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All Journal Variance : Journal of Statistics and Its Applications Jurnal Statistika dan Matematika (Statmat) Multica Science and Technology Prosiding Seminar Nasional Official Statistics STATISTIKA
Wiwit Pura Nurmayanti
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Humanities Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Energy Library & Information Science Mathematics Physics Social Sciences Other

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CLUSTERING PETANI BERDASARKAN DAMPAK COVID-19 YANG TERJADI PADA SEKTOR PERTANIAN Dinda Ayu Lara Saky; Novia Apriana Jayanti; Wiwit Pura Nurmayanti
Seminar Nasional Official Statistics Vol 2020 No 1 (2020): Seminar Nasional Official Statistics 2020
Publisher : Politeknik Statistika STIS

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (143.93 KB) | DOI: 10.34123/semnasoffstat.v2020i1.690

Abstract

As the Covid-19 outbreak continues to spread around the world, it is important to impact the impact on the agricultural sector, both from a food supply and demand perspective. It is imperative to ensure that global and national food supplies continue to function in ensuring food supplies. Lepak hamlet, Lepak village, East Sakra sub-district, East Lombok, NTB is one of the areas with the most dominant farmer profession. Farmers in Lepak hamlet complain that their agricultural output continues to decline due to the Covid-19 outbreak which is increasingly spreading. And there are also farmers who have not felt the impact of the Covid-19 outbreak. Based on this, the researchers wanted to see the grouping of farmers in Lepak hamlet based on the impact of Covid-19 that occurred in the agricultural sector. Based on the results of the clustering hierarchy analysis, namely centroid linkage by taking 3 clustering, it can be concluded that the clustering of the impact of the Covid-19 impact on the agricultural sector in cluster 1, the number of members is 1 farmer, cluster 2 is 18 people, and cluster 3 there are 17 people. So, the clusters with the highest number of farmers are clusters 2 and 3. Based on the results of these clusters, it means that many farmers in Lepak hamlet feel disadvantaged by the Covid-19 outbreak, but not a few farmers also feel that Covid-19 has not significantly affected their agricultural output.
PENERAPAN METODE CLUSTERING SELF ORGANIZING MAPS (SOM) DAN K-AFFINITY PROPAGATION (K-AP) DALAM MENGELOMPOKKAN NILAI TUKAR PETANI DI INDONESIA 2022 Siti Hariati Hastuti; Wiwit Pura Nurmayanti; Apriska Ayu Saputri
VARIANCE: Journal of Statistics and Its Applications Vol 5 No 1 (2023): VARIANCE: Journal of Statistics and Its Applications
Publisher : Statistics Study Programme, Department of Mathematics, Faculty of Mathematics and Natural Sciences, University of Pattimura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/variancevol5iss1page79-88

Abstract

Sektor pertanian masih menjadi sorotan utama di Indonesia, hal ini dikarenakan kontribusi sektor pertanian terhadap perekonomian nasional cukup besar dan penyerapan tenaga kerja pada sektor pertanian terbilang cukup tinggi. Keberhasilan pembangunan di sektor pertanian dapat dilihat pada tingkat kesejahteraan petani dengan indikator Nilai Tukar Petani (NTP). Dalam rangka meningkatkan kesejahteraan petani di Indonesia dibutuhkan suatu analisis pengelompokan wilayah yang berguna untuk memetakan persebaran tingkat kesejahteraan petani. Analisis yang dapat digunakan adalah analisis clustering dengan algoritma Self Organizing Maps (SOM) dan K-Affinity Propagation (K-AP). Kedua metode cluster tersebut dapat diterapkan hampir disemua sektor, salah satunya sektor pertanian. Penelitian bertujuan untuk menguraikan hasil clustering metode SOM dengan K-AP dan untuk mengetahui hasil pengelompokan NTP terbaik antara metode SOM dengan K-AP. Hasil penelitian dengan metode SOM dan K-AP cluster terbaik yang terbentuk sebanyak 3 cluster. Pada metode SOM cluster 1 terdapat 14 provinsi, cluster 2 terdapat 19 provinsi dan cluster 3 terdapat 1 porvinsi. Sedangkan untuk metode K-AP, terdapat 11 provinsi pada cluster 1, 22 provinsi pada cluster 2 dan 1 provinsi cluster 3. Metode SOM memiliki nilai rasio sebesar 18,59997 dan pada metode K-AP memiliki nilai rasio sebesar 38,04833. Dari nilai rasio yang didapatkan pada kedua metode tersebut, dapat disimpulkan bahwa nilai rasio metode SOM lebih kecil dibandingkan K-AP, sehingga analisis cluster data NTP berdasarkan subsektor pertanian di Indonesia tahun 2022 lebih baik jika menggunakan metode SOM dengan 3 cluster.
Comparison of Generalized Poisson Regression and Negative Binomial Regression Models Based on Akaike Information Criterion Values Sinta Qorri Aina; Darnah; Meirinda Fauziyah; Wiwit Pura Nurmayanti
Statistika Vol. 25 No. 1 (2025): Statistika
Publisher : Department of Statistics, Faculty of Mathematics and Natural Sciences, Universitas Islam Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29313/statistika.v25i1.5402

Abstract

Abstract. Poisson regression models discrete data and assumes equidispersion, where the variance equals the mean. It is frequently observed that discrete data exhibits a variance exceeding its mean, a phenomenon known as over-dispersion. Over-dispersion may be addressed through various methodologies, such as Generalized Poisson Regression (GPR) and Negative Binomial Regression (NBR). Model selection is predicated on the smallest Akaike Information Criterion (AIC) value. This study aimed to identify the best model in the comparison of models between GPR and NBR based on the smallest AIC value so that it can be known what factors influence the number of cases of pulmonary tuberculosis (TB) in Indonesia in 2022. The results of the study showed that the NBR model was the best model, with an AIC value of 688.49. Factors that influence cases of pulmonary TB in Indonesia in 2022 are the percentage of households that have access to proper sanitation, nursing staff, and the percentage of education levels completed are high school or equivalent.
TRAFFIC ACCIDENT VICTIM CLASSIFICATION IN BONTANG USING NW-KNN AND BACKWARD ELIMINATION Mangalik, Gerald; Nariza Wanti Wulan Sari; Surya Prangga; Wiwit Pura Nurmayanti; Ika Purnamasari
Multica Science and Technology (ACCREDITED-SINTA 5) Vol. 5 No. 1 (2025): Multica Science and Technology
Publisher : Universitas Mulia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47002/yfbspb33

Abstract

Traffic accidents have been a serious problem caused by various factors such as road conditions, driver behavior, and weather. To understand the pattern of victim severity, a classification approach capable of handling imbalanced data and irrelevant features was needed. This study aimed to classify the status of accident victims using the Neighbor Weighted K-Nearest Neighbor (NW-KNN) method, equipped with backward elimination for feature selection. Backward elimination was employed to reduce insignificant features and improve accuracy.The case study for this research involved the status of accident victims in Bontang City, with a sample size of 93 cases. There were nine features in this study: accident victim status, accident time, road density, road function, road surface condition, speed limit at the location, road slope, and road status.The research results showed that the best parameter combination for classification using the NW-KNN method with backward elimination was K = 7 and E = 3. The "type of accident" feature was eliminated, leaving 8 features. Classification results using the NW-KNN method with backward elimination yielded an accuracy of 88.89%, demonstrating an improvement in classification performance for identifying the status of traffic accident victims. Thus, this method proved to be an effective approach for traffic accident analysis in Bontang City.
ANALISIS FAKTOR_FAKTOR KETENAGAKERJAAN DI INDONESIA DENGAN PENDEKATAN REGRESI LOGISTIK BINER Aprillia Elsada Rinindah; Muhammad Azra Firdaus; Sakila Armayani; Widyaningrum, Erlyne Nadhilah; Wiwit Pura Nurmayanti
STATMAT : JURNAL STATISTIKA DAN MATEMATIKA Vol 7 No 2 (2025)
Publisher : Math Program, Math and Science faculty, Pamulang University

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Tingkat Partisipasi Angkatan Kerja (TPAK) menentukan pertumbuhan ekonomi suatu negara dengan variabel yang mempengaruhi TPAK memiliki dua nilai atau bersift biner. Untuk menganalisis TPAK tersebut digunkana regresi logistik biner yang bisa menganalisis keterkaitan antara variabel prediktor dan variabel respon yang memiliki dua kategori nilai atau bersifat biner. Variabel yang mempengaruhi TPAK yaitu proporsi tenaga kerja formal, tingkat pengangguran terbuka (TPT), Produk Domestik Regional Bruto (PDRB) dan upah rata rata per jam pekerja. Hasil analisis terhadap model terbaik dengan nilai AIC 34,04 menunjukkan bahwa TPT dan rata-rata upah memiliki pengaruh terhadap TPAK, dengan tingkat signifikansi masing-masing sebesar 0,00978 dan 0,06910. Nilai koefisien determinasi sebesar 39,90% mengindikasikan bahwa model mampu menjelaskan sebagian besar variasi TPAK, Sedangkan faktor lainnya dipengaruhi oleh variabel-variabel di luar cakupan model. Tingkat akurasi klasifikasinya mencapai 82,35% menunjukkan bahwa model cukup andal dalam memprediksi status partisipasi angkatan kerja.
Co-Authors Aprillia Elsada Rinindah Apriska Ayu Saputri Darnah Dinda Ayu Lara Saky Erlyne Nadhilah Widyaningrum Ika Purnamasari Mangalik, Gerald Meirinda Fauziyah Muhammad Azra Firdaus Nariza Wanti Wulan Sari Novia Apriana Jayanti Sakila Armayani Sinta Qorri Aina Siti Hariati Hastuti Surya Prangga