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All Journal Bulletin of Computer Science Research Adpebi Science Series LogicLink: Journal of Artificial Intelligence and Multimedia in Informatics
Elmayati
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Agriculture, Biological Sciences & Forestry Humanities Automotive Engineering Computer Science & IT Economics, Econometrics & Finance Environmental Science

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Web Mobile Based on E- Marketing at Jumiran Stores using the Customer Relationship Management (CRM) Method Agung sanjaya; Tri Ragil Saputra; Rian saputra; Elmayati; Lukman Sunardi; Shely yunanda sari; Yurina putri lestari
Adpebi Science Series 2022: 1st AICMEST 2022
Publisher : ADPEBI

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

Abstract

 Jumiran Business's market share is now limited to the neighborhood around the store because sales are still conducted manually or customers come directly to the store.Product data storage for the delivery of information is still done manually utilizing a ledger as a medium for product data recording, resulting in messy and undetailed data storage.The goal of implementing the CRM approach in e-commerce or e-marketing is to simplify the marketing, sales, and support processes for business owners, making it simpler for customers to access product information at Jumiran Stores.The following are the outcomes of the study on the sales system using the CRM method: main page, product page, and customer
Analisis Sentimen Masyarakat di Twitter Mengenai Open AI CHATGPT Menggunakan Metode Support Vector Machine (SVM) Septini, Ayu; Susanto; Elmayati
Bulletin of Computer Science Research Vol. 5 No. 2 (2025): February 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v5i2.475

Abstract

This study aims to analyze public sentiment toward OpenAI ChatGPT technology on Twitter using the Support Vector Machine (SVM) method. The background of this research is based on the increasing global use of the internet and artificial intelligence (AI), as well as the role of social media as a platform for people to express their opinions. This study employs a qualitative research approach using the Support Vector Machine method, with data collection conducted through primary data obtained by crawling data from Twitter. The research uses data collected from 4,305 Indonesian-language tweets gathered between January and September 2023. These tweets were then classified into positive, neutral, and negative sentiments using the SVM method. The results indicate that out of the total collected data, 2,196 tweets had a neutral sentiment, 1,500 tweets had a positive sentiment, and 591 tweets had a negative sentiment. In the model performance evaluation, training data with an 80:20 ratio achieved the highest accuracy of 94.25%, while testing data with a 70:30 ratio achieved the highest accuracy of 93.16%. Additionally, the use of 10-fold cross-validation on training data resulted in an accuracy of 89.94%, while testing data achieved an average accuracy of 78.17%.
Klasifikasi Penyakit Pada Buah Jambu Biji Menggunakan Algoritma Yolo V5 Rezika, Nadiya; Elmayati; Lestari, Novi
LogicLink Vol. 2 No. 2, December 2025
Publisher : Universitas Islam Negeri K.H. Abdurrahman Wahid Pekalongan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28918/logiclink.v2i2.12942

Abstract

Horticultural agriculture, especially guava (Psidium guajava), has great economic potential in Indonesia. However, productivity often declines due to fruit disease attacks, which are still manually diagnosed by farmers. This study aims to develop an artificial intelligence-based guava disease classification system using the You Only Look Once (YOLO) version 5 algorithm. The dataset consists of 600 images divided into three disease classes: Phytophthora, Styler and Root, and Scab. Data were collected through field documentation, then preprocessed and augmented using Roboflow. The dataset was divided into 70% training data, 20% validation, and 10% testing. The YOLOv5 model was trained using Google Collaboratory and consistently evaluated using the Confusion Matrix and accuracy, precision, recall, and F1-score metrics. The test results showed that the model achieved an accuracy of more than 95% with high precision, recall, and F1-score values ​​for each disease class. This proves that YOLOv5 is effective for real-time guava disease detection. This research contributes to the application of artificial intelligence technology to help farmers make early diagnoses quickly and accurately, thereby reducing the risk of reduced crop yields.
Co-Authors agung sanjaya Lestari, Novi Lukman Sunardi Rezika, Nadiya rian saputra Septini, Ayu Shely yunanda sari Susanto Susanto Tri Ragil Saputra Yurina putri lestari