Article Per Year (5 Year)
p-Index From 2020 - 2025
0.61
P-Index
This Author published in this journals
All Journal InComTech: Jurnal Telekomunikasi dan Komputer IJID (International Journal on Informatics for Development) Halal Research Journal
Fathurrohman, Sewin
Unknown Affiliation
Religion Agriculture, Biological Sciences & Forestry Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering

Published : 3 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 3 Documents
Search

 1 
Sentiment Analysis of TIMNAS Indonesia's Participation in the Asian Cup U23 2024 on X Using Naive Bayes and SVM Fathurrohman, Sewin; Afandi, Irfan Ricky; Hasan, Firman Noor
IJID (International Journal on Informatics for Development) Vol. 13 No. 1 (2024): IJID June
Publisher : Faculty of Science and Technology, UIN Sunan Kalijaga Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14421/ijid.2024.4504

Abstract

This study aims to analyze the sentiment of the Indonesian public regarding the participation of the Indonesian National Team in the 2024 U-23 Asian Cup through the social media platform X. Sentiment analysis is crucial for understanding public perception and its impact on support for the national team. The research methodology involves collecting user comments on X related to the team's performance during the tournament, followed by data cleaning. The dataset is manually labeled, with 80% used as training data for algorithmic model training and the remaining 20% as test data, classified using Naive Bayes and Support Vector Machine algorithms. The analysis results indicate that the SVM algorithm achieves a higher % accuracy rate of 95% compared to Naive Bayes, which achieves 87%. The majority of the 3367 opinions analyzed express positive or satisfactory sentiments towards the national team's participation. However, there are fewer negative sentiments, highlighting areas requiring team management's attention. This study provides valuable insights into public perception of the Indonesian National Team. Furthermore, these findings can inform policymakers and team managers' decision-making to enhance the team's quality and performance in the future.
Sentiment Analysis of Mandatory Halal Certification Policy on Twitter Using the Naive Bayes and K-Nearest Neighbors Algorithm Wibowo, Muhammad Wahyu Arif; Afandi, Irfan Ricky; Fathurrohman, Sewin
Halal Research Vol 5 No 1 (2025): February
Publisher : Halal Center ITS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j22759970.v5i1.1806

Abstract

Twitter become a platform for Indonesians to express views on various issues, including the mandatory halal certification policy regulated by Law Number 33 of 2014 on Halal Product Assurance. The first phase of this certification runs from October 17, 2019, to October 17, 2024, covering: (1) food and beverages; (2) raw materials, food additives, and auxiliary materials; and (3) slaughter products and services. This research analyzes public sentiment on Twitter towards this policy using the Naive Bayes and KNN algorithm. Analysis of 536 tweets revealed 307 neutral (57.3%), 145 positive (27.1%), and 84 negative sentiments (15.7%). The findings highlight public support and criticism of the policy. The model Naïve Bayes showed an accuracy of 82.7% and KNN 81.62%, demonstrating its effectiveness in classifying new sentiments. This research aids the government's decision-making process in evaluating the mandatory halal certification policy, ensuring it aligns with public needs and is well-received by Indonesians.
Penerapan FP-Growth dan Random Forest dalam Analisis Data Penjualan Makanan Ringan Afandi, Irfan Ricky; Wahyuningtyas, Irma; Fathurrohman, Sewin; Hasan, Firman Noor
InComTech : Jurnal Telekomunikasi dan Komputer Vol 15, No 1 (2025)
Publisher : Department of Electrical Engineering

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22441/incomtech.v15i1.30260

Abstract

Penelitian ini bertujuan untuk menganalisis pola pembelian produk makanan ringan serta memprediksi penjualan produk dengan menggunakan pendekatan data mining dan machine learning. Dalam industri makanan ringan yang semakin kompetitif pemahaman mendalam tentang pola perilaku konsumen dan tren penjualan produk sangat penting untuk pengambilan keputusan bisnis yang lebih efektif serta peningkatan profitabilitas perusahaan. Tantangan utama dalam penelitian ini adalah mengidentifikasi variabel yang relevan dalam dataset penjualan untuk mengungkap pola asosiasi antar produk dan menghasilkan prediksi penjualan yang akurat. Metodologi yang digunakan dalam penelitian ini melibatkan algoritma FP-Growth untuk menemukan asosiasi produk yang sering dibeli bersamaan serta algoritma Random Forest untuk memprediksi penjualan berdasarkan data historis. Hasil penelitian dari penerapan algoritma FP-Growth mampu mengidentifikasi sembilan aturan asosiasi yang potensial untuk diterapkan dalam sistem rekomendasi produk untuk menyediakan rekomendasi produk yang lebih personal kepada konsumen. Selain itu, model prediksi menggunakan Random Forest menunjukkan performa yang baik dengan nilai Mean Absolute Error (MAE) sebesar 23,54, Root Mean Squared Error (RMSE) sebesar 36,36 dan R-squared sebesar 0,86 dengan keseluruhan menunjukkan tingkat akurasi yang cukup baik. Penelitian ini memberikan kontribusi penting dalam optimasi stok dan strategi pemasaran berbasis data. Penelitian lanjutan disarankan menggunakan data yang lebih bervariasi dan periode waktu yang lebih panjang untuk meningkatkan akurasi prediksi.
Co-Authors Afandi, Irfan Ricky Firman Noor Hasan Wahyuningtyas, Irma Wibowo, Muhammad Wahyu Arif