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All Journal KOMPUTIKA - Jurnal Sistem Komputer Journal of Applied Data Sciences Jurnal Teknoif Teknik Informatika Institut Teknologi Padang
Putra, Muhammad Daffa Arviano
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering

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A Comparative Analysis of Transfer Learning Architecture Performance on Convolutional Neural Network Models with Diverse Datasets Putra, Muhammad Daffa Arviano; Winanto, Tawang Sahro; Hendrowati, Retno; Primajaya, Aji; Adhinata, Faisal Dharma
Komputika : Jurnal Sistem Komputer Vol 12 No 1 (2023): Komputika: Jurnal Sistem Komputer
Publisher : Computer Engineering Departement, Universitas Komputer Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34010/komputika.v12i1.8626

Abstract

Deep learning is a branch of machine learning with many highly successful applications. One application of deep learning is image classification using the Convolutional Neural Network (CNN) algorithm. Large image data is required to classify images with CNN to obtain satisfactory training results. However, this can be overcome with transfer learning architectural models, even with small image data. With transfer learning, the success rate of a model is likely to be higher. Since there are many transfer learning architecture models, it is necessary to compare each model's performance results to find the best-performing architecture. In this study, we conducted three experiments on different datasets to train models with various transfer learning architectures. We then performed a comprehensive comparative analysis for each experiment. The result is that the DenseNet-121 architecture is the best transfer learning architecture model for various datasets.
Efficient Web Mining on MyAnimeList: A Concurrency-Driven Approach Using the Go Programming Language Putra, Muhammad Daffa Arviano; Dewi, Deshinta Arrova; Putri, Wahyuningdiah Trisari Harsanti; Achsan, Harry Tursulistyono Yani
Journal of Applied Data Sciences Vol 5, No 3: SEPTEMBER 2024
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v5i3.352

Abstract

Anime is a globally popular form of entertainment, with the industry experiencing rapid growth in recent years. Despite the wealth of anime data available on MyAnimeList, the largest community-driven platform for anime enthusiasts, existing publicly available datasets are often outdated and incomplete. This presents a challenge for data science research, as the increasing volume of anime information requires more efficient data extraction methods. This research aims to address this challenge by developing a concurrent web mining program using the Go programming language. Leveraging Go's concurrency capabilities, our program efficiently extracted anime data from MyAnimeList, iterating through anime pages from ID 1 to 52,991. To overcome potential issues like rate limits and server timeouts, we implemented a two-phase execution strategy. As a result, the program successfully gathered 23,105 anime records within 8.5 hours. The extracted data has been transformed into a comprehensive dataset and made publicly available in CSV format. This research demonstrates the effectiveness of concurrent web mining for large-scale data extraction and offers a valuable resource for future data-driven research in the anime industry.
PENGEMBANGAN APLIKASI MOBILE FORUM DISKUSI MAHASISWA UNIVERSITAS PARAMADINA BERBASIS OBJEK Putra, Muhammad Daffa Arviano; Darwis, Muhammad; Hendrowati, Retno
Jurnal Teknoif Teknik Informatika Institut Teknologi Padang Vol 11 No 2 (2023): TEKNOIF OKTOBER 2023
Publisher : ITP Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21063/jtif.2023.V11.2.37-44

Abstract

Forum is a platform for exchanging information and ideas. In the digital era, information is highly valuable, and forums have become an appropriate means to obtain it. As a result, forums are now available in the form of applications. A forum application can provide numerous benefits to its users. In this study, a mobile forum application specifically designed for students of Paramadina University is developed. The presence of this student forum application is expected to enhance collaboration and foster discussions among students, thus enriching their learning experience at Paramadina University. The system development process of the forum application is carried out by applying object-oriented design principles, incorporating the creation of Unified Modeling Language (UML) diagrams. Consequently, the system design follows efficient and standard-compliant structures. The designed system is then implemented into a mobile application using the Dart programming language with Flutter framework. For the backend, the application is developed with Go programming language and MySQL relational database. The forum application developed in this study underwent comprehensive testing to ensure its accuracy and usability, using both blackbox and whitebox methods. The testing results indicated that the forum application functions well. Therefore, the forum application is considered suitable and can be used by students of Paramadina University to collaborate with each other.
Co-Authors Achsan, Harry Tursulistyono Yani Dewi, Deshinta Arrova Faisal Dharma Adhinata Hendrowati, Retno Muh. Darwis Wahyuningdiah Trisari Harsanti Putri Winanto, Tawang Sahro