Problematika Perumahan Perkotaan di Kota Yogyakarta

Kamim, Anggalih Bayu Muh.; Amal, Ichlasul; Khandiq, M. Rusmul

doi:10.24815/jsu.v13i1.13494

Cited by 4 publications

(4 citation statements)

References 2 publications

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“…Despite these efforts, the challenge of providing appropriate housing persists. A range of policies has been implemented to mitigate potential issues, encompassing initiatives like Social Rehabilitation of Slum Areas (RSDK), Social Welfare Business Development (PUKSM), Skill Enhancement Projects (PPKT), Clean River Projects (Prokasih), and Urban Settlement Environmental Health Projects (PLKP), among others (Kamim, 2019).…”

Section: Study Areamentioning

confidence: 99%

Implementing Support Vector Machine Algorithm for Early Slum Identification in Yogyakarta City, Indonesia Using Pleiades Images

Widayani,

Fadilah,

Irawan

et al. 2023

For. Geo.

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Slums are one of the urban problems that continue to get the attention of the government and the city of Yogyakarta. Over time, cities continue to experience changes in land use due to population growth and migration. Therefore, it is necessary to monitor the existence of slums continuously. The objectives of this study are to conduct early identification of the slum using the Support Vector Machine (SVM) Algorithm, which is applied to the Pleiades Image in parts of Yogyakarta City, to test the accuracy of the slum mapping results generated from the SVM compared to the Slum Map of the KOTAKU Program. The data used are Pleiades Image, administrative maps, and existing slum maps of the KOTAKU Program, which are used to test the accuracy. The method used is Machine Learning with a Support Vector Machine Algorithm. The parameters used for early identification of the slums are the characteristics of the object (characteristics of buildings), settlement (density and shape), and the environment (location and its proximity to rivers and industries). We separate slum and non-slum based on texture, morphology, and spectral approaches. Based on the accuracy test results between the SVM classification results map of the slum and the map from the KOTAKU Program, the accuracy is 86.25% with a kappa coefficient of 0.796.

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Section: Study Areamentioning

confidence: 99%

Implementing Support Vector Machine Algorithm for Early Slum Identification in Yogyakarta City, Indonesia Using Pleiades Images

Widayani,

Fadilah,

Irawan

et al. 2023

For. Geo.

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“…Berdasarkan Rencana Strategis Direktorat Jenderal Perumahan, Kementerian PUPR 2020-2024, permasalahan yang sedang dihadapi dalam penyelenggaraan perumahan mencakup berbagai aspek yang antara lain dapat dirangkum dalam Tabel 2. Neysa Dianesdhika Jasrul, Ayomi Dita Rarasati Permasalahan Bidang Lahan dalam penyelenggaraan perumahan dalam program perumahan terjangkau berkaitan dengan keterbatasan lahan, terutama di kota besar di Indonesia yang tidak mendukung kebutuhan perumahan yang semakin meningkat (Kamim, Amal, & Khandiq, 2019). Bahkan, di daerah padat penduduk seperti pada beberapa kecamatan di kota Depok, ketersediaan lahannya sudah tidak mampu menampung untuk perumahan (Budiarto, Dwiputri, & Hambali, 2019).…”

Section: B Permasalahan Dalam Program Perumahan Terjangkauunclassified

Faktor-Faktor Paling Berpengaruh Terhadap Penyediaan Perumahan dan Aksesibilitas Pembiayaan Perumahan Bagi Masyarakat Berpenghasilan Rendah

Jasrul

Rarasati

2022

SLJIL

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Setiap negara memiliki tanggung jawab untuk memenuhi kebutuhan akan hunian yang berkualitas bagi masyarakat berpenghasilan rendah (MBR) dalam bentuk pelaksanaan program perumahan terjangkau. Meskipun program-program perumahan terjangkau marak diadakan di berbagai negara, terdapat beragam permasalahan yang dihadapi dalam pelaksanaannya. Di Indonesia sendiri, penyediaan perumahan dengan berbagai program bantuan pembiayaan yang ada tergolong belum terjangkau, terutama bagi MBR. Hal tersebut mendorong untuk dirancangnya strategi yang dapat meningkatkan penyediaan perumahan dan aksesibilitas pembiayaan perumahan bagi MBR. Guna merancang strategi yang tepat untuk meningkatkan penyediaan perumahan dan aksesibilitas pembiayaan perumahan bagi MBR, dibutuhkan analisis yang mendalam terlebih dahulu terhadap permasalahan yang tengah dihadapi dalam penyelenggaraan perumahan. Makalah ini menganalisis permasalahan-permasalahan penyelenggaraan perumahan dalam Program Sejuta Rumah sebagai program perumahan terjangkau di Indonesia guna mengidentifikasi faktor yang paling berpengaruh terhadap penyediaan perumahan dan aksesibilitas pembiayaan perumahan bagi MBR. Permasalahan-permasalahan penyelenggaraan perumahan dalam program perumahan terjangkau tersebut dianalisis menggunakan Interpretive Structural Modeling (ISM) untuk menentukan hubungan timbal balik antara permasalahan-permasalahan tersebut. Hasil analisis dari permasalahan-permasalahan tersebut pun berupa permasalahan yang paling berpengaruh terhadap permasalahan lainnya dalam penyelenggaraan perumahan, yang mana menjadi masukan untuk mengidentifikasi faktor yang paling berpengaruh terhadap penyediaan perumahan dan aksesibilitas pembiayaan perumahan bagi MBR. Makalah ini pun menyimpulkan bahwa faktor dukungan pemangku kepentingan, faktor bidang lahan, serta faktor regulasi mengenai perumahan dan kawasan permukiman merupakan faktor-faktor yang paling berpengaruh terhadap permasalahan penyelenggaraan perumahan dalam Program Sejuta Rumah sebagai program perumahan terjangkau di Indonesia.

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“…The city of Yogyakarta has a very complex development and development trend and is one of the most frequently visited cities because it has the title of being an educational city, tourist city and cultural city (Devi et al, 2020). This causes the City of Yogyakarta to experience acute land limitations and high land and house prices (Rahardjo & Marhaento, 2018;Kamim et al, 2019), where the trend of increasing housing prices in the City of Yogyakarta always occurs every quarter of the year (Kamim et al , 2019). The rapid development of Yogyakarta City has also caused a shift in development boundaries reaching Sleman Regency and Bantul Regency due to urban influence (Artaningtyas et al, 2019;Devi et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

Recommended Strategies Based on The Capability And Purchasing Power of Low-Income Communities (MBR) In Providing MBR Houses In Kartamantul

Kurniati,

Fanani

2024

JIP

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The housing needs that shape urban spatial structures or the relationships between the three cannot be fully understood without considering the state of housing market prices. The reason is that changes in urban housing market prices no longer depend on the city level, but over time begin to be characterized by urban agglomeration processes. Furthermore, agglomeration dynamics that change urban spatial structure and housing market prices will have an impact on socio-economic disparities and challenges in providing affordable housing for people with low incomes in densely populated urban areas. The aim of this research is to find out whether the MBR housing allocated by the regional government is appropriately located in low-income areas/zones in KARTAMANTUL. The research methods used are web scrapping, mapping and comparative evaluation of housing locations with the distribution of MBR in KARTAMANTUL. The results obtained were to simplify the simulation of affordability and household purchasing power regarding home ownership, the researchers tried to average the income of the two categories of low-income households. Therefore, with an average MBR income limit of Rp. 3,900,000 in DIY, then the ability to repay the house in installments is IDR. 1,170,000/month. Furthermore, regarding house buildings, the criteria for a habitable house is a per capita area of at least 9 m2 per person. With the average number of family members in DIY being 4 people, the house area required per household is 36 m2. The recommendations given are a) 25 year house installments are a payment option that can be used for low-income households in KARTAMANTUL, b) the choice of house locations for MBR is mostly in Bantul Regency and Sleman Regency, both of which have urban status. fringe and urban shadow.

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Problematika Perumahan Perkotaan di Kota Yogyakarta

Cited by 4 publications

References 2 publications

Implementing Support Vector Machine Algorithm for Early Slum Identification in Yogyakarta City, Indonesia Using Pleiades Images

Implementing Support Vector Machine Algorithm for Early Slum Identification in Yogyakarta City, Indonesia Using Pleiades Images

Faktor-Faktor Paling Berpengaruh Terhadap Penyediaan Perumahan dan Aksesibilitas Pembiayaan Perumahan Bagi Masyarakat Berpenghasilan Rendah

Recommended Strategies Based on The Capability And Purchasing Power of Low-Income Communities (MBR) In Providing MBR Houses In Kartamantul

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