EFISIENSI INPUT PRODUKSI PADA BUDIDAYA RUMPUT LAUT (Gracilaria sp) DI KOTA PALOPO PROVINSI SULAWESI SELATAN

Abstract

ABSTRAKTingkat produksi hasil budidaya dipengaruhi oleh bekerjanya beberapa faktor produksi seperti luas lahan, pupuk, bibit, dan tenaga kerja. Oleh karena itu, tinggi rendahnya produksi bergantung dari efektif dan efisiennya pemanfaatan faktor-faktor produksi tersebut. Penelitian ini bertujuan untuk menganalisis efisiensi penggunaan faktor input produksi budidaya rumput laut Gracilaria sp di Kota Palopo. Pengumpulan data penelitian dilaksanakan dengan menggunakan dua metode yaitu metode observasi dan wawancara. Data yang diperoleh kemudian dianalisis dengan menggunakan analisis produksi, analisis efisiensi dan analisis skala usaha (return to scale). Hasil analisis produksi dengan menggunakan analisis Cobb-Douglas diperoleh persamaan fungsi Y= 2,377.X1 (0,386).X2 (0,521).X3 (0,223).X4 (0,100).X5 (0,137).e. Terdapat empat variabel yang signifikan mempengaruhi tingkat produksi yaitu modal (X1), bibit (X2), luas lahan (X3), dan pupuk (X5), sedangkan untuk variabel tenaga kerja (X4) cenderung tidak terlalu mempengaruhi tingkat produksi. Hasil analisis efisiensi produksi menunjukkan bahwa beberapa input produksi belum efisien, seperti modal, bibit, luas tambak dan pupuk, dimana rasio NPMX faktor produksi tersebut lebih besar dari satu. Maka untuk mencapai efisiensi, faktor produksi tersebut perlu ditambah guna memaksimalkan keuntungan. Sedangkan penggunaan input tenaga kerja karena rasio NPMX yang diperoleh lebih kecil dari satu, maka faktor tenaga kerja perlu dikurangi. Berdasarkan hasil analisis skala usaha diperoleh nilai ∑bi =1,166 yang berarti bahwa produksi rumput laut Gracilaria sp berada pada skala kenaikan hasil yang semakin meningkat (increasing return to scale). Pengembangan usaha budidaya rumput laut dapat dilakukan dengan cara mengoptimalkan dan mengefisienkan penggunaan faktor produksi, dengan efisiennya faktor produksi maka akan meningkatkan keuntungan yang diperoleh masyarakat.Title : The Efficiency Of Production Inputs On Seaweed Culture (Gracilaria Sp) In Palopo City South Sulawesi ProvinceABSTRACTThe production level of cultivation outcome is influenced by several production factors such as the land area, fertilizer, seedlings and labor. Consequently, the high and the low of production amount is depending on the effectiveness and the efficiency of those production factors implementation. This research aims to analyze the efficiency of the input factors usage on the cultivation of Gracilaria sp seaweed in Palopo City. The data collection of research was conducted by using two methods: observations and interviews. The collected data then analyzed by using production analysis, efficiency analysis and return to scale or RTS analysis. The result of the production analysis by using Cobb-Douglass analysis was obtained function equation Y = 2,377.X1(0,386).X2 (0,521).X3(0,223).X4(0,100).X5(0,137).e. There are four variables that significantly affect the level of production, namely the capital (X1), seedlings (X2), land area (X3), and fertilizer (X5), while for the labor variable (X4) tends not to greatly affect the level of production. The results of the production efficiency analysis indicated some of the production inputs were not efficient such as the capital, seedlings, the pond area and fertilizer, where the NPMX ratio of production factors was greater than one. Therefore to achieve the efficiency, these production factors need to be added to maximize the profits. While the usage of the labor input need to be reduced because the obtained NPMX ratio was smaller than one. Based on the analysis of return to scale, it is obtained the the value of ∑bi = 1.166 which is mean the production of Gracilaria sp is on a increasing scale of outcomes (increasing return to scale). The development of seaweed cultivation can be done by optimizing and making efficient the usage of production factors, the efficient factors of production will increase the profit gained by the community.

Technical efficiency with stochastic production function frontier of seaweed farming in Pantai Amal, Tarakan City

“…This is possible because farmers use seeds from previous harvests or seaweed that is deliberately not harvested for the needs of seeds for the next planting period, so that the number of subsequent plantings was adjusted to the yield. This opinion was in line with the results of research [13], which stated that the used of seaweed seeds by the average respondent cultivators was residual seaweed production that was deliberately not harvested for the purpose of further planting. The labor outpouring variable has a IOP Publishing doi:10.1088/1755-1315/1253/1/012075 6 coefficient value which was also the production elasticity of the variable whose effect was significantly different at the 5% level, which is 0.0001.…”

“…Increasing the used of the number of workers both in quality and quantity has an important role in increasing production. Similar to the opinion [13], that the influence of labor factors on seaweed cultivation activities has no significant effect, presumably because the use of HOK has not been taken into account properly in the production process, especially in terms of quality and specialization. While the estimation of technical inefficiency from the model can be seen in Table 3.…”

Analisis Global Value Chain Pada Komoditas Ekspor Rumput Laut Di Pulau Lombok, Nusa Tenggara Barat Dalam Memasuki Pasar Tiongkok

“…Besarnya angaka total produksi ini membuat Provinsi Nusa Tenggara Barat menjadi daerah penghasil rumput laut terbesar ke empat di Indonesia. Provinsi dengan total hasil produksi diatas Provinsi Nusa Tenggara Barat yaitu Provinsi Sulawesi Selatan, Provinsi Nusa Tenggara Timur dan Provinsi Sulawesi Tengah (Handi, 2021). 3.339.0183.660.9723.287.8933.405.8481.694.010 Nusa Tenggara Timur 1.854.5721.941.7231.803.8061.600.028548.279,9 Sulawesi Tengah 1.297.348691.576,21.136.458932.685,6442.586,4 Nusa Tenggara Barat 1.026.580822.749,1875.894,5896.760,3600.712,9 Sulawesi Tenggara 830.497,0872.171,5492.494,7346.886,3132.956,3 0,00 500.000,00 1.000.000,00 1.500.000,00 2.000.000,00 2.500.000,00 3.000.000,00 3.500.000,00 4.000.000,00…”