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All Journal ComEngApp : Computer Engineering and Applications Journal Jupiter Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Dinamisia: Jurnal Pengabdian Kepada Masyarakat Jurnal Inovasi Hasil Pengabdian Masyarakat (JIPEMAS) Sriwijaya Journal of Informatics and Applications Jurnal Generic Jurnal Pengabdian Kolaborasi dan Inovasi IPTEKS
Alvi Syahrini Utami
Teknik Informatika, Fakultas Ilmu Komputer, Universitas Sriwijaya
Agriculture, Biological Sciences & Forestry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Industrial & Manufacturing Engineering Library & Information Science Social Sciences Other

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Journal : Jupiter

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Prediksi Cuaca di Kota Palembang Berbasis Supervised Learning Menggunakan Algoritma K-Nearest Neighbour Alvi Syahrini Utami; Dian Palupi Rini; Endang Lestari
JUPITER (Jurnal Penelitian Ilmu dan Teknologi Komputer) Vol 13 No 1 (2021): JUPITER Vol. 13 No. 1 April 2021
Publisher : Teknik Komputer Politeknik Negeri Sriwijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

AbstrakPermasalahan cuaca yang dipengaruhi banyak faktor alam menyebabkan kondisi cuaca yang berubah - ubah sehingga kadang sulit diprediksi. Prediksi cuaca yang tepat diperlukan agar masyarakat dan para pengambil kebijakan dapat melakukan antisipasi terhadap hal ini. Banyaknya factor yang mempengaruhi cuaca menyebabkan kesulitan dalam mengklasifikasikan cuaca pada hari tertentu. Locality Sensitive Hashing (LSH) bekerja pada data pelatihan dengan memberikan nilai hash pada tiap vektor yang berisi nilai yang merepresentasikan faktor – faktor yang mempengaruhi cuaca dan melakukan pengklasifikasian cuaca. Untuk selanjutnya algoritma k-Nearest Neighbour (k-NN) yang akan menghitung prediksi terhadap faktor – faktor yang mempengaruhi cuaca pada suatu hari tertentu. Berdasarkan pengujian yang dilakukan, metode k-NN yang dihybrid dengan LSH dapat memprediksi nilai factor – factor yang mempengaruhi cuaca dengan cukup baik dengan nilai Mean Square Error (MSE) sebesar 0,301.  Kata kunci—k-Nearest Neighbour (k-NN), prediksi cuaca, Locality Sensitive Hasihing (LSH) AbstractWeather is influenced by many natural factors causing it to change frequently at any time so that it is sometimes difficult to predict. An accuratet weather prediction is needed so that people and policy makers can anticipate this problem. Many factors that influence the weather cause difficulty in classifying the weather on a particular day. Locality Sensitive Hashing (LSH) works on training data by assigning hash values to a vectors that contain values that represent factors that affect weather and perform weather classification. Furthermore, the k-Nearest Neighbor (k-NN) algorithm will calculate the predictions of the factors that affect the weather on a certain day. Based on the tests carried out, k-NN and LSH in weather prediction has Mean Square Error (MSE) 0,301. Keywords— k-Nearest Neighbou r(k-NN), weather forecasting, Locality Sensitive Hasihing (LSH
Analisis Sentimen di Twitter Menggunakan Algoritma Artificial Neural Network Novi Yusliani; Armenia Yuhafiz; Mastura Diana Marieska; Alvi Syahrini Utami
JUPITER (Jurnal Penelitian Ilmu dan Teknologi Komputer) Vol 15 No 1d (2023): Jupiter Edisi April 2023
Publisher : Teknik Komputer Politeknik Negeri Sriwijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5281./6603/15.jupiter.2023.04

Abstract

Along with the development of social media, the amount of data in the form of opinions is increasing. The opinions in social media can be used to find out the assessments of social media users regarding something, one of which is the assessment of a candidate in politics. In general, the opinions in social media can be classified into two categories, namely positive and negative. Sentiment analysis is one of the research topics in the field of Natural Language Processing which aims to classify opinions into one of these categories. The opinions in social media that are often used as research objects are the opinions of Twitter users. This study uses an Artificial Neural Network (ANN) algorithm to be implemented in sentiment analysis system. The dataset used in this study is 1088 tweets consisting of 700 tweets labeled positive and 388 tweets labeled negative. The test results show that the best performance is produced when the data is divided into 80% for training and 20% for testing. The resulting percentages for each performance parameter used are accuracy is 61.3%, recall is 67.9%, precision is 75.1%, and f1-score is 71.3% using 0.01 for learning rate and 150 for epoch.
Perbandingan Metode Mapreduce Berbasis Single Node Hadoop Pada Aplikasi Word Count Marieska, Mastura Diana; Utami, Alvi Syahrini; Oktaviani, Elvira
JUPITER (Jurnal Penelitian Ilmu dan Teknologi Komputer) Vol 16 No 1 (2024): Jurnal Penelitian Ilmu dan Teknologi Komputer (JUPITER)
Publisher : Teknik Komputer Politeknik Negeri Sriwijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5281/zenodo.11097045

Abstract

In the context of Big Data processing, Hadoop MapReduce serves as a framework used to develop software and process large-scale data in parallel. Word Count is a type of task used to count the occurrences of unique words in a text file. Considering processing time is crusial in adhering to standards of Big Data Processing. The conducted research involved the processing of text files using the MapReduce method on the Hadoop Distributed File System (HDFS) using a single node, comparing the results of Word Count processing with and without the use of MapReduce. The research findings indicate that the implementation of Word Count without using MapReduce offers better speed in processing Indonesian language text data on a Hadoop single node. Additionally, the comparison of processing time between the Word Count program with Hadoop MapReduce and the Word Count program without MapReduce shows that the latter has faster processing time. A significant reduction in processing time, up to 95% for a 5 MB file size, can be achieved by using the Word Count method without MapReduce. However, the level of reduction decreases with increasing file size.
Co-Authors Abdiansah Abdiansah, Abdiansah Abdiansyah Abdiansyah, Abdiansyah Adi Kurniawan Ahmad Fali Oklilas Aisyah Filza Aliyah Ari Wedhasmara Armenia Yuhafiz Arya Yunanta Darmawahyuni, Annisa Desty Rodiah Dian Palupi Rini Dian Palupi Rini Endang Lestari Ruskan Febrivia, Pretty Fujianti Harisatul Aulia Herman, Adelwin Kanda Januar Miraswan Mastura Diana Marieska Mudinillah, Adam Muhammad Fachrurrozi Muhammad Qurhanul Rizqie Naura Qatrunnada Novi Yusliani Oktadini, Nabila Rizky Oktaviani, Elvira Osvari Arsalan Pratama, M Wahyu Putri, Amelia Rizki Kurniati Wiwik Anum Puspita YUNITA