cover
Contact Name
Fahmi Arif Kurnianto
Contact Email
fahmiarif.fkip@unej.ac.id
Phone
-
Journal Mail Official
geografi.fkip@unej.ac.id
Editorial Address
-
Location
Kab. jember,
Jawa timur
INDONESIA
Geosfera Indonesia
Published by Universitas Jember
ISSN : 25989723     EISSN : 26148528     DOI : -
Core Subject : Science, Social,
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.
Arjuna Subject : -
Articles 154 Documents
LEVEL OF KNOWLEDGE OF SENIOR HIGH SCHOOL STUDENTS TO MANGROVE CONSERVATION Fahmi Arif Kurnianto; Bejo Apriyanto; Elan Artono Nurdin; Fahrudi Ahwan Ikhsan
Geosfera Indonesia Vol 1 No 1 (2017): GEOSFERA INDONESIA
Publisher : Department of Geography Education, University of Jember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.19184/geosi.v1i1.6190

Abstract

Geography Learning in XI Social Science Classes still dominated by conventional teaching methods that make students become unmotivated in learning. Therefore, it is necessary to apply a model of learning that can foster activity and student’s level of knowledge. The Group Investigation Learning model (GI) has several advantages. Advantages of Group Investigation Learning model among others: (1) increase the ability to think critically, (2) creating a learning environment that is democratic, (3) enhance the development of soft skills, (4) may improve social solidarity, dan (5) improve student’s level of knowledge to learn. The purpose of this study to analyze the influence of group investigation learning model towards the level of mangrove knowledge of students in senior high school. The type of research is the quasi-experimental study with non equivalent control group posttest only design. The subject of the study consisted of class XI Social Science are selected based on the score of Middle Semester Exam (UTS) 2 on 2014-2015 teachings year that had an average of almost the same (homogeneous).The results of this study are showed significant influence of GI models on Geography level of knowledge to learn of students. It was based on the results of the Independent Sample T-Test analysis showed a p-value of 0.000 level. P-level value is smaller than 0.05 (p <0.05). The average score student’s level of knowledge to learn geography experimental class is higher with a score of 208, while the control class w ith a score of 177. That's because the investigations conducted the mangrove forest, teachers simply deliver early learning problems, frequent interaction between students during learning, and students investigate different sub-themes. Keywords: Group Investigation Learning, Level of knowledge, mangrove Copyright (c) 2017 Geosfera Indonesia Journal and Department of Geography Education, University of Jember Copyright Notice This work is licensed under a Creative Commons Attribution-Share A like 4.0 International License
GEOGRAPHIC INFORMATION SYSTEM (GIS) APPLICATION TO ANALYZE LANDSLIDE PRONE DISASTER ZONE IN JEMBER REGENCY EAST JAVA Fahmi Arif Kurnianto; Bejo Apriyanto; Elan Artono Nurdin; Fahrudi Ahwan Ikhsan; Rosmadi Bin Fauzi
Geosfera Indonesia Vol 2 No 1 (2018): GEOSFERA INDONESIA
Publisher : Department of Geography Education, University of Jember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.19184/geosi.v2i1.7524

Abstract

Jember regency has several areas that are morphology of folding hills and mountain folds. The part of landslide prone zone is closely related to the slope of the slope. Areas with a sloping slope of more than 15º need attention to the possibility of a landslide disaster. Interconnection contacts with weathering of rocks, settlements and land cover also affect the landslide potential. The existence of Ijen Volcano that produces volcanic rock deposits that are generally not yet unified will increase the potential for landslides in Jember Regency. Landslide has occurred one of them on Gunung Gumitir Street which is the main route of Surabaya-Jember-Banyuwangi traffic. In May 2016 this street is hit by landslide, so the flow of traffic through this lane is paralyzed and must be diverted to a further path, which rotates to Situbondo City. The transfer of this pathway resulted in a loss to the local community and who crossed the path.The occurrence of landslide disaster shows that Jember Regency area is vulnerable and potentially return to landslide. Therefore there is a need for a solution to solve this problem. One solution to solve the problem is by utilizing Geographic Information System (GIS) application. The purpose of this research is to analyze zonation prone to landslide in jember district. The design of the research is Geographic Information System overlay analysis. This design combines several parameters in the determination of landslide-prone zones. This design combines several parameters in the determination of landslide-prone zones. The parameter used in this research is (1) land use, (2) topography, and (3) soil.Based on the research results, it can be known zone with highest to lowest vulnerability level. Zone with very high level of vulnerability is located in Panti sub-district, Sumberbaru, Sukorambi, Dyke, Silo and Jelbuk. The zones have similar characteristics that include (1) soil type of andosol, (2) clay texture, (3) uncompacted rock, (4) slope of 30⁰-40⁰ (steep and very steep), and (5) land use for settlements and plantations. Keyword: landslide disaster, jember regency, Geographic Information System Copyright (c) 2018 Geosfera Indonesia Journal and Department of Geography Education, University of Jember Copyright Notice This work is licensed under a Creative Commons Attribution-Share A like 4.0 International License
THE CARRYING CAPACITY ON ECOSYSTEM SERVICES OF LAND USE CHANGE AT BORDER ENTIKONG Robby Irsan; Luthfi Muta'ali; S Sudrajat
Geosfera Indonesia Vol 3 No 2 (2018): GEOSFERA INDONESIA
Publisher : Department of Geography Education, University of Jember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.19184/geosi.v3i2.7896

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 Entikong's 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. Copyright (c) 2018 Geosfera Indonesia Journal and Department of Geography Education, University of Jember Copyright Notice This work is licensed under a Creative Commons Attribution-Share A like 4.0 International License
LEARNING ACTIVITIES IN HIGHER ORDER THINKING SKILL (HOTS) ORIENTED LEARNING CONTEXT N Nofrion; Bayu Wijayanto
Geosfera Indonesia Vol 3 No 2 (2018): GEOSFERA INDONESIA
Publisher : Department of Geography Education, University of Jember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.19184/geosi.v3i2.8126

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 Copyright (c) 2018 Geosfera Indonesia Journal and Department of Geography Education, University of Jember Copyright Notice This work is licensed under a Creative Commons Attribution-Share A like 4.0 International License
EFFORTS TO IMPROVE THE COMPETENCY OF PROFESSIONALISM TEACHER IN JEMBER REGENCY Fahrudi Ahwan Ikhsan; Fahmi Arif Kurnianto; Bejo Apriyanto; Elan Artono Nurdin
Geosfera Indonesia Vol 1 No 1 (2017): GEOSFERA INDONESIA
Publisher : Department of Geography Education, University of Jember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.19184/geosi.v1i1.6191

Abstract

This study aims to explain the condition of professionalism and efforts to improve the competence of teachers IPS in Jember District. The sampling technique was done by random sampling which amounted to 55 person. Methods of data collection with questionnaires and documentation. Data analysis uses a percentage descriptive. The result of the research shows that the competency of professionalism of IPS teachers is as follows: the acquisition of materials, concepts and scholarship of IPS teachers is 87.25% in the high category, the development of learning materials supported by IPS teachers creatively is 92.04% including in the high category; mastery of basic competence and basic competence by 87.05% including high category, utilization of communication technology for self-development of IPS teachers of 86.35% included in the high category. In general, the competency of professionalism of IPS teachers in Jember district is categorized as high, namely 88.17%, while the effort to increase the professional competence of IPS teachers as follows: joining the training and seminar of 66.81% low category; developing syllabus and IPS RPP of 70.50%; doing PTK 48.95% low category; develop science of technology in learning 74.82% high category. Overall, the effort to improve the professionalism competence of IPS Junior High School teachers in Jember District is 71.34 included in the high category. Key words: Professionalism Competence, IPS Teachers References Agung, Iskandar . 2012. Panduan Penelitian Tindakan Kelas bagi Guru. Jakarta: Bestari Musfah, Jejen. 2011. Peningkatan Kompetensi Guru. Jakarta. Kencana Prenada. Sanjaya, Wina. 2006. “Strategi Pembelajaran Berorientasi Standar Proses. Jakarta : Kencana Prenada Media Sumiati. 2007. Metode Pembelajaran Pendekatan Individual. Bandung: Rancaekek Kencana. Copyright (c) 2017 Geosfera Indonesia Journal and Department of Geography Education, University of Jember Copyright Notice This work is licensed under a Creative Commons Attribution-Share A like 4.0 International License
GEOGRAPHY SKILLS DOMAIN TAXONOMY Fahrudi Ahwan Ikhsan; Fahmi Arif Kurnianto; Bejo Apriyanto; Elan Artono Nurdin
Geosfera Indonesia Vol 2 No 1 (2018): GEOSFERA INDONESIA
Publisher : Department of Geography Education, University of Jember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.19184/geosi.v2i1.7525

Abstract

This study aims to explain the geography student skills domain. The focus of this research is the domain of geography skills possessed by students. The research method with the a qualitative approach. Subjects were students of Jember University geography education consisting of 2 men and 2 women with indicators of academic ability value of the national geography exam results. Data collection techniques by observation and interview. Data were analyzed using the processing unit, categorization and interpretation of data. The findings show that the skills of geography for prospective teachers of geography and geographers to be possessed composed as follows: 1st level thinking skills geography (space, phenomena, location and place, region, environment, coordinate, and humans), level 2 skills of analysis geography (scale, distribution, patterns of interaction, interrelation, connectivity, corologi, descriptions, and agglomeration), and level 3 skills of geographic applications (mapping/cartography, remote sensing, geographic information systems, surveying and mapping of the area, and Global Position systems (GPS). This level difference is used to distinguish the use of knowledge and application of the science of geography. Keywords: Students of geography education, geography Skills Copyright (c) 2018 Geosfera Indonesia Journal and Department of Geography Education, University of Jember Copyright Notice This work is licensed under a Creative Commons Attribution-Share A like 4.0 International License
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’ib Musa; Asiribo, Sallau Osisiemo
Geosfera Indonesia Vol 3 No 2 (2018): GEOSFERA INDONESIA
Publisher : Department of Geography Education

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.19184/geosi.v3i2.7934

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, &amp; 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 &nbsp; References Akinrinmade, A., Ibrahim, K., &amp; Abdurrahman, A. (2012). Geological Investigation of Tagwai Dams using Remote Sensing Technique, Minna Niger State, Nigeria. Journal of Environment, 1(01), pp. 26-32. Amadi, A., &amp; Olasehinde, P. (2010). Application of remote sensing techniques in hydrogeological mapping of parts of Bosso Area, Minna, North-Central Nigeria. International Journal of Physical Sciences, 5(9), pp. 1465-1474. Aplin, P., &amp; Smith, G. (2008). Advances in object-based image classification. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 37(B7), pp. 725-728. Ayele, G. T., Tebeje, A. K., Demissie, S. S., Belete, M. A., Jemberrie, M. A., Teshome, W. M., . . . Teshale, E. Z. (2018). Time Series Land Cover Mapping and Change Detection Analysis Using Geographic Information System and Remote Sensing, Northern Ethiopia. Air, Soil and Water Research, 11, p 1178622117751603. Azevedo, J. A., Chapman, L., &amp; Muller, C. L. (2016). Quantifying the daytime and night-time urban heat island in Birmingham, UK: a comparison of satellite derived land surface temperature and high resolution air temperature observations. Remote Sensing, 8(2), p 153. Blaschke, T., Hay, G. J., Kelly, M., Lang, S., Hofmann, P., Addink, E., . . . van Coillie, F. (2014). Geographic object-based image analysis–towards a new paradigm. ISPRS Journal of Photogrammetry and Remote Sensing, 87, pp. 180-191. Bukata, R. P., Jerome, J. H., Kondratyev, A. S., &amp; Pozdnyakov, D. V. (2018). Optical properties and remote sensing of inland and coastal waters: CRC press. Camps-Valls, G., Tuia, D., Bruzzone, L., &amp; Benediktsson, J. A. (2014). Advances in hyperspectral image classification: Earth monitoring with statistical learning methods. IEEE signal processing magazine, 31(1), pp. 45-54. Chen, J., Chen, J., Liao, A., Cao, X., Chen, L., Chen, X., . . . Lu, M. (2015). Global land cover mapping at 30 m resolution: A POK-based operational approach. ISPRS Journal of Photogrammetry and Remote Sensing, 103, pp. 7-27. Chen, M., Mao, S., &amp; Liu, Y. (2014). Big data: A survey. Mobile networks and applications, 19(2), pp. 171-209. Cheng, G., Han, J., Guo, L., Liu, Z., Bu, S., &amp; Ren, J. (2015). Effective and efficient midlevel visual elements-oriented land-use classification using VHR remote sensing images. IEEE transactions on geoscience and remote sensing, 53(8), pp. 4238-4249. Cheng, G., Han, J., Zhou, P., &amp; Guo, L. (2014). Multi-class geospatial object detection and geographic image classification based on collection of part detectors. ISPRS Journal of Photogrammetry and Remote Sensing, 98, pp. 119-132. Coale, A. J., &amp; Hoover, E. M. (2015). Population growth and economic development: Princeton University Press. Congalton, R. G., &amp; Green, K. (2008). Assessing the accuracy of remotely sensed data: principles and practices: CRC press. Corner, R. J., Dewan, A. M., &amp; Chakma, S. (2014). Monitoring and prediction of land-use and land-cover (LULC) change Dhaka megacity (pp. 75-97): Springer. Coutts, A. M., Harris, R. J., Phan, T., Livesley, S. J., Williams, N. S., &amp; Tapper, N. J. (2016). Thermal infrared remote sensing of urban heat: Hotspots, vegetation, and an assessment of techniques for use in urban planning. Remote Sensing of Environment, 186, pp. 637-651. Debnath, A., Debnath, J., Ahmed, I., &amp; Pan, N. D. (2017). Change detection in Land use/cover of a hilly area by Remote Sensing and GIS technique: A study on Tropical forest hill range, Baramura, Tripura, Northeast India. International journal of geomatics and geosciences, 7(3), pp. 293-309. Desheng, L., &amp; Xia, F. (2010). Assessing object-based classification: advantages and limitations. Remote Sensing Letters, 1(4), pp. 187-194. Dewan, A. M., &amp; Yamaguchi, Y. (2009). Land use and land cover change in Greater Dhaka, Bangladesh: Using remote sensing to promote sustainable urbanization. Applied Geography, 29(3), pp. 390-401. Dronova, I., Gong, P., Wang, L., &amp; Zhong, L. (2015). Mapping dynamic cover types in a large seasonally flooded wetland using extended principal component analysis and object-based classification. Remote Sensing of Environment, 158, pp. 193-206. Duro, D. C., Franklin, S. E., &amp; Dubé, M. G. (2012). A comparison of pixel-based and object-based image analysis with selected machine learning algorithms for the classification of agricultural landscapes using SPOT-5 HRG imagery. Remote Sensing of Environment, 118, pp. 259-272. Elmhagen, B., Destouni, G., Angerbjörn, A., Borgström, S., Boyd, E., Cousins, S., . . . Hambäck, P. (2015). Interacting effects of change in climate, human population, land use, and water use on biodiversity and ecosystem services. Ecology and Society, 20(1) Farhani, S., &amp; Ozturk, I. (2015). Causal relationship between CO 2 emissions, real GDP, energy consumption, financial development, trade openness, and urbanization in Tunisia. Environmental Science and Pollution Research, 22(20), pp. 15663-15676. Feng, L., Chen, B., Hayat, T., Alsaedi, A., &amp; Ahmad, B. (2017). The driving force of water footprint under the rapid urbanization process: a structural decomposition analysis for Zhangye city in China. Journal of Cleaner Production, 163, pp. S322-S328. Fensham, R., &amp; Fairfax, R. (2002). Aerial photography for assessing vegetation change: a review of applications and the relevance of findings for Australian vegetation history. Australian Journal of Botany, 50(4), pp. 415-429. Ferreira, N., Lage, M., Doraiswamy, H., Vo, H., Wilson, L., Werner, H., . . . Silva, C. (2015). Urbane: A 3d framework to support data driven decision making in urban development. Visual Analytics Science and Technology (VAST), 2015 IEEE Conference on. Garschagen, M., &amp; Romero-Lankao, P. (2015). 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).&nbsp; Retrieved from http://nigerstate.blogspot.com.ng/ Green, K., Kempka, D., &amp; 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., &amp; 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., &amp; 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., &amp; 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., &amp; 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, VENμS and Sentinel-2 images. Remote Sensing, 7(3), pp. 2668-2691. Hegazy, I. R., &amp; 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., &amp; Deichmann, U. (2017). Has climate change driven urbanization in Africa? Journal of development economics, 124, pp. 60-82. Hu, L., &amp; Brunsell, N. A. (2015). A new perspective to assess the urban heat island through remotely sensed atmospheric profiles. Remote Sensing of Environment, 158, pp. 393-406. Hughes, S. J., Cabral, J. A., Bastos, R., Cortes, R., Vicente, J., Eitelberg, D., . . . Santos, M. (2016). A stochastic dynamic model to assess land use change scenarios on the ecological status of fluvial water bodies under the Water Framework Directive. Science of the Total Environment, 565, pp. 427-439. Hussain, M., Chen, D., Cheng, A., Wei, H., &amp; Stanley, D. (2013). Change detection from remotely sensed images: From pixel-based to object-based approaches. ISPRS Journal of Photogrammetry and Remote Sensing, 80, pp. 91-106. Hyyppä, J., Hyyppä, H., Inkinen, M., Engdahl, M., Linko, S., &amp; Zhu, Y.-H. (2000). Accuracy comparison of various remote sensing data sources in the retrieval of forest stand attributes. Forest Ecology and Management, 128(1-2), pp. 109-120. Jiang, L., Wu, F., Liu, Y., &amp; Deng, X. (2014). Modeling the impacts of urbanization and industrial transformation on water resources in China: an integrated hydro-economic CGE analysis. Sustainability, 6(11), pp. 7586-7600. Jin, S., Yang, L., Zhu, Z., &amp; Homer, C. (2017). A land cover change detection and classification protocol for updating Alaska NLCD 2001 to 2011. Remote Sensing of Environment, 195, pp. 44-55. Joshi, N., Baumann, M., Ehammer, A., Fensholt, R., Grogan, K., Hostert, P., . . . Mitchard, E. T. (2016). A review of the application of optical and radar remote sensing data fusion to land use mapping and monitoring. Remote Sensing, 8(1), p 70. Kaliraj, S., Chandrasekar, N., &amp; Magesh, N. (2015). Evaluation of multiple environmental factors for site-specific groundwater recharge structures in the Vaigai River upper basin, Tamil Nadu, India, using GIS-based weighted overlay analysis. Environmental earth sciences, 74(5), pp. 4355-4380. Koop, S. H., &amp; van Leeuwen, C. J. (2015). 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., &amp; 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., &amp; 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. Population and Environment, 35(3), pp. 286-304. Lillesand, T., Kiefer, R. W., &amp; Chipman, J. (2014). Remote sensing and image interpretation: John Wiley &amp; Sons. Liu, Y., Wang, Y., Peng, J., Du, Y., Liu, X., Li, S., &amp; Zhang, D. (2015). Correlations between urbanization and vegetation degradation across the world’s metropolises using DMSP/OLS nighttime light data. Remote Sensing, 7(2), pp. 2067-2088. López, E., Bocco, G., Mendoza, M., &amp; Duhau, E. (2001). Predicting land-cover and land-use change in the urban fringe: a case in Morelia city, Mexico. Landscape and urban planning, 55(4), pp. 271-285. Luo, M., &amp; Lau, N.-C. (2017). Heat waves in southern China: Synoptic behavior, long-term change, and urbanization effects. Journal of Climate, 30(2), pp. 703-720. Mahboob, M. A., Atif, I., &amp; Iqbal, J. (2015). Remote sensing and GIS applications for assessment of urban sprawl in Karachi, Pakistan. Science, Technology and Development, 34(3), pp. 179-188. Mallinis, G., Koutsias, N., Tsakiri-Strati, M., &amp; Karteris, M. (2008). Object-based classification using Quickbird imagery for delineating forest vegetation polygons in a Mediterranean test site. ISPRS Journal of Photogrammetry and Remote Sensing, 63(2), pp. 237-250. Mas, J.-F., Velázquez, A., Díaz-Gallegos, J. R., Mayorga-Saucedo, R., Alcántara, C., Bocco, G., . . . Pérez-Vega, A. (2004). Assessing land use/cover changes: a nationwide multidate spatial database for Mexico. International Journal of Applied Earth Observation and Geoinformation, 5(4), pp. 249-261. Mathew, A., Chaudhary, R., Gupta, N., Khandelwal, S., &amp; Kaul, N. (2015). Study of Urban Heat Island Effect on Ahmedabad City and Its Relationship with Urbanization and Vegetation Parameters. International Journal of Computer &amp; Mathematical Science, 4, pp. 2347-2357. Megahed, Y., Cabral, P., Silva, J., &amp; Caetano, M. (2015). Land cover mapping analysis and urban growth modelling using remote sensing techniques in greater Cairo region—Egypt. ISPRS International Journal of Geo-Information, 4(3), pp. 1750-1769. Metternicht, G. (2001). Assessing temporal and spatial changes of salinity using fuzzy logic, remote sensing and GIS. Foundations of an expert system. Ecological modelling, 144(2-3), pp. 163-179. Miller, R. B., &amp; Small, C. (2003). Cities from space: potential applications of remote sensing in urban environmental research and policy. Environmental Science &amp; Policy, 6(2), pp. 129-137. Mirzaei, P. A. (2015). Recent challenges in modeling of urban heat island. Sustainable Cities and Society, 19, pp. 200-206. Mohammed, I., Aboh, H., &amp; Emenike, E. (2007). A regional geoelectric investigation for groundwater exploration in Minna area, north west Nigeria. Science World Journal, 2(4) Morenikeji, G., Umaru, E., Liman, S., &amp; Ajagbe, M. (2015). Application of Remote Sensing and Geographic Information System in Monitoring the Dynamics of Landuse in Minna, Nigeria. International Journal of Academic Research in Business and Social Sciences, 5(6), pp. 320-337. Mukherjee, A. B., Krishna, A. P., &amp; Patel, N. (2018). Application of Remote Sensing Technology, GIS and AHP-TOPSIS Model to Quantify Urban Landscape Vulnerability to Land Use Transformation Information and Communication Technology for Sustainable Development (pp. 31-40): Springer. Myint, S. W., Gober, P., Brazel, A., Grossman-Clarke, S., &amp; Weng, Q. (2011). Per-pixel vs. object-based classification of urban land cover extraction using high spatial resolution imagery. Remote Sensing of Environment, 115(5), pp. 1145-1161. Nemmour, H., &amp; Chibani, Y. (2006). Multiple support vector machines for land cover change detection: An application for mapping urban extensions. ISPRS Journal of Photogrammetry and Remote Sensing, 61(2), pp. 125-133. Niu, X., &amp; Ban, Y. (2013). 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., &amp; dos Santos, J. A. (2017). Towards better exploiting convolutional neural networks for remote sensing scene classification. Pattern Recognition, 61, pp. 539-556. Oguz, H., &amp; 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., &amp; 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., &amp; 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., &amp; Miller, J. (2015). Detection and attribution of urbanization effect on flood extremes using nonstationary flood‐frequency models. Water resources research, 51(6), pp. 4244-4262. Rawat, J., &amp; 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., &amp; 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., &amp; 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., &amp; 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., &amp; 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., &amp; 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., &amp; 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., &amp; 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., &amp; Jóźków, G. (2016). Remote sensing platforms and sensors: A survey. ISPRS Journal of Photogrammetry and Remote Sensing, 115, pp. 22-36. Tuholske, C., Tane, Z., López-Carr, D., Roberts, D., &amp; Cassels, S. (2017). Thirty years of land use/cover change in the Caribbean: Assessing the relationship between urbanization and mangrove loss in Roatán, Honduras. Applied Geography, 88, pp. 84-93. Tuia, D., Flamary, R., &amp; 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., &amp; Argialas, D. (2008). Support vector machine classification for object-based image analysis Object-Based Image Analysis (pp. 663-677): Springer. Wang, L., Sousa, W., &amp; 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., &amp; 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., &amp; 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 &amp; Business. Wellmann, T., Haase, D., Knapp, S., Salbach, C., Selsam, P., &amp; 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., &amp; 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. Willhauck, G., Schneider, T., De Kok, R., &amp; Ammer, U. (2000). Comparison of object oriented classification techniques and standard image analysis for the use of change detection between SPOT multispectral satellite images and aerial photos. Proceedings of XIX ISPRS congress. Winker, D. M., Vaughan, M. A., Omar, A., Hu, Y., Powell, K. A., Liu, Z., . . . Young, S. A. (2009). Overview of the CALIPSO mission and CALIOP data processing algorithms. Journal of Atmospheric and Oceanic Technology, 26(11), pp. 2310-2323. Yengoh, G. T., Dent, D., Olsson, L., Tengberg, A. E., &amp; Tucker III, C. J. (2015). Use of the Normalized Difference Vegetation Index (NDVI) to Assess Land Degradation at Multiple Scales: Current Status, Future Trends, and Practical Considerations: Springer. Yu, Q., Gong, P., Clinton, N., Biging, G., Kelly, M., &amp; Schirokauer, D. (2006). Object-based detailed vegetation classification with airborne high spatial resolution remote sensing imagery. Photogrammetric Engineering &amp; Remote Sensing, 72(7), pp. 799-811. Zhou, D., Zhao, S., Zhang, L., &amp; Liu, S. (2016). Remotely sensed assessment of urbanization effects on vegetation phenology in China's 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 &nbsp; Copyright (c) 2018&nbsp;Geosfera Indonesia Journal and Department of Geography Education, University of Jember Copyright Notice This work is licensed under a Creative Commons Attribution-Share&nbsp;A like&nbsp;4.0 International License
GEOGRAPHY LITERACY OF OBSERVATION INTRODUCTION LANDSCAPE REPRESENTATION PLACE FOR STUDENT EXPERIENCE Fahrudi Ahwan Ikhsan; Fahmi Arif Kurnianto; Elan Artono Nurdin; Bejo Apriyanto
Geosfera Indonesia Vol 3 No 2 (2018): GEOSFERA INDONESIA
Publisher : Department of Geography Education, University of Jember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.19184/geosi.v3i2.8384

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. © 2018 Department of Geography Education, University of Jember Copyright Notice This work is licensed under a Creative Commons Attribution-Share A like 4.0 International License
LEVEL OF KNOWLEDGE OF SENIOR HIGH SCHOOL STUDENTS TO NORTH COASTAL OF JAVA CONSERVATION Fahmi Arif Kurnianto; Bejo Apriyanto; Elan Artono Nurdin; Fahrudi Ahwan Ikhsan
Geosfera Indonesia Vol 1 No 1 (2017): GEOSFERA INDONESIA
Publisher : Department of Geography Education, University of Jember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.19184/geosi.v1i1.6192

Abstract

The purpose of this study to analysis the influence of group investigation learning model towards the level of conservation knowledge of students in senior high school. The type of research is the quasi-experimental study with nonequivalent control group posttest only design. The subject of the study consisted of class XI Social Science are selected based on the score of Final Semester Exam on 2014-2015 teachings year that had an average of almost the same (homogeneous).The results of this study are showed significant influence of GI models on Geography level of knowledge to learn of students. It was based on the results of the Independent Sample T-Test analysis showed a p-value of 0.000 level. P-level value is smaller than 0.05 (p <0.05). Keywords: Group Investigation Learning, Level of knowledge, conservation References Huda, Miftahul. 2011. Cooperative Learning. Yogyakarta: Pustaka Pelajar. Jacksen, Sherri L. 2011. Research Methode: Moduler Approach. Stamford: Cengage Learning. Majid, Abdul. 2013. Strategi Pembelajaran. Bandung : PT Remaja Rosdakarya. Mushodik. 2013. Pengaruh Model Pembelajaran Group Investigation terhadap Kemampuan Berpikir Kritis Siswa Madrasah Aliyah Negeri 6 Jakarta. Tesis tidak diterbitkan. Malang: PPS Universitas Negeri Malang. Rusman. 2012. Model-Model Pembelajaran. Jakarta: Raja Grafindo Persada. Sharan, Shlomo. 2014. Handbook of Cooperative Learning. New York: Teachers College Press. Slavin, Robert E. 2005. Cooperative Learning: theory, research and practice (N. Yusron. Terjemahan). London: Allymand Bacon. Buku asli diterbitkan tahun 2005. Sumarmi. 2012. Model-Model Pembelajaran Geografi. Malang : Aditya Media Publishing. Tan, Ivy Geok Chin. 2004. Effects of cooperative learning with group investigation on secondary students’ achievement, level of knowledge and perceptions. Singapore: National Institute of Education. Trianto. 2007. Model-model Pembelajaran Inovatif Berorientasi Konstruktivistik. Jakarta: Prestasi Pustaka. Copyright (c) 2017 Geosfera Indonesia Journal and Department of Geography Education, University of Jember Copyright Notice This work is licensed under a Creative Commons Attribution-Share A like 4.0 International License
DEMOGRAPHIC FACTORS INFLUENCE ON POPULATION ADDED IN SUMBERSARI JEMBER DISTRICT Elan Artono Nurdin; Fahrudi Ahwan Ikhsan; Bejo Apriyanto; Fahmi Arif Kurnianto
Geosfera Indonesia Vol 2 No 1 (2018): GEOSFERA INDONESIA
Publisher : Department of Geography Education, University of Jember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.19184/geosi.v2i1.7515

Abstract

Population growth is the increasing population changes at any time which is calculated in the number of individuals. This study aimed to determine the effect of demographic factors on the growth of population in the district of Jember in East Java Sumbersari. Selection of research areas using purposive sampling technique which is in District SumbersariJember. The number of samples is equal to the number of population is the whole population in Jember in 2012 - 2016.The results of this study show the influence of demographic factors include fertility, mortality, and migration on population growth is the F> M and positive migration rises (N) in the District SumbersariJember, East Java. Keywords: population growth, demographics, migration Copyright (c) 2018 Geosfera Indonesia Journal and Department of Geography Education, University of Jember Copyright Notice This work is licensed under a Creative Commons Attribution-Share A like 4.0 International License

Page 1 of 16 | Total Record : 154