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All Journal Journal of Artificial Intelligence and Engineering Applications (JAIEA) Jurnal Penelitian Rumpun Ilmu Teknik
Ihsan Zulfahmi
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Automotive Engineering Computer Science & IT Control & Systems Engineering Engineering

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Analisis Sentimen Aplikasi PLN Mobile Menggunakan Metode Decission Tree Ihsan Zulfahmi
Jurnal Penelitian Rumpun Ilmu Teknik Vol. 3 No. 1 (2024): Februari : Jurnal Penelitian Rumpun Ilmu Teknik
Publisher : Pusat Riset dan Inovasi Nasional

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55606/juprit.v3i1.3096

Abstract

This study utilizes Twitter data to understand user opinions and emotions towards an application. The Decision Tree method was chosen due to its ability to describe the relationship between input variables and the target. The TF-IDF method was used to weight words in the text, and the confusion matrix was used to evaluate the accuracy of the classification model. The research included the research process flow, data preprocessing, and data modeling. Word cloud visualization was used to display the frequency of words in the text. Data was collected from Twitter using Python and the Tweepy library. After preprocessing, the data was categorized into positive, negative, and neutral labels. The evaluation results using the decision tree algorithm showed an accuracy of 96%. The word cloud revealed that the word "aplikasi" (application) has the highest frequency, which shows the importance of the PLN Mobile application but also shows the need for further development. This study provides insights into user sentiment towards the PLN Mobile application and demonstrates the effectiveness of the Decision Tree method in sentiment analysis
Implementation of MobileNet V3 In Classifying Butterfly Species with Android and Cloud Based Application Development Ihsan Zulfahmi; Said Iskandar Al Idrus; Hermawan Syahputra; Insan Taufik; Kana Saputra S
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 4 No. 2 (2025): February 2025
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59934/jaiea.v4i2.797

Abstract

This research aimed to develop an Android application capable of classifying butterfly species using cloud computing and deep learning technologies. MobileNetV3-Large, a Convolutional Neural Network (CNN) architecture, was employed to process and classify six butterfly species. The dataset was divided into two ratios, 70:30 and 80:20, for training and testing. Evaluation results indicated that the optimal model was achieved with an 80:20 ratio, yielding an accuracy of 94% and precision, recall, and F1-Score values exceeding 90% for each species class. Google Cloud Platform (GCP) was utilized to manage and run the model using the Cloud Run service, enabling the application to function efficiently even with limited resources on Android devices. The application incorporates an encyclopedia of species and a camera scanning feature, making it a valuable educational tool
Co-Authors Hermawan Syahputra Insan Taufik Kana Saputra S Said . Iskandar