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All Journal IJOG : Indonesian Journal on Geoscience OLDI (Oseanologi dan Limnologi di Indonesia) International Journal of Remote Sensing and Earth Sciences (IJReSES) Indonesian Journal of Electrical Engineering and Computer Science
Tri Muji Susantoro, Tri Muji
Bidang Penginderaan Jauh PPPTMGB LEMIGAS
Biochemistry, Genetics & Molecular Biology Earth & Planetary Sciences Environmental Science

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Kontaminasi Logam Berat di Kawasan Pesisir Tanjung Selor Kalimantan Utara Susantoro, Tri Muji; Andayani, Ariani
Oseanologi dan Limnologi di Indonesia Vol 4, No 1 (2019)
Publisher : Indonesian Institute of Sciences

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (3221.714 KB) | DOI: 10.14203/oldi.2019.v4i1.181

Abstract

Terbentuknya Provinsi Kalimantan Utara mengubah wilayah Tanjung Selor dan sekitarnya menjadi ibukota provinsi, sehingga akan berkembang pesat, baik dari segi pembangunan maupun aktivitas lainnya. Monitoring kondisi lingkungan, salah satunya logam berat di perairan perlu dilakukan sebagai rona awal lingkungan sebelum wilayah tersebut berkembang. Hal ini penting dilakukan mengingat sifat logam berat yang berubah toksik pada konsentrasi yang melebihi ambang batas. Tujuan kajian ini untuk mengidentifikasi potensi adanya kontaminasi logam berat pada wilayah pesisir Tanjung Selor, Kabupaten Bulungan, Provinsi Kalimantan Utara. Logam berat yang dikaji dibatasi pada air raksa (Hg), kromium (Cr), arsen (As), kadmium (Cd), tembaga (Cu), timbal (Pb) dan seng (Zn). Penentuan lokasi sampling dilakukan menggunakan citra Landsat 8 yang dirancang agar dapat mewakili kondisi kawasan pesisir tersebut. Sampel berasal dari air sumur, air sungai, air laut dan sedimen laut pada masing-masing empat, sembilan dan lima stasiun pengamatan. Sampel diambil pada bulan Agustus 2018 dengan metode grab sample (sampel sesaat) dan dianalisis kandungan logam beratnya menggunakan metode standar American Public Health Association (APHA) dengan instrumen Atomic Absorption Spectroscopy (AAS). Dari 23 sampel yang dihasilkan, hanya pada tiga stasiun pengamatan yang tidak terkontaminasi logam berat. Adapun pada 20 sampel lainnya ditemui satu hingga tiga jenis logam berat yang melebihi ambang batas. Secara umum Cu merupakan logam terbanyak yang terdeteksi melebihi ambang batas pada daerah kajian yang dijumpai di sampel air sungai, air laut, dan sedimen. Zn ditemui melebihi ambang batas pada air sumur. Pb dijumpai melebihi ambang batas pada sampel lima air sungai. Cd dijumpai melebihi ambang batas pada sampel air sungai, air laut dan sedimen. Sumber pencemaran diduga berasal dari aktivitas pertambangan batubara, kebun kelapa sawit dan sampah rumah tangga. Keseluruhan hasil kajian ini menunjukkan bahwa muara sungai cenderung mengakumulasi logam berat.
MONITORING OF MANGROVE GROWTH AND COASTAL CHANGES ON THE NORTH COAST OF BREBES, CENTRAL JAVA, USING LANDSAT DATA Susantoro, Tri Muji; Wikantika, Ketut; Yayusman, Lissa Fajri; Tan, Alex; Ghozali, M. Firman
International Journal of Remote Sensing and Earth Sciences (IJReSES) Vol 16, No 2 (2019)
Publisher : National Institute of Aeronautics and Space of Indonesia (LAPAN)

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (2075.632 KB) | DOI: 10.30536/j.ijreses.2019.v16.a3221

Abstract

Severe abrasion occurred in the coastal area of Brebes Regency, Central Java between 1985 and 1995. Since 1997, mangroves have been planted around the location as a measure intended to prevent further abrasion. Between 1996 and 2018, monitoring has been carried out to assess coastal change in the area and the growth and development of the mangroves. This study aims to monitor mangrove growth and its impact on coastal area changes on the north coast of Brebes, Central Java Province using Landsat series data, which has previously proven suitable for wetland studies including mangrove growth and change. Monitoring of mangrove growth was analysed using the normalised difference vegetation index (NDVI) and the green normalised difference vegetation index (GNDVI) of the Landsat data, while the coastal change was analysed based on the overlaying of shoreline maps. Visual field observations of WorldView 2 images were conducted to validate the NDVI and GNDVI results. It was identified from these data that the mangroves had developed well during the monitoring period. The NDVI results showed that the total mangrove area increased between 1996 and 2018 about 9.82 km2, while the GNDVI showed an increase of 3.20 km2. Analysis of coastal changes showed that the accretion area about 9.17 km2 from 1996 to 2018, while the abrasion being dominant to the west of the Pemali River delta about 4.81 km2. It is expected that the results of this study could be used by government and local communities in taking further preventative actions and for sustainable development planning for coastal areas.
Geographic information system-based approaches for evaluating CO2 storage in Kalimantan basins, Indonesia Susantoro, Tri Muji; Sugihardjo, Sugihardjo; Suliantara, Suliantara; Widarsono, Bambang; Usman, Usman; Setiawan, Herru Lastiadi; Romli, Mohamad; Sukarno, Panca W.; Nurkamelia, Nurkamelia; Suhartono, Rudi
Indonesian Journal of Electrical Engineering and Computer Science Vol 38, No 2: May 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v38.i2.pp904-914

Abstract

To achieve the energy transition towards more environmentally friendly energy, various approaches must be taken, one of which is CO2 source-to-sink matching. A basin evaluation study has been carried out through classifying, weighting, and scoring in the geographic information system (GIS) for screening and ranking basins for CO2 storage on the island of Kalimantan, Indonesia. The region covers 13 sedimentary basins that have the potential to serve as CO2 sinks. As many as 21 parameters have been analyzed through classification and weighting using a pairwise comparison matrix method to produce scores and ranks for each basin. The results show that the Kutai, Tarakan, and Barito basins are the top three basins for CO2 storage potential. Singkawang, Nangapinoh, Pangkalanbun Utara, and Embaluh Selatan basins have been found to have the lowest sink potential.
Hyperparameter Tuning on Machine Learning-Based Landslide Susceptibility Mapping (Case study: Palu City and Its Surrounding areas) Sukristiyanti, Sukristiyanti; Pamela, Pamela; Putra, Moch Hilmi Zaenal; Arifianti, Yukni; Rozie, Andri Fachrur; Lestiana, Hilda; Susantoro, Tri Muji; Sumaryono, Sumaryono; Kristiawan, Yohandi; Putra, Iqbal Eras
Indonesian Journal on Geoscience Vol. 12 No. 1 (2025)
Publisher : Geological Agency

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.17014/ijog.12.1.43-53

Abstract

Landslide susceptibility mapping (LSM) produces a zonation map of landslide susceptibility levels, representing the future probability of landslides. It is necessary to give a guideline regarding spatial planning. A machine learning method was used, namely a random forest (RF) algorithm to map landslide susceptibility in Python. The case study is Palu City and its surrounding areas, which were attacked by a big earthquake on September 28th, 2018. Some earlier LSM studies did not discuss hyperparameter tuning, and several others did not mention the training accuracy. Therefore, this study is to find out whether the fast model without hyperparameter tuning and frequently overfitting, can well produce landslide susceptibility maps. The research questions were addressed by comparing two landslide susceptibility maps built with and without hyperparameter tuning using receiver operating characteristics (ROC) and landslide density (LD) analyses. This study shows that the area under the curve (AUC) of the landslide susceptibility map from the fast RF model without hyperparameter tuning is as high as the AUC from the tuned model map. It also happened in both landslide density (LD) maps, and there is no anomaly in the fast model map. Nevertheless, there are strange appearances in the fast model map. Therefore, hyperparameter tuning to obtain the optimal model with no overfitting is mandatory to predict landslide susceptibility spatially.
Co-Authors Agung Budi Harto Andayani, Ariani Bambang Widarsono Chairil Anwar Crystiana, Indah Crystiana, Indah Desrina, R Firdaus, Nurus Firdaus, Nurus Ghozali, M. Firman Hadimuljono, Jonathan Setyoko Hilda Lestiana Julikah, Julikah Ketut Wikantika Kristiawan, Yohandi Matahelemual, Godfried Junio Sebastian Nurkamelia, Nurkamelia Pamela, Pamela Prayoga, Ongki Ari Putra, Iqbal Eras Putra, Moch Hilmi Zaenal Romli, Mohamad Rozie, Andri Fachrur Setiawan, Herru Lastiadi Sugihardjo Sugihardjo Suhartono, Rudi Sukarno, Panca W. Sukristiyanti Sukristiyanti, Sukristiyanti Suliantara, Suliantara Sumaryono Sumaryono Sunardjanto, Djoko Tan, Alex Usman Usman Yayusman, Lissa Fajri Yensusminar, Desi Yukni Arifianti, Yukni