GEOSFERA INDONESIA, Journal of Geography
Published by Universitas Jember
Geosfera Indonesia : | ISSN: 2598-9723 (Print)| ISSN: 2614-8528 (Online) is published by Department of Geography Education, University of Jember, Indonesia. We accept mainly research-based articles related to geography. Geosfera Indonesia welcomes contributions in such areas of current analysis in: (1) Geography Education, (2) Geography (Physical Geography and Human Geography), (3) Geographic Information System (GIS), (4) Remote Sensing, (5) Environmental Science, and (6) Disaster Mitigation. Since volume 1, it is published three times a year in April, August, and December. Every issue consisted of 12 articles.
Articles
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Articles
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ANALYSIS OF VEHICLE DENSITY IN WIROLEGI, SUMBERSARI DISTRICT, JEMBER REGENCY

Pramesty, Dinda Ayu, Kurnianto, Fahmi Arif, Ikhsan, Fahrudi Ahwan

Geosfera Indonesia Vol 3 No 3 (2018): GEOSFERA INDONESIA
Publisher : Department of Geography Education

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Abstract

Transportation is an important aspect of life, especially to fulfill living needs, people need transportation. Congestion that occurs in Jember Regency shows that this area is an industrial center and education center for the surrounding area, so that the high demand for goods and people in Jember Regency. This observation aims to determine the intensity of the density of motorized vehicles in Wirolegi Village, Sumbersari District, Jember Regency. Our data is obtained directly by using quantitative descriptive methods, which are supported by observation, documentation and description analysis techniques. The results of these observations show that Pakusari is the main road used by residents to go to the city center. According to data we get more people using motorbikes than other transportation, this is because motorbikes are one way to quickly get to the desired place. With the large number of motorized vehicles and causing traffic jams, it affects the surrounding air pollution.

AN ANALYSIS OF CALCULATION FACTORS IN BASUKI RAHMAT ROAD, KALIWATES DISTRICT, JEMBER REGENCY

Pramesty, Dinda Ayu, Apriyanto, Bejo, Nurdin, Elan Artono

Geosfera Indonesia Vol 3 No 3 (2018): GEOSFERA INDONESIA
Publisher : Department of Geography Education

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Abstract

Congestion is the obstruction of traffic flow which is one of the problems in developing countries. The main factor that causes congestion is the rapid population growth and lifestyle of the population. This observation aims to determine the factors that have caused congestion on Basuki Rahmat Street, Kaliwates District, Jember Regency. Our data is obtained directly by using descriptive survey methods, which are supported by observation, documentation and description analysis techniques. The results of this observation show that rapid population growth and the lifestyle of the population who prefer to use private transportation have a significant influence on the congestion that occurs on Basuki Rahmat Street, Kaliwates District, Jember Regency. According to the data we obtain from population growth and the lifestyle of the population, there needs to be self-awareness from the users of private transportation and the need for government support or attention to society and the environment.

THE EFFECTIVENESS OF PROJECT-BASED LEARNING AND PROBLEM- BASED LEARNING MODELS TOWARDS GEOGRAPHY LEARNING OUTCOMES IN TERMS OF STUDENTSí LOCUS OF CONTROL

Sari, Nur Hafidah Yuniar, Masruri, Muhsinatun Siasah

Geosfera Indonesia Vol 3 No 3 (2018): GEOSFERA INDONESIA
Publisher : Department of Geography Education

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Abstract

Nowadays, Indonesianís education seems to have an improvement in its quality. The government is establishing the 2013 curriculum. However, its implementation has not been well-implemented. In regard to this problem, it is necessary to apply scientific learning models: PjBL and PBL models. With these models and according to the studentsí locus of control, it is expected that it will improve studentsí learning outcomes. This study is aimed at determining the effectiveness of PjBL and PBL  models towards learning outcomes in terms of the studentsí Locus of Control. The method used in this study was experiment, with 2x2 factorial design. The population was XI IPS students of  SMAN 1 Ngaglik. The samples were 32 experimental students of PBL model and 28 experimental students of PjBL model. The data were collected using questionnaires and tests. The data were analyzed using Two Ways ANOVA. The result of the analysis shows that there is an influence between the models and studentsí locus of control towards the learning outcomes. The value of Fcal5.488>Fstd4.00 and the value of the probability is 0.023 < 0.05.

THE CARRYING CAPACITY ON ECOSYSTEM SERVICES OF LAND USE CHANGE AT BORDER ENTIKONG

Irsan, Robby, Mutaali, Luthfi, Sudrajat, S

GEOSFERA INDONESIA, Journal of Geography Vol 3 No 2 (2018): GEOSFERA INDONESIA
Publisher : Department of Geography Education

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Abstract

Entikong Region is located in Sanggau Regency, West Kalimantan Province, Indonesia, which is directly adjacent to Malaysia. Land use in the Border Area, which is massive and irregular, results in environmental degradation, deculturization, and lack of living standards of the community. High population growth in the border areas leads to excessive use of natural resources, and used land is not appropriately allocated. The land has limited function, and if the demand for the land is greater than the carrying capacity, there will be an imbalance that results in land degradation and its environment. The purpose of this study is to identify the type and extent of land function switch, analyze provider services as part of the Land Support Capacity Ecosystem services, and identify the Accuracy of Image Interpretation. The results showed that the increasing area of massive land use comes from a mixed plantation in 2017 increased by 60.6% of the total area of Entikong District. Degradation occurs in primary forest land use component which is only 18.6% of Entikongs total area in 2017. This indicates that the use of mixed plantation land acquires the protected forest, with many palm, rubber, and pepper. Similarly, the percentage of accuracy test from the interpretation result reaches 83.33% from 42 sample points in accordance with the real conditions. The Value of Clean Water Ecosystem Service Providers in 2011 was 0.36 and was 0.33 in 2017. Then within the period of almost 7 years, it is decreased by 0.03. Thus, the Ecosystem Service Index of clean water providers has a value less than 1, it means the function of the area as a provider of clean water is very small. Similarly, the Provider Ecosystem Services Index for Foodstuffs, the Value of Food Ecosystem Services Index in 2011 was 0.32 and was 0.31 in 2017, then within the nearly 7-year period, it is decreased by 0.01. The ecosystem services index as a food supply provider for the Entikong border area is very low (less than 1) which means the carrying capacity of the environment is not good enough for supplying food needs in Entikong. This indicates that there is a reduction in the availability of environmental services, and if it continues, then Environmental Assets declines sharply and services derived from nature will be lost or will be expensive in the near future. Thus, optimization and revitalization of land use are necessary by applying various policies related to development in the border area in Entikong District. Keywords: Borders, Land Use, Ecosystem Provider Services.   References Admadhani, D. N., Hajil, A. H. S., & Susanawati, L. D. (2013). Analysis of Water Supply and Water Demand for Carrying Capacity Assessment ( Case Study of Malang ). Journal of Natural Resources and Environment. Asdak, C., & Salim, H. (2006). Water Resource Capacity As a Spatial Planning Consideration. Journal of Environmental Engineering P3TL-BPPT. Ernan Rustiadi, Sunsus Saefulhakim, D. R. P. (2011). Planning and Regional Development. Restpent Press. Ghozali. (2013). Referral of Land Use Utilization Through Ecological Footprint in Gresik Regency. Territory and Environment, 1 No.1, 67‚??78. Hamidy, Z. (2003). Land Cover Change, Composition, and Life Type in Suakaidupan Cikepuh. Faculty of Forestry, IPB. Muta‚??ali, L. (2015). Regional Analysis Techniques For Regional Planning, Spatial Planning, and Environment (Februari). Yogyakarta: Faculty of Geography UGM. National Standardization Department. (2010). Classification of Land Cover. Purwadhi. (2008). Introduction Remote Sensing Imagery Interpretation. Semarang: LAPAN. Riqqi, A. (2014). Design Concept Techniques Determination of Supporting Capacity and Capacity of the National Environment and Islands / Islands And Provinces. Bali: KLH. Saripin, I. (2003). Identify Land Use Using Landsat TM Imagery. Agricultural Engineering Bulletin. Varika. (2015). Monitoring of Ecosystem Service-Based Ecotourism (Recreation and Ecotourism) Capacity in 2000 and 2015 Using Landsat Image in Badung Regency, Bali. Viska. (2012). Land Use Direction in Batu City Based on Ecological Ecosystem Approach. Pomits Technique, 1 No.1, 1‚??6.    

LEARNING ACTIVITIES IN HIGHER ORDER THINKING SKILL (HOTS) ORIENTED LEARNING CONTEXT

Nofrion, N, Wijayanto, Bayu

GEOSFERA INDONESIA, Journal of Geography Vol 3 No 2 (2018): GEOSFERA INDONESIA
Publisher : Department of Geography Education

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Abstract

The development of 21st century life requires the higher-level thinking ability developmental for learners. HOTS learning is characterized by; 1) Analysis, Evaluation and Creating, 2) Logical reasoning, 3) Consideration and critical thinking, 4) Problem Solving and Creative Thinking. One effort that can be done by educators to develop higher-level thinking ability for learners is by facilitating learners to do Advanced Learning Activities (ABL) that include: 1) processing/ analyzing, 2) Communicating/ dialogue, 3) discuss/ collaborate, 4) presents/ constructs. ABL is a continuation of Basic Learning Activities which includes; 1) Observing (combination of seeing and hearing), 2) trying/ questioning, 3) searching/ collecting. Therefore, in learning, educators should be more focused on giving learners space to do ABL while still allowing time for ABD as a warm-up activity or initial activity (Schaffolding). The way that can be done as a trigger of Advanced Learning Activities is to present more questions/ tasks/ problems on high cognitive level that is C4, C5, and C6 in every learning. High-level questions/ tasks/ problems will also encourage learners to dialogue and discuss so that collaboration in learning will be created. Keywords: Basic Learning Activity/ABD, Advanced Learning Activities/ ABL, HOTS oriented Learning   References Brookhart, L. Susan. 2010. How to assess Higher Order Thinking Skills in Your Class. ASCD. Alexandria, Virginia USA Hamalik, Oemar. 2001. Learning strategies. Jakarta. Bumi Aksara -------------------- 2010. Learning strategies. Jakarta. BumiAksara Hanafiah, Nanang & Suhana, Cucu. 2010. Learning Strategies. Bandung. Refika Aditama Marzano, R. J., & Kendall, J. S. (2007) .The new taxonomy of educational objectives (2nd ed.). Thousand Oaks, CA: Sage Marzano, R. J & Heflebower, T. 2012.Teaching & Assssing 21st Century Skills (The Classroom Strategies Series). EBook from marzanoresearch.com Nofrion. 2017. Geography Learning Models and Strategies (Designing HOTS and Learning Collaborative Learning). Padang. Sukabina Publisher N, Nofrionet al.2018. Effectiveness of EXO OLO TASK Learning Model Based on Lesson Study in Geography Learning IOP Conf. Ser .: Earth Environ. Sci. 145 012038 Parjito. 2015. Vision of 21st Century Geography Education. Proceedings of the P3GI National Seminar. Poor. ISBN: 978 - 602 - 71506 - 3 ‚?? 8 Prayitno.2009 Basic Teaching and Praxis Education. Grasindo. Jakarta Sardiman. 2010. Interaction and Motivation of Learning Teaching. Jakarta. Rajawali Press Silbermen, L. Melvin. 2006. Active Learning: 101 Learning Methods Active Students. Bandung. Nusamedia Law Number 20 of 2003 concerning National Education Systems Woolfolk. 2009. Educational Psychology (Active Learning Edition), Tenth Edition. Yogyakarta. Student Library

AN ASSESSMENT OF SPATIAL VARIATION OF LAND SURFACE CHARACTERISTICS OF MINNA, NIGER STATE NIGERIA FOR SUSTAINABLE URBANIZATION USING GEOSPATIAL TECHNIQUES

Yakubu, Bashir Ishaku, Hassan, Shua√ʬĬôib Musa, Asiribo, Sallau Osisiemo

GEOSFERA INDONESIA, Journal of Geography Vol 3 No 2 (2018): GEOSFERA INDONESIA
Publisher : Department of Geography Education

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Abstract

Rapid urbanization rates impact significantly on the nature of Land Cover patterns of the environment, which has been evident in the depletion of vegetal reserves and in general modifying the human climatic systems (Henderson, et al., 2017; Kumar, Masago, Mishra, & Fukushi, 2018; Luo and Lau, 2017). This study explores remote sensing classification technique and other auxiliary data to determine LULCC for a period of 50 years (1967-2016). The LULCC types identified were quantitatively evaluated using the change detection approach from results of maximum likelihood classification algorithm in GIS. Accuracy assessment results were evaluated and found to be between 56 to 98 percent of the LULC classification. The change detection analysis revealed change in the LULC types in Minna from 1976 to 2016. Built-up area increases from 74.82ha in 1976 to 116.58ha in 2016. Farmlands increased from 2.23 ha to 46.45ha and bared surface increases from 120.00ha to 161.31ha between 1976 to 2016 resulting to decline in vegetation, water body, and wetlands. The Decade of rapid urbanization was found to coincide with the period of increased Public Private Partnership Agreement (PPPA). Increase in farmlands was due to the adoption of urban agriculture which has influence on food security and the environmental sustainability. The observed increase in built up areas, farmlands and bare surfaces has substantially led to reduction in vegetation and water bodies. The oscillatory nature of water bodies LULCC which was not particularly consistent with the rates of urbanization also suggests that beyond the urbanization process, other factors may influence the LULCC of water bodies in urban settlements. Keywords: Minna, Niger State, Remote Sensing, Land Surface Characteristics   References Akinrinmade, A., Ibrahim, K., & Abdurrahman, A. (2012). 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Exploring the relationships between urbanization trends and climate change vulnerability. Climatic Change, 133(1), pp. 37-52. Gokturk, S. B., Sumengen, B., Vu, D., Dalal, N., Yang, D., Lin, X., . . . Torresani, L. (2015). System and method for search portions of objects in images and features thereof: Google Patents. Government, N. S. (2007). Niger state (The Power State).  Retrieved from http://nigerstate.blogspot.com.ng/ Green, K., Kempka, D., & Lackey, L. (1994). Using remote sensing to detect and monitor land-cover and land-use change. Photogrammetric engineering and remote sensing, 60(3), pp. 331-337. Gu, W., Lv, Z., & Hao, M. (2017). Change detection method for remote sensing images based on an improved Markov random field. Multimedia Tools and Applications, 76(17), pp. 17719-17734. Guo, Y., & Shen, Y. (2015). Quantifying water and energy budgets and the impacts of climatic and human factors in the Haihe River Basin, China: 2. Trends and implications to water resources. Journal of Hydrology, 527, pp. 251-261. Hadi, F., Thapa, R. B., Helmi, M., Hazarika, M. K., Madawalagama, S., Deshapriya, L. N., & Center, G. (2016). Urban growth and land use/land cover modeling in Semarang, Central Java, Indonesia: Colombo-Srilanka, ACRS2016. Hagolle, O., Huc, M., Villa Pascual, D., & Dedieu, G. (2015). A multi-temporal and multi-spectral method to estimate aerosol optical thickness over land, for the atmospheric correction of FormoSat-2, LandSat, VENőľS and Sentinel-2 images. Remote Sensing, 7(3), pp. 2668-2691. Hegazy, I. R., & Kaloop, M. R. (2015). Monitoring urban growth and land use change detection with GIS and remote sensing techniques in Daqahlia governorate Egypt. International Journal of Sustainable Built Environment, 4(1), pp. 117-124. Henderson, J. V., Storeygard, A., & Deichmann, U. (2017). Has climate change driven urbanization in Africa? Journal of development economics, 124, pp. 60-82. Hu, L., & Brunsell, N. A. (2015). 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Assessment of the sustainability of water resources management: A critical review of the City Blueprint approach. Water Resources Management, 29(15), pp. 5649-5670. Kumar, P., Masago, Y., Mishra, B. K., & Fukushi, K. (2018). Evaluating future stress due to combined effect of climate change and rapid urbanization for Pasig-Marikina River, Manila. Groundwater for Sustainable Development, 6, pp. 227-234. Lang, S. (2008). Object-based image analysis for remote sensing applications: modeling reality‚??dealing with complexity Object-based image analysis (pp. 3-27): Springer. Li, M., Zang, S., Zhang, B., Li, S., & Wu, C. (2014). A review of remote sensing image classification techniques: The role of spatio-contextual information. European Journal of Remote Sensing, 47(1), pp. 389-411. Liddle, B. (2014). Impact of population, age structure, and urbanization on carbon emissions/energy consumption: evidence from macro-level, cross-country analyses. 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Multi-temporal RADARSAT-2 polarimetric SAR data for urban land-cover classification using an object-based support vector machine and a rule-based approach. International journal of remote sensing, 34(1), pp. 1-26. Nogueira, K., Penatti, O. A., & dos Santos, J. A. (2017). Towards better exploiting convolutional neural networks for remote sensing scene classification. Pattern Recognition, 61, pp. 539-556. Oguz, H., & Zengin, M. (2011). Analyzing land use/land cover change using remote sensing data and landscape structure metrics: a case study of Erzurum, Turkey. Fresenius Environmental Bulletin, 20(12), pp. 3258-3269. Pohl, C., & Van Genderen, J. L. (1998). Review article multisensor image fusion in remote sensing: concepts, methods and applications. International journal of remote sensing, 19(5), pp. 823-854. Price, O., & Bradstock, R. (2014). Countervailing effects of urbanization and vegetation extent on fire frequency on the Wildland Urban Interface: Disentangling fuel and ignition effects. Landscape and urban planning, 130, pp. 81-88. Prosdocimi, I., Kjeldsen, T., & Miller, J. (2015). Detection and attribution of urbanization effect on flood extremes using nonstationary flood‚?źfrequency models. Water resources research, 51(6), pp. 4244-4262. Rawat, J., & Kumar, M. (2015). Monitoring land use/cover change using remote sensing and GIS techniques: A case study of Hawalbagh block, district Almora, Uttarakhand, India. The Egyptian Journal of Remote Sensing and Space Science, 18(1), pp. 77-84. Rokni, K., Ahmad, A., Solaimani, K., & Hazini, S. (2015). A new approach for surface water change detection: Integration of pixel level image fusion and image classification techniques. International Journal of Applied Earth Observation and Geoinformation, 34, pp. 226-234. Sakieh, Y., Amiri, B. J., Danekar, A., Feghhi, J., & Dezhkam, S. (2015). Simulating urban expansion and scenario prediction using a cellular automata urban growth model, SLEUTH, through a case study of Karaj City, Iran. Journal of Housing and the Built Environment, 30(4), pp. 591-611. Santra, A. (2016). Land Surface Temperature Estimation and Urban Heat Island Detection: A Remote Sensing Perspective. Remote Sensing Techniques and GIS Applications in Earth and Environmental Studies, p 16. Shrivastava, L., & Nag, S. (2017). MONITORING OF LAND USE/LAND COVER CHANGE USING GIS AND REMOTE SENSING TECHNIQUES: A CASE STUDY OF SAGAR RIVER WATERSHED, TRIBUTARY OF WAINGANGA RIVER OF MADHYA PRADESH, INDIA. Shuaibu, M., & Sulaiman, I. (2012). Application of remote sensing and GIS in land cover change detection in Mubi, Adamawa State, Nigeria. J Technol Educ Res, 5, pp. 43-55. Song, B., Li, J., Dalla Mura, M., Li, P., Plaza, A., Bioucas-Dias, J. M., . . . Chanussot, J. (2014). Remotely sensed image classification using sparse representations of morphological attribute profiles. IEEE transactions on geoscience and remote sensing, 52(8), pp. 5122-5136. Song, X.-P., Sexton, J. O., Huang, C., Channan, S., & Townshend, J. R. (2016). Characterizing the magnitude, timing and duration of urban growth from time series of Landsat-based estimates of impervious cover. Remote Sensing of Environment, 175, pp. 1-13. Tayyebi, A., Shafizadeh-Moghadam, H., & Tayyebi, A. H. (2018). Analyzing long-term spatio-temporal patterns of land surface temperature in response to rapid urbanization in the mega-city of Tehran. Land Use Policy, 71, pp. 459-469. Teodoro, A. C., Gutierres, F., Gomes, P., & Rocha, J. (2018). Remote Sensing Data and Image Classification Algorithms in the Identification of Beach Patterns Beach Management Tools-Concepts, Methodologies and Case Studies (pp. 579-587): Springer. Toth, C., & J√≥Ňļk√≥w, G. (2016). Remote sensing platforms and sensors: A survey. ISPRS Journal of Photogrammetry and Remote Sensing, 115, pp. 22-36. Tuholske, C., Tane, Z., L√≥pez-Carr, D., Roberts, D., & Cassels, S. (2017). Thirty years of land use/cover change in the Caribbean: Assessing the relationship between urbanization and mangrove loss in Roat√°n, Honduras. Applied Geography, 88, pp. 84-93. Tuia, D., Flamary, R., & Courty, N. (2015). Multiclass feature learning for hyperspectral image classification: Sparse and hierarchical solutions. ISPRS Journal of Photogrammetry and Remote Sensing, 105, pp. 272-285. Tzotsos, A., & Argialas, D. (2008). Support vector machine classification for object-based image analysis Object-Based Image Analysis (pp. 663-677): Springer. Wang, L., Sousa, W., & Gong, P. (2004). Integration of object-based and pixel-based classification for mapping mangroves with IKONOS imagery. International journal of remote sensing, 25(24), pp. 5655-5668. Wang, Q., Zeng, Y.-e., & Wu, B.-w. (2016). Exploring the relationship between urbanization, energy consumption, and CO2 emissions in different provinces of China. Renewable and Sustainable Energy Reviews, 54, pp. 1563-1579. Wang, S., Ma, H., & Zhao, Y. (2014). Exploring the relationship between urbanization and the eco-environment‚??A case study of Beijing‚??Tianjin‚??Hebei region. Ecological Indicators, 45, pp. 171-183. Weitkamp, C. (2006). Lidar: range-resolved optical remote sensing of the atmosphere: Springer Science & Business. Wellmann, T., Haase, D., Knapp, S., Salbach, C., Selsam, P., & Lausch, A. (2018). Urban land use intensity assessment: The potential of spatio-temporal spectral traits with remote sensing. Ecological Indicators, 85, pp. 190-203. Whiteside, T. G., Boggs, G. S., & Maier, S. W. (2011). Comparing object-based and pixel-based classifications for mapping savannas. International Journal of Applied Earth Observation and Geoinformation, 13(6), pp. 884-893. 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Photogrammetric Engineering & Remote Sensing, 72(7), pp. 799-811. Zhou, D., Zhao, S., Zhang, L., & Liu, S. (2016). Remotely sensed assessment of urbanization effects on vegetation phenology in Chinas 32 major cities. Remote Sensing of Environment, 176, pp. 272-281. Zhu, Z., Fu, Y., Woodcock, C. E., Olofsson, P., Vogelmann, J. E., Holden, C., . . . Yu, Y. (2016). Including land cover change in analysis of greenness trends using all available Landsat 5, 7, and 8 images: A case study from Guangzhou, China (2000‚??2014). Remote Sensing of Environment, 185, pp. 243-257.            

GEOGRAPHY LITERACY OF OBSERVATION INTRODUCTION LANDSCAPE REPRESENTATION PLACE FOR STUDENT EXPERIENCE

Ikhsan, Fahrudi Ahwan, Kurnianto, Fahmi Arif, Nurdin, Elan Artono, Apriyanto, Bejo

Geosfera Indonesia Vol 3 No 2 (2018): GEOSFERA INDONESIA
Publisher : Department of Geography Education

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Abstract

This study aims to describe the understanding of geography literacy and student experience with landscape recognition observations using an ethnometodology perspective. The subject of this study was the chairman of each landscape recognition practice group student geography education program from University of Jember. The results of this study that geography literacy has a dimension of relevance to geographic skills in representing contextual phenomena and places from landscape recognition observation activities. The results of both observational studies provide research experience, motivation, critical and scientific thinking skills for students represented in the mapping of the area. Keywords: Geography Literacy, Student Experience, Ethnometodology References Bogdan, R. And Biklen, S.K.(1998). Qualitative Research for Education: An introduction to theories and methods. Boston: Allyn and Bacon, Inc. Boogart II, Thomas A. (2001). The Powwer of Place: From Semiotics to Ethnogeography, Middle States Geograher, 2001, 34: 38-47. Boyle, A., Maguire, S., Martin, A., Milsom, C., Nash, R., Rawlinson, S., Turner, A., Wurthmann, S. & Conchie, S.(2007). Fieldwork is Good: The Student Perception and the Affective Domain, Journaal of Geography in Higher Education, 31(2), 299-317. Chappell, Adrian.(2007). Using Teaching Observations and Reflective Practice to Challenge Conventions and Conceptions of Teaching in Geography, Journal of Geography in Higher Education, 32(2), 257-268. Comber, Barbara.(2017). Literacy Geography and Pedagogy: Imagining Translocal Research Alliances for Educational Justice, Journal Literacy Research: Theory, Method, and Practice, Sagepub, University of South Australia, 66, 53-72. Cotton, Debby R.E., Stokes, Alison, & Cotton, Peter A.(2010).Using Observational Methods to Research the Student Experience, Journal of Geography in Higher Education, 34(3), 463-473. Denzin, Norman K. And Lincoln Yvonna S. (2008). Strategies of Qualitative Inquiry. California: Sage Publications, Inc. Fatchan, Achmad. (2015). Methodology Research Qualitative of Ethnography and Ethnometodology Approaches for Social Sciences. Yogyakarta: Ombak. Guertin, L., Stubbs, C., Millet, C., Lee, T., & Bodek, M.(2012). Enchancing Geographic and Digital Literacy with a Student Generated Course Portfolio in Google Earth, Journal of College Science Teaching, 42(2), 32-37. Hunter, Nancee.(2016). Assesing Sense of Place and Geo-literacy Indicatorc as Learning Outcomes of an International Teacher Professional Development Program, Dissertation, Porland State University. Johnston, B. And Webber, S. (2003). Information Literacy in Higher Education: a review and case study, Studies in Higher Education, 28 (3), 335-352. Levinson, S.C.(2003). Space in Language and Cognition: Explorations in Cognitive Disversity. New York: Cambridge University Press. Lloyd, Annemaree.(2006). Information Literacy Landscapes: an emerging picture, Journal of Documentation, 62 (5), 570-583. Miles, Matthew B, Huberman, A. Michael, and Saldana, Johnny.(2015). Qualitative Data Analysis A Methods Sourcebook. Thousand Oaks, CA: Sage Publications. Minca, Claudio.(2013). The Cultural Geographies of Landscape, Hungarian Geographical Bulletin 62(1), 47-62. National Research Council.(2005). Learning to Think Spatially. GIS as a Support System in the K12 Curriculum. Washington DC: National Research Council and National Academies Press. Ottati, Daniela F.(2015). Geographical Literacy, Attitudes, adn Experiences of Freshman Students: A Qualitative Study at Florida International University, Dissertation. Miami: Florida International University. Patton, M.Q.(2002). Qualitative Research and Evaluation Methods (3rd ed.). Thousand Oasks CA: Sage Publications. Stokes, A. & Boyle, A.P.(2009). The Undergraduate Geoscience Fieldwork Experience: Influencing Factors and Implications for Learning, in: S.J. Whitmeyer, D.W. Mogk & E.J. Pyle (Eds) Field Geology Education-Historical Perspectives and Modern Approach, 461, Geological Society of America, 313-321. Turner, S., & Leydon, J.(2012). Improving Geography Literacy among First Year Undergraduate Students: Testing the Effectivess of Online Quizzes, Journal of Geography, 111(2), 54-66.

THE SPATIAL STUDY OF HEALTH CONDITION OF SOCIETY TOWARDS INCOME GRADE IN PRAMBANAN, KLATEN REGENCY

Fatonah, Ayu, Setiyani, Fitria Febri, Winardany, Hafiz, Samidu, S, Murdiansyah, Bagas Anindra, Aini, Fitri Nur

GEOSFERA INDONESIA, Journal of Geography Vol 3 No 2 (2018): GEOSFERA INDONESIA
Publisher : Department of Geography Education

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Abstract

Health is the most important aspect of the human being life. A good health will affect other aspects, one of which will affect the economic aspect of the income earned by individuals. The purpose of this research is to know the correlation between health condition and income of society in Prambanan Sub-district. This research uses quantitative descriptive research, with the research variables such as health data and income data. The sample in this study amounted to 2,235 respondents from 16 Villages in Prambanan Sub-district, Klaten. The Data Collection Method used in this research are interview, observation and questionnaire with the data analysis technique is quantitative descriptive analysis which is non statistical analysis with frequency table. The results show that there is not a significant correlation between health and income. Pereng Village has an average of "High" income with the population  average monthly income > Rp. 1.500.000,00 of 52.04% but has health condition in the "Medium" level. Randusari Village has health condition in "Medium" level whereas Randusari Village has average "Low" income with population with average income per month < Rp. 500.000,00 for 44,44%. Keywords: Health Conditions, Income   References Alviana, Hendika. 2017. The Development of the Economic Potential of the Local Areas to Reduce the Gap in Growth between Subdistrict in Sukoharjo Regency years 2010-2015. [Article Scientific Publications]. Surakarta: Muhammadiyah University Of Surakarta Atmawikarta, Arum. (2009). "A health Investment For economic development". BAPPENAS: Director of Community Health and Nutrition BPS Klaten Regency. (2016). Human development Index Klaten 2016. Klaten Regency and Klaten: BPS/BPS-Statistics of Klaten Regency. BPS Klaten Regency. (2016). Sub Prambanan in Number 2016. Klaten Regency and Klaten: BPS/BPS-Statistics of Klaten Regency. Fadhilah, Achmad. Spatial Analysis of the Level of Insecurity Dengue Feverfor Priority Handling System Using geographical InfromasiPrambanan Sub-district Klaten Regency. Analysis of the Spatial Level, 295-305. Hartono, Budiantoro. (2008). Analysis of the Imbalance of Economic Development in the Province of Central Java. [Thesis]. Semarang: University of Diponegoro Klaten Regency. (2015). Community Health Profile Klaten 2015. Head Of Department: Klaten Regency. Ismail M. (2013).The Influence of Family Income Level, Education Level of the Mother and the Mothers Employment Status for Nutritional Status in the District of Darul Makmur, Nagan Raya Regency [Thesis]. University Of Teuku Umar. Mutaali, Lutfi. 2015. The Regional Analysis Techniques for Planning regions, Spatial, and Environment. Yogyakarta: Faculty Of Geography Gadjah Mada University, Publisher. Putra, Andi NepiErtanta. (2012). Economic Growth Imbalances between Klaten Regency in Central Java province Year 1999 ‚?? 2009 [Thesis]. Yogyakarta: Atma Jaya University of Yogyakarta Sudarlan. (2015). Economic Growth, Inequality,and Poverty in Indonesia. Journal Exists, Vol 11 (1), 3036-3213 Susilo, Abdi. (2017). Analysis of Economic Growth and Inequality of Income Distribution between the Central Java Regency [Thesis]. Surakarta: Muhammadiyah University Of Surakarta. Syamsurijal. (2008). The Influence of the Level of Health of the Revenue capita Growth Rate Against in South Sumatra. Journal of Economics and Development Policy, Vo1 6 (1), 1-9. Wisana, I Dewa Gede Karma. (January 2001). Health as an Investment. Journal of Economics and the Development of Indonesia, vol. 1 (1), 42-51. Yuhendri. (2013). Influence the Quality of Education, Health,and Investment towards Growth of West Sumatra[Article Publication]. Padang: State University of Padang Yuliartika, Febriyana Niken., Larasati, Dheya Amalia., Sehan, Septia Mahadeka Putri., Okctaviana, Angel., Alfredo, Septian Briantama., (2017). Study of the Level of Knowledge of Early Warning Systems Individual and Household in the Face of Devastating Earthquake in Wonogiri. Presented at the National Seminar on Geography UMS 2017, Surakarta, Central Java, May 22, 2017  

INVESTIGATED THE IMPLEMENTATION OF MAP LITERACY LEARNING MODEL

Segara, Nuansa Bayu, Maryani, Enok, Supriatna, Nana, Ruhimat, Mamat

GEOSFERA INDONESIA, Journal of Geography Vol 3 No 2 (2018): GEOSFERA INDONESIA
Publisher : Department of Geography Education

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Abstract

This article presents the results of the first implementation of map literacy learning model in middle school classes - this is the preliminary test. The implementation of this learning model will gain optimal results when it is conducted by following all the component of the model such as the syntax, theoretical framework, social system, teachers roles, and support system. After the model implementation has been completed, the results showed that there was significantly different in students spatial thinking skills before and after the treatment. However, the implementation also revealed that the model has some technical issues and thus to be improved. In a social system revision, the teacher has to be flexibly provide scaffolding every time he/she sees that the students need it. Teachers book is significantly important to help a teacher lead the learning process. After improvement of the model has been completed, then it is ready to be implemented in the main field testing stage. Keywords: map literacy, social studies learning, spatial thinking   References Abbasnasab, S., Rashid, M., & Saad, M. (2012). Knowledge with Professional Practice A Sociocultural Perspective on Assessment for Learning‚?Į: The Case of a Malaysian Primary School ESL Context, 66, 343‚??353. http://doi.org/10.1016/j.sbspro.2012.11.277 Adeyemi, S. B., & Cishe, E. N. (2015). Effects of Cooperative and Individualistic Learning Strategies on Students‚?? Map Reading and Interpretation. International Journal of Arts & Sciences, 8(7), 383‚??395. Bednarz, S. W., Acheson, G., & Bednarz, R. S. (2006). Maps and Map Learning in Social Studies. Social Education, 70(7), 398‚??404. http://doi.org/10.4324/9780203841273 Brophy, J., & Alleman, J. (2009). Meaningful social studies for elementary students. Teachers and Teaching, 15(3), 357‚??376. http://doi.org/10.1080/13540600903056700 Cohen, L., Manion, L., Morrison, K., & Wyse, D. (2010). A Guide To Teaching Practice (5th ed.). London and New York: Rotledge. Churcher, K. M. A., Downs, E., & Tewksbury, D. (2014). ‚?? Friending ‚?Ě Vygotsky‚?Į: A Social Constructivist P edagogy of Knowledge Building Through Classroom Social Media Use, 14(1), 33‚??50. DurmuŇ?, Y. T. (2016). Effective Learning Environment Characteristics as a requirement of Constructivist Curricula: Teachers‚?? Needs and School Principals‚?? Views. International Journal of Instruction, 9(2), 183‚??198. http://doi.org/10.12973/iji.2016.9213a Fani, T., & Ghaemi, F. (2011). Implications of Vygotsky ‚?? s Zone of Proximal Development ( ZPD ) in Teacher Education‚?Į: ZPTD and Self-scaffolding. Procedia - Social and Behavioral Sciences, 29(Iceepsy), 1549‚??1554. http://doi.org/10.1016/j.sbspro.2011.11.396 Gauvain, M. (1993). The Development of Spatial Thinking in Everyday Activity. Developmental Review, 13, 92‚??121. Hribar, G. C. (2015). Using Map-Based Investigations with Elementary Students. In ESRI Education GIS Conference (pp. 1‚??26). Huynh, N. T., & Sharpe, B. (2013). An Assessment Instrument to Measure Geospatial Thinking Expertise An Assessment Instrument to Measure Geospatial Thinking Expertise. Journal of Geography, 112(October 2014), 3‚??41. http://doi.org/10.1080/00221341.2012.682227 Ishikawa, T. (2012). Geospatial Thinking and Spatial Ability: An Empirical Examination of Knowledge and Reasoning in Geographical Science. The Professional Geographer, (July 2015), 121018062625002. http://doi.org/10.1080/00330124.2012.724350 Jessie A. (1951). Maps and Slow-Learners. Journal of Geography, 50:4, 145-149, DOI: 10.1080/00221345108982661 Jo, I., Bednarz, S., & Metoyer, S. (2010). Selecting and Designing Questions to Facilitate Spatial Thinking. The Geography Teacher, 7(2), 49‚??55. http://doi.org/10.1080/19338341.2010.510779 Joyce, B.R., Weil, M., & Calhoun, E. (2014). Models of Teaching (8th Ed). New Jersey: Pearson Education. Key, L.V., Bradley, J.A., & Bradley, K.A. (2010).Stimulating Instruction in Social Studies. The Social Studies, 101:3, 117-120, DOI: 10.1080/00377990903283932 Leinhardt, G., Stainton, C., & Bausmith, J. M. (1998). Constructing Maps Collaboratively. Journal of Geography, 97(1), 19‚??30. http://doi.org/10.1080/00221349808978821 Logan, J. R. (2012). Making a Place for Space: Spatial Thinking in Social Science. Annual Review of Sociology, 38(1), 507‚??524. http://doi.org/10.1146/annurev-soc-071811-145531 Logan, J. R., Zhang, W., & Xu, H. (2010). Applying spatial thinking in social science research. GeoJournal, 75(1), 15‚??27. http://doi.org/10.1007/s10708-010-9343-0 National Reseach Council. (2006). Learning to Think spatially. Washington, D.C.: The National Academic Press. Retrieved from www.nap.edu NCSS. (2016). A Vision of Powerful Teaching and Learning in the Social Studies, 80(3), 180‚??182. Saekhow, J. (2015). Steps of Cooperative Learning on Social Networking by Integrating Instructional Design based on Constructivist Approach. Procedia - Social and Behavioral Sciences, 197(February), 1740‚??1744. http://doi.org/10.1016/j.sbspro.2015.07.230 Uttal, D. H. (2000). Maps and spatial thinking: a two-way street. Developmental Science, 3(3), 283‚??286. http://doi.org/10.1111/1467-7687.00121 Verma, K. (2014). Geospatial Thinking of Undergraduate Students in Public Universities in The United States. Texas State University. Wiegand, P. (2006). Learning and Teaching with Maps. London and New York: Routledge Taylor & Francis Group. Retrieved from http://cataleg.udg.edu/record=b1373859~S10*cat          

LEVEL OF LANDSLIDE SUSCEPTIBILITY IN CIBAL DISTRICT OF MANGGARAI EAST NUSA TENGGARA

Bate, Dominikus Victorius, Karyanto, Puguh, Rindarjono, Moh. Gamal

GEOSFERA INDONESIA, Journal of Geography Vol 3 No 2 (2018): GEOSFERA INDONESIA
Publisher : Department of Geography Education

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Abstract

This research aims to determine the degree of susceptibility of landslides in Cibal Districts. The method used in this research is descriptive survey method. The population in this research is all of the land in Cibal Districts. data collection technique is done by using observation technique and documentation study. Data analysis technique in this research uses crosstab technique. The results of this study can be seen that the level of susceptibility of landslides in Cibal based on crosstab results, there are three categories of vulnerability of landslide , they are low, medium and high. Low landslide susceptibility rate of 6.979,65 hectares or 64.09% is found in Nine sub-districts / village. The moderate landslide susceptibility rate has an area of 3,634.67 hectares or 33.38%, in seven villages. While the high landslide susceptibility rate is found only in one village with an area of 275.65 hectares or 2.35% of the total area of Cibal districts. Keywords: Susceptibility, Landslide, Cibal District   References Aditya, Triyas & Marjuki, Bramantyo, (2009) Preparation of DIY Province Risk Map. Yogyakarta: Provincial Government of DIY & PPMU SCDRRRegional Disaster Mitigation Agency (BPBD) of Manggarai Regency in 2017 Nurjanah. 2012. Disaster Management. Bandung: Alfabeta Regulation of National Agency for Disaster Management 04 of 2008 on guidelines for the preparation of disaster management Ministry of Public Works Regulation No. 22 of 2007 Mutaali. 2012. Environmental Support Capacity for Regional Development Planning. Yogyakarta: Faculty of Geography Gadjah Mada Univercity. Gems, S. 2016. Risk Management And Landslide Mitigation. UGM: YogyakartaSugiyono. 2016. Quantitative Research Methods, Qualitative, And R & D. Bandung: AlfabetaSupretno, J. (1996). Statistics, Theory and Applications. Jakarta: Erlangga Suripin.2002. Preservation of Land and Water Resources.Yogyakarta.