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All Journal Jurnal Sains dan Teknologi
Anisa Lutfiyani
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Aerospace Engineering Agriculture, Biological Sciences & Forestry Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Chemistry Computer Science & IT Control & Systems Engineering Dentistry Electrical & Electronics Engineering Energy Engineering Health Professions Industrial & Manufacturing Engineering Mathematics Mechanical Engineering Medicine & Pharmacology Nursing Public Health Veterinary

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Perbandingan Analisis Sentimen Pengguna Aplikasi Shopee dan Lazada pada Situs Google Play Store Menggunakan Algoritma K-Nearst Neighbor dan Naive Bayes Trisnaeni Faradaningsih; Anisa Lutfiyani
INSOLOGI: Jurnal Sains dan Teknologi Vol. 4 No. 3 (2025): Juni 2025
Publisher : Yayasan Literasi Sains Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55123/insologi.v4i3.5646

Abstract

This study aims to compare the performance of the Naive Bayes and K-Nearest Neighbor (KNN) algorithms in classifying user sentiment towards two popular e-commerce applications in Indonesia, namely Shopee and Lazada. The data used are 40,000 user reviews collected equally from the Google Play Store, 20,000 reviews for each application. The pre-processing process is carried out through the stages of cleaning, case folding, tokenization, stopword removal, normalization, and stemming. Furthermore, weighting is carried out using the TF-IDF method, then classified using Naive Bayes and KNN with a cosine similarity approach. The evaluation results show that in the Shopee application, Naive Bayes produces an accuracy of 84.72% and an F1-score of 81.56%, while KNN produces an accuracy of 84.17% and an F1-score of 82.31%. On the Lazada application, Naive Bayes achieved an accuracy of 82.53% and an F1-score of 79.89%, while KNN obtained an accuracy of 75.04% and an F1-score of 73.20%. Thus, Naive Bayes proved to be superior in terms of classification accuracy and stability, especially on Lazada data. Meanwhile, KNN showed superiority in the balance of precision and recall on Shopee data. This study contributes to the selection of appropriate algorithms for text-based sentiment analysis in the context of e-commerce in Indonesia.
Deteksi Hewan Secara Real-Time Menggunakan Algoritma You Only Look Once (YOLO) Fersellia, Fersellia; Anisa Lutfiyani; Fahmi Fachri; Endang Wahyuningsih
INSOLOGI: Jurnal Sains dan Teknologi Vol. 5 No. 1 (2026): Februari 2026
Publisher : Yayasan Literasi Sains Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55123/insologi.v5i1.7592

Abstract

Forest areas in Indonesia are very vital and are the lungs of the world. The government and forest police need assistance in tackling forest fires and animal rescue, especially system assistance that can be used in real-time so that rescue and first aid can be carried out immediately. This is what moves the research team to conduct research in making a prototype of a real-time animal detection system. The goal to be achieved is to help forest police, SAR teams and teams from local governments to detect animals in forest areas in real-time. This research is quantitative research using experimental methods. The subject of our research is the image images that we get in real time from the webcam, especially animal images. Data was collected using the help of a webcam installed in the forest area. Image and video processing is done using the You Look Only Once (YOLO) and Convolutional Neural Network (CNN) algorithms. This study obtained 82% accuracy, 86.11% precision and 82% recall. The camera angle shooting from the front gets 100% accuracy.
Co-Authors Endang Wahyuningsih Fahmi Fachri Fersellia, Fersellia Trisnaeni Faradaningsih