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All Journal BAREKENG: Jurnal Ilmu Matematika dan Terapan Parameter: Jurnal Matematika, Statistika dan Terapannya
Amalia, Rahmatin Nur
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Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Energy Engineering Mathematics Mechanical Engineering Physics Transportation

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A PRELIMINARY STUDY OF SENTIMENT ANALYSIS ON COVID-19 NEWS: LESSON LEARNED FROM DATA ACQUISITION, PRE-PROCESSING, AND DESCRIPTIVE ANALYTICS Amalia, Rahmatin Nur; Sadik, Kusman; Notodiputro, Khairil Anwar
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 17 No 4 (2023): BAREKENG: Journal of Mathematics and Its Applications
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol17iss4pp1901-1914

Abstract

Sentiment analysis is a method used to analyze opinions and feelings. The goal of sentiment analysis is to determine whether a document contains a positive or negative emotion. Along with the spread of Covid-19 cases, news related to Covid-19 has often become a trending topic in the mass media. Conducting sentiment analysis using all news becomes more challenging because it might take time and cost. Therefore, the sampling method is needed to obtain representative news for the analysis. Web scraping was employed to obtain the news article about Covid-19 in Indonesia. In order to select the representative news, two-step sampling was employed by using stratified and systematic random sampling. According to the topic modelling results using lambda 0.6, news articles are grouped into three topics: updating Covid-19 cases, vaccination, and government policy. In addition, based on the number of positive and negative words, news articles are grouped into news dominated by positive words, news dominated by negative words, and news with the same number of positive and negative words. Methods for representing text in numerical form have been developed. Some of them use tf-idf weighting and word embedding. It does not pay attention to word order or meaning, only based on the frequency of words both locally and globally. Furthermore, this method will form a vector size as large as the number of unique words in the document, so it is less effective when many documents are used. Meanwhile, the vector size generated from the word2vec method is not as much as the number of unique words in the corpus. In addition, word2vec considers the context of the words in the corpus.
Clustering of Water Quality Location Using Self Organizing Maps (SOM) Amalia, Rahmatin Nur; Farady, M. Difa; Aksioma, Diaz Fitra; Ahsan, Muhammad
Parameter: Jurnal Matematika, Statistika dan Terapannya Vol 4 No 2 (2025): Parameter: Jurnal Matematika, Statistika dan Terapannya
Publisher : Jurusan Matematika FMIPA Universitas Pattimura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/parameterv4i2pp197-208

Abstract

A decline in the number of locations meeting drinking water quality standards was observed based on internal monitoring in 2021 and 2022. To address this, clustering was performed on water quality test locations using Self Organizing Maps (SOM). The analysis of data from 60 locations, considering turbidity, pH, iron, and nitrite parameters, indicated very good water quality. Outliers were detected before clustering, with the Ireng location being the most extreme, showing turbidity of 4.95 NTU and pH of 8.41, near specification limits. The clustering process removed one outlier, forming two clusters with a silhouette coefficient of 0.668. Multivariate normality tests showed the samples were not multivariate normal, leading to the use of Kruskal-Wallis testing. The results revealed significant differences between clusters 1 and 2, particularly in turbidity and iron levels. Cluster 2 had better water quality, with lower turbidity and iron content. Some locations in cluster 1 exceeded 1 NTU turbidity and had higher iron levels. The company should improve water quality monitoring and control at locations approaching specification limits.
Co-Authors Ahsan, Muhammad Diaz Fitra Aksioma Farady, M. Difa Kusman Sadik