Boengai, Kezia Aimee (2026) Analisis Faktor-Faktor yang Mempengaruhi Prevalensi Ketidakcukupan Konsumsi Pangan di Indonesia Menggunakan Regresi Nonparametrik Spline Truncated. Other thesis, Institut Teknologi Sepuluh Nopember.
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Abstract
Ketidakcukupan konsumsi pangan masih menjadi permasalahan serius di Indonesia karena berkaitan erat dengan isu ketahanan pangan, kemiskinan, dan kesejahteraan masyarakat. Data Badan Pusat Statistik (BPS) menunjukkan bahwa prevalensi ketidakcukupan konsumsi pangan di Indonesia tetap tinggi di beberapa provinsi dengan tingkat kemiskinan besar. Faktor-faktor seperti tingkat kemiskinan, produktivitas padi, angka harapan hidup, dan pengeluaran per kapita makanan diduga memiliki hubungan yang signifikan terhadap kondisi tersebut. Hubungan antarvariabel ini sering kali bersifat nonlinier sehingga model regresi parametrik klasik kurang mampu menggambarkan pola yang sesungguhnya. Penelitian ini menggunakan pendekatan regresi nonparametrik spline truncated untuk menganalisis faktor-faktor yang mempengaruhi prevalensi ketidakcukupan konsumsi pangan antarprovinsi di Indonesia. Data sekunder diperoleh dari Badan Pusat Statistik dan Kementerian Sosial Republik Indonesia tahun 2024 yang mencakup 38 provinsi. Variabel respon adalah prevalensi ketidakcukupan konsumsi pangan, sedangkan variabel prediktor meliputi tingkat kemiskinan, produktivitas padi, angka harapan hidup, dan pengeluaran per kapita makanan. Penentuan titik knot optimal dilakukan menggunakan metode Generalized Cross Validation (GCV) guna memperoleh model terbaik. Hasil analisis diharapkan dapat menunjukkan pola hubungan nonlinier antarvariabel, mengidentifikasi faktor paling dominan yang mempengaruhi ketidakcukupan konsumsi pangan, serta memberikan kontribusi terhadap pengembangan model statistik nonlinier di bidang ekonomi pangan. Penelitian ini juga diharapkan menjadi dasar bagi perumusan kebijakan yang lebih tepat sasaran dalam upaya peningkatan ketahanan pangan di Indonesia. =====================================================================================================================================
Prevalence of Undernourishment (PoU) remains a serious issue in Indonesia because it is closely related to food security, poverty, and community welfare. Data from Statistics Indonesia (BPS) show that the prevalence of undernourishment remains high in several provinces with high poverty rates. Factors such as poverty level, rice productivity, life expectancy, and per capita food expenditure are suspected to have significant relationships with this condition. The relationships among these variables are often nonlinear, making classical parametric regression models less capable of capturing the actual patterns. This study employs a truncated spline nonparametric regression approach to analyze the factors affecting the Prevalence of Undernourishment across provinces in Indonesia. Secondary data were obtained from Statistics Indonesia (BPS) and the Ministry of Social Affairs of the Republic of Indonesia for the year 2024, covering 38 provinces. The response variable is the Prevalence of Undernourishment, while the predictor variables include poverty level, rice productivity, life expectancy, and per capita food expenditure. The optimal knot points were determined using the Generalized Cross Validation (GCV) method to obtain the best model. The results are expected to reveal the nonlinear relationships among variables, identify the most influential factors affecting the Prevalence of Undernourishment, and contribute to the development of nonlinear statistical modeling in the field of food economics. Furthermore, this study is expected to provide a basis for formulating more targeted policies aimed at improving food security in Indonesia.
| Item Type: | Thesis (Other) |
|---|---|
| Uncontrolled Keywords: | Kemiskinan, Pangan, Regresi Nonparametrik, Spline Truncated, Food, Nonparametric Regression, Poverty, Truncated Spline |
| Subjects: | H Social Sciences > H Social Sciences (General) H Social Sciences > HA Statistics H Social Sciences > HA Statistics > HA31.3 Regression. Correlation. Logistic regression analysis. |
| Divisions: | Faculty of Science and Data Analytics (SCIENTICS) > Statistics > 49201-(S1) Undergraduate Thesis |
| Depositing User: | Kezia Aimee Boengai |
| Date Deposited: | 31 Jul 2026 02:16 |
| Last Modified: | 31 Jul 2026 02:16 |
| URI: | http://repository.its.ac.id/id/eprint/140309 |
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