Article Per Year (5 Year)
p-Index From 2021 - 2026
0.444
P-Index
This Author published in this journals
All Journal Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Jurnal Sisfokom (Sistem Informasi dan Komputer)
Rusdi, Jack Febrian
Unknown Affiliation
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Engineering

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

Found 2 Documents
Search

 1 
Comparison of Machine Learning Algorithms in Detecting Tea Leaf Diseases Ihsan, Candra Nur; Agustina, Nova; Naseer, Muchammad; Gusdevi, Harya; Rusdi, Jack Febrian; Hadhiwibowo, Ari; Abdullah, Fahmi
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 8 No 1 (2024): February 2024
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v8i1.5587

Abstract

Tea is one of the top ten export products sent from Indonesia to foreign countries. However, in recent years, the amount of tea leaf exports from Indonesia has decreased, although the value of the export impacts the country’s economic structure. In addition to market competition, Indonesia must maintain tea leaf production so that the increase in export decline is not significant or even increases tea leaf export production. To improve production quality and reduce production costs, early detection of tea leaf diseases is necessary. This study aims to classify tea leaf images for early detection of tea leaf disease so that appropriate treatment can be carried out early. This study compares machine learning algorithms to determine the best algorithm for detecting tea leaf diseases. The algorithms tested as performance comparisons in classifying tea leaf diseases are random forest (RF), support vector classifier (SVC), extra tree classifier (ETC), decision tree (DT), XGBoost classifier (XGB), and convolutional neural algorithms. Network (CNN). As a result, the average accuracy performance generated by ETC produces a higher value than other algorithms, i.e., getting an average accuracy performance of 77.47%. Another algorithm, SVC, has an average accuracy of 76.57%, RF of 76.12%, DT of 65.31%, XGB of 71.62%, and the lowest is CNN of 59.08%. ETC has been proven to be the most superior machine learning algorithm for detecting tea leaf diseases in this study.
Sentiment Analysis on Ajaib App Using the SVM Method Minggow, Lingua Franca Septha; Vitianingsih, Anik Vega; Kacung, Slamet; Maukar, Anastasia Lidya; Rusdi, Jack Febrian
Jurnal Sisfokom (Sistem Informasi dan Komputer) Vol. 14 No. 4 (2025): NOVEMBER
Publisher : ISB Atma Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32736/sisfokom.v14i4.2402

Abstract

The rapid growth of investment applications has transformed trading accessibility, yet user dissatisfaction persists, particularly regarding transaction delays, technical issues, and inadequate customer support. This study addresses a research gap in sentiment analysis, specifically in the context of the Ajaib investment application, by employing a Support Vector Machine (SVM) model combined with lexicon-based labelling. The objective is to classify user-generated Google Play reviews into positive, negative, and neutral sentiments, providing actionable insights for service improvement. The research follows a structured methodology comprising data crawling, text pre-processing (cleaning, case folding, tokenization, stopword removal, and stemming), TF-IDF feature extraction, and supervised classification with SVM. Model validation utilises a 3×3 confusion matrix to calculate accuracy, precision, and recall, thereby ensuring a robust performance evaluation. Experimental results demonstrate that the SVM classifier achieves high accuracy in sentiment polarity classification, highlighting its suitability for text-based sentiment analysis in the financial domain. The distinct contribution of this research is its implementation of SVM for sentiment classification for Ajaib, offering a replicable framework for leveraging user feedback to enhance digital investment platforms. These findings contribute to the development of automated sentiment analysis systems that support data-driven decision-making for improving customer satisfaction.
Co-Authors Agustina, Nova Anastasia Lidya Maukar Anik Vega Vitianingsih Efendi, Kacung Fahmi Abdullah, Fahmi Gusdevi, Harya Hadhiwibowo, Ari Ihsan, Candra Nur Minggow, Lingua Franca Septha Muchammad Naseer