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All Journal Jurnal Pembangunan Wilayah dan Kota Grimsa Journal of Science Engineering and Technology Geoid - Journal of Geodesy and Geomatics
Iffaty, Athiya
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Agriculture, Biological Sciences & Forestry Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Earth & Planetary Sciences Engineering Environmental Science Medicine & Pharmacology Physics

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Enhancing Water Quality Assessment in Indonesia Through Digital Image Processing and Machine Learning Iffaty, Athiya; Salsabila, Adinda; Rafiqhi, Adis Aufa; Suhendra, Rivansyah; Yusuf, Muhammad; Sasmita, Novi Reandy
Grimsa Journal of Science Engineering and Technology Vol. 1 No. 1 (2023): October 2023
Publisher : Graha Primera Saintifika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61975/gjset.v1i1.3

Abstract

Indonesia's diverse climate types, influenced by its unique geographical features, pose significant environmental challenges, including water quality issues related to turbidity and Total Dissolved Solids (TDS). Many Indonesians lack awareness of water quality, particularly turbidity, which can harbor harmful microorganisms. To address these challenges, this study employs digital image processing and machine learning, specifically Support Vector Machine (SVM) algorithms, for water quality assessment. A dataset of 80 water images, categorized into seven turbidity classes, is used to train and test the model. Results show a clear correlation between turbidity levels and TDS concentrations and pH values. The system accurately assesses water suitability for different sources, offering a user-friendly and cost-effective solution for water quality monitoring in dynamic environmental conditions. However, limitations include the dataset size and the narrow focus on turbidity. Future research could expand to encompass a broader range of water quality factors. This approach holds promise for enhancing water quality management in Indonesia and similar regions.
Pengembangan Wisata Rintisan Berbasis Keunggulan Kompetitif di Desa Wisata Montongsari, Kabupaten Kendal Sunarti, Sunarti; Damayanti, Maya; Rahdriawan, Mardwi; Untari, Rustina; Iffaty, Athiya; Rahmadani, Shahwa
Jurnal Pembangunan Wilayah dan Kota Vol 21, No 1 (2025): JPWK Volume 21 No. 1 March 2025
Publisher : Universitas Diponegoro Publishing Group, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/pwk.v21i1.68072

Abstract

Desa wisata rintisan memiliki potensi besar dalam pengembangan pariwisata pedesaan di Indonesia. Keunggulan kompetitif menciptakan daya saing untuk dapat menarik lebih banyak wisatawan. Namun, Desa Montongsari, Kabupaten Kendal, Provinsi Jawa Tengah yang masih dalam tahap desa wisata rintisan menghadapi tantangan dalam memanfaatkan potensi yang dimiliki. Kapasitas sumber daya yang masih rendah, sulitnya perolehan sumber pembiayaan, dan belum adanya rencana tindak yang menjadi acuan realisasi wisata menjadi permasalahan dalam mengembangkan potensi di Desa Montongsari. Keberlanjutan wisata dapat tercapai dengan pendekatan partisipatif dan pengelolaan yang terstruktur. Tujuan penelitian adalah menganalisis keunggulan kompetitif melalui Community Based Tourism (CBT) dalam pengembangan desa wisata rintisan, Desa Montongsari, Kabupaten Kendal. Metode penelitian menggunakan pendekatan kualitatif melalui Focus Group Discussion (FGD) dengan tim ahli, perangkat desa, dan masyarakat Desa Montongsari. Hasil FGD selanjutnya dianalisis menggunakan teknik analisis deskriptif kualitatif untuk menghasilkan prioritas keunggulan kompetitif. Hasil penelitian menunjukkan bahwa dalam analisis keunggulan kompetitif, daya tarik berupa event menjadi prioritas utama bagi Desa Wisata Montongsari. Hal ini didukung oleh kesiapan sumber daya, pengelola, dan masyarakat. Kebaruan penelitian adalah peran CBT dalam proses menentukan keunggulan kompetitif dan tantangan yang dihadapi. Dengan demikian, penerapan CBT dalam keunggulan kompetitif di Desa Montongsari cenderung mengintegrasikan antara partisipasi masyarakat dalam pengembangan desa wisata rintisan di Desa Montongsari. 
Detection of River Change in Modeling Flood Vulnerability using Support Vector Machine (SVM) Methods in Tallo River Makassar City Izzaty, Atika; Aprian, Syahra Dewi; Wijayanti, Regita Faridatunisa; Iffaty, Athiya; Bakri, Bambang; Karamma, Riswal
GEOID Vol. 20 No. 2 (2025)
Publisher : Departemen Teknik Geomatika ITS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/geoid.v20i2.7536

Abstract

The transformation of river morphology and the rising frequency of flooding in urban environments have emerged as increasingly concerning environmental challenges, particularly in Makassar City. The Tallo River, one of the primary waterways traversing the city, exhibits notable dynamic changes driven by both natural processes. In the contemporary era, flooding stands as one of the most recurrent natural disasters, occurring unpredictably and posing serious risks, especially in major metropolitan areas. Such events frequently disrupt daily activities, leading to traffic congestion and obstructing ground transportation. Residential zones situated near riverbanks are particularly vulnerable to its impacts. Moreover, climate change exacerbates these conditions by contributing to increasing environmental unpredictability and need through a monitoring. The purpose of this research is to analyze river morphology changes and assess flood susceptibility in the Tallo River, Makassar City, using Support Vector Machine (SVM) classification methods. Approximately, there are 20% of the area experienced significant changes during 2018 in Tallo River. As water discharge continues to increase, the volume of water mass also rises accordingly. To detect the spatial distribution of flood vulnerability along the Tallo River, which flows through Makassar City, this study utilizes Land Use and Land Cover (LULC) data from 2017 and 2024. These datasets were classified using the Random Forest model, achieving accuracies of 0.89 and 0.87, respectively values that meet the standards for land use change accuracy. Flood vulnerability is also influenced by low elevation values, particularly areas below 0 meters, which are classified as wetland zones. In the Tallo River area, which is part of the Jeneberang Watershed, the dominant class is moderate flood vulnerability, covering approximately 138.48 hectares. Remote sensing technology combined with machine learning approaches especially supervised classification techniques widely used for both binary and multivariate classification tasks, demonstrating high accuracy in detecting and classifying flood vulnerability.
Co-Authors Aprian, Syahra Dewi Bambang Bakri Izzaty, Atika Mardwi Rahdriawan Maya Damayanti Muhammad Yusuf Rafiqhi, Adis Aufa Rahmadani, Shahwa Riswal Karamma Salsabila, Adinda Sasmita, Novi Reandy Suhendra, Rivansyah Sunarti Untari, Rustina Wijayanti, Regita Faridatunisa