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Ketut Wikantika

Centre for Remote Sensing, Bandung Institute of Technology (ITB) and Faculty of Earth Sciences and Technology, Bandung Institute of Technology (ITB)

Author-ID : 417867

Agriculture, Biological Sciences & Forestry Astronomy Biochemistry, Genetics & Molecular Biology Chemistry Computer Science & IT Earth & Planetary Sciences Education Energy Engineering Environmental Science Immunology & microbiology Materials Science & Nanotechnology Mathematics Physics Social Sciences

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Spatial Analysis to Mitigate the Spread of Covid-19 Based on Regional Demographic Characteristics Mochamad Firman Ghazali; Anggun Tridawati; Mamad Sugandi; Aqilla Fitdhea Anesta; Ketut Wikantika
Forum Geografi Vol 35, No 1 (2021): July 2021
Publisher : Universitas Muhammadiyah Surakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23917/forgeo.v35i1.12325

COVID-19 is currently the hot topic of discussion by scientists because of its ability to quickly spread, in line with everyday human activities. One of the environmental factors related to climatic parameters, such as the air temperature, contributed to the spreading of COVID-19 in the last four months. Its distribution ability is no longer local as it successfully halts the important activities in many countries globally. This study aims to explain the opportunity of geospatial analysis in handling the COVID-19 distribution locally based on the characteristics of demographic data. Various data, including the confirmed positive for COVID-19, age-based population, and Landsat 8 satellite imagery data were used to determine the spatial characteristics of the COVID-19 distribution per September 2020 in Bandung, Indonesia. An inverse distance weighted (IDW), Moran's I index and local indicator spatial association (LISA), and a proposed ratio of the elderly population against the population with confirmed positive for COVID-19 (CoVE) were used as the approach to determine its distribution characteristics. The information derived from Landsat 8 satellite imagery, such as the residential area, surface temperature, and humidity based on the supervised classification, land surface temperature (LST), and the normalized difference water index (NDWI) was used to perform the analysis. The results showed that the positive population of COVID-19 was concentrated in Bandung city. However, with a Moran's I value of 0.316, not all are grouped into the same category. There are only 8, 2, 5, and 3 districts categorized as HH, HL, LL, and LH. However, the areas with a large or small number of elderlies do not always correlate with the high number of confirmed positives for COVID-19. There are only 3, 1, and 3 districts classified as HH, HL, and LL. They were represented by the values of Moran's I, for about 0.057. The positive relationship between confirmed positive for COVID-19 and the built-up area, surface temperature, humidity, and the elderly population based on the coefficient of determination (R2) were 0.03, 0.28, 0.25, and 0.019, respectively. The study also shows that the vulnerability of those areas is relatively low. The study shows that the vulnerabilities in these areas are relatively low and the recommendation for COVID-19 widespread mitigation has to consider the demographic characteristics precisely in the large scale social restrictions (LSSR).

Temporal Decorrelation Effect in Carbon Stocks Estimation Using Polarimetric Interferometry Synthetic Aperture Radar (PolInSAR) (Case Study: Southeast Sulawesi Tropical Forest) Laode M Golok Jaya; Ketut Wikantika; Katmoko Ari Sambodo; Armi Susandi
Forum Geografi Vol 31, No 1 (2017): July 2017
Publisher : Universitas Muhammadiyah Surakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23917/forgeo.v31i1.2518

This paper was aimed to analyse the effect of temporal decorrelation in carbon stocks estimation. Estimation of carbon stocks plays important roles particularly to understand the global carbon cycle in the atmosphere regarding with climate change mitigation effort. PolInSAR technique combines the advantages of Polarimetric Synthetic Aperture Radar (PolSAR) and Interferometry Synthetic Aperture Radar (InSAR) technique, which is evidenced to have significant contribution in radar mapping technology in the last few years. In carbon stocks estimation, PolInSAR provides information about vertical vegetation structure to estimate carbon stocks in the forest layers. Two coherence Synthetic Aperture Radar (SAR) images of ALOS PALSAR full-polarimetric with 46 days temporal baseline were used in this research. The study was carried out in Southeast Sulawesi tropical forest. The research method was by comparing three interferometric phase coherence images affected by temporal decorrelation and their impacts on Random Volume over Ground (RvoG) model. This research showed that 46 days temporal baseline has a significant impact to estimate tree heights of the forest cover where the accuracy decrease from R2=0.7525 (standard deviation of tree heights is 2.75 meters) to R2=0.4435 (standard deviation 4.68 meters) and R2=0.3772 (standard deviation 3.15 meters) respectively. However, coherence optimisation can provide the best coherence image to produce a good accuracy of carbon stocks.

Advanced Applications of Synthetic Aperture Radar (SAR) Remote Sensing for Detecting Pre- and Syn-eruption Signatures at Mount Sinabung, North Sumatra, Indonesia Asep Saepuloh; Prima Rizky Mirelva; Ketut Wikantika
Indonesian Journal on Geoscience Vol 6, No 2 (2019)
Publisher : Geological Agency

DOI:10.17014/ijog.6.2.123-140Mount Sinabung was re-activated at August 28th, 2010 after a long repose interval. The early stage of a phreatic eruption was then followed by magmatic eruptions at September 15th, 2013 for years until now. To understand the ground surface changes accompanying the eruption periods, comprehensive analyses of surface and subsurface data are necessary, especially the condition in pre- and syn-eruption periods. This study is raised to identify ground surface and topographical changes before, intra, and after the eruption periods by analyzing the temporal signature of surface roughness, moisture, and deformation derived from Synthetic Aperture Radar (SAR) data. The time series of SAR backscattering intensity were analyzed prior to and after the early eruption periods to know the lateral ground surface changes including estimated lava dome roughness and surface moisture. Meanwhile, the atmospherically corrected Differential Interferometric SAR (D-InSAR) method was also applied to know the vertical topographical changes prior to the eruptions. The atmospheric correction based on modified Referenced Linear Correlation (mRLC) was applied to each D-InSAR pair to exclude the atmospheric phase delay from the deformation signal. The changes of surface moistures on syn-eruptions were estimated by calculating dielectric constant from SAR polarimetric mode following Dubois model. Twenty-one Phased Array type L-band SAR (PALSAR) data on board Advanced Land Observing Satellite (ALOS) and nine Sentinel-1A SAR data were used in this study with the acquisition date between February 2006 and February 2017. For D-InSAR purposes, the ALOS PALSAR data were paired to generate twenty interferograms. Based on the D-InSAR deformation, three times inflation-deflation periods were observed prior to the early eruption at August 28th 2010. The first and second inflation-deflation periods at the end of 2008 and middle 2009 presented migration of magma batches and dike generations in the deep reservoir. The third inflation-deflation periods in the middle of 2010 served as a precursor signal presenting magma feeding to the shallow reservoir. The summit was inflated about 1.4 cm and followed by the eruptions. The deflation of about 2.3 cm indicated the release pressure and temperature in the shallow reservoir after the early eruption at August 28th, 2010. The last inflation-deflation period was also confirmed by the increase of the lava dome roughness size from 5,121 m2 on July to 6,584 m2 on August. The summit then inflated again about 1.1 cm after the first eruption and followed by unrest periods presented by lava dome growth and destruction at September 15th, 2013. The volcanic products including lava and pyroclastics strongly affected the moisture of surface layer. The volcanic products were observed to reduce the surface moisture within syn-eruption periods. The hot materials are presumed responsible for the evaporation of the surface moisture as well.

Peningkatan Akurasi Interpretasi Foto Udara Menggunakan Metode Pembobotan Berbasis Objek untuk Pembuatan Peta Skala 1:5000 Marlonroi Lumbantobing; Ketut Wikantika; Agung Budi Harto
REKA GEOMATIKA Vol 2017, No 1
Publisher : Institut Teknologi Nasional

Show Abstract | Download Original | Original Source | Check in Google Scholar

ABSTRAK Kebutuhan akan adanya pengembangan metode untuk meningkatkan akurasi dari interpretasi objek memerlukan kajian metodologi yang disebut analisis citra berbasis objek. Penelitian ini ditujukan untuk menentukan dan menganalisis akurasi dari interpretasi objek secara otomatis dengan metode berbasis objek dengan memberikan bobot yang berbeda untuk setiap kanal. Data yang digunakan adalah foto hasil pemotretan udara format menengah (medium format) dengan resolusi 16 cm. Ekstrak data menggunakan teknik object based image analysis (OBIA). Data diproses berdasarkan bobot yang yang berbeda untuk setiap kanal. Nilai akurasi ditentukan berdasarkan overall accuracy. Overall accuracy merupakan hasil validasi klasifikasi objek dengan ground truth yang diperoleh dari peta garis skala 1:5000 yang diinterpretasi secara visual. Hasil penelitian menunjukkan terjadi peningkatan nilai akurasi dengan pendekatan OBIA jika setiap kanal diberikan bobot yang berbeda dibandingkan dengan bobot yang sama. Peningkatan akurasi paling tinggi dengan bobot (Red=3, Green=4, Blue=3, IR=4, dan DEM= 3) menghasilkan akurasi 85,88%. Hasil akurasi meningkat sebesar 10,27 % dibandingkan dengan interpretasi tanpa pembobotan. Kata kunci: Interpretasi, Peta 1:5000, Klasifikasi, OBIA, Pembobotan, AkurasiABSTRACT Interpretation of imagery or aerial photo is an attempt to understand or interpret imagery to obtain accurate information and in accordance with the recorded object. The need for developing methods to improve the accuracy of the object interpretation requires assessment methodology which is called as object based image analysis. This study aimed at determining and analyzing the accuracy of the interpretation of the object automatically using object based method by giving different weights to each band. The data used were medium format aerial photos with a resolution of 16 cm. The method of data processing was object based image analysis (OBIA). Data were processed by different weights for each band. Accuracy value is determined based on the overall accuracy. Overall accuracy is the result of the validated object classification with ground truth obtained from the map of 1:5000 which were interpreted visually. The research results showed that the value of the accuracy with OBIA approach increased if each band is given different weights compared with the same weight. The highest accuracy was achieved with weights (Red=3, Green=4, Blue=3 , IR=4, and DEM=3), and resulted overall accuracy 85,88%. Results accuracy increased 10,27% compared with the interpretation without weighting. Keywords: Interpretation, Map 1:5000, Classification, OBIA, Weighting, Accuracy

Land Degradation Model Based on Vegetation and Erosion Aspects Using Remote Sensing Data Adhi Wibowo; Ishak H. Ismullah; Bobby S. Dipokusumo; Ketut Wikantika
Journal of Mathematical and Fundamental Sciences Vol. 44 No. 1 (2012)
Publisher : Institute for Research and Community Services (LPPM) ITB

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5614/itbj.sci.2012.44.1.3

The study of land degradation in various geographic conditions in the world using remote sensing is still become a concern amongst researchers because it has been proven as one of the most effective ways. In Indonesia, East Kalimantan province is one of the experiencing land area degradation due to intensive exploitation of natural resouces since 1970. The degradation model proposed in this study is modeled using a combination of ASTER and Landsat ETM+ imagery, both taken on February 27, 2001. The model composed of both two aspects: erosion aspect and vegetation aspect. Vegetation aspect is a function of suppression of vegetation from Crippen and Blom method and spectral angle a of Spectral Angle Mapper (SAM) algorithm. The erosion aspect is calculated from erosion prediction and depends on the constant factors of b as well, and the latter is said as a function of Normalized Difference Vegetation Index (NDVI) value. Based on the validation using spectral based degradation map and Land Degradation Index of Chikhaoui et al, our model proves the ability to map land degradation, especially to better distinguish the classification of land degradation at very-slightly to very-severe intensity and the ability to differentiate water body, swamp or river.

IDENTIFIKASI POTENSI REMBESAN MIKRO DI LAPANGAN MIGAS MELALUI DETEKSI MINERAL LEMPUNG MENGGUNAKAN CITRA LANDSAT 8 OLI/TIRS, STUDI KASUS LAPANGAN MIGAS CEKUNGAN JAWA BARAT BAGIAN UTARA Tri Muji Susantoro; Ketut Wikantika; Asep Saepuloh; Agus Handoyo Harsolumakso
Jurnal Penginderaan Jauh dan Pengolahan Data Citra Digital Vol. 15 No. 1 Juni 2018
Publisher : Indonesian National Institute of Aeronautics and Space (LAPAN)

Clay minerals in the oil and gas field have changed with an increase of the quantities in the middle of the oil and gas field and reduction in the edges. This reduction is the effect of micro seepage from oil and gas from the subsurface. The aims of the research is to identify the potential oil and gas seepage through clay mineral mapping. The data used where Landsat 8 OLI/TIRS with recording dated September 25, 2015. The method used in the mapping of clay minerals using the ratio of 1.55-1.75 µm (Short Wave Infrared 1) and 2.08-2.35 µm (Short Wave Infrared 2). The result of Landsat 8 OLI/TIRS data processing shows the potential of anomalies in edges of the oil and gas field. The anomaly is a change in the index value of clay minerals that tend to be lower with values 1.0 to 1.5 than the middle of oil and gas field with values 1.5 to 2.0. The potential pattern of the anomaly follows the border of the oil and gas field. Field surveys show that oil and gas field based on grain size analysis is dominated by clay-sized soil. The dominant clay minerals from X-Ray Diffraction analysis are smectite (56%) and kaolinite (6%).ABSTRAKMineral lempung di lapangan migas mengalami perubahan dengan terjadinya peningkatan kandungannya pada tengah lapangan migas dan pengurangan di tepinya. Pengurangan ini merupakan efek adanya rembesan mikro dari migas yang berasal dari bawah permukaan. Kajian ini bertujuan untuk mengidentifikasi adanya potensi rembesan migas melalui pemetaan mineral lempung. Adapun data yang digunakan adalah Landsat 8 OLI/TIRS dengan perekaman tanggal 25 September 2015. Metode yang digunakan pada pemetaan mineral lempung menggunakan perbandingan panjang gelombang 1.55-1.75 µm (Short Wave Infrared 1) dengan 2.08-2.35 µm (Short Wave Infrared 2). Hasil pengolahan data Landsat 8 OLI/TIRS menunjukkan adanya potensi anomali di tepi lapangan migas. Anomali tersebut berupa perubahan nilai indeks mineral lempung yang cenderung lebih rendah yaitu dengan nilai 1,0 – 1,5 dibandingkan lokasi di tengah lapangan yaitu dengan nilai 1,5 – 2,0. Pola potensi anomali tersebut mengikuti batas tepi lapangan migas. Survei lapangan menunjukkan bahwa pada lapangan migas berdasarkan analisis ukuran butir didominasi oleh tanah berukuran lempung. Adapun mineral lempung yang dominan dari hasil analisis XRD berupa smektit (56%) dan terdapat kaolinit (6%).

ANALISIS TRANSFORMASI INDEKS NDVI, NDWI DAN SAVI UNTUK IDENTIFIKASI KERAPATAN VEGETASI MANGROVE MENGGUNAKAN CITRA SENTINEL DI PESISIR TIMUR PROVINSI LAMPUNG Nirmawana Simarmata; Ketut Wikantika; Trika Agnestasia Tarigan; Muhammad Aldyansyah; Rizki Kurnia Tohir; Afi Fauziah; Yustika Purnama
JURNAL GEOGRAFI Geografi dan Pengajarannya Vol 19 No 2 (2021): JURNAL GEOGRAFI Geografi dan Pengajarannya
Publisher : GEOGRAPHY EDUCATION DEPARTMENT Social Science and Law Faculty, Universitas Negeri Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26740/jggp.v19n2.p69-79

Abstrak: Perolehan informasi keberadaan hutan mangrove yang memiliki potensi, peran dan fungsi besar dalam kehidupan, dapat diperoleh melalui data penginderaan jauh. Teknologi penginderaan jauh memiliki efisien tinggi dan banyak kelebihan untuk keperluan monitoring hutan mangrove. Penelitian ini bertujuan untuk mengidentifikasi kerapatan ekosistem mangrove dengan menggunakan transformasi indeks vegetasi serta menguji efektivitas beberapa indeks vegetasi dalam hal ini NDVI, NDWI dan SAVI untuk identifikasi jenis dan kerapatan mengrove. Berdasarkan hasil analisis citra Sentinel dengan menggunakan transformasi indeks NDVI, SAVI, dan NDWI untuk identifikasi kerapatan vegetasi pada transformasi NDVI didominasi kelas kerapatan tinggi yaitu pada rentang nilai 0,67 – 1 yaitu seluas 46975,96 Ha, pada transformasi SAVI didominasi kelas kerapatan sangat jarang yaitu pada rentang nilai 0,99 – 1,38 yaitu seluas 48775,18 Ha, pada transformasi NDWI didominasi kelas kerapatan rendah yaitu pada rentang nilai 0,1 – 0,17 yaitu seluas 27442,26 Ha. Hasil uji akurasi yang dilakukan menggunakan 30 sampel uji diperoleh akurasi sebesar 83,33%. Kata kunci: mangrove, Sentinel, NDVI, NDWI, SAVI

Generating himawari-8 time series data for meteorological application Ahmad Luthfi Hadiyanto; Ketut Wikantika; Ary Setijadi Prihatmanto; Nurjanna Joko Trilaksono; Dedi Irawadi
Indonesian Journal of Electrical Engineering and Computer Science Vol 29, No 2: February 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v29.i2.pp780-787

Optical remote sensing images have been widely used for temporal monitoring. The data is acquired by sensors on satellites with better spatial resolution compared to in-situ measurements by meteorological stations. The problem with utilizing optical images is the cloud, which blocks the ground and near-ground information collected by satellites. To overcome this problem, especially when dealing with thermal bands, we propose a procedure including aggregation and spatial interpolation methods to obtain time series data over a region. There is still no reference to selecting the data period to calculate the aggregate value and apply spatial interpolation. An assessment is proposed by applying Yamane’s formula in the time domain and thresholding the number of pixels in the spatial domain. Himawari-8 data was utilized and collected on an hourly basis over Java Island. This algorithm is applied to a sequence of periodic datasets to obtain a time series of aggregate data for meteorological applications. The result of this study is a recommendation to use three-month periods of data over the eastern part of Java.

Heavy Oil Potentials in Central Sumatra Basin, Indonesia Using Remote Sensing, Gravity, and Petrophysics Data: From Literature Review to Interpretations and Analyses Tri Muji Susantoro; S. Suliantara; Herru Lastiadi Setiawan; Bambang Widarsono; Ketut Wikantika
Indonesian Journal of Science and Technology Vol 7, No 3 (2022): IJOST: VOLUME 7, ISSUE 3, December 2022
Publisher : Universitas Pendidikan Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.17509/ijost.v7i3.51288

Central Sumatra basin is located on Sumatra Island, Indonesia, and is considered an oil prolific basin that produces heavy oil. The basin is believed to have unexplored heavy oil potential. Therefore, this study aims to map the heavy oil potential distribution in the basin using surface and subsurface lineaments analyses interpreted from satellite imagery and gravity data, and assisted by well log/petrophysics analysis. A thorough basin analysis was conducted based on surface/subsurface structures’ control and source rock location settings to map all potential heavy oil traps. The gravity anomaly data interpretation identified the low areas and lineaments in NW – SE, and N – S directions. The interpretation of satellite imageries showed very similar lineament patterns with the same general direction. It was observed that there is continuity between subsurface and surface lineament features, which provide contact between reservoirs and surface water sources, thereby facilitating heavy oil generation. Overlapping the lineament interpretation of gravity and satellite imagery data, supported by petroleum system understanding and verification from wells data have confirmed 7 heavy oil trap potential areas within the sedimentary basin.

Integration of remote sensing and geophysical data to enhance lithological mapping utilizing the Random Forest classifier: a case study from Komopa, Papua Province, Indonesia Hary Nugroho; Ketut Wikantika; Satria Bijaksana; Asep Saepuloh
Journal of Degraded and Mining Lands Management Vol 10, No 3 (2023)
Publisher : Brawijaya University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15243/jdmlm.2023.103.4417

Lithological information is important in mineral resource exploration, geological observations, mine planning or degradation vulnerability assessment. Currently, lithology mapping can be performed in a fast, inexpensive, and easy way using remote sensing data and machine learning. Remote sensing techniques have become a valuable and promising tool for mapping lithological units and searching for minerals. Typically, the integration of remote sensing data with geophysical data provides a better diagnosis to lithological units than single-source mapping methodologies. Accordingly, this study used a combination of remote sensing and airborne geophysical data utilizing the Random Forest algorithm with small training samples to enhance lithology mapping in Komopa, Papua Province, Indonesia. Geophysical data consisting of magnetic, electromagnetic, and radiometric were added one by one gradually to the remote sensing data, which includes Sentinel 2A, ALOS PALSAR, and DEM (digital elevation model) to compare the accuracy of the classification results from each dataset. The results showed that the model that combined remote sensing data and the three types of geophysical data produced the best classification, with an overall accuracy of 0.81, precision of 0.66, recall of 0.47, and F1 score of 0.52. This fused data can increase the accuracy of the classification results by 8% overall accuracy, 6% precision, 11% recall, and 13% F1 score when compared to the model that only used remote sensing data.

Co-Authors Abd. Rasyid Syamsuri Adhi Wibowo Adriana Hiariej, Adriana Afi Fauziah Agung B. Harto Agung Budi Harto Agung Budi Harto Agung Budi Harto Agung Budi Harto Agung Budi Harto Agung Budi Harto Agung Budi Harto Agung Budi Harto Agung Budi Harto Agung Budi Harto Agus Handoyo Harsolumakso Agus Sutanto Agus Sutanto Ahmad Luthfi Hadiyanto Akihiko Kondoh Aminah Kastuari Anesta, Aqilla Fitdhea Anggun Tridawati Aqilla Fitdhea Anesta Armi Susandi Armi Susandi Ary Setijadi Prihatmanto Asep Saepuloh Asep Yusup Saptari Asep Yusup Saptari, Asep Yusup Asmi M. Napitu Asmi M. Napitu Aswin Rahadian Bambang Widarsono Bobby S. Dipokusumo Dandy A. Novresiandi Darmawan S Darmawan S, Darmawan Dedi Irawadi Deni Suwardhi Deni Suwardhi Deni Suwardhi Deni Suwardi Desti Ayunda Dudung M Hakim Dudung Muhally Hakim Dudung Muhally Hakim Fahmi, Muhammad Nurul Farah Nafisa Ariadji Fenny M. Dwivany FENNY MARTHA DWIVANY Ghazali, Mochamad Firman Ghozali, M. Firman Giasintha Stefani Hary Nugroho Herru Lastiadi Setiawan Himasari Hanan Husna Nugrahapraja I Nyoman Dibia I NYOMAN RAI I Wayan Nuarsa Imam A. Sadisun Intan Fatmawati Irland Fardani Ishak H. Ismullah Jaya, La Ode Muhammad Golok Jevon A. Telaumbanua Karlia Meitha Katmoko Ari Sambodo Katmoko Ari Sambodo, Katmoko Ari Laode Muhammad Golok Jaya LILIK BUDIPRASETYO Lissa F. Yayusman Luky Adrianto Lumbantobing, Marlonroi Mamad Sugandi Marlonroi Lumbantobing Mila Olivia Trianaputri Mirelva, Prima Rizky Mochamad Firman Ghazali Mochamad Firman Ghazali Muhammad Aldyansyah Nengah Widiadnyana Nengah Widiadnyana Nisrina Sukriandi Nurjanna Joko Trilaksono Prihanggo, Maundri Prila Ayu Dwi Prastiwi Retno Dammayatri Rian Nurtyawan Riantini Virtriana S. Suliantara Satria Bijaksana Shafarina Wahyu Trisyanti Sigit Nur Pratama Simarmata, nirmawana Soni Darmawan Sony Darmawan, Sony Sugandi, Mamad Sukristiyanti Sukristiyanti Supriadi A Supriadi A, Supriadi Susantoro, Tri Muji Tahjudil Witra Tan, Alex Tohir, Rizki Kurnia Tombayu A. Hidayat Topik Hidayat Tri Muji Susantoro Tri Muji Susantoro Tri Muji Susantoro Tri Muji Susantoro Tri Muji Susantoro Tri Muji Susantoro, Tri Muji Trianaputri, Mila Olivia Tridawati, Anggun Trika Agnestasia Tarigan Yayusman, Lissa Fajri Yudi Setiawan Yustika Purnama

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