Judul: Optimasi Konsumsi Pangan pada Rumah Tangga dengan Pendapatan 20 Persen Terendah di Provinsi Sulawesi Selatan
Abstrak: Optimasi konsumsi pangan pada rumah tangga dengan tingkat pendapatan yang rendah diperlukan untuk memenuhi kebutuhan gizi. Penelitian ini bertujuan untuk mengevaluasi kualitas dan kuantitas konsumsi pangan serta menyusun rekomendasi komposisi pangan yang dapat mencukupi kebutuhan energi dan protein pada rumah tangga dengan pendapatan 20% terendah di Provinsi Sulawesi Selatan dengan biaya yang minimum. Desain penelitian yang digunakan adalah penelitian deskriptif menggunakan data sekunder. Hasil penelitian menunjukkan bahwa rumah tangga dengan pendapatan 20% terendah masih belum mampu memenuhi kebutuhan energi dan protein per kapita per hari (TKE=79,6% dan TKP=87,6%) serta konsumsi pangannya masih tergolong belum beragam dan seimbang (PPH=58,1). Rekomendasi komposisi pangan yang didapatkan melalui linear programming dapat memenuhi kebutuhan energi dan protein per kapita per hari dan skor PPH masih dapat ditingkatkan. Biaya pangan dari susunan komposisi pangan yang direkomendasikan juga mendekati rata-rata pengeluaran pangan per hari rumah tangga dengan pendapatan 20% terendah di Provinsi Selatan tahun 2021. Kata kunci: biaya pangan, optimasi konsumsi pangan, rumah tangga, tingkat kecukupan energi, tingkat kecukupan protein, Optimizing food consumption in households with low-income levels is necessary to meet nutritional needs. This study aims to evaluate the quality and quantity of food consumption and develop food composition recommendations that can fulfill energy and protein requirements among households with the lowest 20% income in South Sulawesi Province at a minimum cost. The research was conducted by processing secondary data with a descriptive research design. The results show that households with the lowest 20% income are still not able to fulfill their energy and protein requirements per capita per day (energy 7.,6% RDA and protein 87.6% RDA) and their food consumption is still not diverse and balanced (DDP score=58.1). The food composition recommendation obtained through linear programming can improve energy and protein adequacy and DDP score can still be improved. The food costs of the recommended food composition models are also close to the average daily food expenditure of households with the lowest 20% income in South Sulawesi Provinces in 2021. Keywords: energy adequacy level, food cost, household, optimization of food consumption, protein adequacy level
Keyword: biaya pangan, optimasi konsumsi pangan, rumah tangga, tingkat kecukupan energi, tingkat kecukupan protein

Judul: Optimization of Food Consumption Among Low Income Households in DKI Jakarta
Abstrak: Rumah tangga miskin sebagai kelompok rentan gizi kurang dan rawan pangan perlu diupayakan untuk dapat memenuhi kecukupan gizi. Penelitian bertujuan mengevaluasi kualitas dan kuantitas konsumsi pangan dan mengembangkan model komposisi pangan yang dapat memenuhi kebutuhan gizi dengan biaya yang terjangkau bagi rumah tangga berpendapatan 20% terendah (kuintil 1). Penelitian ini menggunakan data sekunder Susenas 2021. Desain penelitian ini adalah studi deskriptif. Hasil penelitian menunjukan bahwa pangsa pengeluaran pangan rumah tangga menurun seiring dengan peningkatan kelas pendapatan. Tingkat kecukupan energi, protein, dan skor Pola Pangan Harapan (PPH) meningkat seiring dengan peningkatan kelas pendapatan. Rumah tangga kuintil 1 mengalami kerawanan pangan, defisit energi sedang, dan pola konsumsi kurang beragam. Model optimasi konsumsi pangan menggunakan pemrograman linier dan menghasilkan 4 model. Masing-masing model dapat mencukupi kebutuhan energi dan protein 90-110% AKG serta meningkatkan skor PPH antara 80-97 dengan biaya di bawah pengeluaran aktual yaitu Rp10.649,00-Rp15.756,00. Dengan pemilihan bahan pangan yang tepat dan menggunakan alokasi pengeluaran maksimal sama dengan pengeluaran pangan rumah tangga kuintil 1, kecukupan energi dan protein masih dapat dicapai serta skor PPH masih dapat ditingkatkan., Poor households as nutritional and food insecure groups need to be pursued to be able to meet nutritional adequacy. This study aims to evaluate the quality and quantity of food consumption and develop a recommendation model for food composition that can meet nutritional needs and affordable for households with the lowest 20% income class (quintile 1). This study used secondary data Susenas 2021. The design of this study is a descriptive study. The results showed that the share of household food expenditure decreased along with the increase in income class. The level of energy sufficiency, protein, and the Desirable Dietary Pattern (DDP) score increased along with the increase in income class. Households in quintile 1 had food insecurity, moderate energy deficit, and less diverse consumption patterns. The food consumption optimization model used linear programming and generated 4 models. Each model could fulfill the energy and protein needs of 90-110% RDA and increase the DDP score 80-97 with costs of Rp10.649,00-Rp15.756,00, which was below the actual expenditure. By selecting the right food and using the expenditure allocation maximum equal to quintile 1 household food expenditure, energy and protein adequacy can still be fulfilled and the DDP score can still be increased.
Keyword: affordable food recommendation, food consumption, linear programming, low-income household, share of expenditure

Judul: Identifikasi Karakter Pada Plat Nomor Kendaraan Menggunakan Ekstraksi Fitur ICZ dan ZCZ dengan Metode Klasifikasi K-NN
Abstrak: License plate is a unique code consisting of letters and numbers arrangement. Each vehicle has a license plate that serve as the identity code of the vehicle itself. Since license plates are unique, it often used for recording, such as in the parking area. The character identification characters on the license plates are made automatically using the input in the form of license plate images to facilitate the recording process. K-NN classification and feature extraction ICZ and ZCZ used in the identification of character identification. ICZ and ZCZ feature extraction obtain features by computing the character centroids and divide the image into n equal zones then computing the average distance from the character centroid to every pixel in the image. The character identification accuracy using 14 zones and ICZ are 97.00% while using ICZ+ZCZ feature extraction are 98.17%. The license plate identification accuracy using feature extraction plates ICZ+ZCZ are 72.31% for zero tolerance and 89.23% for one fault tolerance.
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