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All Journal Jumat Pendidikan: Jurnal Pengabdian Masyarakat J-Innovation Journal Of Artificial Intelligence And Software Engineering
Mohamad Khoiron
Universitas Merdeka Pasuruan
Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Social Sciences Other

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Pembuatan dan Pengelolaan Website Desa Sebagai Media Informasi dan Administrasi Desa Kawisrejo Mohamad Khoiron; Syahrul Alfani
Jumat Pendidikan: Jurnal Pengabdian Masyarakat Vol. 5 No. 2 (2024): Agustus
Publisher : Lembaga Penelitian dan Pengabdian Masyarakat UNWAHA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32764/abdimaspen.v5i2.3883

Abstract

The writer wants to provide solutions and innovations to facilitate village administrative activities and access village information in Kawisrejo Village. The author provides the application of technology in Kawisrejo Village by creating a village website which contains village history, village information, village structure, and community services which will later be useful for administrative administrators as admins and the community as users of information and services. This implementation method will be divided into several stage, including the preparation stage, the implementation stage, and the evaluation stage. With the existence of a village website, the village can use it as an information medium and as a website-based village administration media. By making this website, it is hoped that Kawisrejo Village can solve problems in the exchange and delivery of information and promotions can be minimized easily and quickly.
Evaluasi Sentimen Review Produk Roundup Menggunakan Algoritma Support Vector Machine: Evaluasi Sentimen Review Produk Roundup Menggunakan Algoritma Support Vector Machine Mohamad Khoiron; Dian Ahkam Sani; Mohammad Zoqi Sarwani; Muhammad Mahrus Ali; Khoirul Anwar; Muhammad Udin
J-Innovation Vol. 13 No. 2 (2024): Jurnal J-Innovation
Publisher : Politeknik Aceh

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55600/jipa.v13i2.313

Abstract

In today's digital era, more and more internet users are sharing their experiences and opinions about certain products. Sentiment analysis can be used to extract valuable information from the data generated by the shopee application users. This study aims to conduct a sentiment analysis of Roundup product reviews. The method used is the Support Vector Machine (SVM). SVM is an effective machine learning method for classifying text based on positive or negative sentiments. The purpose of this study is the SVM model which can be used to perform sentiment analysis automatically on Roundup product reviews. The results of this analysis can provide important insights for Roundup producers in understanding consumer perceptions of their products. In addition, this research can also be a guide for consumers in choosing and understanding weed killer products that suit their needs and preferences. In this study, the accuracy value was 80%, the precision value was 80%, the recall value was 100% and the value F1 score of 88.89%.
Optimization of the Naïve Bayes Classifier Algorithm Using Cost-Sensitive Learning to Detect Lung Diseases with an Imbalanced Dataset sarwani, mohammad zoqi; Khoiron, Mohamad; Udin, Muhammad
Journal of Artificial Intelligence and Software Engineering Vol 5, No 1 (2025): March
Publisher : Politeknik Negeri Lhokseumawe

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30811/jaise.v5i1.6474

Abstract

Lung diseases are one of the global public health issues that continue to be a primary concern in the medical field. According to data from the World Health Organization (WHO), 91% of the world’s population lives in areas with poor air quality. Continuous exposure to dust, cigarette smoke, air pollutants, and toxic chemicals can increase the risk of developing lung diseases. In efforts to reduce the health impacts on the lungs and assist doctors in classifying lung diseases, a method is needed to predict lung diseases. Naïve Bayes is a classification technique that uses probability and statistics. This research uses a dataset of 30,000, which is divided into training data and testing data, with 80% allocated for training and 20% for testing. The results of this study show that optimization performed on the Naïve Bayes algorithm using cost-sensitive learning achieved an accuracy of 79.6%, which represents a 12% improvement in accuracy compared to the previous result without optimization.
Co-Authors Dian Ahkam Sani KHOIRUL ANWAR Mohammad Zoqi Sarwani Mohammad Zoqi Sarwani, Mohammad Zoqi Muhammad Mahrus Ali Muhammad Udin Syahrul Alfani Udin, Muhammad