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Effect of substitution of coconut coir waste on the compressive strength of non-structural concrete Imam Heru Nurfais; Nur Azizah Affandy; Salwa Nabilah
Jurnal Teknika Vol 17, No 2 (2021): Available Online in November 2021
Publisher : Faculty of Engineering, Universitas Sultan Ageng Tirtayasa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36055/tjst.v17i2.11449

Using coconut fiber waste (LSK) in concrete mixes has good prospects because Indonesia has abundant natural resources. The addition of this fiber aims to improve the properties of concrete. This study aims to determine the effect of adding coconut fiber waste (LSK) as a substitute for coarse aggregate on the compressive strength of non-structural concrete and utilizing coconut fiber waste to reduce the accumulation of coconut waste and increase the selling value of coconut fibers. Making concrete mixtures with standard SNI 03-2834-2000, concrete compressive strength f'c 16.9 MPa concrete age 28 days, with 4 variations of the mixture, namely LSK 0%, LSK 0.5%, LSK 1%, LSK 1.5%, and LSK 2%. The test object is cylindrical with a diameter of 15 cm and a height of 30 cm, with 30 specimens. According to the findings of this study, the addition of coco fiber reduced the compressive strength of the concrete. In the compressive strength test of concrete, aged seven days and 28 days, the highest average compressive strength values of LSK 0 were 14.72 MPa and 21.62 MPa. Of the three variations of LSK, LSK 0.5 and LSK 1 are closest to normal concrete. The relationship between the uses of LSK on the compressive strength of concrete at the age of 28 days. Its effect on the decrease in compressive strength occurs in LSK 0.5, LSK 1, LSK 1.5, and LSK 2. The equation obtained from the linear regression equation y = -4.764+19.88 with a value of R2 = 0.827. The results show that the coconut fiber mixture (LSK) cannot provide a compressive strength of concrete that exceeds the compressive strength of normal concrete. Penggunaan limbah sabut kelapa (LSK) dalam campuran beton memiliki prospek yang bagus karena Indonesia memiliki sumber daya alam yang melimpah. Penambahan serat ini bertujuan untuk memperbaiki sifat-sifat beton. Penelitian ini bertujuan untuk mengetahui pengaruh penambahan limbah serat sabut kelapa (LSK) sebagai subtitusi agregat kasar terhadap kuat tekan beton non-struktural, dan memanfaatkan limbah sabut kelapa sehingga bisa mengurangi penumpukan limbah kelapa dan menambah nilai jual dari sabut kelapa. Pembuatan campuran beton dengan standar SNI 03-2834-2000, kuat tekan beton f’c 16,9 MPa usia beton 28 hari, dengan 4 variasi campuran, yaitu LSK 0%, LSK 0,5%, LSK 1%, LSK 1,5% dan LSK 2%. Benda uji berbentuk silinder dengan diameter 15 cm dan tinggi 30 cm, dengan jumlah benda uji 30 buah. Hasil dari penelitian ini adalah penambahan serat sabut kelapa mengakibatkan terjadinya penurunan kuat tekan pada beton. Pada uji kuat tekan beton umur 7 hari dan 28 hari nilai kuat tekan rata-rata tertinggi LSK 0 adalah 14,72 MPa, dan 21,62 Mpa. Dari ketiga variasi LSK, LSK 0,5 dan LSK 1 yang paling mendekati beton normalnya. Hubungan penggunaan LSK terhadap kuat tekan beton pada umur 28 hari pengaruhnya pada penurunan kuat tekan yang terjadi pada LSK 0,5, LSK 1, LSK 1,5 dan LSK 2. Persamaan yang didapat dari persamaan regresi linear y = -4,764+19,88 dengan nilai R2 = 0,827. Hal ini menunjukkan bahwa campuran sabut kelapa (LSK) tidak bisa memberikan kuat tekan beton melebihi kuat tekan beton normal.

Aplikasi Penggunaan Sistem Informasi Geografis (SIG) Dalam Pemetaan Potensi di Desa Pataan Irfan Falih; Salwa Nabilah
Journal of Science Nusantara Vol 1 No 1 (2021): September 2021
Publisher : LPPM Universitas Nahdlatul Ulama Blitar

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (735.842 KB)

Pataan adalah desa yang bertempat di Kecamatan Sambeng Kabupaten Lamongan dengan area seluas 97302,13 Ha. Desa ini akan diproyeksikan menjadi desa wisata, maka diperlukan data potensi dari desa tersebut. Dengan pemanfataan teknologi Sistem Informasi Geografis (SIG) yang merupakan rekaman objek keruangan dijadikan data nonspasial dalam pembuatan peta. Metode yang dipakai dalam pembuatan peta ini yaitu dengan menggabungkan data nonspasial dan data spasial menggunakan aplikasi ArcGIS yang akan memudahkan dalam pembuatan peta potensi desa Pataan. Penyajian data sudah diatur dalam peraturan kepala Badan Informasi Geospasial (BIG) No. 3 Tahun 2016 tentang Spesifikasi Teknis Penyajian Peta Desa bahwa untuk mendukung penyelanggaraan informasi geospasial tematik terkait pembuatan peta desa yang akurat, terintegrasi dan dapat dipertanggungjawabkan maka diperlukan spesifikasi teknis yang sesuai peraturan tersebut

Reducing the Risk of Flood Disasters in Lamongan Regency Using the Geographic Information System (GIS) Salwa Nabilah; Nur Azizah Affandy; N. Anwar; M. A. Maulana; N. Nurwatik
U Karst Vol 5, No 2 (2021): NOVEMBER
Publisher : Kadiri University

Flood disasters cause negative impacts, such as damage to facilities to the onset of fatalities. Reducing the risk of flooding needs to be done to reduce the impact caused by this disaster. Lamongan Regency is one of the regencies in East Java affected by floods every year in most of its areas. This study aims to reduce the risk caused by flooding by using GIS (Geographic Information System). Mitigation is done by determining areas with a high potential risk of being affected by flooding. The study used spatial analysis functions in ArcGIS. Supporting variables used rainfall, land cover, slope, soil texture, and watershed area, and it becomes important in determining flood-prone areas. From the results, the largest soil classification is the Kpl soil type. Litosol Gray Grumosol, The wide distribution of rainfall from 1500-1750 mm has the widest distribution is 66,67 ha. The slope of 0-8% has the widest distribution of 92,257 ha, making Lamongan a very vulnerable high flood area. Laren District is the District with the greatest flood potential, and Irrigated Field is the dominant land cover type affected by the flood. With the flood disaster map generated from this research, local governments can seek prevention in areas with high flood potential. They can carry out socialization based on disaster mitigation, especially for districts with potential flooding.

Socialize the Biopore Modification and Utilization as a Composting Media and Disaster Mitigation Efforts in Blawi Village, Karangbinangun, Lamongan Marsha Savira Agatha Putri; Nur Lathifah Syakbanah; Nur Azizah Affandy; Salwa Nabilah; Adisti Risma Putri Azzahra Arismaya
ABDIMAS: Jurnal Pengabdian Masyarakat Vol. 5 No. 2 (2022): ABDIMAS UMTAS: Jurnal Pengabdian Kepada Masyarakat
Publisher : LPPM Universitas Muhammadiyah Tasikmalaya

Blawi is a village where geographically located near the Bengawan Solo River crosses Lamongan Regency. During the wet season, Blawi Village often experiences flooding, however the contrary during the dry season it often experiences drought. Thus, with the background of these problems, it is necessary to carry out outreach the “Socialization and Utilization of Biopori with modification of Giving Effective Microorganism 4 (EM4)" to overcome the society problems in Blawi. This activity was held on Saturday, July 23, 2022 at 18.00 WIB until it is finished at the Blawi Village Hall, Karangbinangun District, Lamongan Regency. The event started with the opening, counseling, focus group discussion and question and answer sessions, until the closing. The participants of this activity were Blawi residents along with Karangtaruna and women society called PKK who enthusiastically participated in this activity. The counseling activity went smoothly and was well implemented, and it is hoped that similar activities can be carried out in the future so that collaboration between the academic community of the Faculty of Health Sciences, Universitas Islam Lamongan and the Blawi Village Government.

Reducing the Risk of Flood Disasters in Lamongan Regency Using the Geographic Information System (GIS) Salwa Nabilah; Nur Azizah Affandy; N. Anwar; M. A. Maulana; N. Nurwatik
U Karst Vol. 5 No. 2 (2021): NOVEMBER
Publisher : Kadiri University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30737/ukarst.v5i2.2079

Flood disasters cause negative impacts, such as damage to facilities to the onset of fatalities. Reducing the risk of flooding needs to be done to reduce the impact caused by this disaster. Lamongan Regency is one of the regencies in East Java affected by floods every year in most of its areas. This study aims to reduce the risk caused by flooding by using GIS (Geographic Information System). Mitigation is done by determining areas with a high potential risk of being affected by flooding. The study used spatial analysis functions in ArcGIS. Supporting variables used rainfall, land cover, slope, soil texture, and watershed area, and it becomes important in determining flood-prone areas. From the results, the largest soil classification is the Kpl soil type. Litosol Gray Grumosol, The wide distribution of rainfall from 1500-1750 mm has the widest distribution is 66,67 ha. The slope of 0-8% has the widest distribution of 92,257 ha, making Lamongan a very vulnerable high flood area. Laren District is the District with the greatest flood potential, and Irrigated Field is the dominant land cover type affected by the flood. With the flood disaster map generated from this research, local governments can seek prevention in areas with high flood potential. They can carry out socialization based on disaster mitigation, especially for districts with potential flooding.

Analisis Peta Kekeringan Lahan Pertanian Di Kabupaten Lamongan Menggunakan NDDI (Normalized Difference Drought Index) Nabilah, Salwa; Faroh, Rifky Aisyatul; Affandy, Nur Azizah; Nisa, Tika Ziadhatin; Wijaya, Bara Ramadani
Jurnal Ilmiah MITSU (Media Informasi Teknik Sipil Universitas Wiraraja) Vol 13 No 1 (2025): Jurnal Ilmiah MITSU
Publisher : Fakultas Teknik, Universitas Wiraraja

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24929/ft.v13i1.3761

Agricultural land is a natural resource that plays an important role in the continuity of human life. Lamongan is one of the regencies with vast agricultural land, around 80% of the Lamongan Regency area is agricultural land consisting of rice fields and non-rice fields. However, every year Lamongan is hit by drought in several regions. Drought in Lamongan occurs on average around June - October. Several sub-districts in Lamongan are hit by drought, one of the causes of which is low rainfall during the dry season. This study used rainfall data and Landsat 8 satellite imagery with a time span of August - October 2019 - 2023. Landsat 8 imagery was adjusted to the months used, and image processing to obtain drought results in Lamongan Regency using the NDDI (Normalized Difference Drought Index). From the processing, the results obtained in October 2023 were that the area hit by severe drought was Sarirejo District with an area value of 32,490,000 m2 from the total area of Sarirejo District of 47,380,000 m2. Around 68.57% of the Sarirejo District area was hit by severe drought. This study aims to determine the area affected by drought in the Lamongan area. So it can be used for mitigation in future drought disasters.

Coastline segmentation on Landsat 8 OLI images using majority voting with deep learning models Nur Nafiiyah; Salwa Nabilah; Nur Azizah Affandy; Rifky Aisyatul Faroh; Esa Prakasa
International Journal of Informatics and Communication Technology (IJ-ICT) Vol 15, No 2: June 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijict.v15i2.pp588-596

Coastlines are highly dynamic due to both natural processes and anthropogenic factors, including global warming and sea level rise. Accurate coastline segmentation is essential for effective monitoring and management. Although previous studies have applied deep learning for coastline detection, many existing models still suffer from instability across scenes, blurred boundaries, and segmentation artifacts, indicating that model generalization remains a challenge. This study aims to develop a more robust coastline segmentation approach by introducing an automated majority voting strategy that integrates three deep learning models: ResNet50, ResNet18, and MobileNet-V2. Landsat 8 OLI imagery is used for training and testing. The Jaccard index results show that ResNet18, ResNet50, and MobileNet-V2 achieved scores of 0.96, 0.98, and 0.95 respectively, while the proposed majority voting method also achieved 0.98. Despite the producing a similar numerical score to the best individual model (ResNet50), the ensemble method improves segmentation consistency by reducing artifacts such as unwanted peripheral shapes and cracks within land areas. These findings demonstrate that combining multiple segmentation outputs yields more stable and reliable coastline detection than using single models. Future work will apply this approach to broader Indonesian coastal regions to further assess its generalizability across diverse shoreline conditions.

Sea Land Segmentation of East Java’s North Coast Using Landsat 9 and ResNet50 Nur Nafiiyah; Ilyas; Rifky Aisyatul Faroh; Salwa Nabilah; Nur Azizah Affandy
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 10 No 2 (2026): April 2026
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v10i2.7435

Coastal regions are among the most vulnerable ecosystems due to the combined impacts of natural processes and human activities. Climate change, population growth, and coastal development accelerate shoreline dynamics, increasing the need for accurate and efficient coastal monitoring. Satellite-based remote sensing, combined with deep learning techniques, provides a promising solution for large-scale and continuous shoreline analysis. This study proposes a deep learning–based approach for coastal land–sea segmentation using the ResNet50 architecture applied to Landsat 9 OLI imagery of the North Coast of East Java, Indonesia. The dataset consists of multispectral images processed into 224×224 pixel tiles, accompanied by manually generated ground truth segmentation maps. Two optimization strategies, Adam and Stochastic Gradient Descent (SGD), are evaluated to determine the most effective optimizer for improving segmentation performance. Experimental results demonstrate that the Adam optimizer outperforms SGD across multiple training epochs, achieving the highest segmentation accuracy with mean Intersection over Union (IoU) and Dice coefficient values of 0.888 and 0.934, respectively. These findings indicate that optimizer selection significantly influences the performance of ResNet50-based coastal segmentation. The proposed approach shows strong potential for supporting automated and large-scale coastal monitoring applications using medium-resolution satellite imagery.

Analisis peramalan data kekeringan lahan pertanian di kabupaten lamongan menggunakan metode Fuzzy Time Series-Markov Chain Rifky Aisyatul Faroh; Salwa Nabilah; Nur Azizah Affandy; Nur Nafi'iyah; Muhammad Said
AKSIOMA: Jurnal Matematika dan Pendidikan Matematika Vol. 16 No. 2 (2025): AKSIOMA: Jurnal Matematika dan Pendidikan Matematika
Publisher : Universitas PGRI Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26877/bd1eqb34

The agricultural sector in Lamongan Regency is severely affected by the threat of drought, potentially affecting food production and farmers' welfare. This study aims to forecast the area of agricultural drought with a very severe category in Lamongan Regency using the Fuzzy Time Series-Markov Chain (FTS-MC) method. The data used includes the average drought area based on the processing of Landsat 8 imagery from 2020–2024, rainfall data, and historical drought data from the Regional Disaster Management Agency. The selection of the FTS-MC method is based on its strength in dealing with uncertain data and modeling drought condition transitions probabilistically. The forecasting results show that the pattern produced by FTS-MC closely follows the actual pattern, with good accuracy indicated by a MAPE value of 13.25%. The forecast result for January 2025 incerased 925,421.43 m². This finding is expected to serve as a reference for local governments and other stakeholders in planning drought mitigation, irrigation management, and more adaptive, data-driven food security strategies.

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