achieved equivalent and superior accuracy of 0.937, with macro F1-scores of 0.908 and 0.907, respectively, outperforming Random Forest which recorded an accuracy of 0.853 and a macro F1-score of 0.777. These findings indicate that SVM and Neural Network are more optimal and reliable for sentiment classification of Indonesian-language Spotify reviews, while Random Forest requires further improvement, particularly in recognizing minority classes.
ANALISIS PENGARUH MEDIA SOSIAL TERHADAP KESEHATAN MENTAL REMAJA  Wanty Eka Jayanti; Sahrul Bariyah; ALBERTUS BELO; Muhammad Rizqi Pratama
Journal of Data Analytics, Information, and Computer Science Vol. 3 No. 2 (2026): April
Publisher : Yayasan Nuraini Ibrahim Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70248/jdaics.v3i2.2458

Abstract

Fenomena maraknya penggunaan media sosial di kalangan remaja menimbulkan kekhawatiran terhadap dampaknya pada kesehatan mental, seperti kecemasan, depresi, dan perasaan kesepian. Penelitian ini bertujuan untuk menganalisis pengaruh media sosial terhadap kesehatan mental remaja, baik dari sisi positif maupun negatif, serta mengidentifikasi faktor-faktor yang memperkuat atau melemahkan pengaruh tersebut. Metode yang digunakan adalah studi literatur dengan pendekatan kualitatif, melalui telaah pustaka dari berbagai jurnal dan publikasi ilmiah terbitan tahun 2020–2025. Hasil penelitian menunjukkan bahwa media sosial dapat memberikan manfaat berupa dukungan emosional, peningkatan rasa percaya diri, dan akses informasi positif jika digunakan secara bijak. Namun, penggunaan berlebihan, paparan konten negatif, serta kurangnya literasi digital dan pengawasan orang tua dapat meningkatkan risiko kecemasan, depresi, dan masalah kesehatan mental lainnya. Temuan ini menegaskan pentingnya literasi digital dan keterlibatan orang tua dalam mendampingi aktivitas media sosial remaja untuk mendorong pola penggunaan yang sehat dan bertanggung jawab.
Co-Authors ALBERTUS BELO Frizi Olivian Grant Christo Budiyanto Lady Agustin Fitriana Muhammad Rizqi Pratama Riski Annisa Wanty Eka Jayanti Weiskhy Steven Dharmawan