Article Per Year (5 Year)
p-Index From 2021 - 2026
0.408
P-Index
This Author published in this journals
All Journal INTENSIF: Jurnal Ilmiah Penelitian dan Penerapan Teknologi Sistem Informasi Zero : Jurnal Sains, Matematika, dan Terapan
Ali Ikhwan
School of Computer and Communication Engineering
Agriculture, Biological Sciences & Forestry Chemistry Computer Science & IT Decision Sciences, Operations Research & Management Mathematics Physics

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

Found 2 Documents
Search

 1 
Palm Oil Quality Based on Free Fatty Acid Using SVM Andi Prayogi; Moustafa H. Aly; Ali Ikhwan; Muhammad Akbar Syahbana Pane; Ratu Mutiara Siregar
INTENSIF: Jurnal Ilmiah Penelitian dan Penerapan Teknologi Sistem Informasi Vol 9 No 2 (2025): August 2025
Publisher : Universitas Nusantara PGRI Kediri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29407/intensif.v9i2.24797

Abstract

Background: Palm oil is one of the key commodities in both the food and non-food industries, with its quality largely influenced by the level of Free Fatty Acid (FFA). Obejctive: High FFA content can reduce the stability and market value of the oil. Classify palm oil quality based on FFA levels using the Support Vector Machine (SVM) algorithm. Methods: FFA levels were measured across multiple samples with varying usage frequencies (0, 5, 7, and 9 cycles) using the alkalimetric titration method. The measured data was categorized as "Suitable" if FFA ≤ 0.3% and "Unsuitable" if it exceeded this threshold. The developed SVM model was trained using 70% of the data and tested with the remaining 30%. Results: Evaluation results indicate that the model achieved an accuracy of 95%, a precision of 92%, and a recall of 94%, demonstrating SVM's effectiveness in classifying data. Additionally, hyperplane visualization using PCA provided a clearer distinction between oil categories based on FFA levels. Conclusion: This study highlights that SVM can serve as an effective alternative for FFA-based palm oil quality classification. The implementation of this model is expected to enhance efficiency in the palm oil industry, particularly.
Analyzing Criteria Count Impact on SAW and TOPSIS Stability in Decision Support Systems Alif Catur Murti; Muhammad Imam Ghozali; Indra Lina Puta; Ali Ikhwan
ZERO: Jurnal Sains, Matematika dan Terapan Vol 9, No 2 (2025): Zero: Jurnal Sains Matematika dan Terapan
Publisher : UIN Sumatera Utara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30829/zero.v9i2.25707

Abstract

This study investigates how increasing the number of decision criteria (5-30) affects the ranking stability and computational efficiency of Simple Additive Weighting (SAW) and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). Previous studies compared these methods in domains such as scholarship selection and food assistance but did not examine how rankings evolve under greater complexity. Using a synthetic dataset of five fixed alternatives with multiple random seeds, results show that SAW is more prone to ranking fluctuations, while TOPSIS demonstrates greater stability. Kendall's Tau reveals variability across scenarios, and sensitivity tests confirm that agreement depends on data generation. Computationally, SAW exhibits quasi-linear growth in processing time (≈0.002-0.008 s), whereas TOPSIS remains efficient (≈0.002-0.004 s) with minimal variance. These findings highlight a context-dependent choice SAW offers simplicity in low-dimensional settings, while TOPSIS provides scalability and robustness for complex, high-stakes decision support.
Co-Authors Alif Catur Murti, Alif Catur Andi Prayogi Indra Lina Puta Moustafa H. Aly Muhammad Akbar Syahbana Pane Muhammad Imam Ghozali Ratu Mutiara Siregar