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All Journal Sistem Komputer & Teknologi Intelegensi Artifisial (SIKOMTIA) Jurnal Sains, Nalar, dan Aplikasi Teknologi Informasi Scientific Journal of Informatics
Ardiansyah, Fahmi
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering

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Penerapan Metode Color Based Detection Menggunakan Platform ESP32-Cam Sebagai Alat Pendeteksi Ikan Betta Varian Albino Ardiansyah, Fahmi; Prambodo, Yoga Listi; Iswidodo, Iswidodo
Sistem Komputer dan Teknologi Intelegensi Artifisial (SIKOMTIA) Vol. 1 No. 3 (2023): December
Publisher : FIKOM Universitas Bung Karno

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59039/sikomtia.v1i3.17

Abstract

This research aims to overcome the problems that arise in identifying Betta fish variants, especially Albino variants, which are often difficult to distinguish by ordinary people due to difficulties in distinguishing visual differences. This problem encourages the need to develop a tool that is able to automatically distinguish Betta Albino fish variants. This research presents the concept of implementing the ESP32-Cam platform as a Betta Albino fish variant detection tool. The novelty of this research lies in the use of the ESP32-Cam platform which is an innovation as a detection tool. The urgency of this research lies in the need for a tool that can help ordinary people in identifying Betta Albino fish variants easily without requiring knowledge of Betta fish species. The method used in this research is colour-based detection using the eloquent surveillance library. The image capture process is done through an ESP32-Cam camera by utilising the concept of object detection and image processing. This research also involves the application of components such as TCS 3200, LM393, and LCD 1602 I2C to support the function of the detection tool. The results show that the Betta Albino fish variant object detection device is able to identify colour differences accurately. Through the ESP32-Cam platform, this tool successfully creates an automated solution that can distinguish Betta Albino fish variants well. In conclusion, this study confirms that the use of the ESP32-Cam platform in designing the Betta Albino fish variant detection tool can be practically applied. This tool has the potential to contribute in facilitating the accurate identification of Betta Albino fish variants to the general public.
Music Genre Classification Using Mel Frequency Cepstral Coefficients and Artificial Neural Networks: A Novel Approach Alamsyah, Alamsyah; Ardiansyah, Fahmi; Kholiq, Abdul
Scientific Journal of Informatics Vol. 11 No. 4: November 2024
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v11i4.13660

Abstract

Purpose: Music is an artistic expression with many categories in various genres and styles, characterized by its melodic and harmonic compositions. Music genre classification is crucial because genres serve as descriptors commonly used to organize large music collections, especially on the internet and in widely used applications like JOOX and Spotify. The aim of this research is to implement the Mel Frequency Cepstral Coefficients (MFCC) feature extraction method to generate numerical features from a set of specific music tracks. This collection of information will then be classified using machine learning. Methods: The method used in this study begins with combining the "GTZAN Dataset - Music Genre Classification" with additional data from TikTok and YouTube. The total dataset consists of 1,200 audio files, divided into 12 classes. The MFCC extraction process generates numerical representations of acoustic characteristics, which are then processed using Artificial Neural Networks. Result: The experiments demonstrate that increasing the amount of data is crucial, as it can enhance both variation and accuracy. The average accuracy achieved in this study is 91.42%, while the highest accuracy reaches 92.16%. These findings indicate that this study outperforms previous studies. Novelty: The novelty of this research lies in the integration of dynamic social media data (TikTok and YouTube) to enrich the standard GTZAN dataset, the repetition of the MFCC feature extraction process, and the combination of MFCC with Artificial Neural Networks (ANN).
Evaluation of SDCA, LBFGS, LightGBM and FastTree in ML.NET for Diabetes Prediction Taufan, Resi; Ardiansyah, Fahmi; Augustia, Annisa Elfina
Jurnal Sains, Nalar, dan Aplikasi Teknologi Informasi Vol. 5 No. 2 (2026)
Publisher : Department of Informatics Universitas Islam Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20885/snati.v5.i2.49517

Abstract

This study aims to develop a machine learning-based diabetes risk prediction model using the ML.NET framework. The dataset utilized is a balanced-split version of the 2015 BRFSS, consisting of 70,692 respondents and 21 health indicator variables. Two training approaches were applied to analyze model performance: a baseline with default parameters and hyperparameter tuning. The preprocessing stage involved combining variables into feature vectors, Min-Max normalization, and an 80:20 train-test data split. The models were trained using four algorithms: SDCA Logistic Regression, LBFGS Logistic Regression, LightGBM, and FastTree. Evaluation results showed that LightGBM with the hyperparameter tuning approach, delivered the most consistent performance, achieving 75.37% accuracy, 82.86% AUC, 76.22% F1-score, 72.92% precision, and 79.83% recall. Feature analysis confirmed that GenHlth, HighBP, BMI, HighChol, and Age contributed dominantly to diabetes risk, aligning with medical literature regarding metabolic factors. The best-performing LightGBM model was then integrated into a .NET-based prototype application with a Razor Pages web interface. The practical contribution of this research is proof of concept for machine learning integration into e-health systems to support early detection and digital prevention of diabetes complications in the future.
Co-Authors Abdul Kholiq Alamsyah - Augustia, Annisa Elfina Iswidodo Iswidodo Prambodo, Yoga Listi Taufan, Resi