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All Journal Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi)
Muhammad Haekal
Lambung Mangkurat University
Computer Science & IT Electrical & Electronics Engineering Engineering

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Jatropha Curcas Disease Identification using Random Forest Triando Hamonangan Saragih; Vivi Nur Wijayaningrum; Muhammad Haekal
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol 7, No 1 (2021): April
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v7i1.20141

Abstract

As one of the most versatile plants, Jatropha curcas is spread in various regions around the world because of the great benefits it provides. However, Jatropha curcas is easily attacked by viruses which then cause damage to the plant, such as yellowing leaves and secreting sap, making it necessary to identify Jatropha curcas disease to deal with the problem as early as possible so that the losses incurred are not too large. An expert system was built to be able to identify Jatropha curcas disease by adopting expert knowledge. The use of the Random Forest algorithm as one of the classification algorithms was applied in this study. By using a random forest, several disease prediction classes are generated by each decision tree that has been formed. The disease class with the most votes was used as the final result. In this study, the data used were 166 data with 9 diseases and 30 symptoms. The results showed that Random Forest outperformed other algorithms such as Fuzzy Neural Network and Extreme Learning Machine with an accuracy of 98.002% using the composition of training data and test data of 70:30.
Using Social Media Data to Monitor Natural Disaster: A Multi Dimension Convolutional Neural Network Approach with Word Embedding Mohammad Reza Faisal; Irwan Budiman; Friska Abadi; Muhammad Haekal; Mera Kartika Delimayanti; Dodon Turianto Nugrahadi
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 6 No 6 (2022): Desember 2022
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v6i6.4525

Abstract

Social media has a significant role in natural disaster management, namely as an early warning and monitoring when natural disasters occur. Artificial intelligence can maximize the use of natural disaster social media messages for natural disaster management. The artificial intelligence system will classify social media message texts into three categories: eyewitness, non-eyewitness and don't-know. Messages with the eyewitness category are essential because they can provide the time and location of natural disasters. A common problem in text classification research is that feature extraction techniques ignore word meanings, omit word order information and produce high-dimensional data. In this study, a feature extraction technique can maintain word order information and meaning by using three-word embedding techniques, namely word2vec, fastText, and Glove. The result is data with 1D, 2D, and 3D dimensions. This study also proposes a data formation technique with new features by combining data from all word embedding techniques. The classification model is made using three Convolutional Neural Network (CNN) techniques, namely 1D CNN, 2D CNN and 3D CNN. The best accuracy results in this study were in the case of earthquakes 78.33%, forest fires 81.97%, and floods 78.33%. The calculation of the average accuracy shows that the 2D and 3D v1 data formation techniques work better than other techniques. Other results show that the proposed technique produces better average accuracy.
Co-Authors Dodon Turianto Nugrahadi Friska Abadi Irwan Budiman Mera Kartika Delimayanti Muhammad Reza Faisal, Muhammad Reza Saragih, Triando Hamonangan Vivi Nur Wijayaningrum