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All Journal JURNAL MATEMATIKA STATISTIKA DAN KOMPUTASI GRADIEN AKSIOLOGIYA : Jurnal Pengabdian Kepada Masyarakat MUST: Journal of Mathematics Education, Science and Technology BAREKENG: Jurnal Ilmu Matematika dan Terapan Martabe : Jurnal Pengabdian Kepada Masyarakat Teorema: Teori dan Riset Matematika Jurnal Aplikasi Statistika & Komputasi Statistik FIBONACCI: Jurnal Pendidikan Matematika dan Matematika Cendekia : Jurnal Pengabdian Masyarakat Jurnal Berdaya Mandiri Dharma Raflesia : Jurnal Ilmiah Pengembangan dan Penerapan IPTEKS TRIBUTE: JOURNAL OF COMMUNITY SERVICES JAPI: Jurnal Akses Pengabdian Indonesia Prosiding Seminar Nasional Official Statistics Innovative: Journal Of Social Science Research International Journal of Trends in Mathematics Education Research (IJTMER) Jurnal Hasil-Hasil Pengabdian dan Pemberdayaan Masyarakat (JHP2M)
Herlin Fransiska
Jurusan Matematika FMIPA Universitas Bengkulu
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POVERTY PANEL DATA MODELING IN SOUTH SUMATERA Hidayati, Nurul; Fransiska, Herlin; Agwil, Winalia
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 17 No 3 (2023): BAREKENG: Journal of Mathematics and Its Applications
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol17iss3pp1203-1214

Abstract

Poverty is still a major problem on Sumatra's island, despite abundant natural resources potential, such as mining and plantation products. Sumatra Island consists of 10 provinces divided into three regions: Northern Sumatra, Central Sumatra, and Southern Sumatra. The island of Sumatra has the highest number of poor people in the Southern Sumatra region, which reaches 2.88 million people. Poverty is an integrated concept with five dimensions: poverty, powerlessness, vulnerability to emergencies, dependency, and alienation, both geographically and sociologically. One method that can be used to analyze poverty data problems is panel data regression analysis, which combines two data, namely cross-sectional data and time series data. It is expected to produce more in-depth and comprehensive information, both the interrelationships between the variables and their development within a certain period. The panel data was related to poverty and included 60 districts in the southern Sumatra region from 2018 to 2020. This study aimed to model poverty panel data in Southern Sumatra. Three estimation methods were used in the panel data regression, including the Common Effect Model (CEM), Fixed Effect Model (FEM), and Random Effect Model (REM. The results of the model specification test show that the best model for estimating the percentage of poor people in the Southern Sumatra region is the Fixed Effect Model (FEM), with a value of R2 = 75.57%. The results of the significance test show that the variables that significantly influence the percentage of poverty in the Southern Sumatra region using the FEM model are the open unemployment rate (), life expectancy (), and the average length of schooling ().
MODELING CLUSTERWISE LINEAR REGRESSION ON POVERTY RATE IN INDONESIA Meylisah, Eni; Rini, Dyah Setyo; Fransiska, Herlin; Agwil, Winalia; Sartono, Bagus
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 17 No 3 (2023): BAREKENG: Journal of Mathematics and Its Applications
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol17iss3pp1653-1662

Abstract

When a person's income is so low that it cannot cover even the most basic living expenses, they are said to be poor. Data on poverty levels and hypothesized causes are used in this study. If the data pattern forms clusters, one of the regression analyses that can be used is Clusterwise Linear Regression (CLR). Therefore, this study aimed to determine the poverty rate modeling in Indonesia with the CLR method. The results showed that the best model is with 3 clusters, that for cluster 1, the factors that significantly affect the percentage of poverty are the percentage of electricity users , the number of small and micro industries and the number of tourist villages n cluster 2, the amount of village tours . In cluster 3, the percentage of users of electricity and the percentage of villages that have mining and quarrying .
Pelatihan Pengelolaan Sampah di SD Negeri 02 Kota Bengkulu Sebagai Upaya Peningkatan Literasi Lingkungan Bagi Siswa Sekolah Dasar Yuliza, Elfi; Rini, Dyah Setyo; Fransiska, Herlin; Rosdalena, Nutia; Valeriyan, Reyvo; Purwanto, Eko Heru; Syah, Muhammad Taufiqurrahman
JAPI (Jurnal Akses Pengabdian Indonesia) Vol 8, No 2 (2023)
Publisher : Universitas Tribhuwana Tunggadewi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33366/japi.v8i2.4856

Abstract

Permasalahan sampah di Kota Bengkulu merupakan masalah klasik dan krusial yang belum ada solusinya. Padahal, masalah ini sudah berimbas pada berbagai aspek kehidupan seperti kesehatan, ekonomi, pariwisata, kebencanaan dan lain sebagainya. Salah satu penyebab belum teratasinya masalah sampah ini berkaitan dengan budaya dan karakter masyarakat yang belum peduli terhadap sampah dan efeknya terhadap lingkungan. Salah satu upaya yang dapat dilakukan untuk menumbuhkan budaya dan karakter peduli lingkungan adalah melalui pemberian dan peningkatan literasi lingkungan sejak dini. Pemahaman tentang pengelolaan sampah sudah mulai dilakukan di SD Negeri 02 Kota Bengkulu, namun pandemi Covid-19 yang melanda 2 tahun terakhir telah merubah tatanan dalam penerapan program pengelolaan sampah. Saat ini, untuk melakukan penerapan terkait pemahaman peduli lingkungan, pihak sekolah memiliki keterbatasan pada belum terintegrasinya tema khusus literasi lingkungan pada kurikulum. Disamping itu, terdapat tiga angkatan yang belum mendapatkan pemahaman terkait peduli lingkungan. Berdasarkan permasalahan ada, dua masalah utama yang ditemukan, yaitu (1) metode penyampaian dan penerapan materi peduli lingkungan yang aktif, interaktif, menarik serta mudah dipahami oleh siswa kelas III SD, (2) kekurangan fasilitas bank sampah dengan pemisah jenis serta pengelolaan sampah menjadi barang yang bernilai guna. Kedua permasalahan ini dapat diselesaikan dengan menerapkan sains teknologi masyarakat melalui penyediaan media pembelajaran yang lebih interaktif menggunakan video animasi edukasi, poster-poster dengan ilustrasi yang menarik, penyediaan bank sampah, dan pendampingan pemanfaatan sampah. 
Meningkatkan Kinerja Model Klasifikasi Curah Hujan Melalui Penanggulangan Missing Value Dengan Imputasi Berbasis Model Agwil, Winalia; Agustina, Dian; Fransiska, Herlin; Hasani, Idam Abdurrohim
Innovative: Journal Of Social Science Research Vol. 4 No. 1 (2024): Innovative: Journal Of Social Science Research
Publisher : Universitas Pahlawan Tuanku Tambusai

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/innovative.v4i1.9158

Abstract

Penanggulangan terhadap data yang tidak lengkap telah banyak dikembangkan. Salah satu metode yang digunakan untuk menangani data hilang (missing value) adalah imputasi. Beberapa metode imputasi berbasis model untuk menduga nilai hilang pada variabel numerik adalah menggunakan metode regresi dan Random Forest. Penerapan metode imputasi ini dilakukan pada data curah hujan di Provinsi Bengkulu dari tahun 2013 sampai 2022 dengan variabel yang digunakan Y1 = Curah Hujan, X1 = Suhu Rata-rata, X2 = Suhu Maksimum, X3 = Suhu Minimum, X4 = Tekanan Udara diatas Permukaan Laut, X5 = Arah Angin, X6 = Kecepatan Angin Maksimum, X7 = Tingkat Awan, X8 = Lama Penyinaran Matahari. Hasil diperoleh bahwa kedua metode imputasi berbasis model yang digunakan memberikan nilai akurasi yang tidak terlalu berbeda. Nilai akurasi konsisten antara data training dan data testing, hal ini mengindikasikan bahwa imputasi yang dilakukan sudah baik. Pemodelan dilakukan dengan menggunakan metode CART dengan variabel dengan kontribusi tinggi adalah variabel yang memiliki kontribusi paling besar adalah arah angin pada hari sebelumnya dan variabel tutupan awan pada satu hari sebelumnya.
STRATEGI PENINGKATAN LITERASI KEBENCANAAN DENGAN PENDEKATAN SAINS DAN TEKNOLOGI MASYARAKAT BAGI SISWA SEKOLAH DASAR DI WILAYAH PESISIR KOTA BENGKULU Rini, Dyah Setyo; Fransiska, Herlin; Rosdalena, Nutia; Valeriyan, Reyvo; Purwanto, Eko Heru; Syah, M. Taufiqurrahman; Yuliza, Elfi
Martabe : Jurnal Pengabdian Kepada Masyarakat Vol 5, No 10 (2022): Martabe : Jurnal Pengabdian Kepada Masyarakat
Publisher : Universitas Muhammadiyah Tapanuli Selatan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31604/jpm.v5i10.3539-3546

Abstract

As the capital of Bengkulu Province, Bengkulu City is located not far from the coast, so most community activities are also located around the coastal area, including schools, both elementary and high schools. Bengkulu City is prone to earthquakes and tsunamis, so disaster preparedness is very needed from an early age through earthquake and tsunami disaster literacy training in order to reduce the impact of these disasters. This community service activity carries the theme of disaster literacy using the application of science and technology, including the creation of interesting animated videos, the use of drones for making evacuation route maps, and pocketbooks that are easily understood by students of SD Negeri 2 Bengkulu City. Based on the results of the paired data t-test for evaluation used to pretest and posttest scores, it was found that the earthquake and tsunami disaster literacy training had a significant effect on the knowledge and understanding of third-grade students of SDN 2 Bengkulu City.
MIXED-EFFECTS MODELS WITH GENERALIZED RANDOM FOREST: IMPROVED FOOD INSECURITY ANALYSIS Fransiska, Herlin; Soleh, Agus Mohamad; Notodiputro, Khairil Anwar; Erfiani, Erfiani
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 20 No 2 (2026): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol20iss2pp1111-1124

Abstract

Food insecurity is a complex issue that requires a deep understanding of its influencing factors. Accurate predictions are crucial for effective interventions. Machine learning is well-suited to the large and complex data available in the big data era. However, machine learning generally does not accommodate hierarchical or clustered data structures, making them challenging for machine learning modeling. One model that accommodates hierarchical data structures is the mixed-effects model. This study introduces a novel approach to predict food insecurity by integrating mixed-effects models and a generalized random forest. Mixed-effects models capture variations in hierarchical or clustered data, such as differences between regions, and the generalized random forest, as extended and developed from the traditional random forest, is integrated to model fixed effects and improve prediction accuracy. The empirical data used were the food insecurity data from 2021 in West Java, Indonesia. The results show that mixed-effects models with a generalized random forest significantly improve the prediction accuracy compared to other models. The average performance measure shows GMEGRF is a good model and has a balanced accuracy value of 0.6789709, which is the highest result compared to other methods. This methodological advancement offers a new robust model for understanding and potentially mitigating food insecurity, ultimately informing efforts towards SDG 2 (Zero Hunger).
Co-Authors Agus Mohamad Soleh Agustina Agwil, Winalia Anida Pasaribu Bagus Sartono Baki Swita Dian Agustina Dian Agustina Dian Agustina Dian Agustina Dian Agustina Dian Agustina Diyah Astuti Dyah Setyo Rini Dyah Setyo Rini Dyah Setyo Rini Dyah Yunita hartanti Edi Susanto Eko Heru Purwanto Eko Heru Purwanto Elfi Yuliza Elfi Yuliza Erfiani Erfiani Etis Sunandi Hasani, Idam Abdurrohim M. Taufiqurrahman Syah Meylisah, Eni Moradarma Taksiyah Muhammad Taufiqurrahman Syah Nur Afandi Nur Afandi Nurul Hidayati Nurul Hidayati Nutia Rosdalena Nutia Rosdalena Pepi Novianti Purwanto, Eko Heru Reyvo Valeriyan Reyvo Valeriyan Rini, Dyah Setyo Riwi Dyah Pangesti Rosdalena, Nutia Sigit Nugroho Siska Yosmar Sunandi, Etis Susanta, Agus Susi Tri Nurhayanti Syah, M. Taufiqurrahman Syah, Muhammad Taufiqurrahman Valeriyan, Reyvo Winalia Agwil Winalia Agwil Winalia Agwil