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All Journal Media Jurnal Informatika Bakti Sekawan : Jurnal Pengabdian Masyarakat Edu Elektrika Journal
Husaini, Rahayun Amrullah
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Religion Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Electrical & Electronics Engineering Health Professions Social Sciences Other

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ANALISIS SENTIMEN KINERJA TENAGA MEDIS INDONESIA MENGGUNAKAN MODELING ROBERTA DAN METODE MACHINE LEARNING Husaini, Rahayun Amrullah; Priyanto, Dadang; Hendro Martono, Galih
Edu Elektrika Journal Vol. 13 No. 1 (2025)
Publisher : LPPM Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/eduel.v13i1.22163

Abstract

The development of information technology has led to the emergence of various social media applications, such as X, which allow users to share information and opinions. However, social media can also function as a platform for the spread of hoaxes and hate speech. One of the challenges faced is determining whether user comments are negative, neutral, or positive through sentiment analysis. This study aims to compare the performance of various classification algorithms in sentiment analysis of medical personnel services in X, using the Support Vector Machine (SVM), Random Forest (RF), K-Nearest Neighbors (KNN), and Naïve Bayes (NB) algorithms with RoBERTa model-based labeling. Data was collected through the tweet-harvest library and processed using the Python programming language. The results showed a significant increase in accuracy, with the model able to classify public opinion into positive, negative, or neutral categories. The SVM model achieved the highest accuracy of 91.8%, outperforming other models such as Random Forest and Naïve Bayes, and provided insight into sentiment towards government health services. These findings provide valuable insights for policymakers in improving the provision of health services and managing public perceptions of medical personnel.
Rice Leaf Disease Classification Based on ResNet50 and MobileNetV3 Feature Extraction Using Random Forest Pratama, Gede Yogi; Husaini, Rahayun Amrullah; Nasri, Muhammad Haris; Hammad, Rifqi
Media Jurnal Informatika Vol 17, No 2 (2025): Media Jurnal Informatika
Publisher : Teknik Informatika Universitas Suryakancana Cianjur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35194/mji.v17i2.5939

Abstract

Diseases in rice plants are one of the main factors contributing to decreased agricultural productivity. Early and accurate disease identification is crucial to support effective decision-making in plant disease management. This study aims to compare the performance of deep learning models based on Convolutional Neural Networks (CNN), namely ResNet50 and MobileNetV3, as well as their integration with the Random Forest (RF) algorithm for rice leaf disease classification. The dataset used consists of rice leaf images categorized into several disease classes. Model performance was evaluated using accuracy, precision, recall, and F1-score metrics with a macro-average approach. The results show that the standalone ResNet50 and MobileNetV3 models achieved accuracies of 62.5% and 65.7%, respectively, with macro F1-scores below 0.65, indicating moderate classification performance. However, combining CNN models with Random Forest significantly improved classification performance. The ResNet50 + RF model achieved an accuracy of 99.6%, while the MobileNetV3 + RF model attained the highest accuracy of 99.8%, along with equally high macro-averaged precision, recall, and F1-score values. These findings demonstrate that integrating CNN-extracted features with the Random Forest algorithm enhances the model’s ability to distinguish disease classes more accurately and consistently. Therefore, the hybrid CNN–Random Forest approach shows strong potential as an effective solution for image-based rice plant disease detection systems.
Autism Classification Using MobileNetV3 Feature Extraction and K-Nearest Neighbor Algorithm Husaini, Rahayun Amrullah; Pratama, Gede Yogi; Latif, Kurniadin Abd.; Zulfikri, Muhammad; Augustin, Kartarina
Media Jurnal Informatika Vol 17, No 2 (2025): Media Jurnal Informatika
Publisher : Teknik Informatika Universitas Suryakancana Cianjur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35194/mji.v17i2.5934

Abstract

Autism Spectrum Disorder (ASD) is a neurodevelopmental disorder characterized by difficulties in social interaction, communication, and repetitive behaviors. Early detection of ASD is crucial; however, conventional diagnostic methods rely heavily on clinical observation and expert assessment, which can be time-consuming and resource-intensive. Along with the rapid development of artificial intelligence, especially in computer vision and machine learning, automated image-based approaches have gained attention as alternative tools for ASD screening. This study proposes a hybrid classification approach that integrates MobileNetV3 as a feature extraction model with the K-Nearest Neighbor (KNN) algorithm for autism classification using facial image data. Unlike previous CNN–KNN approaches, this study specifically explores the use of MobileNetV3’s lightweight architecture to generate compact and discriminative facial features, which are then classified using KNN to evaluate its effectiveness in low-complexity and resource-efficient settings. This design highlights the novelty of combining an optimized lightweight CNN with a distance-based classifier for autism detection from facial images. The dataset used in this research was obtained from Kaggle and consists of 2,940 labeled facial images of children categorized into Autism and non-Autism classes. This study proposes a hybrid classification approach that combines MobileNetV3 as a lightweight feature extraction model with the K-Nearest Neighbor (KNN) algorithm for autism classification. Experimental evaluations were conducted over multiple independent runs to improve statistical reliability, and model performance was assessed using accuracy, precision, recall, and F1-score. The results indicate that the proposed hybrid model achieves satisfactory and consistent performance while maintaining computational efficiency. These findings suggest that integrating lightweight deep learning models with classical machine learning algorithms can provide an effective and resource-efficient approach for autism classification, with potential applicability as a supportive tool for early ASD screening rather than a definitive clinical diagnosis.
Intervensi Edukasi Digital Marketing untuk Peningkatan Pengetahuan Siswa Siswi Madrasah Aliyah Nasri, Muhammad Haris; Hammad, Rifqi; Husaini, Rahayun Amrullah; Roodhi, Mohammad Najid; Pratama, Gede Yogi
Bakti Sekawan : Jurnal Pengabdian Masyarakat Vol. 6 No. 1 (2026): Juni
Publisher : Puslitbang Sekawan Institute Nusa Tenggara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35746/bakwan.v6i1.913

Abstract

Advances in digital technology require young people to possess adequate digital literacy skills, particularly in digital marketing, which is now a crucial skill in education, the workplace, and entrepreneurship. However, observations indicate that Madrasah Aliyah (MA) students still lack a grasp of digital marketing concepts and practices. This community service activity aims to improve MA students' knowledge and understanding of basic digital marketing concepts, social media promotion strategies, digital branding principles, and consumer behavior in the digital world. The activity is divided into three stages: preparation, implementation, and evaluation. The preparation phase includes initial discussions with schools and the development of training materials. During the implementation phase, materials are delivered through interactive lectures and discussions, followed by simple practices using digital platforms. Assessments were conducted using pre- and post-tests to measure student knowledge gains. The results showed significant improvement, with an average pre-test score of 42.7 rising to 82.4 in the post-test. This 39.7-point increase indicates that the training successfully strengthened students' understanding of digital marketing concepts. This activity is effective in improving MA students' digital literacy and is relevant to continue with mentoring to better prepare them to face the challenges of the digital era
Co-Authors Augustin, Kartarina Hammad, Rifqi Hendro Martono, Galih Kurniadin Abd Latif Muhammad Haris Nasri Pratama, Gede Yogi Roodhi, Mohammad Najid Zulfikri, Muhammad