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All Journal Jurnal Ilmu dan Teknologi Kelautan Tropis Journal of Degraded and Mining Lands Management Justek : Jurnal Sains Dan Teknologi Geodika: Jurnal Kajian Ilmu dan Pendidikan Geografi El-Jughrafiyah : Jurnal Geografi dan Terapannya Applied Environmental Science
Parluhutan Manurung
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Agriculture, Biological Sciences & Forestry Humanities Biochemistry, Genetics & Molecular Biology Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Education Environmental Science Library & Information Science Mathematics Physics Social Sciences Other

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SEA LEVEL VARIATION AND GEOSTROPHIC CURRENT OF THE SUNDA STRAIT BASED ON TIDAL AND WIND DATA IN YEAR 2008 Resni Oktavia; John Iskandar Pariwono; Parluhutan Manurung
Jurnal Ilmu dan Teknologi Kelautan Tropis Vol. 3 No. 2 (2011): Elektronik Jurnal Ilmu dan Teknologi Kelautan Tropis
Publisher : Department of Marine Science and Technology, Faculty of Fisheries and Marine Science, IPB University

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (2177.519 KB) | DOI: 10.29244/jitkt.v3i2.7827

Abstract

Sea level variation from four tide-gauge data in the Sunda Strait, Indonesia, in the year 2008 has been studied by using Wavelet 1 D Daubechius 1 level 5 type and Fast Fourier Transform methods. The hourly sea level variation in April and November (representing transitional seasonal conditions) is approximately +0.49 m; whereas in January (representing Northwest Monsoon condition) and July (representing Southeast Monsoon condition) can reach up to -0.48 m. In 2008, sea level variation in the Sunda Strait is mainly influenced by the monsoon. Results from this study show that there are at least three phenomena of sea level variations in the Sunda Strait, namely (1) seasonal variation (periodicity between 4-6 months) where it is believed influenced by the Java Sea; (2) intra-seasonal (periodicity between 1-3 months), which is more influenced by the Indian Ocean; and (3) tidal forcing (periodicity between 14-17 days, suggesting fortnightly tidal forces) which propagates from the Indian Ocean into the Sunda Strait. Result from surface geostrophic approximation calculation suggests that during Southeast Monsoon (June-August), monthly mean current flows southwestwardly towards the Indian Ocean with a velocity of 0.14-0.16 m/s. Whereas during Northwest monsoon (December-February), current flows northeastwardly towards the Java Sea with a velocity of 0.14-0.17 m/s. Furthermore, on the daily time scale, tidal current in the Sunda Strait flows into the Java Sea (Indian Ocean) during high tide (low tide) with a velocity ranging from 0.51 to 0.72 m/s (0.48 to 0.51 m/s).Keywords: sea level variation, geostrophic approximation, tides, monsoon, Sunda Strait
Visualisasi dan Analisis Sebaran Data Sekolah (SD, SMP dan SMA) di Kota Bengkulu Menggunakan Geocoding R Alyudin, Dyah Rizky; Manurung, Parluhutan; Mandini Manessa, Masita Dwi
Justek : Jurnal Sains dan Teknologi Vol 7, No 2 (2024): Juni
Publisher : Unversitas Muhammadiyah Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31764/justek.v7i2.22131

Abstract

Abstract:  Schools are a means of infrastructure needed to fulfill the law's educational obligations, so the distribution of schools needs to be a concern so that access to education for every citizen can be achieved. Analysis of school distribution is one way to see the needs of schools in an area through visualization of the distribution of school data in Indonesia, including Bengkulu City. However, access to school coordinates is limited, so a method is needed to obtain coordinate points for mapping and distribution analysis. Meanwhile, there is still little research regarding taking coordinate points from addresses for school data distribution in Indonesia, including Bengkulu City. Even though Geocoding with R is one way to get the coordinates of an address well. By using geocoding and visualization using the Google API, mapview, shiny and the ggplot function in R, we can show variations in the distribution of geocoding data so that distribution analysis can be carried out. The results of the visualization of the distribution of Bengkulu City school data look good, with the Muara Bangkahulu District lacking a high school, while Teluk Segara, Ratu Agung and Muara Bangkahulu lack a junior high school, and the Kampung Melayu and Sungai Serut Districts lack an elementary school. Visualizing the distribution of this data would be better done by combining four methods, namely Google API, mapview, shiny and ggplot because each method shows the advantages and disadvantages of the display.Abstrak: Sekolah menjadi suatu sarana prasarana yang diperlukan untuk memenuhi Undang-Undang dalam kewajiban pendidikan, sehingga sebaran sekolah perlu menjadi perhatian agar akses menerima pendidikan bagi setiap warga negara dapat terlaksana. Analisis sebaran sekolah menjadi salah satu cara untuk melihat kebutuhan sekolah di suatu wilayah melalui visualisasi sebaran data sekolah di Indonesia termasuk Kota Bengkulu. Akan tetapi, akses mengenai koordinat sekolah terbatas, sehingga diperlukan metode untuk mendapatkan titik koordinat untuk melakukan pemetaan dan analisis sebaran. Sementara itu, penelitian mengenai pengambilan titik koordinat dari alamat untuk sebaran data sekolah masih sedikit di Indonesia termasuk Kota Bengkulu. Padahal Geocoding dengan R adalah salah satu cara untuk mendapatkan koordinat dari suatu alamat dengan baik. Dengan menggunakan geocoding dan visualisasi menggunakan Google API, mapview, shiny dan fungsi ggplot di R, dapat memperlihatkan variasi sebaran data hasil geocoding sehingga analisis sebaran dapat dilakukan. Hasil visualisasi sebaran data sekolah Kota Bengkulu tampak baik dengan wilayah yang Kecamatan Muara Bangkahulu kekurangan SMA, sementara Teluk Segara, Ratu Agung dan Muara Bangkahulu kekurangan SMP, serta Kecamatan Kampung Melayu dan Sungai Serut kekurangan SD. Visualisasi sebaran data ini akan lebih baik dilakukan dengan mengkombinasikan dari empat metode yaitu Google API, mapview, shiny dan ggplot dikarenakan masing-masing metode menunjukkan kelebihan dan kekurangan tampilan.
Model Spasial Prediktif Bahaya Bullying di Kota Depok Al Kautsar, Azhari; Manurung, Parluhutan
EL-JUGHRAFIYAH Vol 5, No 2 (2025): El-Jughrafiyah : August, 2025
Publisher : Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/jej.v5i2.36357

Abstract

Tujuan dari penelitian ini adalah untuk memprediksi spasial tingkat bahaya bullying di Kota Depok. Kemudian tujuan lainnya adalah seberapa penggunaan data lokasi pendidikan terhadap potensi prediksi kekerasan bullying pada usia remaja di Kota Depok. Metodologi penelitian ini menggunakan analisis multi-kriteria (AHP), kriging, regresi OLS, REML, PLS, GB, RF, dan INLA. Temuan utama pada penelitian ini adalah terjadi perbedaan pengukuran model spasial prediktif yang dikatakan tinggi seperti krigring, GB, REML, dan INLA demikian juga yang terendah seperti PLS dan RF. Temuan berikutnya dari model spasial prediktif tingkat bahaya bullying tercermin dari lokasi kegiatan pelajar usia remaja seperti pendidikan, kegiatan hiburan remaja, pemerintah dan keamanan, fasilitas kesehatan, dan tempat ibadah. Kesimpulan yang diperoleh dalam penelitian ini adalah keseluruhan tingkat bahaya bullying tinggi dengan nilai 0,6 – 0,8.
Application of spectral indices and deep learning (convolutional neural network model) on land cover change analysis Hikmah, Nur ‘Izzatul; Manurung, Parluhutan
Applied Environmental Science Vol. 3 No. 1: (July) 2025
Publisher : Institute for Advanced Science, Social, and Sustainable Future

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61511/aes.v3i1.2025.1883

Abstract

Background: Understanding land cover change is crucial for sustainable urban development, particularly in rapidly growing coastal cities such as Semarang City, Central Java, Indonesia. Methods: This study investigates spatial and temporal patterns of land cover change from 2000 to 2025 by integrating multi-temporal Landsat satellite imagery, key spectral indices—namely the normalized difference vegetation index, normalized difference water index, and normalized difference built-up index—and a deep learning approach based on convolutional neural networks. Annual Landsat images were preprocessed for atmospheric correction, cloud masking, and spatial subsetting using Google Earth Engine. Adaptive thresholding was then applied to each spectral index to delineate vegetation, water bodies, and built-up areas. Findings: Quantitative analysis revealed a significant decline in vegetation cover, with the normalized difference vegetation index dropping from 53.66% (397.59 km²) in 2000 to 46.83% (346.98 km²) in 2025, driven by urban expansion and landscape conversion, especially in coastal and lowland areas. Normalized difference water index analysis indicated a reduction and fragmentation of water bodies after 2015, linked to reclamation, sedimentation, and urban encroachment. Conversely, built-up areas expanded steadily, confirming accelerated urbanization. Scatter plot and regression analyses showed strong inverse relationships among vegetation, water, and built-up land, emphasizing ecological trade-offs and the loss of green-blue infrastructure. Conclusion: To enhance classification accuracy, a convolutional neural network was trained and validated on image patches, achieving a validation accuracy of 60%—outperforming conventional threshold-based methods by better capturing complex spatial patterns. The integrated remote sensing and deep learning framework offers robust potential for long-term, large-area land cover monitoring. Novelty/Originality of this article: The novelty of this research lies in its combined use of spectral indices and deep learning for multi-decadal land cover change analysis, providing a transferable methodology for other rapidly urbanizing coastal cities.
Land use and land cover (LULC) change from 2010 to 2015 driven by mining industries: A case study in Obi Island, Indonesia Listyono, Girlly Marchlina; Manurung, Parluhutan
Journal of Degraded and Mining Lands Management Vol. 12 No. 5 (2025)
Publisher : Brawijaya University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15243/jdmlm.2025.125.8677

Abstract

This study investigated land use and land cover (LULC) changes in Obi Subdistrict, Indonesia, from 2010 to 2015, driven by the expansion of the nickel mining industry. Using Landsat 7 and Landsat 8 imagery, Random Forest classification and change detection were conducted to evaluate annual LULC dynamics. Preprocessing included cloud masking and the calculation of NDVI, NDBI, and NDWI to enhance class separability. Four land cover classes were defined: dense vegetation, sparse vegetation, bare soil, and urban areas. The results showed a significant increase in urban/built-up area from 2,052 ha (2010) to 4,843 ha (2015), alongside a decrease in sparse vegetation from 92,770 ha to 84,848 ha. Dense vegetation increased to 10,236 ha in 2015, suggesting potential regrowth. Chord diagrams and pixel-based change maps reveal that transitions from sparse vegetation to urban and dense vegetation dominate the landscape change. Accuracy assessment indicates classification reliability improved from Landsat 7 to Landsat 8, with dense vegetation F1-score increasing from 0.21 to 0.81. This study demonstrated the utility of spectral indices and machine learning in early-stage LULC detection. It recommends future improvements using object-based classification, ground-truth validation, and deep learning for more robust environmental monitoring in resource-rich areas. This study contributes an early-stage LULC assessment framework for mining zones in Indonesia, which can inform future land governance and remote sensing policy applications.
Analisis Distribusi Fasilitas Sekolah Menengah Pertama melalui Pemodelan Spasial Studi Kasus di Kota Malang Kuncoro Adi Pradono; Manurung, Parluhutan; Wibowo, Adi
Geodika: Jurnal Kajian Ilmu dan Pendidikan Geografi Vol 8 No 2 (2024): September 2024
Publisher : Universitas Hamzanwadi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29408/geodika.v8i2.25692

Abstract

Dalam upaya membangun bangsa melalui pemerataan wajib belajar sembilan tahun merupakan bagian amanat UUD 1945 dan menjadi prioritas program pemerintah. Penelitian ini bertujuan untuk menganalisis distribusi fasilitas sekolah SMP di Kota Malang. Metode pemodelan spasial yang digunakan metode krigging dan regresi machine learning untuk prediksi perkembangan fasilitas sekolah SMP. Pemodelan spasial akan menilai sejauh mana pola spasial fasilitas sekolah terdistribusi dan analisis regresi yang akan melakukan prediksi fasilitas sekolah SMP dengan variabel peserta didik, guru, pegawai dan rombongan belajar. Hasil pemodelan spasial dengan teknik krigging menunjukan distribusi variogram antar fasilitas sekolah berjarak 3-4 km dan merata. Adapun regresi dengan model terbaik secara berurutan REML, RF, OLS, GB, INLA dan PLS. Dengan model terbaik didapatkan akurasi 0,98 dan RMSE sebesar 0.79. Melalui hasil dari penelitian ini didapatkan gambaran bahwa sebaran distribusi fasilitas sekolah tingkat SMP di era zonasi saat ini masih terpusat di wilayah tengah Kota Malang sehingga terdapat peluang untuk pembangunan dan pengembangan fasilitas sekolah SMP di daerah pinggiran. Analisis pemodelan spasial dapat memberikan sudut pandang dan pertimbangan bagaimana fasilitas sekolah di perbaikan sesuai untuk pemerataan pendidikan.
Co-Authors Adi Wibowo Al Kautsar, Azhari Alyudin, Dyah Rizky Girlly Marchlina Listyono, Girlly Marchlina Hikmah, Nur ‘Izzatul John Iskandar Pariwono Kuncoro Adi Pradono Mandini Manessa, Masita Dwi Resni Oktavia