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All Journal Jurnal Gaussian Science and Technology Indonesia Sinkron : Jurnal dan Penelitian Teknik Informatika BAREKENG: Jurnal Ilmu Matematika dan Terapan Jurnal BPPK : Badan Pendidikan dan Pelatihan Keuangan Jurnal Kreativitas PKM Sulolipu: Media Komunikasi Sivitas Akademika dan Masyarakat Jurnal Informatika dan Rekayasa Elektronik Unri Conference Series: Community Engagement Operations Research: International Conference Series Jurnal Ketenagakerjaan Parameter: Journal of Statistics Journal of Community Engagement Research for Sustainability Jurnal Ilmiah Pengabdian Masyarakat Bidang Kesehatan Sulolipu: Media Komunikasi Sivitas Akademika dan Masyarakat Journal of Mathematics, Computation and Statistics (JMATHCOS)
Gustriza Erda
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Humanities Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Economics, Econometrics & Finance Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Immunology & microbiology Law, Crime, Criminology & Criminal Justice Materials Science & Nanotechnology Mathematics Mechanical Engineering Medicine & Pharmacology Nursing Physics Public Health Social Sciences Transportation Other

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Journal : Parameter: Journal of Statistics

 1 
WORLD GREENHOUSE GAS EMISSION CLASSIFICATION USING SUPPORT VECTOR MACHINE (SVM) METHOD Ramadani, Kurnia; Gustriza Erda
Parameter: Journal of Statistics Vol. 4 No. 1 (2024)
Publisher : Fakultas Matematika dan Ilmu Pengetahuan Alam Universitas Tadulako

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22487/27765660.2024.v4.i1.17051

Abstract

The phenomenon of Heatwaves has struck several countries across the globe due to climate change. This climate change has led to an increase in greenhouse gas emissions surpassing the limits set by the IPCC Fourth Assessment Report GWPs. This study utilizes the Support Vector Machine (SVM) classification method to identify and categorize greenhouse gas emission data from 1990 to 2020 using four kernels function such as linear, polynomial, radial basis function (RBF), and sigmoid. The SVM method demonstrates excellent performance in constructing classification models with a polynomial kernel function. This is evidenced by high values of training accuracy, testing accuracy, and F1-score, accompanied by short training and testing analysis times. Successively, these values are 97.39%, 97.69%, 96.82%, 0.59 seconds, and 0.22 seconds.
EXPLORATION OF STUDENTS INTERESTS IN MBKM AT RIAU UNIVERSITY USING A MACHINE LEARNING APPROACH Safitri, Nuraini; Zahra, Lathifah; Lafina, Melanie Maria; Erda, Gustriza; Yolanda, Anne Mudya
Parameter: Journal of Statistics Vol. 4 No. 2 (2024)
Publisher : Fakultas Matematika dan Ilmu Pengetahuan Alam Universitas Tadulako

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22487/27765660.2024.v4.i2.17158

Abstract

This study aims to analyze the factors that have a significant influence on the interest of Riau University students in the Merdeka Belajar Kampus Merdeka (MBKM) program using a machine learning approach. MBKM is an innovation initiated by the Ministry of Education and Culture with the aim of improving student competence through its various programs. The Riau University as one of the universities supports this program by providing opportunities for its students to participate in various activities provided in the MBKM program. This study will specifically use a machine learning approach by utilizing several methods to analyze significant factors that have not been analyzed in depth by previous studies. The methods used in this analysis are logistic regression, decision trees, random forests, and naive bayes by utilizing secondary data on the level of interest of Riau University students to participate in the MBKM program in 2023. The variables used in this study include gender, generation, faculty, knowledge, self-confidence, feeling benefits, family support, friend support, lecturer support, self-ability, and facilities as independent variables and MBKM interest as a dependent variable. The results of the analysis of several methods show that the logistic regression method provides the best performance in modeling with an accuracy level of 95%. Variables that have a significant influence on students' interest in the MBKM program have also been successfully identified. The variables that have a significant effect are self-ability and family support. The development strategy of MBKM at the University of Riau can be optimized by paying attention to and focusing on these variables. The optimization of this strategy aims to make the implementation of the program more effective and efficient. Supportive policies such as workshops for the development of students' soft skills can be one of the strategic steps to improve students' abilities to the maximum
Classifiying The Factors Influencing The Human Development Index in Riau Province using Principal Component Analysis Erda, Gustriza; Mega Aulia, Sartika; Erda, Zulya
Parameter: Journal of Statistics Vol. 2 No. 3 (2022)
Publisher : Fakultas Matematika dan Ilmu Pengetahuan Alam Universitas Tadulako

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22487/27765660.2022.v2.i3.16203

Abstract

The Human Development Index is a critical indicator of economic growth. Several factors, including average length of schooling (X1), expected length of schooling (X2), life expectancy at birth (X3), number of health workers (X4), number of health facilities (X5), spending per capita (X6), open unemployment rate (X7), number of poor people (X8), percentage of households with proper drinking water sources (X9), and GRDP growth rate (X10), can influence the Human Development Index. The purpose of this research was to simplify the factors that influence the human development index in Riau Province in 2021. Data analysis used R-Studio software by applying descriptive statistical analysis, Principal Component analysis, and Biplot analysis. The analysis revealed that the ten variables that influence human development index in Riau in 2021 can be divided into three categories: community service quality, health facilities, access, and economic conditions. These three factors can describe up to 80% of the diversity of the data.
GROUPING OF POVERTY IN INDONESIA USING K-MEANS WITH SILHOUETTE COEFFICIENT Erda, Gustriza; Gunawan , Chairani; Erda, Zulya
Parameter: Journal of Statistics Vol. 3 No. 1 (2023)
Publisher : Fakultas Matematika dan Ilmu Pengetahuan Alam Universitas Tadulako

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22487/27765660.2023.v3.i1.16435

Abstract

Poverty is an enormous problem in numerous nations including Indonesia. Poverty can be measured using several indicators, including the unemployment rate, the percentage of poor people, expenditures per capita, and the poverty line. The purpose of this study is to categorize Indonesian provinces based on poverty indicators in 2021 using K-Means with the Silhouette Coefficient approach. Based on the silhouette coefficient approach, there are two clusters that are created. The first cluster is a high-poverty-rate regional group that includes the provinces of Aceh, Bengkulu, West Nusa Tenggara, East Nusa Tenggara, Central Sulawesi, Gorontalo, Maluku, West Papua, and Papua. On the other hand, the second cluster is an association of regions with a low poverty rate, and it includes 25 provinces. The greater number of provinces in the low poverty rate cluster implies that the poverty rate in Indonesia in 2021 is included in the low category
IMPLEMENTATION OF THE K-MEDOIDS METHOD IN CLUSTERING HUMAN DEVELOPMENT INDEXES IN INDONESIA Erda, Gustriza; Usdika, Radhiatul Khaira; Pitri, Rizka; Erda, Zulya
Parameter: Journal of Statistics Vol. 3 No. 2 (2023)
Publisher : Fakultas Matematika dan Ilmu Pengetahuan Alam Universitas Tadulako

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22487/27765660.2023.v3.i2.16906

Abstract

The Human Development Index (HDI), which takes into account three fundamental aspects of human existence, a long and healthy life, knowledge, and a reasonable level of living, is one tool used to assess the effectiveness of human progress. Clustering provinces based on the human development index is important so that development disparities can be identified and help identify provinces with high, medium or low levels of development. The purpose of this study was to use the k-medoids approach to perform a cluster analysis of HDI in Indonesia based on life expectancy, average years of schooling, expected years of schooling, and expenditure per capita adjusted for 2022. The analysis indicate that two clusters were created: cluster 1 had a high human development index, while cluster 2 had a low human development index. More provinces belonged to cluster 1 than cluster 2 suggesting that human development index in Indonesia in 2022 was largely in the high category
APPLICATION OF THE LIGHTGBM ALGORITHM IN THE CLASSIFICATION OF GREENHOUSE GAS EMISSIONS Rini Latifah; Gustriza Erda
Parameter: Journal of Statistics Vol. 4 No. 1 (2024)
Publisher : Fakultas Matematika dan Ilmu Pengetahuan Alam Universitas Tadulako

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22487/27765660.2024.v4.i1.17055

Abstract

Ada banyak dampak negatif yang dapat ditimbulkan oleh peningkatan emisi gas rumah kaca. Oleh karena itu, penting untuk mengetahui tingkat emisi gas rumah kaca di masa depan dengan membuat prediksi sehingga kita dapat merencanakan kebijakan untuk memitigasi dampaknya. Pada penelitian ini, klasifikasi tingkat emisi gas rumah kaca dilakukan dengan menggunakan metode lightGBM. Tujuannya untuk melihat kinerja metode light GBM dalam melakukan klasifikasi emisi rumah kaca. Hasil yang diperoleh dari penelitian ini adalah akurasi sebesar 96,26%, sensitivitas sebesar 97,62%, spesifisitas sebesar 93,97%, dan MAE sebesar 0,0374.
Co-Authors Adia Syaputri Agustiarni, Efni Al Baihaqi, Habib Alika, Elsa Riesta Althoff Hibban Anisa, Rahmatul Anne Mudya Yolanda Arisman Adnan Aulia, Felia Rizki Ayu Sofia Bustami, Haposan Sirait Dien, Zulfanita Dimas Abyan Fatkhin Al-Aswad Divo Dharma Silalahi Efendi, MHD Eky Erie Sadewo Fandi Gusriyanda Ferdinan, Tri Agus Finda, Ingla Fitri Ramadhani Goldameir, Noor Ell Gumanti Gunawan , Chairani Indra, Zul Kholilah Samosir Kurnia Ramadani Lafina, Melanie Maria Lathifah Zahra M. SYIFA RAMADHAN Mariza Mega Aulia, Sartika Miftahul Jannah Mistika, Lensi Musraini M Nurhaliza Nurhannifah Rizky Tampubolon Nusantri Purba Okta Bella Syuhada Pitri, Rizka Putri, Nelsa Rahman, Fadhila Rahmi, Afrilia Ramadani, Kurnia Rezzy Eko Caraka Rini Latifah Riris Putri Kinanti Russel, Edwin Rustam Efendi Safitri, Nuraini Sarasmita Apriyenti Simbolon, Veronika Amelia Sirait, Tesa Theresia Siregar, Nurainun Siti Farika Sari SITI SUHAILA Sukamto Susilawati, Susilawati Syaftiani Dwi Astuti Syamsudhuha Syamsudhuha Syamsudhuha T, Masrina Munawarah T, Nurhannifah Rizky Tata, Tata Tatik Widiharih Thearas, Lia Titi Solfitri Usdika, Radhiatul Khaira Wamiliana Wawan Sugiyarto Yenita Roza Yuciana Wilandari Zahra, Lathifah Zulya Erda