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All Journal The Journal of Experimental Life Sciences (JELS) Jurnal EECCIS SIGMA: Jurnal Pendidikan Matematika PYTHAGORAS: Jurnal Program Studi Pendidikan Matematika Jurnal Matematika Sains dan Teknologi Jurnal Saintek Lahan Kering Variance : Journal of Statistics and Its Applications Publik: Jurnal Manajemen Sumber Daya Manusia, Administrasi dan Pelayanan Publik Jurnal Statistika dan Aplikasinya 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 Research Review: Jurnal Ilmiah Multidisiplin Jurnal Riset Mahasiswa Matematika Journal of Artificial Intelligence and Digital Business Jurnal Ilmiah Ekonomi dan Manajemen Sciencestatistics: Journal of Statistics, Probability, and Its Application Journal of Mathematics, Computation and Statistics (JMATHCOS)
Siti Nurmardia Abdussamad
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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 Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mathematics Medicine & Pharmacology Neuroscience Nursing Physics Public Health Social Sciences Other

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PENERAPAN SMOTE DAN CLUSTER-BASED UNDERSAMPLING TECHNIQUE DALAM KLASIFIKASI OPINI PUBLIK BERBASIS SUPPORT VECTOR MACHINE Dina Zulfiana Matiyeni; Djihad Wungguli; Siti Nurmardia Abdussamad
SIGMA: JURNAL PENDIDIKAN MATEMATIKA Vol. 18 No. 1: Juni 2026
Publisher : Universitas Muhammadiyah Makassar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26618/n8cyqc26

Abstract

Tujuan: Penelitian ini bertujuan untuk menerapkan metode hybrid yang menggabungkan SMOTE dan Cluster-Based Undersampling Technique guna mengatasi ketidakseimbangan data dalam klasifikasi sentimen terhadap Rancangan Undang-Undang Perampasan Aset menggunakan Support Vector Machine (SVM). Metode: Penelitian ini menggunakan pendekatan kuantitatif dengan rancangan eksperimental komparatif. Data dikumpulkan dari media sosial X terkait Rancangan Undang-Undang Perampasan Aset, dilanjutkan dengan preprocessing, pelabelan, ekstraksi fitur, serta pemisahan data latih dan data uji. Ketidakseimbangan data diatasi dengan menggabungkan metode SMOTE dan Cluster-Based Undersampling Technique pada data latih. Selanjutnya, klasifikasi sentimen dilakukan menggunakan Support Vector Machine (SVM). Hasil: Hasil penelitian menunjukkan bahwa model SVM tanpa penyeimbangan data menghasilkan akurasi 70,10%, presisi 62%, recall 46%, dan F1-score 47%, dengan recall kelas negatif yang sangat rendah sebesar 8%. Setelah penerapan metode resampling hybrid SMOTE dan Cluster-Based Undersampling Technique, performa model meningkat signifikan dengan akurasi 82%, presisi 84%, recall 82%, dan F1-score 82%, yang mengindikasikan bahwa metode hybrid mampu mengatasi dominasi kelas mayoritas dan meningkatkan sensitivitas model secara merata pada seluruh kelas sentimen. Simpulan: Temuan penelitian ini mengindikasikan bahwa penerapan metode SMOTE dan Cluster-Based Undersampling Technique berkontribusi signifikan dalam meningkatkan keadilan prediksi model SVM pada data yang tidak seimbang. Oleh karena itu, kombinasi kedua metode tersebut dapat dijadikan solusi yang efektif dalam pengembangan sistem klasifikasi sentimen opini publik, khususnya pada kasus dengan distribusi kelas yang tidak proporsional.
The Implementation of Random Under-Sampling and Synthetic Minority Oevrsampling Techniques to Evaluate the Performance of the Classification and Regression Tree Method Rifandi Pratama Putra Kasadi; Nurwan Nurwan; La Ode Nashar; Djihad Wungguli; Siti Nurmardia Abdussamad
Jurnal Matematika Sains dan Teknologi Vol. 26 No. 1 (2025)
Publisher : LPPM Universitas Terbuka

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33830/jmst.v26i1.11381.2025

Abstract

Class imbalance in datasets poses a significant challenge in the application of classification models, including the Classification and Regression Tree (CART) method. This study aims to evaluate the performance of CART combined with two data balancing techniques: Random Under Sampling (RUS) and Synthetic Minority Oversampling Technique (SMOTE). The data set used in this research is the Heart Failure Clinical Records from Kaggle.com, which exhibits an imbalance where the number of deceased patients is 1,568 records (minority class) and the number of survivors is 3,432 records (majority class), with a total of 5,000 records. The RUS technique reduced the total number of records to 2,526, with each class containing 1,263 records. Conversely, after applying SMOTE, the total number of records increased to 5,474, with each class containing 2,737 records. Model performance evaluation was conducted using precision, recall, and F1-score metrics, both before and after implementing data balancing techniques. The results of the study showed that combining CART with SMOTE produced better performance in recognizing the minority class compared to RUS, achieving accuracy and F1-score of 88.203% and 88.195%, respectively. Meanwhile, RUS achieved an accuracy of 86.345% and an F1-score of 86.332%. Therefore, the use of SMOTE improved model accuracy by approximately 1.85% and F1-score by 1.86% compared to RUS. This study makes a significant contribution to improving prediction accuracy on imbalanced datasets and enriches scientific references related to the application of the CART method and data balancing techniques.
Anxiety Contributing Factors in College Students during the Final Project: Ordinal Logistic Regression Analysis Ni Wayan Tiarawati; Lailany Yahya; Amanda Adityaningrum; Putri Ayuningtias Mahdang; Nikmatisni Arsad; Siti Nurmardia Abdussamad; Muhammad Rezky Friesta Payu; Salmun K. Nasib
International Journal of Health, Economics, and Social Sciences (IJHESS) (Special Issue) - International Journal of Health, Economics, and Social Sciences (IJHESS) - January
Publisher : Universitas Muhammadiyah Palu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56338/ijhess.v7i1.6964

Abstract

The prevalence of anxiety disorders among university students is high, particularly during periods of high stress, such as when students are completing their final projects. This research aimed to investigate how self-confidence and anxiety levels relate among students working on their final projects at the Statistics Department at Universitas Negeri Gorontalo. Research was conducted on 86 students aged 21-26 years old using an online questionnaire. Self-confidence, self-efficacy, and social support were independent variables, while anxiety levels (mild, moderate, and severe) were dependent variables. Self-confidence was found to be significantly correlated with anxiety levels, while self-efficacy and social support were not significantly correlated. The result of ordinal logistic regression analysis indicated that students with high self-confidence were 0.13 times more likely to experience mild or moderate anxiety compared to those with moderate self-confidence. Those with high levels of self-confidence, however, are more likely to suffer from severe anxiety (26%) than those with moderate levels of self-confidence (4%). In certain academic situations, high self-confidence may not be a hindrance against anxiety. A more comprehensive understanding of anxiety will require further research considering additional factors that contribute to anxiety, factors that were not considered in this study.
Pemodelan Faktor-Faktor Yang Mempengaruhi Perilaku Konsumen Pia Jagung Dumati menggunakan Structural Equation Modeling-Partial Least Square Siti Nurmardia Abdussamad; Siti Nurmeylisya Naue; Nadia Kasmin Hasan
Research in the Mathematical and Natural Sciences Vol. 4 No. 1 (2025): November 2024-April 2025
Publisher : Scimadly Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55657/rmns.v4i1.192

Abstract

The development of industrial competition requires producers to continue to evaluate and innovate the products they produce. This research aims to analyze consumer behavior by looking at the factors that influence the purchase of Pia Jagung Dumati products in Gorontalo Regency. The method used is Structural Equation Modeling (SEM)-Partial Least Square (PLS) to provide a clearer picture of the influence on consumer behavior. Questionnaires distributed to 100 respondents became the data used in this research. The sampling technique used is purposive sampling. This research uses the variables price, product quality, promotions, and working hours. The results of the analysis are that the price, product quality and promotion variables have a significant influence on consumer behavior, while the working hours variable has no significant influence. This research provides recommendations for manufacturers to focus on flavor innovation, packaging and more creative digital marketing strategies to attract the attention of consumers, especially among the productive age group
Klasifikasi Tingkat Depresi Mahasiswa Menggunakan Image Recognition dengan Support Vector Machine Siti Nurmardia Abdussamad; Nadya Pratiwi Doholio; Wahyu Pratama Lasaleng; Putu Ayu Indah N. Usia; Mohamad Iswanto Rahman; Dwi Putri Juniar Adam
Research in the Mathematical and Natural Sciences Vol. 4 No. 1 (2025): November 2024-April 2025
Publisher : Scimadly Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55657/rmns.v4i1.193

Abstract

Mental health problems in Indonesia are increasing, with university students being one of the groups vulnerable to depression due to academic pressure, social expectations, and exposure to negative information. Early detection of depression still relies on questionnaire methods that have limitations in objectivity and accuracy. Therefore, this research aims to develop a classification system for student depression using image recognition technology with Support Vector Machine (SVM). The system analyses students' facial expressions and combines them with questionnaire results to improve the accuracy of early depression detection. The results showed that out of 131 respondents, 74% experienced moderate depression, with academic pressure as the main factor. This finding is consistent with the condition of final-year students who face high academic loads. With this method, early detection of depression is more accurate than conventional methods, which can help intervene more quickly in dealing with student mental health crises.
Pemilihan Metode Optimal Untuk Prediksi Angka Kemiskinan Di Provinsi Gorontalo: Perbandingan Double Exponential Smoothing dan Bayesian Structural Time Series Meitasya wolah; Salmun K. Nasib; Armayani Arsal; Isran K. Hasan; Asriadi; Siti Nurmardia Abdussamad
Research in the Mathematical and Natural Sciences Vol. 4 No. 1 (2025): November 2024-April 2025
Publisher : Scimadly Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55657/rmns.v4i1.202

Abstract

Kajian ini mengevaluasi angka kemiskinan di Indonesia yang masih tinggi dengan fokus pada Provinsi Gorontalo yang menjadi urutan kelima sebagai provinsi termiskin di Indoneisa. Meskipun angka kemiskinan ekstrem nasional menurun menjadi 1,12% pada Maret 2023, Gorontalo mencatat masih 183,71 ribu penduduk miskin dengan garis kemiskinan per kapita sebesar Rp 442.194. Tujuan penelitian ini untuk membandingkan dua teknik peramalan, yaitu Bayesian Structural Time Series (BSTS) dan Double Exponential Smoothing (DES) untuk menilai efektivitas masing-masing metode dalam memprediksi angka kemiskinan di Provinsi Gorontalo. Hasil analisis menunjukkan bahwa model Double Exponential Smoothing (DES) memiliki Mean Absolute Percentage Error (MAPE) sebesar 6,6%, lebih rendah dibandingkan MAPE Bayesian Structural Time Series (BSTS) yang mencapai 7,39%. MAPE yang lebih rendah pada Double Exponential Smoothing (DES) menunjukkan kemampuannya yang lebih baik dalam mengidentifikasi pola data dan menghasilkan perkiraan yang lebih akurat. Meskipun BSTS mampu menangkap komponen musiman dan Trend dengan teknik probabilistik yang canggih, hasil ini menegaskan bahwa Double Exponential Smoothing (DES) adalah metode yang lebih efektif untuk memprediksi angka kemiskinan di Provinsi Gorontalo.
Co-Authors Abdussamad, Siti Nurcahyati Agustina, Melisa Agusyarif Rezka Nuha Alaina, Silvana Rahmayanti Amanda Adityaningrum Amanda Adityaningrum Amanda Adityaningrum Armayani Arsal Asriadi Asriadi Asriadi Desya Neydi Putri Pakaya Dewi Rahmawaty Isa Dina Zulfiana Matiyeni Djafar, Fikriyanto Djihad Wungguli Doholio, Nadya Pratiwi Dwi Putri Juniar Adam effendi, Achmad FAHREZAL ZUBEDI Fajar Putrawan Djabar Febrya Christin Handayani Buan Hanz Franklyn Bachruddin Wewengkang Hinelo, Ikrar Prasetyo Ikhlas Ul Aqmal Ilato, Mutiara Ismail Djakaria Ismail Djakaria Isran K Hasan Isran K Hasan Isran K. Hasan Juriko Abdussamad Karim, Finansiya S. Abd. La Ode Nashar Lailany Yahya Marukai, Nur Amalia Meitasya wolah Mohamad Iswanto Rahman Mohamad, Rini Wahyuni Muhammad Rezky F. Payu Muhammad Rezky Friesta Payu Nadia Kasmin Hasan Nadya Pratiwi Doholio Naue, Siti Nurmeylisya Ni Luh Diyani Swarningsih Ni Wayan Tiarawati Nikmatisni Arsad NISKY IMANSYAH YAHYA Nurwan Nurwan Nur’ain Manoppo Pakaya, Desya Neydi Putri Putri Ayuningtias Mahdang Putu Ayu Indah N. Usia Putu Ayu Indah Nazwa Usia Qamarani Zatil Isma Darise Rahmi, Emli Rifandi Pratama Putra Kasadi Rizal Usman Salmun K. Nasib Shafiah Poliyama Siti Nurmeylisya Naue Suci Astutik Sulista Kamah Syafri Yandi Kudo Taufik, Mohamad Alfiransyah Vidya Avianti Hadju Wahyu Pratama Lasaleng Wahyu Pratama Lasaleng Widya Reza Widya Reza Zuchri Abdussamad