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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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Peningkatan Kompetensi Guru dan Siswa melalui Pengembangan Virtual Lab Terintegrasi untuk Pengenalan Internet of Things Koprawi, Muhammad; Destya, Senie; Pramitasari, Rina
Jurnal Inovasi Penelitian dan Pengabdian Masyarakat Vol. 5 No. 2 (2025): Desember
Publisher : Indonesia Emerging Literacy Education

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53621/jippmas.v5i2.686

Abstract

Program pengabdian ini bertujuan memperkuat kemampuan guru dan siswa dalam bidang Internet of Things (IoT) melalui virtual lab yang dipadukan dengan perangkat fisik. Program dirancang untuk mengatasi keterbatasan fasilitas laboratorium di sekolah mitra, sehingga praktik IoT dapat dilakukan secara optimal. Pendekatan Participatory Action Research (PAR) diterapkan agar guru dan siswa terlibat langsung dalam pengkajian kebutuhan, perencanaan kegiatan, hingga refleksi hasil pelatihan. Pelaksanaan program melalui lima tahap: sosialisasi, pelatihan, penerapan teknologi, pendampingan dan evaluasi, serta keberlanjutan. Virtual lab digunakan sebagai langkah awal untuk memudahkan pemahaman konsep dasar sensor, aktuator, dan mikrokontroler sebelum praktik dengan ESP32 dan sensor fisik. Pendampingan teknis diberikan secara berkala agar peserta dapat menyelesaikan tantangan praktik dan meningkatkan keterampilan secara bertahap. Hasil menunjukkan peningkatan kemampuan signifikan. Sebelum pelatihan, mayoritas peserta berada pada kategori sangat rendah (40%) dan rendah (33%). Setelah pelatihan, kategori sangat rendah hilang, kategori rendah turun menjadi 20%, kategori sedang naik menjadi 47%, dan kategori tinggi serta sangat tinggi mencapai 33%. Hal ini membuktikan bahwa kombinasi virtual lab, praktik langsung, dan pendekatan PAR efektif dalam meningkatkan literasi dan keterampilan IoT di sekolah mitra.
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.
Regression Based Prediction of Roblox Game Popularity Using Extreme Gradient Boosting with Hyperparameter Optimization Amalina, Inna Nur; Norhikmah, Norhikmah; Ariyus, Dony; Koprawi, Muhammad; Prasetyo, Rafli Ilham
Jurnal Teknik Informatika (Jutif) Vol. 7 No. 1 (2026): JUTIF Volume 7, Number 1, February 2026
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2026.7.1.5648

Abstract

The rapid growth of the digital gaming industry has increased the importance of predicting game popularity on user-generated content platforms such as Roblox, where diverse games and highly variable user engagement patterns create challenges in modeling long-term popularity trends. This study aims to develop a regression-based popularity prediction model using the Extreme Gradient Boosting (XGBoost) algorithm based on user interaction indicators, including visits, likes, dislikes, favorites, and active players. To investigate the effect of model optimization, hyperparameter tuning is performed using GridSearchCV. Model performance is evaluated using Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and the Coefficient of Determination (R²). Experimental results show that the baseline XGBoost model achieves an R² value of 80.74%, indicating strong capability in capturing non-linear popularity patterns. However, the optimized model yields a lower R² value of 77.71%, accompanied by slight increases in prediction error metrics, revealing that hyperparameter optimization does not always improve performance for highly skewed popularity data. Feature importance analysis further indicates that interaction-based attributes, particularly likes and dislikes, are the most influential predictors. These findings provide an important contribution to Informatics research by demonstrating the effectiveness of ensemble regression models for digital entertainment analytics while highlighting the need for critical evaluation of optimization strategies rather than assuming universal performance gains.
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