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All Journal Prosiding Seminar Nasional Official Statistics Emerging Statistics and Data Science Journal
Nurcahayani, Helida
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Humanities Computer Science & IT Economics, Econometrics & Finance Mathematics Social Sciences

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Studi Klasterisasi Usaha Pertanian Perorangan di Kabupaten Bantul Tahun 2023 dengan Pendekatan Hirarki: Studi Klasterisasi Usaha Pertanian Perorangan di Kabupaten Bantul Tahun 2023 dengan Pendekatan Hirarki Nafri, Tania Chelsia; Pinasty, Salsabila; Kartika Dini, Sekti; Nurcahayani, Helida
Emerging Statistics and Data Science Journal Vol. 3 No. 1 (2025): Emerging Statistics and Data Science Journal
Publisher : Statistics Department, Universitas Islam Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20885/esds.vol3.iss.1.art3

Abstract

Penelitian ini bertujuan untuk menganalisis struktur dan pengelompokkan usaha pertanian perorangan di Kabupaten Bantul menggunakan pendekatan hirarki dengan metode Ward. Data yang digunakan merupakan data sekunder dari hasil pencacahan lengkap Sensus Pertanian 2023. Metode Ward dipilih karena kemampuannya dalam mengelompokkan data berdasarkan kesamaan karakteristik dan meminimalkan jumlah kuadrat dalam setiap klaster yang terbentuk. Hasil penelitian menunjukkan bahwa terdapat tiga klaster utama usaha pertanian di Kabupaten Bantul. Klaster pertama didominasi oleh usaha pertanian dengan intensitas rendah di hampir semua sektor kecuali peternakan dan perikanan. Klaster kedua menunjukkan intensitas usaha pertanian yang sedang dengan sektor perikanan sebagai sektor unggulan. Klaster ketiga memiliki intensitas usaha pertanian yang tinggi di hampir semua sektor kecuali perikanan. Hasil analisis ini diharapkan dapat memberikan wawasan yang lebih mendalam mengenai dinamika usaha pertanian di Kabupaten Bantul dan mendukung pengambilan kebijakan yang lebih tepat guna oleh pemerintah daerah.
Klasifikasi Status NEET dengan XGBoost di Pulau Jawa Tahun 2023 Nurcahayani, Helida; P. Wirahadi, Rivana Marinda
Seminar Nasional Official Statistics Vol 2025 No 1 (2025): Seminar Nasional Official Statistics 2025
Publisher : Politeknik Statistika STIS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34123/semnasoffstat.v2025i1.2589

Abstract

The proportion of individuals categorized as Not in Education, Employment, or Training (NEET) is one of the key indicators of the success of creative digital economic development among the youth. This study investigates NEET status on the island on Java island in 2023 using a machine learning approach. Despite Java being the economic and infrastructural center of Indonesia, there exist significant disparities in NEET rates across its provinces. These disparities reflect unequal access to education and employment opportunities, thereby hindering the achievement of Sustainable Development Goal (SDG) 8. By employing the XGBoost algorithm, this study successfully developed a classification model with exceptional performance. The XGBoost model, optimized through SMOTENN resampling and hyperparameter tuning, achieved a validation accuracy of 98.69%, a training loss of 0.0320, a validation loss of 0.0491, and a ROC-AUC score of 0.9978. These results represent a substantial improvement over the baseline model, which attained an accuracy of approximately 80%. The findings reveal that the primary factors influencing NEET status include age, marital status, education level, work experience, household size, gender, disability status, and training experience. Furthermore, participation in training programs and residence in urban areas are associated with a lower risk of becoming NEET, as they enhance individual skill sets and facilitate greater access to educational and employment opportunities.
Co-Authors Kartika Dini, Sekti Nafri, Tania Chelsia P. Wirahadi, Rivana Marinda Pinasty, Salsabila