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Support Vector Machine: Classification of Trade Balance of Provincies in Indonesia Based on Gross Regional Domestic Product and Large Trade Price Index in 2023 Rati Assyifa Putri; Bahriddin Abapihi; Dian Christien Arisona
International Journal of Economics, Management and Accounting Vol. 1 No. 2 (2024): June : International Journal of Economics, Management and Accounting
Publisher : Asosiasi Riset Ekonomi dan Akuntansi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61132/ijema.v1i2.68

The aim of this research is to classify Indonesia's trade balance data using the SVM (Support Vector Machine) method with two features, namely Gross Regional Domestic Product (X1) and Wholesale Price Index (X2). Classification is carried out by comparing two types of kernels, namely polynomial kernels and RBF (Radial Basis Function) kernels. Equality Hyperplaneobtained from the polynomial kernel is: . The Hyperplane equation obtained from the RBF kernel is: Experimental results show that classification with polynomial kernels provides better performance than RBF kernels. This can be seen in the evaluation results which show that the Polynomial kernel has an average model goodness of 75.93% and for the RBF kernel the average model goodness is 74.07%. Leave One Out cross validation (LOOCV) simulation for training data obtained an average accuracy of 76.67% for the polynomial kernel and 66.67% for the RBF kernel. This shows that in this classification context, kernel polynomials are more effective in separating data classes.

Klasifikasi Judul Berita Online Menggunakan Multinomial Naïve Bayes Laome, Lilis; Dian Christien Arisona; Zelianti; Gusti Ngurah Adhi Wibawa; Irma Yahya; Mukhsar
Al-Khwarizmi : Jurnal Pendidikan Matematika dan Ilmu Pengetahuan Alam Vol. 13 No. 1 (2025): Al-Khwarizmi : Jurnal Pendidikan Matematika dan Ilmu Pengetahuan Alam
Publisher : Prodi Pendidikan Matematika FTIK IAIN Palopo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24256/jpmipa.v13i1.6486

Abstract: News is defined as stories or information about current events. News can be classified into two categories, namely hard news and soft news. Generally, hard news refers to news topics that are timely, important and consequential. Meanwhile, soft news refers to information that is interesting, unique and entertaining. This research investigates online news headline classification using an enhanced Multinomial Naïve Bayes approach combined with SMOTE (Synthetic Minority Oversampling Technique) to address class imbalance issues. The results showed that out of 4,122 data collected, 3821 or 92.70% of news headlines were classified as hard news categories and 301 or 7.30% of other news headlines were classified as soft news categories. With an accuracy value of 91.5%, precision of 96.8%, recall of 93.8%, F1-Score of 95.2% and AUC value of 0.78 which shows Multinomial Naïve Bayes is good enough in distinguishing hard news and soft news categories, although it has not reached the optimal level. Abstract: Berita didefinisikan sebagai cerita atau informasi tentang peristiwa terkini. Berita dapat diklasifikasikan ke dalam dua kategori, yaitu berita keras (hard news) dan berita lunak (soft news). Umumnya, berita keras mengacu pada topik berita yang tepat waktu, penting, dan konsekuen. Sementara itu, berita lunak mengacu pada informasi yang menarik, unik, dan menghibur. Penelitian ini menyelidiki klasifikasi judul berita online menggunakan pendekatan Multinomial Naïve Bayes yang disempurnakan yang dikombinasikan dengan SMOTE (Synthetic Minority Oversampling Technique) untuk mengatasi masalah ketidakseimbangan kelas. Hasil penelitian menunjukkan bahwa dari 4.122 data yang dikumpulkan, 3821 atau 92,70% judul berita diklasifikasikan sebagai kategori hard news dan 301 atau 7,30% judul berita lainnya diklasifikasikan sebagai kategori soft news. Nilai akurasi sebesar 91.5%, precision sebesar 96.8%, recall sebesar 93.8%, F1-Score sebesar 95.2% dan nilai AUC sebesar 0.78 yang menunjukkan Multinomial Naïve Bayes sudah cukup baik dalam membedakan kategori hard news dan soft news, meskipun belum mencapai tingkat optimal.

Klasifikasi Judul Berita Online Menggunakan Multinomial Naïve Bayes Laome, Lilis; Dian Christien Arisona; Zelianti; Gusti Ngurah Adhi Wibawa; Irma Yahya; Mukhsar
Al-Khwarizmi : Jurnal Pendidikan Matematika dan Ilmu Pengetahuan Alam Vol. 13 No. 1 (2025): Al-Khwarizmi : Jurnal Pendidikan Matematika dan Ilmu Pengetahuan Alam
Publisher : Prodi Pendidikan Matematika FTIK IAIN Palopo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24256/jpmipa.v13i1.6486

Abstract: News is defined as stories or information about current events. News can be classified into two categories, namely hard news and soft news. Generally, hard news refers to news topics that are timely, important and consequential. Meanwhile, soft news refers to information that is interesting, unique and entertaining. This research investigates online news headline classification using an enhanced Multinomial Naïve Bayes approach combined with SMOTE (Synthetic Minority Oversampling Technique) to address class imbalance issues. The results showed that out of 4,122 data collected, 3821 or 92.70% of news headlines were classified as hard news categories and 301 or 7.30% of other news headlines were classified as soft news categories. With an accuracy value of 91.5%, precision of 96.8%, recall of 93.8%, F1-Score of 95.2% and AUC value of 0.78 which shows Multinomial Naïve Bayes is good enough in distinguishing hard news and soft news categories, although it has not reached the optimal level. Abstrak: Berita didefinisikan sebagai cerita atau informasi tentang peristiwa terkini. Berita dapat diklasifikasikan ke dalam dua kategori, yaitu berita keras (hard news) dan berita lunak (soft news). Umumnya, berita keras mengacu pada topik berita yang tepat waktu, penting, dan konsekuen. Sementara itu, berita lunak mengacu pada informasi yang menarik, unik, dan menghibur. Penelitian ini menyelidiki klasifikasi judul berita online menggunakan pendekatan Multinomial Naïve Bayes yang disempurnakan yang dikombinasikan dengan SMOTE (Synthetic Minority Oversampling Technique) untuk mengatasi masalah ketidakseimbangan kelas. Hasil penelitian menunjukkan bahwa dari 4.122 data yang dikumpulkan, 3821 atau 92,70% judul berita diklasifikasikan sebagai kategori hard news dan 301 atau 7,30% judul berita lainnya diklasifikasikan sebagai kategori soft news. Nilai akurasi sebesar 91.5%, precision sebesar 96.8%, recall sebesar 93.8%, F1-Score sebesar 95.2% dan nilai AUC sebesar 0.78 yang menunjukkan Multinomial Naïve Bayes sudah cukup baik dalam membedakan kategori hard news dan soft news, meskipun belum mencapai tingkat optimal.

ANALISIS SENTIMEN ULASAN APLIKASI WATTPAD DI GOOGLE PLAY STORE DENGAN METODE RANDOM FOREST Safira Nur Adhan; Gusti Ngurah Adhi Wibawa; Dian Christien Arisona; Irma Yahya; Ruslan Ruslan
AnoaTIK: Jurnal Teknologi Informasi dan Komputer Vol 2 No 1 (2024): Juni 2024
Publisher : Program Studi Ilmu Komputer FMIPA-UHO

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33772/anoatik.v2i1.32

Wattpad is an application and online community site that allows users to write or read informational content in the literary sphere with various genres or categories such as short stories, classics, action, adventure, romance, fantasy, humor, spiritual, mystery, horror, poetry, science fiction, historical fiction, teen fiction, general fiction, fan fiction, and non-fiction. By December 2023, 90 million users spent more than 23 billion minutes accessing the app each month. This study aims to provide an overview of user sentiment while classifying it as negative or positive sentiment text using Random Forest and Random Forest methods optimized with the SMOTE (Synthetic Minority Oversampling Technique) on Wattpad App user reviews that experience class imbalance. The results showed that out of 9.975 data collection results, only 8.743 data could be used with a percentage of positive sentiment of 64,2% (5.616) and 35,8% (3.127) negative sentiment. The Random Forest method without SMOTE optimization tends to be superior in predicting unbalanced sentiment classification, this can be seen from the accuracy value which reaches 84,05%, precision 84,71%, recall 91,60%, F1-Score 88,02%, FPR 8,40%, and AUC value 0,9166 are categorized as excellent classification. SMOTE Random Forest modeling is able to improve the ability to classify the minority class, negative sentiment, as can be seen from the increase in precision value from 84,71 % to 86,70% (1,99%). Unfortunately, this class balancing resulted in a decrease in the performance of accuracy, recall, f1-score and AUC values. In addition, based on the feature importance values, the most influential features in both models are the word attributes "kecewa", "bagus", and "baik".

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