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All Journal Jurnal Teknologi Informasi dan Ilmu Komputer Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Tematik : Jurnal Teknologi Informasi Komunikasi
Ihsan, Candra Nur
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Computer Science & IT Engineering

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Image Classification using Machine Learning Algorithms to Detect Cloud Types Agustina, Nova; Ihsan, Candra Nur; Sussolaikah, Kelik
TEMATIK Vol. 10 No. 2 (2023): Tematik : Jurnal Teknologi Informasi Komunikasi (e-Journal) - Desember 2023
Publisher : LPPM POLITEKNIK LP3I BANDUNG

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Study of atmospheric are currently growing rapidly to analyze the negative effects of climate change, weather and air quality. Unstable atmospheric conditions have a negative impact, as extreme weather. The combination of technology and analysis of atmospheric conditions is currently developing rapidly. While atmospheric research using machine learning technology and algorithms is advancing swiftly, challenges persist in identifying the optimal machine learning model for precise cloud type classification. The application of Machine Learning algorithms in atmospheric research has been widely carried out to predict wind direction and cloud imagery to detect weather using satellite data. Detecting cloud type is important for predicting the upcoming weather. However, to detect the type of cloud, it is necessary to choose the algorithm with the best performance. This study applies the Convolutional Neural Network (CNN) with EfficienNetB3 method, Support Vector Classifier (SVC), XGBoost Classifier (XGB), Extra Tree Classifier (ETC), Random Forest (RF), and Decision Tree (DT) algorithms in classifying cloud images to detect clouds type. The method used in this research involves an experimental approach in the hope of gaining a deeper understanding of the factors that influence the performance of machine learning models in classifying cloud types. The dataset used in this research is 1500 cloud data (1200 training data, 300 testing data). Researchers conducted a comparison of algorithms to find out the best algorithm performance in classifying cloud type images. The results showed that doing the CNN algorithm showed better performance with an average accuracy got of 81.03% compared to the SVC algorithm (34.44%), XGB (33.79%), ETC (39.25%), RF (36.18), and DT (29.35%). Our contribution to this research is that we compare machine learning algorithms to detect cloud types along with the impact and characteristics of cloud types from the prediction results.
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.
Pendekatan Ensemble untuk Analisis Sentimen Covid19 Menggunakan Pengklasifikasi Soft Voting Agustina, Nova; Ihsan, Candra Nur
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 10 No 2: April 2023
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jtiik.20236215

Abstract

Covid19 berdampak pada sektor kehidupan, mulai dari sektor ekonomi, pendidikan, kesehatan, invertasi, pariwisata hingga menimbulkan krisis lain yaitu fenomena ketakutan dan kepanikan masyarakat yang dipicu oleh informasi yang tidak lengkap dan akurat. Ketakutan dan kepanikan massa menyebabkan publik mempublikasikan sentimen di media sosial untuk memberikan tanggapan atau kritik terhadap keputusan yang dibuat oleh negara. Pandangan masyarakat terhadap Covid19 perlu dijadikan landasan sebagai pendukung keputusan untuk menyusun kebijakan pemerintah dalam menangani Covid19 di Indonesia. Penelitian ini bertujuan untuk membandingkan dan menerapkan algoritma Logistic Regression, Naïve Bayes, dan Support Vector Machine menggunakan pengklasifikasi dari ensemble, yaitu Soft Voting untuk analisis sentimen perihal Covid19 pada media sosial Twitter. Implementasi Soft Voting untuk analisis sentiment masyarakat Indonesia terhadap Covid19 menjadi kebaruan pada penelitian ini. Soft Voting akan menentukan prediksi baru berdasarkan rekomendasi maksimum dari berbagai model yang diperlukan untuk analisis sentimen. Pada penelitian ini, semua algoritma mendapatkan akurasi yang sama untuk analisis sentimen, yaitu sebesar 89%. Penerapan metode ensemble meningkatkan akurasi model untuk prediksi sentimen menjadi 91%.Abstract Covid-19 has impacted all sectors of life, ranging from the economic sector, education, health, investment, tourism to causing another crisis, i.e., the phenomenon of public fear and panic triggered by incomplete and accurate information. Fear and panic cause the public to publish sentiments on social media to provide feedback or criticism of decisions made by the state. The public's view of Covid-19 needs to be used as a basis for decision support to formulate government policies in dealing with Covid-19 in Indonesia. This study aims to compare and apply the Logistic Regression, Naïve Bayes, and Support Vector Machine algorithms using the classifier from ensemble, i.e., Soft Voting for sentiment analysis related to Covid19 on Twitter social media. The application of Soft Voting for the analysis of Indonesian public's sentiments towards Covid19 is a novelty in this research. Soft Voting will determine new predictions based on maximum recommendations from various models needed for sentiment analysis. In this study, all algorithms get the same accuracy for sentiment analysis, which is 89%. The application of the ensemble method increases the accuracy of the model for sentiment prediction by up to 91%.
Co-Authors Agustina, Nova Fahmi Abdullah, Fahmi Gusdevi, Harya Hadhiwibowo, Ari Muchammad Naseer Rusdi, Jack Febrian Sussolaikah, Kelik