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All Journal MANAJEMEN HUTAN TROPIKA Journal of Tropical Forest Management Jurnal Ilmu Pertanian Indonesia Jurnal Silvikultur Tropika (JST) MEDIA KONSERVASI Indonesian Journal of Geography Journal of Tropical Soils TELKOMNIKA (Telecommunication Computing Electronics and Control) Jurnal Teknosains Jurnal Kawistara : Jurnal Ilmiah Sosial dan Humaniora Jurnal Penelitian Hutan dan Konservasi Alam Jurnal Penelitian Hutan Tanaman BioWallacea Journal of Biological Research MAJALAH ILMIAH GLOBE Jurnal Penelitian Sosial dan Ekonomi Kehutanan Jurnal Penelitian Pengelolaan Daerah Aliran Sungai JURNAL METEOROLOGI DAN GEOFISIKA Indonesian Journal of Electrical Engineering and Computer Science Jurnal Sylva Lestari Jurnal Ilmu-Ilmu Kehutanan Aceh International Journal of Science and Technology Jurnal Penelitian Kehutanan Wallacea Jurnal Kawistara

Muhammad Buce Saleh

Departement Of Forest Management, Faculty Of Forestry And Environment, IPB University, Academic Ring Road, Campus IPB Dramaga, Bogor, Indonesia 16680

Author-ID : 369156

Agriculture, Biological Sciences & Forestry Humanities Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Energy Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Materials Science & Nanotechnology Mechanical Engineering Physics Social Sciences Veterinary

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Identification and Evaluation of Potential Land Resources to Support the Development of Agricultural Commodities for Food Crops Zone Nurdiyanto Agung Prasetya; . Hikmatullah; . Asisah; Muhamad Buce Saleh; Suria Darma Tarigan
JOURNAL OF TROPICAL SOILS Vol 19, No 1: January 2014
Publisher : UNIVERSITY OF LAMPUNG

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5400/jts.2014.v19i1.53-61

To support the goverment purpose to reach the food security, a land use study is needed. The aim of the research was to provide an information of characteristics of land resources through the identification and evaluation of potential landresources and that suitable for food crops in Mamuju District South Sulawesi. The research method used landscape approach to mapping land units as the basis for preparing the soil map unit/DEM compared with field data survey. A case study was done in Mamuju District, West Sulawesi the results showed that the land in Mamuju for paddy covering was suitable enough of 115,250 ha and 54,883 ha of marginal fit, while for dryland crops were 106 978 ha was quite suitable and appropriate marginal was 82,592 ha. However, for cocoa fit enough land was 153,397 ha and corresponding marginal was 485,743 ha. Biophysical constraints were the erosion of land use/steep slopes, drainage, seasonal flooding, toxicity and nutrient retention. Direction of land use for agriculture in Mamuju for Rice crop area was 49,345 ha (6.23%), food crops rice and dry land was 10,680 ha (1.35%), dryland crops/crops was 101,785 ha (12.85%), perennial/Cocoa was 90,488 ha (11.42%), and conservation land was 532,245 ha (67.18%).Keywords : Cland crops, land identification, soil evaluation [How to Cite: Nurdiyanto AP, Hikmatullah, Asisah, MB Saleh, and SD Tarigan. 2014. Identification and Evaluation of Potential Land Resources to Support the Development of Agricultural Commodities for Food Crops Zone. J Trop Soils 19: 53-61. Doi: 10.5400/jts.2014.19.1.53]

IMPLIKASI HAK KEPEMILIKAN DAN KONVERSI HUTAN RAKYAT: STUDI KASUS DAERAH TANGKAPAN AIR WADUK JATIGEDE Nunung Parlinah; Bramasto Nugroho; Muhamad Buce Saleh; Hendrayanto Hendrayanto
Jurnal Penelitian Sosial dan Ekonomi Kehutanan Vol 17, No 2 (2020): Jurnal Penelitian Sosial dan Ekonomi Kehutanan
Publisher : Pusat Penelitian dan Pengembangan Sosial, Ekonomi, Kebijakan dan Perubahan Iklim

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20886/jpsek.2020.17.2.137-151

Small-scale forest as private property has implications on autonomous management decisions, including whether it will be preserved or converted. Changes in land management at the water catchment area of Jatigede reservoir will impact on the dam condition. The purposes of this study are to determine the financial feasibility level of the small-scale forest business compare to other forms of land management, to identify factors influencing farmer’s decisions, and to evaluate implication of property rights to the conversion of small-scale forest. The results show that small-scale forest management in Jatigede catchment area is generally found in the form of woodlands and agroforestry. Other forms of land management are rice fields and crops. Financial analysis shows that all land management patterns are feasible. The economic factors in the form of profits, savings, and selffulfillment are the dominant motivations for timber planting. The financial benefit difference between agroforestry and crops management is not very large, but the potential for conversion is still exists due to daily need fulfillment. One approach that can be applied to prevent small-scale forest conversion is through policy interventions on payment for environmental services, where the target is more intended for empowerment activities to meet the needs of daily living.

PREDIKSI PERUBAHAN TUTUPAN LAHAN DENGAN MODEL MARKOV CHAIN DAN ANN-MARKOV DI DAS KRUENG ACEH (Land cover change prediction using Markov Chain and ANN-Markov Model in Krueng Aceh Watershed) Yudi Armanda Syahputra; Muhammad Buce Saleh; Nining Puspaningsih
Jurnal Penelitian Pengelolaan Daerah Aliran Sungai (Journal of Watershed Management Research) Vol 5, No 2 (2021): Jurnal Penelitian Pengelolaan Daerah Aliran Sungai (Journal of Watershed Managem
Publisher : Center for Implementation of Standards for Environmental and Forestry Instruments Solo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20886/jppdas.2021.5.2.185-206

ABSTRACT Prediction of land cover change will be a consideration in determining the development strategy in the future. There are many methods for predicting land cover change. It depends on data availability, model algorithms and output needed. The objective of this reasearch was to predict land cover change from 2007 to 2020 in the Krueng Aceh watershed. The method used remote sensing and GIS. The Markov Chain (MC) and Artificial Neural Network-Markov (ANN-M) models were used to understand the spatio-temporal dynamics of land cover. The accuracy of the classified imagery was obtained from on-screen digitation using medium resolution landsat-8 OLI image in 2020 with Kappa Accuracy around 84%. Both prediction algorithms used year 2007 (T1) and year 2017 (T2) land cover data to calculated the probability of land cover change prediction in year 2020 (T3). The Kappa Accuracy of both models shows a strong correlation between the simulated land cover maps and the results of visual interpretation (ANN=87.81% and MC=88.69%), this proves high accuracy of both models. Key words: model; ANN-Markov; landcover change prediction; Markov Chain ABSTRAKPrediksi perubahan tutupan lahan yang baik akan menjadi pertimbangan dalam menentukan strategi pembangunan di masa depan. Terdapat banyak metode dalam melakukan prediksi perubahan tutupan lahan yang tergantung pada kebutuhan data, algoritma pemodelan yang dilakukan dan output apa saja yang diperlukan. Penelitian ini dilakukan untuk mengkaji model prediksi perubahan tutupan lahan dari tahun 2007 hingga 2020 di DAS Krueng Aceh. Pendekatan yang dilakukan menggunakan penginderaan jauh dan SIG. Model Markov Chain (MC) dan Artificial Neural Network-Markov (ANN-MC) digunakan untuk memahami dinamika spatio-temporal tutupan lahan. Akurasi dari citra penginderaan jauh yang diklasifikasikan diperoleh dari hasil interpretasi visual pada citra resolusi sedang Landsat OLI tahun 2020 dengan nilai Kappa Accuracy sebesar 84%. Kedua model prediksi menggunakan data tutupan lahan tahun 2007 (T1) dan 2017 (T2) untuk membuat probabilitas perubahan yang digunakan dalam memprediksi tutupan lahan pada tahun 2020 (T3). Validasi kedua algoritma menunjukkan korelasi yang kuat dengan peta tutupan lahan 2020, hal tersebut membuktikan kehandalan model kedua simulasi (ANN=87,81% dan MC=88,69%).Kata kunci: model; ANN-Markov; prediksi tutupan lahan; Rantai Markov

ASSESSMENT OF ATMOSPHERIC CORRECTION METHODS FOR OPTIMIZING HAZY SATELLITE IMAGERIES Umara Firman Rizidansyah; Muhammad Buce Saleh; Antonius Bambang Wijanarto
Jurnal Meteorologi dan Geofisika Vol 15, No 3 (2014)
Publisher : Pusat Penelitian dan Pengembangan BMKG

Tujuan penelitian ini untuk menguji kesesuaian tiga jenis metode koreksi haze terhadap kejelasan obyek permukaan di wilayah tutupan vegetasi dan non vegetasi, berkenaan menghilangkan haze di wilayah citra satelit optis yang memiliki karakteristik tertentu dan diduga proses pembentukan partikel hazenya berbeda. Sehingga daerah penelitian dibagi menjadi wilayah rural yang diasumsikan sebagai daerah vegetasi dan urban sebagai non vegetasi. Pedesaan terpilih kecamatan Balaraja dan Perkotaan terpilih kecamatan Penjaringan. Tiap lokasi menggunakan Avnir-2 dan Landsat 7. Untuk mendapatkan hasil pengurangan kabut di kedua lokasi tersebut digunakan metode Dark Object Substraction (DOS), Virtual Cloud Point (VCP) dan histogram Match (HM) dengan persamaan nilai optimasi kabut HOT = DNbluesin(∂)-DNredcos(∂). hasil penelitian ini sebagai berikut: dalam hal AVNIR-Rural, VCP memiliki hasil yang baik di Band-1 sedangkan HM memiliki hasil yang baik pada band-2, 3 dan 4 sehingga dalam kasus AVNIR-Rural dapat diterapkan HM. Dalam hal AVNIR-Urban, DOS memiliki hasil yang baik pada band-1, 2 dan 3. Sementara HM memiliki hasil yang baik pada band 4, sehingga dalam kasus AVNIR-Urban dapat diterapkan DOS. Dalam kasus Landsat-Rural, DOS memiliki hasil yang baik pada band-1, 2 dan 6, Sementara VCP memiliki hasil yang baik pada band 4 dan 5. Sehingga dalam kasus Landsat-Rural dapat diterapkan DOS. Dalam hal Landsat-Urban, DOS memiliki hasil yang baik pada band-1, 2 dan 6 sedangkan VCP memiliki hasil yang baik pada band-3, 4, dan 5. Sehingga dalam hal Landsat-Urban dapat diterapkan VCP. Semakin baik citra hasil koreksi semakin kecil nilai optimasi kabut, nilai rata–rata terkecil adalah 106,547 dengan VCP di Landsat-Rural. The purpose of this research is to examine the suitability of three types of haze correction methods toward the distinctness of surface objects in land cover. Considering the formation of haze, therefore, the main research is divided into both region namely rural assumed as vegetation and urban assumed as non-vegetation area. Region of interest for rural selected Balaraja and urban selected Penjaringan. Haze imagery reduction utilized techniques such as Dark Object Subtraction, Virtual Cloud Point and Histogram Match. By applying an equation of Haze Optimized Transformation HOT = DNbluesin(∂)-DNredcos(∂), the main result of this research includes: in the case of AVNIR-Rural, VCP has good results on Band 1 while the HM has good results on band 2, 3 and 4, therefore in the case of Avnir-Rural can be applied to HM. in the case of AVNIR-Urban, DOS has good result on band 1, 2 and 3 meanwhile HM has good results on band 4, therefore in the case of AVNIR-Urban can be applied to DOS. In the case of Landsat-Rural, DOS has a good result on band 1, 2 and 6 meanwhile VCP has good results on band 4 and 5 and the smallest average value of HOT is 106.547 by VCP, therefore in the case of Lansat-Rural can be applied to DOS and VCP. In the case of Landsat-Urban, DOS has a good result on band 1, 2 and 6 meanwhile VCP has good results on band 3, 4 and 5, therefore in the case of Landsat-Urban can be applied to VCP.

Information Required for Estimating The Indicator of Forest Reclamation Success in Ex Coal-Mining Area Hasriani Muis; I Nengah Surati Jaya; Muhammad Buce Saleh; Kukuh Murtilakono
Indonesian Journal of Electrical Engineering and Computer Science Vol 3, No 1: July 2016
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v3.i1.pp182-193

This paper describes how the information of the key indicators for assessing the degree of forest reclamation success in ex coal-mining area was identified. Those indicators were analyzed using the descriptive statistic as well as the discriminant analysis on the basis of biophysical data representing age class of vegetation after reclamation. The main objective of the study was to find out the predominant key indicator that determines the success of forest reclamation in ex coal-mining areas. This study found that the variance of basal area, green biomass and increment was relatively high between young plantation and old plantation. The study confirmed that the variation of the success of reclamation was strongly influenced by site quality. . The study concluded that the best indicators to be used for assessing the success of forest reclamation was the increment providing accuracy more than 79.6% either for indicator five or three classes.

Algorithm for assessing forest stand productivity index using leaf area index Faid Abdul Manan; Muhammad Buce Saleh; I Nengah Surati Jaya; Uus Saepul Mukarom
Indonesian Journal of Electrical Engineering and Computer Science Vol 16, No 3: December 2019
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v16.i3.pp1311-1319

This paper describes a development of an algorithm for assessing stand productivity by considering the stand variables. Forest stand productivity is one of the crucial information that required to establish the business plan for unit management at the beginning of forest planning activity. The main study objective is to find out the most significant and accurate variable combination to be used for assessing the forest stand productivity, as well as to develop productivity estimation model based on leaf area index. The study found the best stand variable combination in assessing stand productivity were density of poles (X2), volume of commercial tree having diameter at breast height (dbh) 20-40 cm (X16), basal area of commercial tree of dbh >40 cm (X20) with Kappa Accuracy of 90.56% for classifying into 5 stand productivity classes. It was recognized that the examined algorithm provides excellent accuracy of 100% when the stand productivity was classified into only 3 classes. The best model for assessing the stand productivity index with leaf area index is y = 0.6214x - 0.9928 with R2= 0.71, where y is productivity index and x is leaf area index.

Crown closure segmentation on wetland lowland forest using the mean shift algorithm Beni Iskandar; I Nengah Surati Jaya; Muhammad Buce Saleh
Indonesian Journal of Electrical Engineering and Computer Science Vol 24, No 2: November 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v24.i2.pp965-977

The availability of high and very high-resolution imagery is helpful for forest inventory, particularly to measure the stand variables such as canopy dimensions, canopy density, and crown closure. This paper describes the examination of mean shift (MS) algorithm on wetland lowland forest. The study objective was to find the optimal parameters for crown closure segmentation Pleiades-1B and SPOT-6 imageries. The study shows that the segmentation of crown closure with the red band of Pleiades-1B image would be well segmented by using the parameter combination of (hs: 6, hr: 5, M: 33) having overall accuracy of 88.93% and Kappa accuracy of 73.76%, while the red, green, blue (RGB) composite of SPOT-6 image, the optimal parameter combination was (hs:2, hr: 8, M: 11), having overall accuracy of 85.72% and kappa accuracy of 68.33%. The Pleiades-1B image with a spatial resolution of (0.5 m) provides better accuracy than SPOT-5 of (1.5 m) spatial resolution. The differences between single spectral, synthetic, and RGB does not significantly affect the accuracy of segmentation. The study concluded that the segmentation of high and very high-resolution images gives promising results on forest inventory.

KARAKTERISTIK SIFAT FISIK DAN KIMIA TANAH DI KAWASAN HUTAN LINDUNG SENTAJO KABUPATEN KUANTAN SINGINGI, PROVINSI RIAU Pebriandi .; Omo Rusdiana; Muhamad Buce Saleh
JURNAL ILMU-ILMU KEHUTANAN Vol 5, No 1 (2021)
Publisher : Fakultas Pertanian, Universitas Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31258/jiik.5.1.1-6

Forest is an ecosystem based on the complexity of its components. One of the components of a forest is soil. The importance of soil for human survival and growth for trees. In this ,research we analyzed the physical and chemical characteristics of soil in Sentajo Protected Forest. Soil samples were taken using composite and ring samples techniques. Soil samples were taken from five plot points measuring 20 m x 20 m in two depth level namely, 0-20 cm and 20-40 cm. mixed evenly to get one composite soil mixture. The results of research in Sentajo Protected Forest showed that the land in Sentajo Protected Forest was classified as acidic with a value of 3.68 - 4.34 with organic C content that is classified as low to high and a low KTK value. Moreover, the physical characteristics of the soil in Sentajo Protected Forest were more sandy texture. The values of moisture content, bulk density and porosity at a depth of 0 - 20 cm were higher when compared to a depth of 20 - 40 cm.

Tinjauan Pemanfaatan Citra Sentinel dan Machine Learning Dalam Pendugaan Volume Tegakan Hutan Zulkarnain Zulkarnain; Muhammad Buce Saleh
BioWallacea : Jurnal Penelitian Biologi (Journal of Biological Research) Vol 9, No 2 (2022): Biodiversitas Wallacea
Publisher : University of Halu Oleo

This literature review is carried out systematically and aims to identify and analyze research trends, data collections, methods, and frameworks used in estimating forest stand volume (VTH) research with sentinel and (machine learning) ML from 2014 to 2021 so that it can provide answers. on the research questions of this study. The results of the analysis of 24 selected articles based on inclusion-exclusion criteria showed that VTH studies with sentinel and ML generally combine sentinel with other images, such as ALOS-2 L Band, Landsat, ALOS DSM and DEM data. S2-spectral band, S2-vegetation index, slope, S1-backscatter, elevation, are the variables most widely used as predictors. RF (random forest regression), SVR (support vector regression), MLR (multi linear regression) and kNN (k-nearest neighbor) algorithms are the most widely used algorithms. The potential for significantly increasing the accuracy of VTH estimation results can be done by adding environmental factors as a predictor variable.The study of Mauya et al, Reis et al and Chen et al which combines optical, SAR and topographic data, reported a significant increase in the accuracy of VTH estimation.Furthermore Mauya et al suggested that the weighted average approach of pixels in extracting variables from image based on the position of the field plot produces a better estimation model than using the centroid approach Optimizing the use of red edge bands in VTH estimation conducted by Jiang et al, Ahmadi et al and Hu et al, shows a more significant correlation between red edge bands and VTH than other S2-derived features s proves that the modification of the vegetation index formula by replacing the band (NIR) with a red edge band is proven to significantly increase its correlation with stand volume and performs very well on the RF, SVM and MLR algorithms in estimating VTH. The kriging geostatistical method by Chen et al and Bolat concluded that SVR-kriging and Regression-kriging each outperformed SVR and had better accuracy in predicting VTH. AbstrakTinjauan literatur ini dilakukan secara sistematis dan bertujuan untuk mengidentifikasi serta menganalisis tren penelitian, kumpulan data, metode, dan kerangka kerja yang digunakan dalam penelitian pendugaan volume tegakan hutan (VTH) dengan sentinel dan machine learning (ML) tahun 2014 sampai 2021 sehingga dapat memberikan jawaban atas pertanyaan penelitian kajian ini. Hasil analisis terhadap 24 artikel terpilih berdasarkan kriteria inklusi-eksklusi menunjukan bahwa kajian VTH dengan sentinel dan ML, umumnya mengkombinasikan sentinel dengan citra lain, seperti ALOS-2 L Band, Landsat, ALOS DSM dan data DEM. Band S2-spektral, S2-indeks vegetasi, slope, S1-backscatter, elevation, adalah variable variable yang paling banyak digunakan sebagai prediktor. Algoritma RF (random forest regression), SVR (support vector regression), MLR (multi linear regression) dan kNN (k-nearest neighbor merupakan algotirma yang ditemukan paling banyak digunakan. Potensi peningkatan akurasi hasil pendugaan VTH secara signifikan dapat dilakukan dengan menambahkan faktor lingkungan sebagai variabel prediktor. Studi Mauya et al, Reis et al dan Chen et al yang mengkombinasikan data optis, SAR dan topografi, melaporkan adanya peningkatan akurasi pendugaan VTH yang signifikan. Selanjutnya Mauya et al mengemukakan bahwa pendekatan rata rata tertimbang dari piksel dalam mengekstraksi variabel dari citra berdasarkan posisi plot lapangan menghasilkan model pendugaan yang lebih baik dari pada menggunakan pendekatan centroid. Optimalisasi pemanfaatan band red edge dalam pendugaan VTH yang dilakukan oleh Jiang et al, Ahmadi et al dan Hu et al, menunjukan koleasi yang lebih signifikan antara band red edge dengan VTH daripada fitur lain yang diturunkan dari S2. Chyrysafis membuktikan bahwa modifikasi rumus indeks vegetasi dengan mengganti band (NIR) dengan band red edge terbukti meningkatkan korelasinya dengan volume tegakan secara signifikan dan berkinerja sangat baik pada algoritma RF, SVM maupun MLR dalam menduga VTH. Metode geostatistik kriging yang dilakukan Chen et al dan Bolat, menyimpulkan bahwa SVR-kriging dan Regresi-kriging masing-masing mengungguli kinerja SVR dan memiliki akurasi yang lebih baik dalam memprediksi VTH. Katakunci: Citra Sentinel, Machine Learning, Tegakan Hutan

Developing a Spatial Mathematical Model for Assessing the Rate of Natural Forest Changes Dahlan Dahlan; I Nengah Surati Jaya; Muhammad Buce Saleh; Nining Puspaningsih; Muzailin Affan
Aceh International Journal of Science and Technology Vol 12, No 1 (2023): April 2023
Publisher : Graduate Program of Syiah Kuala University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.13170/aijst.12.1.31703

Establishing a spatial mathematical model that uses diverse data types such as ratio data, interval data, and ordinal and nominal data is a challenge. This paper describes how the mathematical model of the rate of natural forest cover change was developed by considering the causes and/or driving forces that come from the society's biophysical and/or socioeconomic aspects. The main objective of this research is to establish a spatial mathematical model using the environmental and socioeconomic variables that play a significant role in determining the rate of natural forest cover change. From a number of variables considered in the analysis, coupled with any other reason, the rate of natural forest cover change (y), in units of ha per year), this study found that there are 10 potential variables, namely the proximity of the road (x4), the proximity of the river (x5), the proximity of the settlement (x6), proximity from the regency capital (x8), the proximity of the capital city of the district (x9), proximity of the edge of the forest in 2015 (x11), the proximity of the plantation area in 2009 (x12), the proximity of the plantation in 2015 (x13), slope class (x16), and elevation class (x17). The standardization process successfully transformed the non-ratio data type into a ratio data type. Using the standardized data, the study obtained spatially mathematical models that are reliable in estimating the rate of forest cover change, namely y = 0.017 + 0.00040x9 with SR of 17.3% and R2 is 88.0%. The study concludes that the most significant factor affecting the natural forest cover change in the study site is the proximity of the district's capital city (x9). Therefore, a spatial mathematical model can facilitate the government in monitoring forest cover.

Co-Authors . Asisah . HIKMATULLAH . Hikmatullah Afandi Ahmad Afandi Ahmad Agus Rudi Darmawan Ahsana Riska Ahyar Gunawan Andrie Ridzki Prasetyo Antonius B Wijanarto Antonius Bambang Wijanarto Anugrah Andini Natsir Arif Kurnia Wijayanto Asisah, . Bahruni . Beni Iskandar Bramasto Nugroho Budi Kuncahyo Budi Kuncahyo Dahlan Dahlan Dahlan Dahlan Dede Kosasih Deki Zulkarnain Dewayany Sutrisno Dewayany Sutrisno Dwi Noventasari Eva Achmad Faid Abdul Manan Fibo Adhitya Hanifah Ikhsani Hardian, Dwika Hasriani Muis Hendrayanto . Hendri Nurwanto I Gusti Bagus Wiksuana I Nengah Surati Jaya Ibnu Sofian, Ibnu Irlan Ita Carolita Kartodihardjo, Hariadi Kukuh Murtilakono LILIK BUDIPRASETYO Liu Qian Liu Qian M. Akbar AS, M. Akbar M. Ali Sharifi Malta Daerangga Mika Lestaria Muhamad Rizal Muhammad Abdul Qirom Muhammad Abdul Qirom Muzailin Affan Muzailin Affan Nanin Anggraini Nining Puspaningsih Nitya Ade Santi Nitya Ade Santi Nitya Ade Santi Nobuyuki Abe Nunung Parlinah Nunung Parlinah Nurdin, Nurjannah Nurdiyanto Agung Prasetya Nurwadjedi Nurwadjedi Omo Rusdiana Pebriandi, Pebriandi Prasetya, Nurdiyanto Agung Putu Ananta Wijaya Retno Kuswandari Ricca Rohani Hutauruk Rosima Wati Dewi Rudi Ichsan Ismail Sahid Hudjimartsu Setia Hadi Sigit Nugroho Soedomo, Sudarsono Sri Lestari Munajati Suria Darma Tarigan Syamsu Rijal Tatang Tiryana Teddy Rusolono Umara Firman Rizidansyah Uus Saepul Mukarom Wang Xuenjun Wang Xuenjun Yudi Armanda Syahputra Yudi Setiawan Yudi Setiawan Zhang Yuxing Zulkarnain

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