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agriTECH
Published by Universitas Gadjah Mada
ISSN : 02160455     EISSN : 25273825     DOI : 10.22146/agritech
Core Subject : Agriculture,
Agriculture, Biological Sciences & Forestry
Agritech with registered number ISSN 0216-0455 (print) and ISSN 2527-3825 (online) is a scientific journal that publishes the results of research in the field of food and agricultural product technology, agricultural and bio-system engineering, and agroindustrial technology. This journal is published by Faculty of Agricultural Technology, Universitas Gadjah Mada, Yogyakarta in colaboration with Indonesian Association of Food Technologies (PATPI).
Arjuna Subject : Ilmu Pertanian dan Biologi (Semua) - Ilmu Pertanian dan Biologi (Lain-Lain)
Articles 18 Documents
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Search results for , issue "Vol 32, No 4 (2012)" : 18 Documents clear
Model Jaringan Syaraf Tiruan untuk Memprediksi Parameter Kualitas Tomat Berdasarkan Parameter Warna RGB Rudiati Evi Masithoh; Budi Rahardjo; Lilik Sutiarso; Agus Hardjoko
agriTECH Vol 32, No 4 (2012)
Publisher : Faculty of Agricultural Technology, Universitas Gadjah Mada, Yogyakarta, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (184.081 KB) | DOI: 10.22146/agritech.9585

Abstract

Artificial neural networks (ANN) was used to predict the quality parameters of tomato, i.e. Brix, citric acid, total carotene, and vitamin C. ANN was developed from Red Green Blue (RGB) image data of tomatoes measured using a developed computer vision system (CVS). Qualitative analysis of tomato compositions were obtained from laboratory experiments. ANN model was based on a feedforward backpropagation network with different training functions, namely gradient descent (traingd), gradient descent with the resilient backpropagation (trainrp), Broyden, Fletcher, Goldfrab and Shanno (BFGS) quasi-Newton (trainbfg), as well as Levenberg Marquardt (trainlm).  The network structure using logsig and linear (purelin) activation function at the hidden and output layer, respectively, and using  the trainlm as a training function resulted in the best performance. Correlation coefficient (r) of training and validation process were 0.97 - 0.99 and 0.92 - 0.99, whereas the MAE values ranged from 0.01 to 0.23 and 0.03 to 0.59, respectively.ABSTRAKJaringan syaraf tiruan (JST) digunakan untuk memprediksi parameter kualitas tomat, yaitu Brix, asam sitrat, karoten total, dan vitamin C. JST dikembangkan dari data Red Green Blue (RGB)  citra tomat yang diukur menggunakan computer vision system. Data kualitas tomat diperoleh dari analisis di laboratorium. Struktur model JST didasarkan pada jaringan feedforward backpropagation dengan berbagai fungsi pelatihan, yaitu gradient descent (traingd), gradient descent dengan resilient backpropagation (trainrp), Broyden, Fletcher, Goldfrab dan Shanno (BFGS) quasi-Newton (trainbfg), serta Levenberg Marquardt (trainlm). Fungsi pelatihan yang terbaik adalah menggunakan trainlm, serta pada struktur jaringan digunakan fungsi aktivasi logsig pada lapisan tersembunyi dan linier (purelin) pada lapisan keluaran. dengan 1000 epoch. Nilai koefisien korelasi (r) pada tahap pelatihan dan validasi secara berturut-turut adalah 0.97 - 0.99 dan 0.92 - 0.99; sedangkan nilai MAE berkisar antara 0.01-0.23 dan 0.03-0.59.
Model Matematis Kenaikan Suhu pada Butiran selama Pengepresan pada Pembuatan Tablet Effervescen Buah Markisa Ansar Ansar; Sirajuddin Sirajuddin
agriTECH Vol 32, No 4 (2012)
Publisher : Faculty of Agricultural Technology, Universitas Gadjah Mada, Yogyakarta, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (367.975 KB) | DOI: 10.22146/agritech.9586

Abstract

The objective of this research was proposing mathematical models to predict the temperature increasing in the spherical form particles during compression on processing of the passion fruit effervescent tablet. The materials used in this research were passion fruit granule. The data of temperature increase on the materials during compression was recorded by the thermocouple. Result of the research was showed the temperature increase will be distributed by conduction to all position in the particle that can predict with the diffusion equation for dimensions three spherical coordinate. The temperature at the particle surface where friction among the contact points of the particle occurred was higher than Tg of materials of the effervescent tablet component. The particles temperature average increase during compression is 33.03oC.ABSTRAKTujuan penelitian ini adalah membuat model matematis untuk memprediksi kenaikan suhu pada butiran yang dianggap berbentuk bola selama pengepresan pada pembuatan tablet effervescen buah markisa. Bahan yang digunakan dalam penelitian ini adalah granula markisa. Data kenaikan suhu pada bahan selama pengepresan diukur dengan termokopel. Hasil penelitian menunjukkan bahwa kenaikan suhu di permukaan butiran terdistribusi secara konduksi ke seluruh bagian butiran yang dapat didekati dengan persamaan difusivitas panas koordinat bola 3 dimensi. Suhu di permukaan butiran tempat terjadinya gesekan antar titik singgung butiran jauh lebih tinggi dibandingkan dengan suhu Tg bahan komponen penyusun tablet effervescen. Kenaikan rerata suhu butiran selama pengepresan adalah 33,03oC.
Studi Emisi Tungku Masak Rumah Tangga Agus Haryanto; Sugeng Triyono
agriTECH Vol 32, No 4 (2012)
Publisher : Faculty of Agricultural Technology, Universitas Gadjah Mada, Yogyakarta, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (252.083 KB) | DOI: 10.22146/agritech.9587

Abstract

The objective of this research was to study emission characteristic of household stoves. Five stoves were tested, namely clay pot biomass stove, brick biomass stove, kerosene stove, coal stove, and LPG stove.  Emission parameters to be measured were CO, NO2, SO2, and particulates. Gas emission was measured using gas analyzer Wolfsense TG 501, while particulate was determined based on Indonesian National Standard (SNI: 19-7117.12-2005). Results showed that LPG stove emitted no CO indicating that complete burning existed. Other stoves emitted CO with kerosene stove exhibited the highest CO emission of 1074 μg/m3. Biomass pot stoves produced SO2 (722 μg/m3) which is lower than LPG stove (1488 μg/m3) and kerosene stove (1055 μg/m3), but higher than coal stove (290 μg/m3). On the other side, biomass pot stoves produced more NO2 (99 μg/m3 with pot stove) as compared to kerosene stove (25 μg/m3). Particulate emission increased based on the fuels used with an order from the lowest was LPG stove, kerosene stove, coal stove, and biomass stove.ABSTRAKTujuan penelitian ini adalah untuk mengkaji karakteristik emisi beberapa tungku atau kompor dapur rumah tangga. Penelitian dilakukan dengan menggunakan lima jenis tungku atau kompor, yaitu tungku biomassa pot tebal, tungku biomassa bata, kompor minyak tanah, kompor batubara, dan kompor LPG. Parameter emisi yang diukur meliputi CO, NO2, SO2 dan partikel. Emisi gas diukur menggunakan gas analyser Wolfsense TG 501, sedangkan emisi partikel debu ditentukan berdasarkan standar SNI 19-7117.12-2005. Hasil penelitian menunjukkan bahwa kompor LPG tidak menghasilkan emisi CO. Kompor minyak tanah menghasilkan emisi CO paling tinggi yaitu (1074 μg/m3). Kompor LPG menghasilkan emisi SO2 paling banyak (1488 μg/m3), diikuti kompor minyak tanah (1055 μg/m3), tungku kayu pot (722 μg/m3), dan kompor batubara (290 μg/m3). Di pihak lain, tungku biomassa pot tebal menghasilkan NO2 lebih banyak (99 μg/m3) dibandingkan kompor minyak tanah (25 μg/m3). Emisi partikulat meningkat menurut jenis bahan bakar yang digunakan dengan urutan dari yang paling rendah adalah LPG, minyak tanah, batubara, dan biomassa.
Analisis Spasial Distribusi Bulan Basah dan Bulan Kering di Jawa Timur Indarto Indarto; Boedi Susanto; Ardian Nur Fakrudin
agriTECH Vol 32, No 4 (2012)
Publisher : Faculty of Agricultural Technology, Universitas Gadjah Mada, Yogyakarta, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (2111.045 KB) | DOI: 10.22146/agritech.9588

Abstract

This paper expose the spatial distribution of Wet Month (WM) and Dry Month (DM)  in East Java region.  Daily Rainfall data was collected from 943 pluviometres spread around the region.  Monthly rainfall data is calculated from cumulatif of daily rainfall data. Then, wet and dry month were determined from monthly rainfall data using Oldeman Classification method.  Furthermore, Spatial statistics were analysed by means of ESDA (Exploratory Spatial Data Analysis) available on Geostatistical Analyst extention of ArcGIS (9.x.).  Statistical tools exploited to analise the data include:  (1) Histogram, (2) Voronoi Map and (3) QQ-Plot.   The result show that histogram and QQ-Plot of WM and DM are close to normal distribution. Statistical value of Wet Month (WM) obtained from the analysis are: minimum = 1 month/year, maximum = 9 month/year,  average = 3,67 month/year,    and median = 4 month/year.  Other statistical value summarised are: standard deviation = 1,2; skewness = 0,05;  dan curtosis = 3,09.  While for Dry Month (DM), statistical value obtained are: minimum = 2 month/year, maximum = 11 month/year, average = 6,4 month/year,    and median = 6 month/year. Other statistical value summarised are: standard deviation = 1,21; skewness = 0,11;  and curtosis = 3,6.  The research demonstrate the capability and benefit of those statistical tool to describe detailed  spatial variability of Wet Month (WM) and Dry Month (DM).ABSTRAKMakalah ini memaparkan distribusi spasial Bulan Basah (BB) dan Bulan Kering (BK) di Jawa Timur.  Data hujan diperoleh dari 943 lokasi stasiun hujan yang tersebar merata di seluruh wilayah Provinsi Jawa Timur.  Hujan bulanan dihitung dari kumulatif hujan harian. Selanjutnya, nilai BB dan BK ditentukan berdasarkan metode klasifikasi Iklim Oldeman.  Analisa spasial dilakukan menggunakan tool  ESDA (Exploratory Spatial Data Analysis) yang terdapat pada ArcGIS Geostatistical Analyst. Tool yang digunakan mencakup:  Histogram, Voronoi Map, dan QQ-Plot.  Hasil analisa menunjukkan grafik Histogram dan Normal QQPlot untuk Bulan Basah dan Bulan Kering (BK) mendekati distribusi normal.  Nilai statistik BB  yang diperoleh adalah: minimal = 1 bulan/tahun dan maksimal = 9 bulan/tahun. Nilai bulan basah (BB) rerata dari seluruh stasiun untuk semua periode adalah 3,67 bulan/tahun dan nilai median = 4 bulan/tahun.  Histogram bulan basah  menghasilkan nilai standar deviasi = 1,2; koefisien skewness = 0,05;  dan koefisien curtosis sebesar (3,09).  Histogram Bulan Kering, menghasilkan nilai minimal 2 bulan/tahun dan maksimal = 11 bulan/tahun.  Sedangkan, nilai BK rerata dari seluruh stasiun untuk semua periode adalah 6,4 bulan/tahun dan nilai median = 6 bulan/tahun.  Histogram juga menampilkan nilai standar deviasi = 1,21; koefisien skewness = 0,11;  dan koefisien curtosis = (3,6).   Penelitian menunjukkan bahwa analisa menggunakan : histogram, Voronoi Map, Normal QQ-Plot dapat menggambarkan lebih detail variabilitas spasial Bulan Basah  dan Bulan Kering di Jawa Timur.
Model Pengendalian Aset Nirwujud dalam Manajemen Sistem Irigasi Nugroho Tri Waskitho; Sigit Supadmo Arif; Mochammad Maksum; Sahid Susanto
agriTECH Vol 32, No 4 (2012)
Publisher : Faculty of Agricultural Technology, Universitas Gadjah Mada, Yogyakarta, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (265.761 KB) | DOI: 10.22146/agritech.9589

Abstract

The research aimed at developing model of controlling intangible assets in irrigation system management. The research method consisted of two stages. The first stage was building the model of controlling intangible assets in irrigation system management using neuro-fuzzy. The model had three submodels: (i) knowledge management, (ii) intangible assets, and (iii) performance of irrigation system. The second stage was evaluating the model in Sapon irrigation system in Kulon Progo, Yogyakarta. Data collecting was done by questionnaire and interview on nine Water Use Associations. Data analysis was done by Adaptive Neuro Fuzzy Inference System. The model had been evaluated by correlation coefficient, Mean Absolute Percentage Error and Root Mean Square Error. The research result indicated that the model of controlling intangible assets in irrigation system management could predict intangible assets and performance of irrigation system well. The model linked knowledge management, intangible assets and performance of irrigation system.  Knowledge management felt into four main components: (i) learning organization, (ii) principle of organization, (iii) policy and strategy of organization, and (iv) information and communication technology which controlling intangible assets in irrigation system. Intangible assets consisted of moral intelligence, emotional intelligence, creativity attitude, institutional culture, and farmer participation which  controlling effectiveness of irrigation system.ABSTRAKTujuan penelitian adalah mengembangkan model pengendalian aset nirwujud dalam manajemen sistem irigasi. Metode penelitian terdiri dari dua tahap. Tahap pertama adalah pembangunan model pengendalian aset nirwujud dalam manajemen sistem irigasi dengan prinsip neuro-fuzzy. Model mempunyai tiga sub model yaitu manajemen pengetahuan, aset nirwujud dan kinerja sistem irigasi. Tahap kedua adalah pengujian model di Daerah  Irigasi Sapon di kabupaten Kulon Progo, propinsi Daerah Istimewa Yogyakarta. Pengunpulan data dilakukan dengan kuesioner dan wawancara dengan sembilan Perkumpulan Petani Pemakai Air. Analisa data dilakukan dengan   Adaptive Neuro Fuzzy Inference System. Model dievaluasi dengan koefisien korelasi, Mean Absolute Percentage Error dan Root Mean Square Error. Penelitian menghasilkan bahwa model pengendalian aset nirwujud dalam manajemen sistem irigasi yang menggunakan prinsip neuro-fuzzy dapat memprediksi aset nirwujud dan efektivitas sistem irigasi dengan baik. Model menghubungan manajemen pengetahuan, aset nirwujud dan kinerja sistem irigasi.  Manajemen pengetahuan yang terdiri dari organisasi pembelajar, prinsip organisasi, kebijakan dan strategi organisasi, teknologi informasi dan komunikasi mempengaruhi aset nirwujud sistem irigasi. Aset nirwujud yang terdiri dari kecerdasan moral, kecerdasan emosional, sikap kreatif, budaya lembaga, dan partisipasi petani mempengaruhi efektivitas sistem irigasi.
Reviewer Volume 32, Tahun 2012 Reviewer Volume 32, Tahun 2012
agriTECH Vol 32, No 4 (2012)
Publisher : Faculty of Agricultural Technology, Universitas Gadjah Mada, Yogyakarta, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (90.115 KB) | DOI: 10.22146/agritech.22034

Abstract

Abdul Rozaq, Jurusan Teknik Pertanian, Fakultas Teknologi Pertanian, Universitas Gadjah Mada, Jl. Flora No. 1, Bulaksumur Yogyakarta 55281Agnes Murdiati, Jurusan Teknologi Pangan dan Hasil Pertanian, Fakultas Teknologi Pertanian, Universitas Gadjah MadaJl. Flora No. 1, Bulaksumur, Yogyakarta 55281Ambar Rukmini, Fakultas Teknologi Pertanian, Universitas Widya Mataram, nDalem Mangkubumen KT III/237, Yogyakarta 55132Anang Mohamad Legowo, Fakultas Peternakan dan Pertanian, Universitas Diponegoro, Kampus Tembalang, Semarang 50275Atris Suyantohadi, Jurusan Teknologi Industri Pertanian, Fakultas Teknologi Pertanian, Universitas Gadjah, Mada, Jl. Flora No. 1, Bulaksumur, Yogyakarta 55281Aulanni’am, Jurusan Kimia, Fakultas Matematika dan Ilmu Pengetahuan Alam, Universitas Brawijaya, Jl. Veteran Malang 65145
Indeks Penulis Volume 32, Tahun 2012 Indeks Penulis Volume 32, Tahun 2012
agriTECH Vol 32, No 4 (2012)
Publisher : Faculty of Agricultural Technology, Universitas Gadjah Mada, Yogyakarta, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (86.964 KB) | DOI: 10.22146/agritech.22035

Abstract

Abidin, M.Z. 370Aisyah, Y. 207Amperawati, S. 191Andriyani, I. 126Anggrahini, S. 223, 392Ansar. 418Arif, S.S. 51, 446Ariyani, D. 370Assagaf, M. 240, 383Astawan, M. 308Astuti, M. 60
Indeks Subjek Volume 32, Tahun 2012 Indeks Subjek Volume 32, Tahun 2012
agriTECH Vol 32, No 4 (2012)
Publisher : Faculty of Agricultural Technology, Universitas Gadjah Mada, Yogyakarta, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (146.734 KB) | DOI: 10.22146/agritech.22036

Abstract

Acceptance 98ACE inhibitor 259Acetobacter xylinum 98Aloe vera drink 73Altitudes 200Amelogenin 371Antibacterial 309Antioxidant 73, 167Antioxidant activity 249, 295, 392Aroma 98, 105Artifi cial neural network 411ASLT 301Autonomous agricultural vehicle 144

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