Article Per Year (5 Year)
p-Index From 2020 - 2025
0.408
P-Index
This Author published in this journals
All Journal JTT (Jurnal Teknologi Terpadu) JATI (Jurnal Mahasiswa Teknik Informatika)
Ardhillah, Onky
Unknown Affiliation
Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Materials Science & Nanotechnology Mechanical Engineering

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

Found 2 Documents
Search

 1 
ANALISIS SENTIMEN MASYARAKAT TERHADAP DANANTARA DI PLATFORM X DENGAN METODE SVM Lucky Hermanto, Muhammad; Fathoni, Fathoni; Ardhillah, Onky; Ibrahim, Ali
JATI (Jurnal Mahasiswa Teknik Informatika) Vol. 9 No. 4 (2025): JATI Vol. 9 No. 4
Publisher : Institut Teknologi Nasional Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36040/jati.v9i4.14189

Abstract

Danantara merupakan lembaga pengelola investasi baru yang diluncurkan oleh pemerintah Indonesia dan telah menjadi perbincangan hangat di media sosial, khususnya platform X (sebelumnya Twitter). Penelitian ini bertujuan untuk menganalisis sentimen masyarakat terhadap Danantara dengan menggunakan metode Support Vector Machine (SVM). Sebanyak 1.175 tweet yang mengandung kata kunci “Danantara” dikumpulkan dari Januari hingga April 2025. Setelah melalui tahapan preprocessing seperti pembersihan teks, tokenisasi, penghapusan stopword, stemming, dan ekstraksi fitur menggunakan TF-IDF, dilakukan pelabelan otomatis dan klasifikasi sentimen menjadi positif dan negatif. Hasil evaluasi menunjukkan bahwa model SVM mampu mengklasifikasikan sentimen dengan tingkat akurasi sebesar 79,57%, precision 79,54%, recall 69,62%, dan F1-score 71,63%. Visualisasi dengan word cloud mengungkapkan kata-kata dominan seperti “koruptor”, “asing”, dan “danantara”, yang menunjukkan fokus perhatian publik. Penelitian ini memberikan wawasan penting bagi pengambil kebijakan dalam memahami opini publik serta menyusun strategi komunikasi yang lebih efektif terhadap program Danantara
PREDIKSI GANGGUAN PANIK MENGGUNAKAN KNOWLEDGE DISCOVERY IN DATABASE DENGAN ALGORITMA GRADIENT BOOSTING Maulizidan, Muammar Ramadhani; Hermanto, Muhammad Lucky; Ardhillah, Onky; Azra, Muhammad Azyumardi; Purba, Kevin Agustin; Zidan, Umar Rahman; Tania, Ken Ditha; Meiriza, Allsella
Jurnal Teknologi Terpadu Vol 13, No 2 (2025): JTT (Jurnal Terpadu Terpadu)
Publisher : Pusat Penelitian dan Pengabdian Kepada Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32487/jtt.v13i2.2518

Abstract

In an effort to enhance the diagnosis and intervention of panic disorder, this study develops a predictive model for determining the severity level of panic disorder using the Knowledge Discovery in Databases (KDD) approach. The dataset comprises variables such as age, gender, personal and family history, current stressors, symptom severity, impact on daily life, demographics, medical history, psychiatric history, substance use, coping mechanisms, social support, and lifestyle factors. The Gradient Boosting algorithm was employed to analyze the data and uncover complex patterns among the variables. The results indicate that the proposed model is capable of classifying the severity of panic disorder with high accuracy, aligning with findings from previous studies that utilized similar approaches. Other research also supports the effectiveness of machine learning algorithms in predicting panic attacks using data from wearable devices and mobile applications. These findings are expected to contribute to the development of decision support systems in the field of mental health. 
Co-Authors Ali Ibrahim Azra, Muhammad Azyumardi Fathoni Fathoni Hermanto, Muhammad Lucky Ken Ditha Tania Lucky Hermanto, Muhammad Maulizidan, Muammar Ramadhani Meiriza, Allsella Purba, Kevin Agustin Zidan, Umar Rahman