Article Per Year (5 Year)
p-Index From 2021 - 2026
10.492
P-Index
This Author published in this journals
All Journal JURNAL ILMIAH PENDIDIKAN FISIKA AL BIRUNI Teknisia JOIV : International Journal on Informatics Visualization Jurnal Qua Teknika KURVATEK Aviation Electronics, Information Technology, Telecommunications, Electricals, and Controls (AVITEC) G-Tech : Jurnal Teknologi Terapan DEVICE Indonesian Community Journal Aptekmas : Jurnal Pengabdian Kepada Masyarakat JITU (Jurnal Ilmiah Teknik Unida) International Journal of Hydrological and Environmental for Sustainability Journal of Community Service JOMPA ABDI: Jurnal Pengabdian Masyarakat Jurnal Ekonomi dan Bisnis JUSTER: Jurnal Sains dan Terapan STORAGE: Jurnal Ilmiah Teknik dan Ilmu Komputer Prosiding Seminar Nasional Rekayasa Teknologi Industri dan Informasi ReTII Sewagati Jurnal Pengabdian Masyarakat Bangsa GEMBIRA (Pengabdian Kepada Masyarakat) Masyarakat: Jurnal Pengabdian Jurnal Gerakan Edukasi dan Masyarakat Nusantara (GEMANUSA)
Sely Novita Sari
Institut Teknologi Nasional Yogyakarta
Religion Aerospace Engineering Agriculture, Biological Sciences & Forestry Arts Humanities Astronomy Automotive Engineering Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Immunology & microbiology Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Medicine & Pharmacology Neuroscience Nursing Physics Public Health Social Sciences Transportation Veterinary Other

Published : 88 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 88 Documents
Search

 5   6   7   8   9 
Implementation of Artificial Neural Network (ANN) for identifying design indicators of temporary modular shelters Sari, Sely Novita; Sarwidi; Nugraheni, Fitri; Musyafa', Albani
Teknisia Vol 30 No 2 (2025): Teknisia
Publisher : Jurusan Teknik Sipil, Fakultas Teknik Sipil dan Perencanaan, Universitas Islam Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20885/teknisia.vol30.iss2.art6

Abstract

The demand for fast, efficient, and adaptive emergency housing continues to increase, especially in disaster-prone areas and large-scale displacement situations. The determination of the design of Temporary Modular Shelter (TMS) so far still depends a lot on subjective considerations, so a more systematic and data-based approach is needed. This study develops and validates an Artificial Neural Network (ANN) model to identify the most suitable TMS design based on performance indicators and expert assessment. The approach was carried out through the Systematic Literature Review (SLR) stage, the determination of eight key design indicators, and assessment by 150 multidisciplinary respondents. The ANN model was built using a dense four-layer architecture with a total of 1,780 parameters and trained for 400 epochs using the TensorFlow and Keras libraries. The results showed a validation accuracy of 96% and a macro F1-score of 0,9146, indicating the stability and reliability of the model. Analysis of the contribution of features with the SHAP method revealed that the indicators of assembly methods, availability of human resources, and availability of local materials had the greatest influence on the classification results. This model has proven to be effective as a decision support system that is able to increase objectivity and efficiency in the TMS design process. Further development is suggested through integration into web-based digital platforms or mobile applications to support rapid and adaptive emergency response planning.
Uji Pasar Produk Eco enzyme Berbasis IoT sebagai Inovasi Pengelolaan Sampah Organik di Rumah Sampah Ringas Trengginas, Bantul, Yogyakarta Bagus Gilang Pratama; Sely Novita Sari; Oni Yuliani; Nanda Ramadhani; Ilham Nawawi; Silfia Dwi Putri
I-Com: Indonesian Community Journal Vol 5 No 3 (2025): I-Com: Indonesian Community Journal (September 2025)
Publisher : Fakultas Sains Dan Teknologi, Universitas Raden Rahmat Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70609/i-com.v5i3.7687

Abstract

Produk eco enzyme merupakan hasil fermentasi limbah organik yang ramah lingkungan dan multifungsi, seperti untuk pembersih alami, pupuk cair, dan pengusir serangga. Kegiatan pengabdian ini bertujuan untuk mendukung penguatan kapasitas ekonomi masyarakat melalui uji pasar produk eco enzyme berbasis IoT yang dikembangkan oleh Rumah Sampah Ringas Trengginas di Bantul, Yogyakarta. Produk ini dinilai potensial, namun belum memiliki strategi pemasaran berbasis data. Metode pelaksanaan meliputi penyebaran kuesioner, distribusi sampel, wawancara, dan simulasi penjualan terbatas kepada 150 responden dari lima segmen pasar. Hasil menunjukkan respons positif dari konsumen, dengan tingkat ketertarikan tinggi dan potensi pembelian ulang yang signifikan. Temuan ini menjadi dasar penyusunan strategi pemasaran awal yang dapat diterapkan mitra secara mandiri. Hasil pengabdian ini penting sebagai langkah awal dalam memperluas pasar produk berbasis lingkungan dan teknologi, serta mendukung pemberdayaan ekonomi lokal yang berkelanjutan.
Penerapan Standar Operasional Prosedur (SOP) dalam Produksi Eco Enzyme sebagai Upaya Penguatan Kapasitas Mitra Pengelola Sampah Rivan Muhfidin; Sely Novita Sari; Ratna Kartikasari; Yulius Wijanarko; Khairul Mahbubi
I-Com: Indonesian Community Journal Vol 5 No 3 (2025): I-Com: Indonesian Community Journal (September 2025)
Publisher : Fakultas Sains Dan Teknologi, Universitas Raden Rahmat Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70609/i-com.v5i3.7732

Abstract

The Mukti Jaya Waste Management Group faced irregularities in eco enzyme production, resulting in low product quality and inefficiency. This community engagement aimed to enhance the group's technical and managerial capacity through the implementation of Standard Operating Procedures (SOPs). The methods included initial observation, participatory SOP development, hands-on technical training, and implementation mentoring. Results showed an increase in production volume from 12 to 20 liters per batch, a 40% improvement in time efficiency, and a rise in active member participation from 44.4% to 84.4%. Understanding of standardized procedures also improved from a score of 2.7 to 4.4. Evaluations using questionnaires and field observations confirmed enhanced technical and organizational capabilities. Additional impacts included the adoption of production and financial recording systems, the development of a collective work culture, and increased environmental awareness. SOPs proved to be not only a technical guide but also a community empowerment tool promoting efficiency, self-reliance, and sustainability in organic waste management.
Flood Modeling of Telomoyo Watershed in Kebumen Using HEC-RAS for Mapping Flood Risk Zones Khalid S Ridwan; Sely Novita Sari; Anggi Hermawan
G-Tech: Jurnal Teknologi Terapan Vol 9 No 2 (2025): G-Tech, Vol. 9 No. 2 April 2025
Publisher : Universitas Islam Raden Rahmat, Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70609/gtech.v9i2.6801

Abstract

Flooding is a frequent natural disaster in Indonesia, especially in areas with low topography and high rainfall. The Telomoyo watershed in Kebumen Regency, with an area of 553.22 km², faces a high risk of flooding even though its water flow is controlled by the Sempor Reservoir. Geographical conditions, topography and high rainfall in the upstream area often cause inundation in the downstream area. This study modeled the flood potential in the Telomoyo watershed using HEC-RAS software, based on rainfall data and a Digital Elevation Model (DEM). The methodology includes hydrological analysis to determine peak discharge using the Nakayasu Synthetic Unit Hydrograph (HSS) method and hydraulic analysis to map flood-prone areas. The results showed that the combination of rainfall and topographic data resulted in accurate mapping of flood-prone areas. The peak discharge of the 25-year return period reached 986.177 m³/second, with inundation up to a depth of more than 3 meters in certain areas. Integration of satellite rainfall data from NASA's Giovanni with 2D hydraulics modelling based on HEC-RAS, which produced flood risk maps for the Telomoyo watershed with a high level of accuracy that was previously under-explored. The outcome of this research for flood management in the Telomoyo watershed is the provision of a scientific basis for more effective mitigation strategies, such as strengthening drainage infrastructure, and risk zone-based spatial planning. This information can be used as a basis for more effective flood mitigation planning and risk management in the Telomoyo watershed area.
Planning Analysis of the Upper Structure of the Disaster Logistics Warehouse of BPBD Bantul Regency with SAP2000 Adam Sulton; Sely Novita Sari; Rizal Maulana
G-Tech: Jurnal Teknologi Terapan Vol 9 No 2 (2025): G-Tech, Vol. 9 No. 2 April 2025
Publisher : Universitas Islam Raden Rahmat, Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70609/gtech.v9i2.6802

Abstract

Indonesia is a country that is very vulnerable to natural disasters, especially earthquakes, because of its position in the Pacific Ring of Fire region. Bantul Regency, Yogyakarta, is one of the earthquake-prone areas that requires disaster-resistant infrastructure, including disaster logistics warehouses as emergency logistics storage and distribution centers. This study aims to analyze the upper structure of the disaster logistics warehouse of BPBD Bantul Regency to be more optimal and earthquake-resistant using SAP2000 software. The secondary data used is technical data ranging from soil type, earthquake zone, dead load, live load, and earthquake load, which are analyzed according to SNI 1726:2019 and SNI 1729:2020 standards. The results showed that the steel profiles used were IWF 350x175x11x7 for the rafter, IWF 200x150x9x6 for front and rear regels, IWF 250x125x9x6 for side regels, and HB 300x300x18x10 for columns. It was found that the regel was unstable due to the failure to withstand lateral loads, but overall, the upper structure of the warehouse worked optimally according to the results of the check design and steel stress check.
Application of Genetic Algorithm Neural Network in Identifying Buildings in Landslide-Prone Areas Bagus Gilang Pratama; Sely Novita Sari; Joko Prasojo
G-Tech: Jurnal Teknologi Terapan Vol 9 No 3 (2025): G-Tech, Vol. 9 No. 3 July 2025
Publisher : Universitas Islam Raden Rahmat, Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70609/g-tech.v9i3.7168

Abstract

Indonesia is a disaster-prone country, one of which is landslides, which often occur in hilly areas with high rainfall. The impact damages the environment and infrastructure, especially buildings. For effective mitigation, a risk identification system based on artificial intelligence technology is needed. This study applies Genetic Algorithm Neural Network (GANN) in identifying buildings in landslide-prone areas. GANN was chosen for its ability to optimize network weights globally through selection, crossover, and mutation mechanisms, thus avoiding suboptimal local solutions. The dataset consists of 169 data with 12 structural features of the building. The model was configured with genetic parameters such as the number of generations 500, population size of 50, mutation rate of 10%, and the Stochastic Universal Sampling selection method. To Evaluate the performance of model created from dataset, we employed accuracy, precision, recall, and F1-score. The results showed an accuracy of 81% and an average F1-score of 0.82, with the best performance in the "Unsafe" class (recall 0.84). Although it still needs improvement, GANN has proven to have the potential as a decision support tool in data-driven landslide risk mitigation.
Flood Modeling Analysis of the Cokroyasan Watershed in Bayan District, Purworejo Regency Using HEC-RAS Software Muhammad Rizky Fajar; Sely Novita Sari; Anggi Hermawan
G-Tech: Jurnal Teknologi Terapan Vol 9 No 3 (2025): G-Tech, Vol. 9 No. 3 July 2025
Publisher : Universitas Islam Raden Rahmat, Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70609/g-tech.v9i3.7393

Abstract

Floods are a frequent natural disaster in Indonesia. Heavy rainfall and poor watershed management make the Cokroyasan River Basin (DAS), Bayan District, Purworejo Regency, especially susceptible. The purpose of this study is to analyze the flood inundation area and calculate the peak discharge in the Cokroyasan Watershed using satellite rainfall data and the Digital Elevation Model (DEM). The study calculates the highest water flow during floods using the Nakayasu Synthetic Unit Hydrograph (HSS) method and examines the water flow with HEC-RAS and ArcGIS to create maps showing areas that could flood. The results showed that, using the Log Pearson Type III distribution, the design rainfall for a 25-year return period with a flow coefficient of 0.288 was 117.6711 mm. The maximum flood discharge, as determined by the Nakayasu HSS, was 1684.028 m³/second. Hydraulic analysis was able to map the region of floods based on land cover, covering 16,200 km² in total. 4,961 km² were covered the low flood depth category (0–1 m), 3,175 km² the medium inundation (1–3 m), and 8,061 km² the high inundation (>3 m). The results of the simulation might lead to a more effective approach for managing and reducing the danger of flooding.
Quantitative Study on Iron Reinforcement Waste Utilization for Material Efficiency in Boarding House Structures in Bantul Rizal Maulana; Sely Novita Sari
G-Tech: Jurnal Teknologi Terapan Vol 9 No 4 (2025): G-Tech, Vol. 9 No. 4 October 2025
Publisher : Universitas Islam Raden Rahmat, Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70609/g-tech.v9i4.8083

Abstract

The construction sector is one of the largest consumers of material resources, with reinforcement bars (rebar) contributing significantly to project costs. However, standard cutting practices often generate leftover steel, typically regarded as waste, despite its potential for reuse. This study aims to evaluate the efficiency of reusing rebar waste in the structural components columns, beams, and slabs of a two-story boarding house project in Bantul, Indonesia. A quantitative case study method was applied, analyzing planned versus actual steel usage, and calculating the waste percentage compared to the 5% standard from Indonesia’s AHSP. Field data, including drawings, BoQ, and technical reports, were used to measure actual rebar waste and its financial impact. Results show actual waste levels of 2.17% for columns, 2.55% for beams, and 1.21% for slabs significantly lower than the 5% benchmark. This translates into a cost saving of Rp11,393,643.78 for a medium-scale project. These findings confirm that precise planning and reuse of steel offcuts can minimize waste and promote sustainable construction practices. It is recommended that future projects adopt material reuse strategies and integrate digital tools such as BIM to enhance real-time material tracking and cutting optimization.
Fast Building Identification Using Fuzzy Soft Set Based on Rapid Visual Building (RVS) Sely Novita Sari; Rizqi Prastowo; Iwan Tri Riyadi Yanto; Korhan Cengiz; Basak Ozyurt; Tuna Topac
International Journal of Hydrological and Environmental for Sustainability Vol. 1 No. 2 (2022): International Journal of Hydrological and Environmental for Sustainability
Publisher : CV FOUNDAE

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1249.556 KB) | DOI: 10.58524/ijhes.v1i2.87

Abstract

Building damage can be caused by disasters such as earthquakes, landslides, etc. To minimize the fatality, the identification of buildings is needed to know the condition of buildings and whether the construction of buildings is able to endure if the disasters happen. This research uses the Rapid Visual Building (RVS) method to identify the building condition. The data are collected from  Kalirejo, Kulon Progo. The survey is conducted by taking a simple building evaluation form (typical of the walls ) based on RVS data. The field assessment results are distinguished into several factors that affect the condition of typical building walls: the foundations, structures, walls, and roofs of the 11 categories on the assessment form. From the data obtained, it is used to classify the building condition using Fuzzy Soft Set. The results show that the classification has been made with good performance in terms of accuracy, precision and time response. The accuracy and recall are close to 100% with above 50% of prevision average and time response is quite 0.0051 second. Thus, it can be used to  predict the condition of buildings accurately.
Classification Based on Artificial Neural Network for Regency Road Maintenance Priority Bagus Gilang Pratama; Sely Novita Sari; Oni Yuliani
Aviation Electronics, Information Technology, Telecommunications, Electricals, and Controls (AVITEC) Vol 7, No 3 (2025): November (Special Issue)
Publisher : Institut Teknologi Dirgantara Adisutjipto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28989/avitec.v7i3.3056

Abstract

The priority classification of road maintenance is an important issue in regional infrastructure management. This study developed a classification model based on Artificial Neural Network (ANN) to determine the priority of district road maintenance automatically based on actual condition data. The data covered 141 road sections, reduced from 15 to 9 main variables using Principal Component Analysis (PCA), and normalized with the Min-Max Scaler. The ANN model consists of 10 input neurons, 30 hidden neurons, and 5 priority class outputs. The data is divided in a 55-15-35 ratio for training, validation, and testing. The model produces 92% accuracy, 91.7% accuracy, 90.4% recall, and 90.9% F1-score. These findings demonstrate the reliability of ANN in multi-class classifications to support more efficient road maintenance decision-making. The novelty lies in the integration of actual field data, multi-criteria classification, and the application of ANN in the context of complex and underexplored district roads in the literature.
Co-Authors Adam Sulton Afredo Tubur, Hasi Albani Musyafa Alfinur Insaniyati Umi Sa'adah Alwarizi, Fahrol Amir Machmud Amir Machmud, Amir Andary, Fauziah Andrea Sumarah Asih Andri Daeng Salimung Anggi Hermawan Annisa Fauziah Ardian, Oggi Heical Ariza Tiara Ramadhanti Astuti Umasugi Avon Budiono Bagus Gilang Pratama Bagus Gilang Pratama, Bagus Gilang Basak Ozyurt Bere, Gracensia Bismoko Rahadrian Suseno Chandra Wahyu Herbyanto Clara Anggreini Ines Benge Dandi Pramono Payungan Dandi Pranomo Darlahanus, David Dian Nurcahyani Dika, Resa Priya Do’o, Ricko Rivaldo Ruben Fahrul Nurfajri Mokoagow Fandanu Firdyan Syah Faturrahman Jahrun Trumpi Filipus Alfriyadi Junaidi Filipus Alfriyadi Junaidi Fitri Nugraheni Fitri Nugraheni Fitri Nugraheni Grito, Mortalesel Hadi Riswanto, Teguh Hafid, Anggun Abdul Ibnu Rianto Ilham Mopio Ilham Mopio Ilham Nawawi Ircham Iwan Tri Riyadi Yanto Iwan Tri Riyadi Yanto Jesika Dekrita Uan Joko Prasojo Joko Prasojo Kartika, Erawati Khairul Mahbubi Khalid S Ridwan Korhan Cengiz Kota, Reynaldus Sean Kristin Yunita Luna Amadea Maharani Sudjatmiko Mokoagow, Fahrul Nurfajri Muhammad Hanif Jufri Muhammad Rizky Fajar Mustafa Mat Deris Musyafa', Albani Mutiara Pasande Surugallang Nanda Ramadhani Nanda Ramadhani nico siliansyah Norhalina Senan Nurcahyani, Dian Oggi Heical Ardian Oggi Heicqal Ardian Oni Yuliani Oni Yuliani Payungan, Dandi Pramono Putri Jea, Maria Carvallo Rahmad Junaidi, Rahmad Rahmatullah Gafar kahar Ramadhani, Fauziah ratih dwi indrajad ratih Ratna Kartikasari Rd. Rohmat Saedudin Regita Gusttriana Ricko Rivaldo Ruben Do’o Ridayati Ridayati Rizal Maulana Rizal Maulana Rizal Maulana, Rizal Rizky Tri Astuti Rizqi Prastowo Sa’adah, Alfinur Insaniyati Umi Sabila, Yusrina Nur Amalia Sabrina Putri Puspitasari Sarwidi Sarwidi Setya Winarno sianturi, faldi daud suiyoso Silfia Dwi Putri Siswahyudianto Sogar, Aris Umbu Soru Sophia Anwar Fayyal Jannah Sulton, Adam Syach Reza Fachlevi Syamsul Arifin Tedy Kurniawan Triwuryanto triwuryanto Triwuryanto Triwuryanto Tuna Topac Veronica Diana Anis Anggorowati wahyu anisa dwi bekti Yobel, Felix Yulius Wijanarko Zainul Arifin Zulkahhar Ariga