Article Per Year (5 Year)
p-Index From 2020 - 2025
0.444
P-Index
This Author published in this journals
All Journal International Journal of Engineering, Science and Information Technology JURNAL KIMIA SAINTEK DAN PENDIDIKAN
Tarigan, Nurliana
Unknown Affiliation
Astronomy Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Education Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Physics Social Sciences Transportation

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

Found 2 Documents
Search

 1 
PEMBUATAN NANOKARBON DARI LIMBAH PADAT KELAPA SAWIT MENGGUNAKAN METODE HIDROTERMAL Silitonga, Nelson; Tarigan, Nurliana; Saragih, Gimelliya; Purwandari, Vivi; Akbari, Ahmad Zukhruf
JURNAL KIMIA SAINTEK DAN PENDIDIKAN Vol. 7 No. 1 (2023): JURNAL KIMIA SAINTEK DAN PENDIDIKAN
Publisher : Program Studi Kimia - Universitas Sari Mutiara Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51544/kimia.v7i1.3906

Abstract

Meningkatnya kesadaran tentang polusi telah menyebabkan pengembangan solusi untuk masalah lingkungan dengan memaksimalkan pemanfaatan biomassa yang berlimpah untuk pembuatan nanokarbon. Penelitian ini bertujuan untuk memanfaatkan limbah padat kelapa sawit menjadi material yang memiliki nilai guna dan nilai ekonomis menjadi nanokarbon dan material yang berteknologi tinggi. nanokarbon yang berasal dari pelepah dan cangkang sawit yang dibuat melalui metode hidrotermal dengan 180oC selama 2 jam dengan pelarut air dan dilakukan metode ultrasonikasi selama 15 menit. Nanokarbon yang didapat  dikarakterisasi menggunakan Fourier Tranform Infrared (FTIR), X-Ray Difraktometer (XRD), dan Particle Size Analyzer (PSA). Dari hasil analisa gugus fungsi menggunakan FTIR telah menunjukan gugus fungsi nanokarbon dan ditemukan perubahan ukuran partikel yang sangat signifikan dari proses sebelum dan sesudah proses hidrotermal, yaitu 895,2 nm menjadi 334,2 nm. Peningkatan volume pori dan luas permukaan partikel masing-masing sebesar 3,5% dan 63% setelah proses hidrotermal.
Hybrid Deep Fixed K-Means (HDF-KMeans) Zuhanda, Muhammad Khahfi; Kohsasih, Kelvin Leonardi; Octaviandy, Pieter; Hartono, Hartono; Kurnia, Dian; Tarigan, Nurliana; Ginting, Manan; Hutagalung, Manahan
International Journal of Engineering, Science and Information Technology Vol 5, No 3 (2025)
Publisher : Malikussaleh University, Aceh, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52088/ijesty.v5i3.913

Abstract

K-Means is one of the most widely used clustering algorithms due to its simplicity, scalability, and computational efficiency. However, its practical application is often hindered by several well-known limitations, such as high sensitivity to initial centroid selection, inconsistency across different runs, and suboptimal performance when dealing with high-dimensional or non-linearly separable data. This study introduces a hybrid clustering algorithm named Hybrid Deep Fixed K-Means (HDF-KMeans) to address these issues. This approach combines the advantages of two state-of-the-art techniques: Deep K-Means++ and Fixed Centered K-Means. Deep K-Means++ leverages deep learning-based feature extraction to transform raw data into more meaningful representations while employing advanced centroid initialization to enhance clustering accuracy and adaptability to complex data structures. Complementarily, Centered K-Means improve the stability of clustering results by locking certain centroids based on domain knowledge or adaptive strategies, effectively reducing variability and convergence inconsistency. Integrating these two methods results in a robust hybrid model that delivers improved accuracy and consistency in clustering performance. The proposed HDF-KMeans algorithm is evaluated using five benchmark medical datasets: Breast Cancer, COVID-19, Diabetes, Heart Disease, and Thyroid. Performance is assessed using standard classification metrics: Accuracy, Precision, Recall, and F1-Score. The results show that HDF-KMeans outperforms traditional K-Means, K-Means++, and K-Means-SMOTE algorithms across all datasets, excelling in overall accuracy and F1 Score. While some trade-offs are observed in specific precision or recall metrics, the model maintains a solid balance, demonstrating reliability. This study highlights HDF-KMeans as a promising and effective solution for complex clustering tasks, particularly in high-stakes domains like healthcare and biomedical analysis.
Co-Authors Akbari, Ahmad Zukhruf Ginting, Manan Hartono Hartono Hutagalung, Manahan Kelvin Leonardi Kohsasih Kurnia, Dian Muhammad Khahfi Zuhanda Pieter Octaviandy, Pieter Purwandari, Vivi Saragih, Gimelliya Silitonga, Nelson