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All Journal Jurnal Pendidikan Jasmani Indonesia Medikora: Jurnal Ilmiah Kesehatan Olahraga Media Statistika Jurnal Ilmu Dasar Diponegoro Journal of Accounting Jurnal Inovasi Pendidikan Kimia Jurnal Keolahragaan GRADIEN Jurnal Tata Kota dan Daerah SEMIRATA 2015 Psikis : Jurnal Psikologi Islami Psikohumaniora: Jurnal Penelitian Psikologi Indonesian Journal of Chemical Science BAREKENG: Jurnal Ilmu Matematika dan Terapan Motiva: Jurnal Psikologi Jurnal SPORTIF : Jurnal Penelitian Pembelajaran Al-Hikmah: Jurnal Agama dan Ilmu Pengetahuan AN-NAFS : Jurnal Fakultas Psikologi Performance PROGRESIF: Jurnal Hukum Jurnal Sporta Saintika Alayasastra: Jurnal Ilmiah Kesusastraan Cendekia : Jurnal Pengabdian Masyarakat Jurnal Pengabdian Masyarakat Bumi Raflesia JOSSAE (Journal of Sport Science and Education) Dharma Raflesia : Jurnal Ilmiah Pengembangan dan Penerapan IPTEKS Jorpres (Jurnal Olahraga Prestasi) Inferensi Community Development Journal: Jurnal Pengabdian Masyarakat International Journal of Research in Community Services UNES Journal of Swara Justisia JAGO TOLIS : Jurnal Agrokompleks Tolis Journal of Islamic and Contemporary Psychology Cakrawala: Jurnal Pendidikan Journal of Pragmatics and Discourse Research Jurnal Dunia Pendidikan Online Learning in Educational Research Indonesian Journal of Information Technology and Computing (IMAGING) Jurnal Pengabdian Journal of Global Strategic Studies Reformasi Hukum Jurnal Ilmu Kehutanan Journal of Coaching and Sports Science Tanjungpura Journal of Coaching Research Divinitas Jurnal Filsafat dan Teologi Kontekstual The Manager Reveiew Tematik Jurnal Ekonomi dan Pembangunan Indonesia Indonesian Journal of Research in Physical Education, Sport, and Health Society Journal of Statistics and Data Science Jurnal Jurisprudence
Sigit Nugroho
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Journal : BAREKENG: Jurnal Ilmu Matematika dan Terapan

 1 
EVALUATION OF MULTIVARIATE ADAPTIVE REGRESSION SPLINES ON IMBALANCED DATASET FOR POVERTY CLASSIFICATION IN BENGKULU PROVINCE Sriliana, Idhia; Nugroho, Sigit; Agwil, Winalia; Sihombing, Esther Damayanti
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 19 No 2 (2025): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol19iss2pp1143-1156

Abstract

Classification is a statistical method that aims to predict the class of an object whose class label is unknown. The Multivariate Adaptive Regression Splines (MARS) classification method is a classification model that involves several basis functions with influential predictor variables. The MARS classification model is generally effective in classifying imbalanced data, including poverty data classification. The response variable used is the poverty status of households classified into poor and non-poor households, and the predictor variables consist of several poverty indicators. The problem that often arises in classification methods is a class imbalance in the response variable. Due to the poverty status included in the class imbalance data, the Bootstrap Aggregating (Bagging) and Synthetic Minority Over-sampling Technique (SMOTE) approaches will be used to improve classification accuracy on the MARS model. Bagging works by replicating data to strengthen the stability of classification accuracy, while SMOTE works by synthesizing data from minority data classes. The evaluation results showed that the classification model of poverty in Bengkulu Province using the SMOTE-MARS method provides the best classification accuracy compared to the MARS (25.81%) and Bagging-MARS (32.26%) methods based on the sensitivity value obtained, which is 85.36%.
ANALYSIS OF THE EXISTENCE OF THE AGRICULTURAL SECTOR IN MODELING POVERTY IN BENGKULU PROVINCE USING GAUSSIAN COPULA MARGINAL REGRESSION Nugroho, Sigit; Rini, Dyah Setyo; Novianti, Pepi; Crisdianto, Riki; Karuna, Elisabeth Evelin; Fairuzindah, Athaya
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 19 No 2 (2025): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol19iss2pp1251-1262

Abstract

Bengkulu Province ranks second in the category of the highest percentage of poor people in the Sumatra region, at 14.62% in March 2022, and sixth in Indonesia, which is undoubtedly one of the fundamental problems that requires mutual attention. The phenomenon of high poverty in Bengkulu Province is inseparable from the lives of people whose main livelihood is in the agricultural sector, especially tenant farmers. Therefore, in this study, the Copula and Gaussian Copula Marginal Regression (GCMR) methods are applied to determine how the agricultural sector affects poverty in Bengkulu Province using secondary data obtained from the Bengkulu Provincial Statistics Agency (Susenas 2022). The results show that the Copula model can identify various types of dependency between the number of poor households in each district/city in Bengkulu Province in 2022 and each of the variables, namely the Number of Agricultural Business Households , the Growth Rate of the Agricultural Sector , the Human Development Index , and the Open Unemployment Rate ( ) by considering the different characteristics of dependency such as top-tail, bottom-tail, or negative dependency. Meanwhile, the GCMR model can provide the direction of influence of the independent variables on the dependent variable Y, where it can be seen that the variables , , and have a negative influence on the variable , whie the variable has a positive impact on the variable . Therefore, in general, it can be concluded that either positive or negative dependencies identified by the Copula model can influence the resulting GCMR model by providing more profound complexity regarding the relationship between the variables analyzed.
SPATIAL MODELING OF POVERTY IN BENGKULU PROVINCE WITH MIXED GEOGRAPHICALLY WEIGHTED REGRESSION Nugroho, Sigit; Rini, Dyah Setyo; Jomecho, Tommy; Oktarina, Cinta Rizky; Pratiwi, Stevy Cahya; Karuna, Elisabeth Evelin
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 18 No 2 (2024): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol18iss2pp0759-0772

Abstract

The percentage of poor people in Bengkulu Province is high from year to year. The poverty rate in Bengkulu Province also tends to fluctuate. If there is a decrease in the poverty rate, the decrease is relatively small. Poverty in the regions of Bengkulu Province also varies from district to district, subdistrict, and village to village, because poverty data is spatial data that varies regionally. The diversity of poverty data in Bengkulu Province is influenced by spatial effects, namely spatial dependency and spatial heterogeneity. Spatial dependency occurs due to spatial error correlation in cross section data, while spatial heterogeneity occurs due to random area effects, which is the difference between one region and another. Therefore, classical methods are not qualified enough to analyze the resulting diversity. This research will model the poverty of each district/city in Bengkulu Province using Mixed Geographically Weighted Regression (MGWR), because this method is quite complex in modeling data that contains spatial heterogeneity and variations in geospatial data. This modeling aims to identify and analyze poverty indicators in Bengkulu Province spatially, namely based on poverty data in each district/city in Bengkulu Province. The results showed that by using the MGWR method, the variables that locally influence the percentage of extreme poor people in each district/city in Bengkulu Province are Female Household Head Gender and not having a waterheater . Meanwhile, the variable that has a global effect on the percentage of the extreme poor in each district/city in Bengkulu Province is not having a flat screen television ().
Co-Authors Abda Abda Abiyyu Amajida Achmad Binadja Achmad Djunaedi Adesi, Putri Adi Indrayanto Adriani, Desi Agi Ginanjar Agung Juliarto Agung Tri Prasetya Agwil, Winalia Ahmad Nasrulloh Ahmad, Arimuliani Alvionita, Renny Alwansyah, Muhammad Arib Andri Yanto Ari Agung Nugroho, Ari Agung Arief, Yanwar Arief, Yanwar Aristya, Irma Sendy Arono Arono Asmawati, Puji Asri, Novri Azhar Abdul Rahman, Azhar Abdul Bahri, Syuhada Bayubuana, Muhammad Gabdika Bayubuana Betarina , Nurmalia Buyung Keraman Cerika Rismayanthi Cinta Rizki Oktarina Ciptadi, Zaniar Dwi Prihatin Crisdianto, Riki Danny Eka Wahyu Saputra Dewantara, Julian Doni Pranata Duwi Kurnianto Pambudi Dyah Pangesti, Riwi Edmizal, Eval Eka Swasta Budayati Eka Wahyudhi, Andi Sultan Brilin Susandi Eken, Özgür Fachri Faisal Fadhlia, Tengku Nila Fadli Ihsan Fairuzindah, Athaya Fakhrurozi, Zaza Afnindar Fakhrurozi5, Zaza Afnindar Fatkurahman Arjuna Feriza, Firman Firdaus Firdaus Firmansyah, Didi Fitri, Sabina Fransiska, Welly Gumilar, Andri Tresna Haidar, Muhammad Daffa Hardiansyah Hardiansyah Haryanti, Wenny Herawati, Icha Herlin Fransiska Hermalia, Hermalia Hidayat, Bahril Hikmah, Faidatul Idhia Sriliana Iskandar, Doddy Aditya Istislami, Yosuja Jomecho, Tommy Jose Rizal Jose Rizal Judin Purwanto, Judin Julianti, Dola Juliati, Kurnia Jumrianti, Feni Karuna, Elisabeth Evelin Kurniawan, Fuji Kurniawan, Yohan Lala Septem Riza Lestari, Reza Lestari, Wina Ayu Leybina, Anna V Listyani, Nilla Lubis, Layla Takhfa Mahira, Rina Manihuruk, FransFile Margono, Belinda Arunarwati Mariati, Dian Sri Marwan Masnur Ali Maulana, Rifqi Adrian Mohammad Chozin, Mohammad Muhammad Salman Muliadi, Rahmad Mulyawan, Rizki Murgolo, Michael MUTMAINAH Nanang Wahyudin Napitupulu, Lisfarika Nasrullah, Ahmad Nasution, Silvia Fauziah Naury, Chairullah Neni Lismareni, Neni Novi Susanti Novitasari , Eka Fitri Nur Afandi Nur Fitriyana Nurul Hidayati Oki Candra, Oki Okka Adittio Putra Oktarina, Cinta Rizky Panganiban, Teejay D. Pantjarani, Ari Pepi Novianti Perdana, Satya Pongsiri, Tatpicha Praningrum Pratiwi, Stevy Cahya Pusparani, Annisa Marchelyn PUTRI WAHYUNI Putriasari, Novi Qorifah, Nasha Nuryati Rachmah Laksmi Ambardini Rahayu, Sundari Putri Ramya Rachmawati Razak, Ateerah Abdul Resi Vusvitasari Reza Pahlepi, Reza Rezha Arzhan Hidayat REZKI, REZKI Rifky Riyandi Prastyawan Riky Dwihandaka Rina Yuniana Rini, Dyah Setyo Rohani, Tri Rohmatiah, Ahadiati Salsabilla, Intan Sari, Meyritha Trifina Sarumpaet, Mey Yanti Sebastian, Leonard Seger Handoyo Shahadan, MD Azman Sihombing, Esther Damayanti Singh, Laishram Thambal Siregar, Nofi Marlina Sitohang, Yosep O Slamet Widodo Solly Aryza Sri Wahyuni SRI WARDANI Stojanović, Stefan Sujadmi, Sujadmi Sulistiyono Sulistiyono Sumarjo, Sumarjo Sumaryanti Sumaryanti Sumaryanti Sumaryanto Sumaryanto, Sumaryanto Sunandi, Etis Sunaryo Sunaryo Suparyono, Sita Wardhani Suryadi Suryadi Susi Wijuniamurti Syahada, Sri Syahrul Akbar Talib, Kamal bin Tri Hadi Karyono Umi Kalsum Vira Dila, Sondang Wali, Carles Nyoman Wang, Ziyu Wati, Dewi Rohma Wawan Sundawan Suherman Wenni Anggita, Wenni Widiantoro, Didik Widiyatno Widodo, Fanani Haryo Widodo Widodo, Haryo Wijuniamurti, Susi Wiwin Hendriani Yanuar Rachman Sadewa Yassin, Abdulnassir Yoga Pratama Yudik Prasetyo YULIANA RAHMAWATI, YULIANA Yulianti, Ana