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All Journal PIKSEL : Penelitian Ilmu Komputer Sistem Embedded and Logic Jurnal Edukasi dan Penelitian Informatika (JEPIN) Jurnal Khatulistiwa Informatika JOURNAL OF APPLIED INFORMATICS AND COMPUTING JURNAL TEKNIK INFORMATIKA DAN SISTEM INFORMASI Jurnal Teknik Informatika (JUTIF) Jurnal SAINTIKOM (Jurnal Sains Manajemen Informatika dan Komputer) Jurnal Inovasi Penelitian dan Pengabdian Masyarakat Jurnal Algoritma Digital Transformation Technology (Digitech) Jurnal Pengabdian Masyarakat Bangsa
Koprawi, Muhammad
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Journal : JOURNAL OF APPLIED INFORMATICS AND COMPUTING

 1 
Comparative Analysis of the Performance of Decision Tree and Random Forest Algorithms in SQL Injection Attack Detection Aulianoor, Alfatarizky Budi; Koprawi, Muhammad
Journal of Applied Informatics and Computing Vol. 8 No. 1 (2024): July 2024
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v8i1.8136

Abstract

This study compares the performance of two machine learning algorithms the Decision Tree and Random Forest. SQL Injection attacks continue to threaten web applications because they exploit vulnerabilities by injecting malicious code into SQL statements executed on database servers. Therefore, machine learning algorithms are used to identify SQL Injection attacks. The dataset used is 33761 in the form of random query data input in a CSV tabular containing sentence and label columns. The research software used is Google Colaboratory and Microsoft Edge. The series of research conducted by Collect Data is data collection, Preprocessing handling missing values, deleting rows that contain duplicates, and the same query having different labels. Train and Test is used to build models and prepare test data, Build and Compile involves building Decision Tree and Random Forest models. The final step is to evaluate both algorithm models to determine which performs better. After conducting a series of research processes, the results of the Random Forest algorithm are slightly better than the Decision Tree algorithm, with an accuracy of 99.81%, precision of 99.79%, recall of 99.65%, and an average F1-score of 99.72%.
Optimizing Email Spam Detection through Handling Class Imbalance with Class Weights and Hyperparameter Using GridSearchCV Nursyam, Muhammad Ridho; Koprawi, Muhammad; Ariyus, Dony
Journal of Applied Informatics and Computing Vol. 10 No. 1 (2026): February 2026
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v10i1.12060

Abstract

Email spam is a major problem in digital communication that can disrupt productivity, burden network resources, and pose a security threat. This research focuses on optimizing spam email detection using a machine learning approach by addressing class imbalance through class weighting and hyperparameter tuning using GridSearchCV. To improve model accuracy and sensitivity, a combination of diverse datasets is applied to provide a wider scope of training data. The models used in this study include Support Vector Machine (SVM), Random Forest, Multinomial Naive Bayes (MNB), and XGBoost. Evaluation is carried out based on metrics such as accuracy, precision, recall, and F1-score, before and after hyperparameter tuning. The experimental results show that SVM produces the highest accuracy after tuning, reaching 97.10%, compared to 96.73% before hyperparameter tuning. In addition, Random Forest, MNB, and XGBoost also show significant improvements, with each model achieving better performance after tuning. Overall, this study shows that dataset merging and class weight adjustment can significantly improve the model's ability to detect spam, as well as provide a basis for implementing the model in a more effective email spam detection system.
Smart Glove Design to Improve Accessibility Communication for the Deaf Amanda, Janeri; Destya, Senie; Koprawi, Muhammad
Journal of Applied Informatics and Computing Vol. 10 No. 1 (2026): February 2026
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v10i1.12110

Abstract

Deaf people rely on hand gestures as their primary means of communication; however, communication barriers often arise when surrounding individuals do not understand sign language. This study presents the design and evaluation of an Internet of Things (IoT)-based smart glove to improve communication accessibility for deaf individuals. The proposed system utilizes multiple MPU6050 motion sensors integrated with an Arduino Nano to detect finger and hand movements. Gesture recognition is implemented using a rule-based approach with predefined threshold values, enabling real-time detection without the need for training data. System performance was evaluated through response time and recognition accuracy measurements, as well as qualitative observations related to system stability and usability. Experimental results show response times ranging from 146–147 ms, indicating a fast and stable system. Recognition accuracy varies between 70% and 85%, depending on gesture complexity and finger movement patterns. Although the accuracy is moderate compared to machine learning-based approaches, the proposed system offers advantages in computational efficiency, simplicity, and ease of implementation. These findings demonstrate the potential of the smart glove as a practical assistive communication device, while also highlighting opportunities for further development through improved gesture modeling and user-centered evaluation.
Co-Authors Abdullah Abdullah Amalina, Inna Nur Amanda, Janeri Ashari, Wahid Miftahul Aulianoor, Alfatarizky Budi Destya, Senie Dony Ariyus Ferdiansyah, Pramudhita Gilang Ramadhan, Zaka Hidayat, Rofik Imam Setyo Nugroho Indrayani, Rini Jeki Kuswanto Norhikmah Norhikmah Nugraha, Anggit Ferdita Nugranto, Handika Fajar Nursyam, Muhammad Ridho Prasetyo, Rafli Ilham Putra, Wahyu Sukestyastama Rina Pramitasari