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All Journal Jurnal Pendidikan Informatika dan Sains JOINTECOMS (Journal of Information Technology and Computer Science) Applied Information Technology and Computer Science (AICOMS)
Adikara Alif Nurrahman
Universitas Multi Data Palembang
Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Physics

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Classification of molly ornamental fish using VGG16 architecture Adikara Alif Nurrahman; Dedy Hermanto
Jurnal Pendidikan Informatika dan Sains Vol. 14 No. 2 (2025): Jurnal Pendidikan Informatika dan Sains
Publisher : Universitas PGRI Pontianak

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31571/saintek.v14i2.9889

Abstract

Molly fish (Poecilia sphenops) is one of the ornamental fish species that is widely cultured. This study aims to develop a classification system for ornamental molly fish using the VGG16 model, trained with on-the-fly data augmentation techniques (flip, zoom, rotation, and translation). The dataset used consists of 1,750 images of molly fish, divided into seven different species: Black, Blue Electric, Calico, Dalmatian, Golden Black, Platinum, and Sunkist. Data augmentation is performed dynamically during the training process without saving the transformation results, aiming to increase data diversity and help the model recognize patterns more accurately. The experimental results show that the optimal combination of parameters, namely a learning rate of 1e-5, a batch size of 32, and 50 epochs, achieved a training accuracy of 97.80%, validation accuracy of 99.61%, and test accuracy of 99.62%. Additionally, very high precision (99.63%), recall (99.62%), and F1-Score (99.62%) values were achieved. Although there were minor classification errors in the "Black" class predicted as "Sunkist," these errors were minimal and did not affect the overall results. This study shows that with the right parameter settings and the use of augmentation techniques, the VGG16 model can provide classification results with fairly high accuracy for molly ornamental fish. This model also has the potential to be applied in the ornamental fish aquaculture industry, particularly in image-based automatic detection systems.
Rancang Bangun Sistem Inventaris Barang Kantor Berbasis Web di PT. Bank Sumsel Babel dengan Metode Agile Adikara Alif Nurrahman; Ahmad Wahana Jaya; Muhammad Ezar Al Rivan
Journal of Information Technology and Computer Science Vol. 5 No. 4 (2025): JOINTECOMS : Journal of Information Technology and Computer Science
Publisher : Universitas Palangka Raya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47111/jointecoms.v5i4.23339

Abstract

Di era globalisasi ini, perkembangan teknologi informasi yang pesat telah membawa perubahan signifikan dalam berbagai proses bisnis, termasuk pengelolaan inventaris. Penggunaan teknologi komputer terbukti meningkatkan kecepatan, akurasi, dan konsistensi dalam pengolahan data. PT. Bank Pembangunan Daerah Sumatera Selatan dan Bangka Belitung menghadapi tantangan dalam pengelolaan inventaris kantor yang menggunakan sistem manual berbasis Microsoft Excel, yang mempengaruhi efisiensi dan akurasi data inventaris. Penelitian ini mengusulkan pengembangan sistem manajemen inventaris berbasis web menggunakan framework Laravel dan database MySQL dengan metode Agile. Sistem ini menawarkan pembaruan data secara real-time, mengurangi kebutuhan pengecekan fisik barang, dan mendukung pengelolaan aset yang lebih baik. Diharapkan bahwa penerapan sistem ini akan meningkatkan efisiensi, akurasi, dan pengelolaan inventaris yang lebih terorganisir, serta mendukung kelancaran operasional perusahaan.
Opini Publik terhadap Isu Pengoplosan Pertamax di Youtube Menggunakan Metode Naive Bayes Adikara Alif Nurrahman; Earlando Moza; Ramanda Md; Muhamad Rizvi Roshan; Ahmad Rizky; Hafiz Irsyad
Applied Information Technology and Computer Science (AICOMS) Vol 4 No 2 (2025)
Publisher : Pengelola Jurnal Politeknik Negeri Ketapang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58466/aicoms.v4i2.1990

Abstract

This study aims to explore public perceptions regarding the issue of Pertamax fuel adulteration, a topic that has sparked widespread discussion on YouTube, by employing sentiment analysis techniques based on the Naive Bayes algorithm. This issue has attracted significant public attention and become a trending topic on social media, particularly on the YouTube platform. The data analyzed in this research consist of user comments responding to the issue. The Naive Bayes algorithm is used to classify sentiments in the comments into three categories: positive, negative, and neutral. To address the imbalanced distribution of data, the Synthetic Minority Over-sampling Technique (SMOTE) is applied. The results show that before applying SMOTE, the model achieved an accuracy of only 48%, with a precision of 0.48, recall of 0.36, and an F1-score of 0.41 for the negative category, as well as a precision of 0.48, recall of 0.56, and an F1-score of 0.52 for the positive category. After implementing SMOTE, the model's accuracy increased significantly to 88%, with a precision of 0.91, recall of 0.93, and an F1-score of 0.92 for the negative category. For the positive category, precision improved to 0.80, although recall decreased to 0.75, yielding an F1-score of 0.77. The average precision, recall, and F1-score (macro average) after applying SMOTE reached 0.85, 0.84, and 0.85, respectively, representing a substantial improvement compared to the results before SMOTE. This study highlights the importance of using SMOTE to enhance sentiment analysis accuracy, particularly in addressing class imbalance issues within the dataset.
Co-Authors Ahmad Rizky Ahmad Wahana Jaya Dedy Hermanto Earlando Moza Hafiz Irsyad Muhamad Rizvi Roshan Muhammad Ezar Al Rivan Ramanda Md