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Sistem Evaluasi Pengaruh Daya Rusak Air, Kinerja Lembaga Pengelola Irigasi, Tumbuhan Liar, Sampah dan Prilaku Masyarakat terhadap Kerusakan Bangunan Irigasi

Published: | DOI: 10.5614/jts.2020.27.2.10 | Vol 27, Issue 2 (2020)

Authors

Abstract

Nationally 52% of irrigation networks of 7.2 million hectares are in damaged condition. In West Java, 40% of irrigation networks are also damaged. The research was conducted to study the factors affecting Irrigation Structure Damage (KBI) namely: Water Damage (DRA), Irrigation Management Agency (LPI), Wild Plants (TBL), Waste Volume (SPH), Public Behavior (PLK). The research area is limited to 2 (two) Provinces, West Java and Banten; Sample/ respondent of the research consist of: Irrigation structure 224 pieces with 171 water gate, Performance of Official of PU 101 respondent, P3A / GP3A / IP3A 109 respondent, Community Behavior 100 respondent. The analysis method uses the development of the SEM (Structural Equation Model) model path. Data processing using statistical software S-PLS version 2.0. The research variables consist of Latent Variables and Manifest Variables. Manifest variables obtained through the Field Measurement Survey and Dissemination Survey Questionnaire. The result of SEM analysis is; Simultaneously, the effect of exogenous variables on endogenous variable damage (KBI) was 48.17%. The remaining 51.83% is explained by other influences. (DRA) have no significant effect on (KBI). (LPI) is very significant effect on (KBI). (TBL) does not significantly affect on (KBI). (SPH) significantly affects on (KBI). (PLK) does not significantly affect on (KBI). Part of the irrigation structure on the wing (Y2_SYP) suffered the highest damage amount of 58.7%.AbstrakSecara Nasional 52% jaringan irigasi dari 7,2 juta hektar dalam kondisi rusak. Di Jawa Barat, 40% jaringan irigasi juga rusak. Penelitian dilakukan untuk mempelajari faktor-faktor yang mempengaruhi Kerusakan Bangunan Irigasi (KBI) yaitu: Daya Rusak Air (DRA), Lembaga Pengelola Irigasi (LPI), Tumbuhan Liar (TBL), Volume Sampah (SPH) dan Perilaku Masyarakat (PLK). Daerah penelitian dibatasi pada 2 (dua) Provinsi, Jawa Barat dan Banten. Sampel/ responden penelitian terdiri dari: Bangunan irigasi 224 buah dengan jumlah pintu air 171 buah, Kinerja Petugas PU 101 responden, Peran serta P3A/GP3A/IP3A 109 responden, Perilaku Masyarakat di sekitar bangunan irigasi 100 responden. Metode analisis menggunakan pengembangan jalur model SEM (Structural Equation Model). Pengolahan data menggunakan perangkat lunak statistik S-PLS versi 2.0. Variabel penelitian terdiri atas: Variabel Laten dan Variabel Manifes. Variabel manifes diperoleh melalui Survei Pengukuran Lapangan dan Survei Penyebaran Kuesioner. Hasil analisis SEM adalah; Secara simultan diperoleh bahwa pengaruh variabel eksogen terhadap kerusakan variabel endogen (KBI) sebesar 48,17%. Selebihnya yaitu 51,83% dijelaskan oleh pengaruh lain. (DRA) tidak signifikan berpengaruh terhadap (KBI). (LPI) sangat signifikan berpengaruh terhadap (KBI). (TBL) tidak signifikan mempengaruhi (KBI). (SPH) secara signifikan berpengaruh terhadap (KBI). (PLK) tidak signifikan berpengaruh terhadap (KBI). Bagian struktur sayap (Y2_SYP) mengalami kerusakan tertinggi sebesar 58,7%.

Keywords

Physics; Forestry; Irrigation; Geography; Agronomy; Biology

4.3.4 Pengujian hipotesis simultan (uji F outer model).

Berdasarkan hasil keluaran dari analisis statistik melalui program S-PLS dapat diketahui pengaruh secara simultan atau bersama-sama variabel laten

eksogen terhadap variabel laten endogen, melalui perbandingan nilai F-hitung dengan nilai F-tabel. F-hitung diperoleh dari hasil output software S-PLS yang ditampilkan dalam Tabel 8.

4

Gambar 3. Nilai t-statistik model struktural jalur (inner model)

Tabel 8. Output software S-PLS - Anovab

NoV. Manifest
Eksogen		V. Manifest Endogen
(Pers. Regresi)		RR²SigF HitungF TabelKet.
1X1_PAA
X2_GAP
X3_GAK		Y1_PSP = 77.736 +
9.722X1_PAA +
29.891X2_GAP -
59.164X3_GAK		0.181ª0.0330.829b0.294df1 = 3,
df2 = 26,
a = 0.05,
F = 2.89		Tidak Signifikan
2X1_PAA
X2_GAP
X3_GAK		Y2_SYP = 35.059 +
9.337X1_PAA -
5.945X2_GAP -
96.202X3_GAK		0.218ª0.0470.733b0.430df1 = 3,
df2 = 26,
a = 0.05,
F = 2.89		Tidak Signifikan
3X1_PAA
X2_GAP
X3_GAK		Y3_LTI = 40.124 +
10.392X1_PAA +
36.959X2_GAP -
20.579X3_GAK		0.261ª0.0600.600b0.634df1 = 3,
df2 = 26,
a = 0.05,
F = 2.89		Tidak Signifikan
4X1_PAA
X2_GAP
X3_GAK		Y4_KLO = 53.389 +
22.761X1_PAA +
75.701X2_GAP -
18.306X3_GAK		0.339ª0.1150.358b1.122df1 = 3,
df2 = 26,
a = 0.05,
F = 2.89		Tidak Signifikan
5X4_PLU
X5_P3A		Y1_PSP = 435.904 +
0.608X4_LPU -
18.684X5_P3A		0.625a0.3910.001b8.671df1 = 2,
df2 = 27,
a = 0.05,
F = 3.35		Signifikan
6X4_PLU
X5_P3A		Y2_SYP = 365.889 -
1.199X4_LPU -
14.363X5_P3A		0.742a0.5510.551b16.542df1 = 2,
df2 = 27,
a = 0.05,
F = 3.35		Signifikan
7X4_PLU
X5_P3A		Y3_LTI = 292.864 -
0.585X4_LPU -
10.941X5_P3A		0.578ª0.3340.004b6.764df1 = 2,
df2 = 27,
a = 0.05,
F = 3.35		Signifikan
8X4_PLU
X5_P3A		Y4_KLO = 530.756 -
Y4_KLO = 530.756 -
17.259X5_P3A		0.563ª0.3170.006b6.263df1 = 2,
df2 = 27,
a = 0.05,
F = 3.35		Signifikan
9X6_TBLY1_PSP = 55.831 +
0.292X6_TBL		0.107a0.0110.574b0.324df1 = 1,
df2 = 28,
a = 0.05,
F = 4.20		Tidak Signifikan

Tabel 8. Output software S-PLS - Anovab (lanjutan)

NoV. Manifest
Eksogen		V. Manifest Endogen
(Pers. Regresi)		RR²SigF HitungF TabelKet.
9X6_TBLY1_PSP = 55.831 +
0.292X6_TBL		0.107a0.0110.574b0.324df1 = 1,
df2 = 28,
a = 0.05,
F = 4.20		Tidak Signifikan
10X6_TBLY2_SYP = 24.441 + 0.214X6_TBL0.119ª0.0140.530b0.404df1 = 1,
df2 = 28,
a = 0.05,
F = 4.20		Tidak Signifikan
11X6_TBLY3_LTI = 37.228 + 0.221X6_TBL0.127ª0.0160.505b0.456df1 = 1,
df2 = 28,
a = 0.05,
F = 4.20		Tidak Signifikan
12X6_TBLY4_KLO = 83.725 + 0.042X6_TBL0.014ª0.0000.941b0.006df1 = 1,
df2 = 28,
a = 0.05,
F = 4.20		Tidak Signifikar
13X7_SPHY1_PSP = 63.710 +
10.215X7_SPH		0.059ª0.0040.755b0.099df1 = 1,
df2 = 28,
a = 0.05,
F = 4.20		Tidak Signifikar
14X7_SPHY2_SYP = 29.761 +
8.826X7_SPH		0.078ª0.0060.682b0.171df1 = 1,
df2 = 28,
a = 0.05,
F = 4.20		Tidak Signifikan
15X7_SPHY3_LTI = 45.288 +
1.885X7_SPH		0.017ª0.0000.929b0.008df1 = 1,
df2 = 28,
a = 0.05,
F = 4.20		Tidak Signifikar
16X7_SPHY4_KLO = 76.990 +
23.551X7_SPH		0.127ª0.0160.502b0.462df1 = 1,
df2 = 28,
a = 0.05,
F = 4.20		Tidak Signifikai
17X8_PLKY1_PSP = 10.741 +
3.677X8_PLK		0.113ª0.0130.551b0.361df1 = 1,
df2 = 28,
a = 0.05,
F = 4.20		Tidak Signifikai
18X8_PLKY2_SYP = 6.968 + 0.491X8_PLK0.088ª0.0080.645b0.217df1 = 1,
df2 = 28,
a = 0.05,
F = 4.20		Tidak Signifikal
19X8_PLKY3_LTI = 35.541 +
0.491X8_PLK		0.024ª0.0010.902b0.016df1 = 1,
df2 = 28,
a = 0.05,
F = 4.20		Tidak Signifikal
20X8_PLKY4_KLO = 26.308 + 5.259X8_PLK0.151ª0.0230.427b0.650df1 = 1,
df2 = 28,
a = 0.05,
F = 4.20		Tidak Signifikal
21X8_PLKX7_SPH = 0.688 -
0.016X8_PLK		0.083ª0.0070.663b0.193df1 = 1,
df2 = 28,
a = 0.05,
F = 4.20		Tidak Signifikai
22X8_PLKX6_TBL = -0.734 +
1.894X8_PLK		0.159a0.0250.402b0.725df1 = 1,
df2 = 28,
a = 0.05,
F = 4.20		Tidak Signifikal
23X4_LPU
X5_P3A		X6_TBL = 10.2569 -
10.72X4_LPU -
1.912X5_P3A		0.207a0.4300.554b0.604df1 = 2,
df2 = 27,
a = 0.05,
F = 3.35		Tidak Signifikal
24X4_LPU
X5_P3A		Y1_SPH = 2.262 -
0.046X4_LPU -
00.45X5_P3A		0.376ª0.1410.126b2.221df1 = 2,
df2 = 27,
a = 0.05,
F = 3.35		Tidak Signifikai

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Research Intelligence

Data from OpenAlex ↗

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Topics

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Information Systems › Computer Science › Physical Sciences

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Core Domains
Physics 0.74
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Irrigation 0.51
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