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All Journal Sinkron : Jurnal dan Penelitian Teknik Informatika
Zulpan Hadi
Universitas Amikom Yogyakarta
Computer Science & IT

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PREDIKSI KEBAKARAN HUTAN MENGGUNAKAN ALGORITMA NAIVE BAYES DAN KNN Muhammad Salimy Ahsan; Zakaria Zakaria; Zulpan Hadi; Samuel Everth Andrias Kurni; Kusrini
Sinkron : jurnal dan penelitian teknik informatika Vol. 7 No. 4 (2022): Article Research: Volume 7 Number 4, October 2022
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v7i4.11609

Abstract

Forest fires are one of the disasters that cause problems for the environment. Forest fires can cause damage and threats, not only to forest resources but also to the entire ecosystem, both fauna and plants that can damage biodiversity and the environment of an area and can endanger human life. The source of forest fires was initially thought to come from a dry and hot environment, but in some cases, forest fires are triggered by human activities in clearing land for agriculture or other purposes. One of the factors that influence the spread of forest fires is several variables combined with humidity levels, wind speed, and rainfall. In this study, researchers used machine learning algorithms KNN and Naïve Bayes to predict forest fires and compare the results of the performance levels of each method used. The results obtained indicate that the naive Bayes method has an accuracy value of 53.33% and K-NN has an accuracy value of 62.66%
Detect Fake Reviews Using Random Forest and Support Vector Machine Zulpan Hadi; Ema Utami; Dhani Ariatmanto
Sinkron : jurnal dan penelitian teknik informatika Vol. 8 No. 2 (2023): Research Article, Volume 8 Issue 2 April, 2023
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v8i2.12090

Abstract

With the rapid development of e-commerce, which makes it possible to buy and sell products and services online, customers are increasingly using these online shop sites to fulfill their needs. After purchase, customers write reviews about their personal experiences, feelings and emotions. Reviews of a product are the main source of information for customers to make decisions to buy or not a product. However, reviews that should be one piece of information that can be trusted by customers can actually be manipulated by the owner of the seller. Where sellers can spam reviews to increase their product ratings or bring down their competitors. Therefore this study discusses detecting fake reviews on productreviews on Tokopedia. Where the method used is the distribution post tagging feature to perform detection. By using the post tagging feature method the distribution got 856 fake reviews and 4478 genuine reviews. In the fake reviews, there were 628 reviews written with the aim of increasing product sales or brand names from store owners, while there were 228 reviews aimed at dropping their competitors or competitors. Furthermore, the classification is carried out using the random forest algorithm model and the support vector machine. By dividing the dataset for training data by 80% while 20% for data testing. Here it is known that the support vector machine gets much higher accuracy than the random forest. The support vector machine gets an accuracy of 98% while the random forest gets an accuracy of 60%
Co-Authors Dhani Ariatmanto Ema Utami Kusrini Muhammad Salimy Ahsan Samuel Everth Andrias Kurni Zakaria Zakaria