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All Journal Indonesian Journal of Geography JURNAL TEKNOLOGI LINGKUNGAN
muhamad sadly
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Earth & Planetary Sciences Environmental Science

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SISTEM PENJEJAK IKAN UNTUK PEMANTAUAN KUALITAS LINGKUNGAN PERAIRAN DAN PREDIKSI LOKASI PENANGKAPAN IKAN MENUJU PENGELOLAAN PERIKANAN BERKELANJUTAN Sadly, Muhamad; Awaluddin, Awaluddin
Jurnal Teknologi Lingkungan Vol. 18 No. 1 (2017)
Publisher : Center for Environmental Technology - Agency for Assessment and Application of Technology

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (140.68 KB) | DOI: 10.29122/jtl.v18i1.534

Abstract

ne"> Dalam riset ini diusulkan suatu pendekatan baru di dalam membangun model prediksi lokasi penangkapan ikan dan pemantauan kualitas lingkungan perairan, khususnya ikan pelagis ekonomis. Knowledge-based expert system diintegrasikan dengan penginderaan jauh dan sistem informasi geografis dipilih sebagai pendekatan baru untuk menyempurnakan metode konvensional yang saat ini digunakan. Model yang dikembangkan disebut “Sistem Penjejak Ikan nan Cerdas”. Kelemahan utamametode konvensional, penentuan lokasi penangkapan ikan masih dilakukan secara manual, akibatnya hasil yang diperoleh tidak optimal dan tidak praktis di dalam implementasinya. Data seri satelit penginderaan jauh (suhu permukaan laut, klorofil dan turbiditi) yang diperoleh dari satelit Aqua MODIS periode tahun 2007-2014 digunakan sebagai data input. Peta spasial sistem prediksi lokasi penangkapan ikan dibangun menggunakan ERDAS Imagine Macro Language. Untuk verifikasi dan validasi hasil,dilakukan pengambilan data in-situ fishing ground pada lokasi riset dalam periode waktu yang sama, dan telah di analisa dengan metode statistik untuk mendapatkan tingkat akurasinya. Hasil menunjukkan bahwa densitas fishing ground yang telah di prediksi dan kualitas lingkungan perairan di perairan Banggai Kepulauan dikorelasikan dengan data hasil survei lapangan (in-situ data) diperoleh tingkat akurasi lebih dari 93%. Dari demonstrasi hasil, model yang diusulkan dapat diaplikasikan untuk memprediksi, melokalisasi dan menentukan densitas fishing ground dengan tingkat akurasi lebih tinggi dibanding metode konvensional. Sistem prediksi ini telah diimplementasikan pada sistem online.Kata kunci : sistem pakar, lokasi penangkapan ikan, penginderaan jauh, sistem penjejak ikan nancerdas, sistem informasi geografi
PREDIKSI PARAMETER-PARAMETER BIOFISIK TANAMAN PADI DARI DATA GROunDSPECTROmETER DAN HYPERSPECTRAL PESAWAT TERBANG DENGAN MENGGUNAKAN TEKNIK PARTIAL LEAST SquARE REGRESSIOn (PLSR) DAN nORmALIzED DIFFEREnCE SPECTRAL InDEx (NDSI) Darmawan, Arief; Hariyanto, Teguh; Sukojo, Bangun Muijo; Sadly, Muhamad
Jurnal Teknologi Lingkungan Vol. 12 No. 1 (2011)
Publisher : Center for Environmental Technology - Agency for Assessment and Application of Technology

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (391.434 KB) | DOI: 10.29122/jtl.v12i1.1266

Abstract

Paddy rice canopy hyperspectral was measured by using ground-based spectroradiometer and HyMap sensor onboard an airplane from 350 nm up to 2500 nm in the same timethat covered various growth stages. Coinciding with hyperspectral measurement, biophysical parameters such as leaf area index (LAI), SPAD value were measured onground during the airplane passed over area of interest (AOI). Rice yield was measured at the time of final harvesting by random selected yield (ubinan method) for each sampling area. In finding the best correlation among canopy hyperspectral reflectance with crop development, optimal individual waveband explored with involving all possible waveband combinations to obtain the best fitted two-pair waveband related to crop biophysical parameters. Normalized Difference Spectral Index (NDSI) was appliedfrom spectral transformations (obtained from optimal waveband selected by exploring all possible waveband) to improve sensitivity analysis on plant. Canopy hyperspectralprovided information about plant, soil and water background when plant canopy don’t completely cover soil surface yet. The present study was directed to examine spectralindices and establish the relationships between biophysical parameters of rice by using partial least square regression (PLSR) technique. 
PEMODELAN DISPERSI GAUSSIAN MENGGUNAKAN METODE TRANSFORMASI LAPLACE DAN INTEGRASI DENGAN CITRASATELIT GOOGLE EARTH Sulaiman, Albert; Sadly, Muhamad
Jurnal Teknologi Lingkungan Vol. 15 No. 2 (2014)
Publisher : Center for Environmental Technology - Agency for Assessment and Application of Technology

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (5034.187 KB) | DOI: 10.29122/jtl.v15i2.1597

Abstract

Dispersi polutan disekitar pabrik akan diselidiki dengan model dispersi Gaussian. Persamaan adveksidifusi yang memberikan sebaral polusi udara dengan sumber banyak cerobong pabrik akandiselesaikan dengan metode transformasi laplace dan simulasi hasil akan diintegrasikan denganGoogle Earth. Hasil menunjukkan bahwa koefisien difusi yang merepresentasikan lebardari sebaranpolutan akan melebar sejalan dengan semakin jauh dari sumber. Simulasi juga menunjukkan bahwasemakin tinggi sumber maka polutan akan semakin menyebar ke area yang lebih luas. Integrasi hasilsimulasi dengan Google Earth menghasilkan informasi yang lebih mudah diinterpretasikan danmenarik.
ASSESSING THEHYPERSPECTRAL REMOTE SENSING DATA TO DIAGNOSIS CROP VARIABLES MODEL IN TROPICAL IRRIGATED WETLAND RICE Muhamad Evri; Muhamad Sadly; Arief Darmawan
Indonesian Journal of Geography Vol 40, No 2 (2008): Indonesian Journal of Geography
Publisher : Faculty of Geography, Universitas Gadjah Mada

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijg.2253

Abstract

Canopy spectral measureme~t using ground-based hyperspectral r;leviceand rice crop variables such as leaf area index (LAI), leaf dry weight (LDW) andSPAD values were done periodically during growth season with involving threerice cultivars (Pandanwangi, Ciherang and IR Jumbo) ahd four nitrogenapplication levels (NO,N80, N92 and NI03 kg/ha). Thestudy is directed to exploreall possible waveband combinations tested in reflectance of vegetation indices(VIs) and to develop a predictive model of relation between hyperspectral-basedvegetation indiceswith rice crop variables. .Analysis of all possible two-pair waveband combinations used in VIs wasinvestigated with 6,786 combinations to gain optimal waveband attributed to cropvariables. To develop.efficient and accurate model, various multivariate regressionmodels were examined with ten-fold cross validations. Accuracy validation ofpredicted model was performed using reflectance and FDR, NDVI, RVI, RDVI andSA VI data. Validation of predictive model using flJR implied better accuracy toestimate LAI using whole season data (R2=0.856). Meanwhile, the model usingSA VI denoted highest values (R2=0.852)for predicting LAI While the validation ofpredictive model using RVI implied the highest values (K=O. 797) for predictingLDW. Moreover, the test of predictive model using SAVI indicated the highestvalue (R2=0.658) for predicting SPAD values. According to overall validationusing VIs, it seems that RVI has the best accuracy to validate the predictive modelof LAI than those of LDW or SPAD values. Meanwhile, the most significant of K tovalidate the predictive model was obtained on FDR data with R2=0.859for LAl
Co-Authors Albert Sulaiman Arief Darmawan Arief Darmawan Awaluddin Awaluddin Muhamad Evri Sukojo, Bangun Muijo Teguh Hariyanto, Teguh