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All Journal MUST: Journal of Mathematics Education, Science and Technology Journal of Mathematics, Computation and Statistics (JMATHCOS)
Nadya Pratiwi Doholio
Universitas Negeri Gorontalo
Computer Science & IT Education Mathematics

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PENERAPAN CLUSTERING TIME SERIES UNTUK MENGELOMPOKKAN PROVINSI DI INDONESIA BERDASARKAN JUMLAH PRODUKSI KOPI TAHUN 2008-2021 Nadya Pratiwi Doholio; Rifindu Putra Oktavian; Dian Febrianti Jafar; Prycillia M. C. Nelwan; Moh. Adyan Balada; Asrul S. Ulopo; Stevani Hanapi; Sarah Aulia Djafar; Rahmat Septian Daud; La Ode Nashar
MUST: Journal of Mathematics Education, Science and Technology Vol 7 No 2 (2022): DECEMBER
Publisher : Universitas Muhammadiyah Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30651/must.v7i2.18562

Abstract

Kopi merupakan salah satu komoditas perkebunan di Indonesia yang peranannya begitu penting. Indonesia terdapat pada posisi keempat sebagai negara penghasil kopi terbesar. Hal ini bisa saja dikarenakan wilayah Indonesia yang memungkinkan untuk memproduksi tanaman kopi lebih banyak. Karakteristik wilayah di Indonesia berbeda-beda, sehingga penghasilan tanaman kopi juga berbeda-beda setiap tahunnya. Tujuan diadakannya penelitian ini adalah untuk mengelompokkan provinsi-provinsi di Indonesia dengan melakukan penerapan Clustering Time Series pada data nilai produksi kopi setiap provinsi di Indonesia pada periode tahun 2008-2021. Jarak terbaik yang dapat digunakan untuk Clustering Time Series Analysis pada data ini yaitu jarak Dynamic Time Warping (DTW) dengan metode Average Linkage dan menghasilkan 3 cluster, yaitu cluster 1 terdiri dari 2 provinsi, cluster 2 terdiri dari 4 provinsi, dan cluster 3 terdiri dari 28 provinsi. Secara rata-rata, 3 cluster yang terbentuk menghasilkan nilai koefisien silhouette 0.81 atau dikategorikan Strong Classification.
Comparison of Word2vec and CountVectorizer with Mutual Information in Support Vector Machine (SVM) for Public Sentiment Analysis Nadya Pratiwi Doholio; Isran K Hasan; Siti Nurmardia Abdussamad
Journal of Mathematics, Computations and Statistics Vol. 8 No. 1 (2025): Volume 08 Nomor 01 (April 2025)
Publisher : Jurusan Matematika FMIPA UNM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35580/jmathcos.v8i1.6640

Abstract

Social media is widely used today. Along with the development of social media, it makes it not only a means of communication but also a means of exchanging opinions. One of the social media that is widely used to exchange opinions is X (Twitter). X is widely used to express opinions, particularly on controversial issues, such as the relocation of IKN. Therefore, sentiment analysis is needed to analyse public opinion regarding this national issue. SVM is widely used to classify sentiment based on several required categories, such as positive or negative. However, SVM will work even more effectively if the features used have good quality. Therefore, feature extraction and selection are necessary to enhance SVM classification accuracy. The selection of appropriate feature extraction is very important for classification. Therefore, this study aims to compare two feature extractions, namely Word2Vec and CountVectorizer by adding Mutual Information feature selection to SVM in classifying public sentiment from X. The results show that SVM with Word2Vec and CountVectorizer is more effective than SVM with Mutual Information feature selection. The results show that SVM with Word2Vec feature extraction and Mutual Information feature selection is more effective overall with 84% accuracy, 90% precision, 90% recall, and 90% f1-score, compared to SVM with CountVectorizer feature extraction and Mutual Information feature selection which has 80% accuracy, 83% precision, 92% recall, and 87% f1-score.
Co-Authors Asrul S. Ulopo Dian Febrianti Jafar Isran K Hasan La Ode Nashar Moh. Adyan Balada Prycillia M. C. Nelwan Rahmat Septian Daud Rifindu Putra Oktavian Sarah Aulia Djafar Siti Nurmardia Abdussamad Stevani Hanapi