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All Journal YASIN: Jurnal Pendidikan dan Sosial Budaya Sistem Komputer & Teknologi Intelegensi Artifisial (SIKOMTIA) 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 Social Sciences Other

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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.
Penerapan Media Pembelajaran Berbasis Quizizz dalam Meningkatkan Hasil Belajar pada Pembelajaran IPAS Kelas V MI Nurul Hidayah Kabupaten Bogor Ardiansyah, Fahmi; Asmahasanah, Salati; Rivani, Falizar
YASIN Vol 5 No 6 (2025): DESEMBER
Publisher : Lembaga Yasin AlSys

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58578/yasin.v5i6.7769

Abstract

The limited number of studies on the use of interactive technology-based learning media—particularly Quizizz—in Ilmu Pengetahuan Alam dan Sosial (IPAS) instruction at madrasah ibtidaiyah forms the background of this research, considering the low learning outcomes and limited student participation in conventional learning, which impact the effectiveness of the teaching and learning process. This study aims to analyze the effectiveness of using Quizizz in improving student learning outcomes and engagement in Grade V IPAS at MI Nurul Hidayah, Bogor Regency. The method employed is Classroom Action Research (CAR) conducted over two cycles, encompassing the stages of planning, action implementation, observation, and reflection, with 25 purposively selected students as research subjects. Data were collected through observations of teacher and student activities, as well as learning outcome tests in each cycle, and analyzed using descriptive quantitative and qualitative methods. The results showed an increase in the average learning outcomes from 49% in the pre-cycle to 76% in Cycle I and 81% in Cycle II. Classical mastery learning improved from 20% in the pre-cycle to 64% and 72% in each subsequent cycle. Teacher activity increased from 83% to 87%, and student participation from 81% to 87%. These findings align with constructivist theory and the gamification approach, which emphasize active engagement and enjoyable learning experiences in improving academic performance. Quizizz proved to offer an interactive experience through digital quizzes and instant feedback, enhancing student motivation, focus, and retention of IPAS content. The main conclusion of this study is that Quizizz-based learning media is effective in improving both learning outcomes and student participation. Theoretically, this research enriches the literature on digital technology integration in 21st-century education, while practically, it provides recommendations for teachers to utilize interactive media to create engaging, meaningful, and student-responsive learning environments. This study also opens pathways for future research comparing the effectiveness of various digital learning platforms on student motivation and long-term retention.
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).
Co-Authors Abdul Kholiq Alamsyah - Iswidodo, Iswidodo Prambodo, Yoga Listi Rivani, Falizar Salati Asmahasanah