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All Journal Indonesian Journal of Data and Science Ilmu Komputer untuk Masyarakat
Aulia Putri Utami
Universitas Muslim Indonesia
Computer Science & IT Decision Sciences, Operations Research & Management Mathematics Other

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Bimbingan Teknis Aplikasi Xsia Microservice sebagai Media Pelaporan Nilai Siswa di SDN 133 Pari’risi Kabupaten Takalar Poetri Lestari Lokapitasari Belluano; Amaliah Faradibah; Rahmadani Rahmadani; Aulia Putri Utami; Muh Fachrul Islam; Muh Taufik Rifaat
Ilmu Komputer untuk Masyarakat Vol 3, No 2 (2022)
Publisher : Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkomas.v3i2.1554

Abstract

Sistem Informasi Akademik (xSIA) menggunakan teknologi microservice adalah sistem yang memiliki salah satu fungsi untuk mengelola data penilaian akhir siswa sehingga memberikan kemudahan kepada Guru sebagai pengguna utama dalam aktivitas merekam nilai akhir hasil belajar siswa setiap semester dengan luaran berupa nilai Angka Komulatif. Model Pelatihan yang diterapkan kepada mitra SDN 133 Inpres Paririsi Takalar menggunakan model latihan Preceptorship dan Partisipatif. sedangkan Tahap perancangan aplikasi digunakan model Prototyping untuk merepresentasikan secara grafis alur kerja sistem. Target luaran penelitian ini yakni: 1) Adanya aplikasi xSIA untuk pelaporan penilaian siswa agar guru secara mandiri melaksakan pelaporan secara otomatis dan memiliki dokumentasi nilai dengan baik. 2) Keluaran berupa Jurnal Nasional terakreditasi. Serta 3) pengayaan bahan ajar dalam mata kuliah Rekayasa Perangkat Lunak.
Improving Multi-Class Classification on 5-Celebrity-Faces Dataset using Ensemble Classification Methods Nurul Rismayanti; Aulia Putri Utami
Indonesian Journal of Data and Science Vol. 4 No. 2 (2023): Indonesian Journal of Data and Science
Publisher : yocto brain

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56705/ijodas.v4i2.78

Abstract

This study aims to compare the performance between Random Forest Classifier and Gaussian Naïve Bayes Classifier in classification. Several evaluation metrics such as accuracy, precision, recall, and F1-score were used to analyze the performance of both models. The dataset used has specific characteristics that influence the evaluation results. The research findings indicate that Random Forest Classifier outperforms Gaussian Naïve Bayes Classifier in most of the evaluation metrics. Random Forest Classifier achieves higher accuracy and better precision, recall, and weighted F1-score. However, it should be noted that Random Forest Classifier also has more outliers compared to Gaussian Naïve Bayes Classifier when visualized using boxplots. Therefore, in selecting a classification model, a trade-off between higher performance and sensitivity to outliers needs to be considered. Further statistical testing and advanced evaluation are required to gain a deeper understanding of the impact and interpretation of the obtained results. This study provides valuable insights into understanding the comparison between these two classification models and their implications in different contexts.
Co-Authors Faradibah, Amaliah Muh Fachrul Islam Muh Taufik Rifaat Nurul Rismayanti Poetri Lestari Lokapitasari Belluano Rahmadani Rahmadani