Article Per Year (5 Year)
p-Index From 2020 - 2025
0.408
P-Index
This Author published in this journals
All Journal KOMPUTIKA - Jurnal Sistem Komputer Building of Informatics, Technology and Science
Hulvi, Alfajri
Unknown Affiliation
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering

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

Found 2 Documents
Search

 1 
Optimasi Rekomendasi Sustainable Development Goals (SDGs) di Indonesia menggunakan Content-Based Filtering dan Algoritma Machine Learning Hulvi, Alfajri; Kusrini, Kusrini
Building of Informatics, Technology and Science (BITS) Vol 6 No 2 (2024): September 2024
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v6i2.5807

Abstract

Abstrak−Lahirnya program tentang Tujuan Pembangunan Berkelanjutan atau Sustainable Development Goals (SDGs) pada tahun 2015 membuat masyarakat di semua negara mulai memandang penting pembangunan berkelanjutan untuk diimplementasikan. Indonesia, sebagai bagian dari komunitas global, juga telah mengadopsi SDGs ini sebagai kerangka kerja dalam upaya mencapai Indonesia Emas 2045. Dengan visi ini, Indonesia bercita-cita menjadi negara maju yang berdaulat, adil, dan makmur tepat pada peringatan 100 tahun kemerdekaannya. Untuk mencapai tujuan secara efektif, penting untuk menerapkan sistem rekomendasi berbasis Artificial Intelligence (AI) yang mempertimbangkan tantangan sosial, ekonomi, dan lingkungan hidup yang dihadapi oleh negara Indonesia di masa mendatang. Content-Based Filtering (CBF) adalah teknik yang populer untuk membangun sistem tersebut. Penelitian ini membahas teknik untuk optimasi CBF menggunakan beberapa algoritma machine learning tradisional yaitu SVM, KNN, DT dan algoritma Deep Learning yaitu MLP. Teknik pengambilan sample dan penyetelan hiperparameter juga diperhatikan dalam penelitian ini. Algoritma Deep Learning MLP menghasilkan akurasi tertinggi yaitu 84%.
Mengukur Faktor Demografi Psikologis: Memprediksi Depresi, Kecemasan, dan Stres dengan menggunakan Machine Learning Juwariyah, Siti; Hulvi, Alfajri; Riduan, Nor; Kusrini, Kusrini
Komputika : Jurnal Sistem Komputer Vol. 13 No. 2 (2024): Komputika: Jurnal Sistem Komputer
Publisher : Computer Engineering Departement, Universitas Komputer Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34010/komputika.v13i2.11793

Abstract

Mental health is an important aspect of human life. Depression, anxiety and stress are some of the most common mental health disorders. These disorders can negatively impact daily life, including productivity, social relationships, and an individual's quality of life, requiring accurate prediction for early intervention. One of the psychological measurement tools used to assess a person's level of depression, anxiety, and stress is the DASS-42 (Depression Anxiety Stress Scales - Long Form). In addition to the DASS-42 results, demographic factors such as age, gender, education level, and social status are important to analyze to strengthen the analysis. Machine learning (ML) is a powerful tool for analyzing complex data such as predicting psychological demographic factors associated with these mental health conditions. This study explores the potential of ML using a comprehensive dataset, using K-Nearest Neighbor and Support Vector Machine algorithms to assess prediction performance. The findings highlighted the effectiveness of ML models in predicting depression, anxiety and stress with high accuracy. The best algorithm in this study for the classification of depression, anxiety and stress is SVM with 99% accuracy but the use of Exploratory Data Analysis (EDA) technic to process additional variables affects the accuracy of the model so it can be concluded that demographic variables have an influence on the classification of depression, anxiety and stress.
Co-Authors Juwariyah, Siti Kusrini, Kusrini Riduan, Nor