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All Journal DEVICE STORAGE: Jurnal Ilmiah Teknik dan Ilmu Komputer Clean Energy and Smart Technology TECHNOMEDIA : Informatics and Computer Science Servis : Jurnal Pengabdian dan Layanan kepada Masyarakat
muhamad Fuat Asnawi
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Religion Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering Environmental Science Industrial & Manufacturing Engineering Mechanical Engineering Other

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Journal : Clean Energy and Smart Technology

 1 
THE APPLICATION OF XGBOOST CLASSIFICATION FOR FRAUD DETECTION IN CREDIT CARD TRANSACTIONS Muhamad Fuat Asnawi; Nur Fitriyanto; M. Agoeng Pamoengkas
Clean Energy and Smart Technology Vol. 3 No. 2 (2025): April
Publisher : Nacreva Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58641/cest.v3i2.131

Abstract

Credit card fraud detection remains a critical challenge due to the inherent imbalance in transaction datasets, where fraudulent transactions are significantly fewer than normal ones. This study investigates the application of the XGBoost classification algorithm to address this issue using the publicly available Kaggle Credit Card Fraud Detection dataset. The research incorporates data preprocessing techniques such as normalization and SMOTE to handle the dataset's imbalance. Hyperparameter tuning using GridSearchCV optimizes the model’s parameters, enhancing its performance. The results indicate that the model achieves an Area Under the Curve (AUC) of 0.97, demonstrating its high accuracy in distinguishing between fraudulent and normal transactions. The evaluation metrics reveal an F1-score of 0.77 for fraudulent transactions, showing the model's reasonable effectiveness in detecting fraud. While the model performs exceptionally well in identifying normal transactions, reducing false negatives remains a challenge. This study underscores the potential of combining advanced machine learning techniques with preprocessing and optimization strategies to develop robust fraud detection systems.
SMARTPHONE RECOMMENDATION DECISION SUPPORT SYSTEM USING THE TOPSIS METHOD Dani Sifa Abdillah; muhamad Fuat Asnawi
Clean Energy and Smart Technology Vol. 2 No. 2 (2024): April
Publisher : Nacreva Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58641/cest.v2i2.80

Abstract

Many smartphone brands with various specifications and competitive prices often make consumers confused when deciding which smartphone to buy. There are many choice factors for consumers to buy the right smartphone according to their use, while the choice of smartphone is still considered subjective, so it is not uncommon for the choice of smartphone to be less than optimal. The aim of this research is to recommend the best smartphone based on predetermined usability and price criteria, including gaming needs, content creators and low price using the Technique for Others Preference by Similarity to Ideal Selection (TOPSIS) method. The results of the process of implementing the TOPSIS method can display alternative ranking data from the largest value to the smallest value.
IMPLEMENTATION OF CONVOLUTIONAL NEURAL NETWORK (CNN) ALGORITHM IN MOBILE APPLICATION-BASED VOICE EMOTION CLASSIFICATION SYSTEM Naufal Ammar Raihan; Muhamad Fuat Asnawi; Iman Ahmad Ihsannuddin; Nahar Mardiyantoro; Muhammad Alif Muwafiq Baihaqy
Clean Energy and Smart Technology Vol. 4 No. 2 (2026): April
Publisher : Nacreva Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58641/cest.v4i2.211

Abstract

The ability of machines to recognize emotions from voice is known as Speech Emotion Recognition (SER). This study developed a voice emotion classification system using a Convolutional Neural Network (CNN) and implemented it in the form of an Android mobile application. The main problem raised is how to recognize human emotions through voice signals accurately, efficiently, and in real-time on mobile devices. The study was conducted with two training stages, namely pre-training using the RAVDESS dataset and fine-tuning with the IndoWaveSentiment dataset. Audio data was converted into a 128×128×1 Mel-spectrogram to be input to the CNN. The CNN model consists of three convolution and pooling blocks, as well as dense and softmax layers. After training, the model was converted to TensorFlow Lite format and integrated with the Android application through a client-server architecture using Flask. The test results showed that the system was able to recognize neutral, happy, disappointed, and surprised emotions with a high level of accuracy both on test data but not as good on live recorded voice. The system also features a SQLite-based history feature. Test results showed 96% accuracy on external test data and 55% on live recorded audio, with an average accuracy of 75.5%. This indicates the model performs very well in structured conditions, but still needs improvement for real-world input.
Co-Authors Adi Suwondo Afra Nurul Fadhila Agus Triyadi Ahmad Afif Ahmad Irfa’i Al Ashar, Ilham Ariawan Alif Muhammad Nurman Maulana Alya Faradisa Dani Sifa Abdillah dian asmarajati Dimas Prasetyo Utomo Dwi Murni Oktaviani Farah Dina Alifah Fuad Najib Gusti Ilman Prayoga HERMAWAN Hindun Choirun Nisa’ Ilham, Ilham Ariawan Al Ashar Iman Ahmad Ihsannuddin Jenny Febrina Andini Lina Setiawati Lutfi Ziddan Fahriza M Alif Muwafiq B M. Agoeng Pamoengkas Mora Arief Nasucha Muhammad Alif Muwafiq Baihaqy Muhammad Bondan Abhyoso Munaya Putri Khaura Anjali Muslim Hidayat Mutiara Cahyaningrum Rangkuti Naf'ul Huja, Wisnu Nahar Mardiyantoro Najwa Millah Hanifa Naufal Ammar Raihan Nur Fitriyanto NUR HASANAH Rina Mahmudati Sukowiyono Suwondo, Adi Wahyu Aji Bayu Pemungkas Warohmatika Rofikhoh