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All Journal The Journal of Experimental Life Sciences (JELS) Jurnal EECCIS PYTHAGORAS: Jurnal Program Studi Pendidikan Matematika Jurnal Saintek Lahan Kering Variance : Journal of Statistics and Its Applications Publik: Jurnal Manajemen Sumber Daya Manusia, Administrasi dan Pelayanan Publik Jurnal Sibermas (Sinergi Pemberdayaan Masyarakat) JAMBURA JOURNAL OF PROBABILITY AND STATISTICS International Journal of Health, Economics, and Social Sciences (IJHESS) Griya Journal of Mathematics Education and Application Parameter: Journal of Statistics Jurnal Info Sains : Informatika dan Sains Jurnal Kolaboratif Sains Jurnal Pijar MIPA Research in the Mathematical and Natural Sciences Jurnal Riset Mahasiswa Matematika Journal of Artificial Intelligence and Digital Business Jurnal Ilmiah Ekonomi dan Manajemen Journal of Mathematics, Computation and Statistics (JMATHCOS)
Siti Nurmardia Abdussamad
Department Of Statistics, Universitas Negeri Gorontalo
Religion Agriculture, Biological Sciences & Forestry Humanities Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Health Professions Immunology & microbiology Law, Crime, Criminology & Criminal Justice Mathematics Medicine & Pharmacology Neuroscience Nursing Physics Public Health Social Sciences Other

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Journal : Journal of Mathematics, Computation and Statistics (JMATHCOS)

 1 
Comparison of Word2vec and CountVectorizer with Mutual Information in Support Vector Machine (SVM) for Public Sentiment Analysis Doholio, Nadya Pratiwi; Hasan, Isran K; Abdussamad, Siti Nurmardia
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.
Evaluation of the Adaptive Fuzzy Neuro Inference System and Fuzzy Model Time Series Markov Chains in Forecasting Crude Oil Prices Hinelo, Ikrar Prasetyo; Nuha, Agusyarif Rezka; Hasan, Isran K; Nasib, Salmun K; Abdussamad, Siti Nurmardia
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.v8i1.6763

Abstract

The development of a country's economy is greatly influenced by global economic conditions, given the increasingly close links between countries through economic relations and international cooperation. One of the main factors in economic growth is international trade, particularly export and import activities. Crude oil is one of the most actively traded commodities. Given the highly volatile crude oil market, accurate price forecasts are crucial in economic and financial decision-making. This study compares the performance of Adaptive Neuro-Fuzzy Inference System (ANFIS) and Fuzzy Time Series Markov Chain (FTSMC) in forecasting the price of West Texas Intermediate (WTI) crude oil using time series data from 2020 to 2024 with saturated sampling technique. The implementation of both methods is carried out through Matlab Online and R-Studio software, with results showing that ANFIS has higher accuracy than FTSMC, as evidenced by the Mean Absolute Percentage Error (MAPE) value of 1,8010% for ANFIS and 3,7567% for FTSMC. Further analysis shows that ANFIS with a triangular membership function as well as significant lags at lag 1, lag 3, lag 4, and lag 7 is able to produce more accurate predictions and match the trend of actual data. Therefore, ANFIS is recommended as a more effective method in forecasting WTI crude oil prices, which can provide valuable insights for policy makers and industry stakeholders.
Partial Least Square-Path Modeling Analysis of Factors Influencing the Consumptive Behaviour of Generation Z Agustina, Melisa; Djakaria, Ismail; Abdussamad, Siti Nurmardia; Payu, Muhammad Rezky Friesta; Adityaningrum, Amanda
Journal of Mathematics, Computations and Statistics Vol. 8 No. 2 (2025): Volume 08 Nomor 02 (Oktober 2025)
Publisher : Jurusan Matematika FMIPA UNM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35580/jmathcos.v8i2.8014

Abstract

Consumptive Behaviour refers to individuals’ purchasing behaviour without considering long-term needs and financial conditions. This research presents the results of an analysis of the consumptive behaviour of Generation Z in Dungingi Sub-District, Gorontalo City, selected because it represents the second-largest Generation Z population in the city. The study used the Partial Least Square-Path Modeling (PLS-PM) method to measure factors influencing consumptive behaviour: financial literacy, fear of missing out (FOMO), and hedonistic lifestyle. The sampling technique used was purposive sampling, resulting in 378 respondents aged 17-27 years who are employed. The analysis results indicate that financial literacy and FOMO significantly influence consumptive behaviour, with FOMO being the most dominant factor. The resulting model has a value of 0,930, meaning that the three latent variables can explain 93,0% of the consumptive behaviour of Generation Z. This study is expected to provide useful insights for policymakers and related parties in adressing consumptive behaviour issues among Generation Z. Keywords: PLS-PM; Consumptive Behaviour; Generation Z
Comparison of Multiple Kernel Learning and Single Kernel Support Vector Machine for Public Opinion Classification Poliyama, Shafiah; Achmad, Novianita; Abdussamad, Siti Nurmardia
Journal of Mathematics, Computations and Statistics Vol. 9 No. 1 (2026): Volume 09 Issue 01 (March 2026)
Publisher : Jurusan Matematika FMIPA UNM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35580/qea76e33

Abstract

Abstract. Social media has become a digital public space where public opinion is expressed on various government policies. Social media platform X has become a major venue for openly expressing support and criticism, making it relevant to sentiment analysis. This condition is useful for understanding public perceptions of government policies, such as the Makan Bergizi Gratis (MBG) Programme, which has elicited various public responses since its implementation. Support Vector Machine (SVM) is a widely used method for sentiment classification, but its performance is highly dependent on kernel selection. Using a single kernel type often fails to capture both linear and non-linear patterns in social media texts. Therefore, this study aims to compare the performance of Single Kernel and Multiple Kernel Learning (MKL) in classifying public sentiment from social media X. The research methods included collecting Indonesian language tweets through scraping techniques, text pre-processing, feature extraction using Term Frequency–Inverse Document Frequency (TF–IDF), data division with a ratio of 80:20, and the classification process using SVM with linear kernel, Radial Basis Function (RBF) kernel, and a combination of both through the MKL approach. The results show that MKL based SVM provides the best performance with an accuracy of 93.17%, while Linear and RBF kernels produce accuracies of 91.81% and 92.49%, respectively, on the same dataset and testing scheme.
Co-Authors Abdussamad, Siti Nurcahyati Adam, Dwi Putri Juniar Agustina, Melisa Agusyarif Rezka Nuha Alaina, Silvana Rahmayanti Armayani Arsal Asriadi Asriadi Asriadi Darise, Qamarani Zatil Isma Dewi Rahmawaty Isa Djafar, Fikriyanto Djihad Wungguli Doholio, Nadya Pratiwi effendi, Achmad Febrya Christin Handayani Buan Hasan, Nadia Kasmin Hinelo, Ikrar Prasetyo Ikhlas Ul Aqmal Ilato, Mutiara Ismail Djakaria Isran K Hasan Juriko Abdussamad Karim, Finansiya S. Abd. Kudo, Syafri Yandi La Ode Nashar Lailany Yahya Lasaleng, Wahyu Pratama Marukai, Nur Amalia Naue, Siti Nurmeylisya Nikmatisni Arsad NISKY IMANSYAH YAHYA Pakaya, Desya Neydi Putri Poliyama, Shafiah Putri Ayuningtias Mahdang Rahman, Mohamad Iswanto Rahmi, Emli Salmun K. Nasib Suci Astutik Sulista Kamah Taufik, Mohamad Alfiransyah Tiarawati, Ni Wayan Usia, Putu Ayu Indah N. Usman, Rizal Wewengkang, Hanz Franklyn Bachruddin Widya Reza Widya Reza wolah, Meitasya Zuchri Abdussamad