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All Journal Jurnal Penelitian Rumpun Ilmu Teknik Mutiara: Jurnal Penelitian dan Karya Ilmiah Journal of Computer Science Advancements
Sandy Andika Maulana
Universitas Negeri Medan
Religion Humanities Computer Science & IT Electrical & Electronics Engineering Engineering Languange, Linguistic, Communication & Media Other

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Penerapan Metode Naïve Bayes dalam Peramalan Polusi Udara di Kota Jakarta Sandy Andika Maulana; Shabrina Husna Batubara; Wahyu Kurnia Rahman
Mutiara : Jurnal Penelitian dan Karya Ilmiah Vol. 1 No. 6 (2023): Desember: Mutiara : Jurnal Penelitian dan Karya Ilmiah
Publisher : STAI YPIQ BAUBAU, SULAWESI TENGGARA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59059/mutiara.v1i6.702

Abstract

This research aims to analyze and predict the level of air pollution in Jakarta City using ISPU data of DKI Province, adopting the Naïve Bayes method. The test results show that the Naïve Bayes algorithm has excellent performance, with 93% accuracy, 98% precision, 100% recall, and 99% f1-score. The implication is that this model can be effectively used for air pollution forecasting in Jakarta City, assisting authorities in making decisions related to air quality and environmental improvement efforts.
Decision Support System for Determining Tutoring Institutions for SNBT Preparation Using the Weighted Product Method Shabrina Husna Batubara; Sandy Andika Maulana; Wahyu Abadi Harahap; Debi Yandra Niska
Journal of Computer Science Advancements Vol. 2 No. 2 (2024)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/jsca.v2i2.1108

Abstract

There are several stages in choosing a state university, one of which is through the Joint Selection for State University Admission. To prepare for this, many SNBT candidates attend Learning Guidance Institutions. Learning guidance is a service or educational program designed to help students understand the subject matter taught in school. Its main goal is to improve students' academic abilities through various methods. This research aims to develop a Decision Support System (DSS) using the Weighted Product (WP) method to help choose the most appropriate Tutoring Institution. The system is designed to help parents and prospective students make decisions that suit their needs, taking into account factors such as teaching quality, facilities, and costs. With this DSS, it is hoped that the process of selecting Tutoring Institutions can be carried out more efficiently and accurately so that prospective participants can prepare themselves better to face SNBT. Based on the completed case study, the highest preference value is obtained by Adzkia (A4) with a value of 0.231, while the lowest preference value is Ganesha Operation (A1) with a value of 0.168. The results from the DSS built, and the manual calculations, show the same values.
Penerapan Metode CNN (Convolutional Neural Network) Dalam Mengklasifikasi Jenis Ubur-Ubur Sandy Andika Maulana; Shabrina Husna Batubara; Tasya Ade Amelia; Yohanna Permata Putri Pasaribu
Jurnal Penelitian Rumpun Ilmu Teknik Vol. 2 No. 4 (2023): November : Jurnal Penelitian Rumpun Ilmu Teknik
Publisher : Lembaga Pengembangan Kinerja Dosen

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55606/juprit.v2i4.3084

Abstract

The purpose of this research is to apply the Convolutional Neural Network (CNN) method to classify various types of jellyfish. Jellyfish as sea creatures have a variety of shapes and sizes. This research includes data acquisition, data pre-processing, classification, and evaluation. The Keras Sequential model was chosen to implement the CNN model in this study. The results of the study showed an accuracy rate of 87%. In addition, the CNN model training accuracy rate reached 0.9037 with a loss value of 0.2097, while in CNN model testing, the accuracy rate reached 0.7944 with a loss of 0.5228.
Co-Authors Niska, Debi Yandra Shabrina Husna Batubara Tasya Ade Amelia Wahyu Abadi Harahap Wahyu Kurnia Rahman Yohanna Permata Putri Pasaribu