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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, Controls (AVITEC) G-Tech : Jurnal Teknologi Terapan DEVICE Jurnal Penelitian Inovatif 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 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
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

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Application of Genetic Algorithm Neural Network in Identifying Buildings in Landslide-Prone Areas Pratama, Bagus Gilang; Sari, Sely Novita; Prasojo, Joko
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.
Rapid visual screening of building for potential ground movement in Kalirejo, Kulonprogo, Yogyakarta Sari, Sely Novita; Prastowo, Rizqi; Junaidi, Rahmad; Machmud, Amir
Jurnal Ilmiah Pendidikan Fisika Al-Biruni Vol 9 No 1 (2020): Jurnal Ilmiah Pendidikan Fisika Al-Biruni
Publisher : Universitas Islam Negeri Raden Intan Lampung, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24042/jipfalbiruni.v9i1.5190

Abstract

Landslides are the biggest threat in the Kalirejo area. The dynamics of land movements in the mountains often cause cracks and potentially collapse. Landslides due to land fractures caused building damage. This study aimed to analyze the condition of a simple building on the influence of land fracture. The method used was conducting a field survey of existing buildings in the Kalirejo area. The data of the surveys were the percentage of building damage and building categorization. From the results of the analysis, the percentages of buildings in the safe category were 78 buildings or 54.17%, the buildings of the unsafe category were 51 buildings or 35.42%, and buildings with the unsafe category were 15 buildings out of 144 surveyed building with the percentage of 10.42%. Based on the results of the analysis using the Rapid Visual Screening (RVS) method, 15 buildings with unsafe conditions need to be relocated because they do not use the minimum structure required for simple buildings while the 51 buildings with unsafe conditions, repairs must be made to the structure according to the minimum requirements of simple buildings
Identifikasi Karakteristik Desain Temporary Modular Shelter pada Bencana di Indonesia melalui Nvivo dan Review Literatur Sarwidi, Sarwidi; Nugraheni, Fitri; Musyafa, Albani; Sari, Sely Novita
Jurnal Penelitian Inovatif Vol 5 No 3 (2025): JUPIN Agustus 2025
Publisher : CV Firmos

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54082/jupin.1592

Abstract

Kurangnya pemahaman sistematis mengenai faktor-faktor utama yang memengaruhi desain Temporary Modular Shelter (TMS) dalam berbagai konteks kebencanaan menjadi tantangan dalam pengembangan hunian darurat yang efektif. Penelitian ini bertujuan untuk mengidentifikasi karakteristik desain TMS yang paling sering dibahas dalam literatur ilmiah internasional. Metode yang digunakan adalah systematic literature review terhadap 120 artikel yang diperoleh dari ScienceDirect, SpringerLink, dan Google Scholar. Artikel yang memenuhi kriteria inklusi dianalisis menggunakan perangkat lunak NVivo 12 melalui pendekatan thematic coding untuk mengevaluasi tema dan indikator yang paling dominan. Hasil penelitian menunjukkan bahwa kemudahan akses ke lokasi bencana (188), tingkat keterampilan tenaga kerja (178), dan logistik material (175) merupakan tiga tema dengan frekuensi tertinggi. Sebaliknya, dimensi bangunan hanya muncul sebanyak 120 kali. Temuan ini menegaskan bahwa aspek sumber daya manusia dan logistik lebih krusial dibandingkan spesifikasi teknis bangunan dalam konteks perancangan TMS. Penelitian ini berkontribusi terhadap pengembangan desain hunian darurat yang lebih adaptif, efisien, dan berbasis bukti dalam bidang teknik sipil dan perencanaan tanggap bencana.
Flood Modeling Analysis of the Cokroyasan Watershed in Bayan District, Purworejo Regency Using HEC-RAS Software Fajar, Muhammad Rizky; Sari, Sely Novita; Hermawan, Anggi
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.
Uji Pasar Produk Eco enzyme Berbasis IoT sebagai Inovasi Pengelolaan Sampah Organik di Rumah Sampah Ringas Trengginas, Bantul, Yogyakarta Pratama, Bagus Gilang; Sari, Sely Novita; Yuliani, Oni; Ramadhani, Nanda; Nawawi, Ilham; Putri, Silfia Dwi
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 Muhfidin, Rivan; Sari, Sely Novita; Kartikasari, Ratna; Wijanarko, Yulius; Mahbubi, Khairul
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.
Classification Based on Artificial Neural Network for Regency Road Maintenance Priority Pratama, Bagus Gilang; Sari, Sely Novita; Yuliani, Oni
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.
Quantitative Study on Iron Reinforcement Waste Utilization for Material Efficiency in Boarding House Structures in Bantul Maulana, Rizal; Sari, Sely Novita
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.
Pemodelan Artificial Neural Network (ANN) Untuk Identifikasi Bangunan Daerah Rawan Longsor Sari, Sely Novita; Pratama, Bagus Gilang; Prastowo, Rizqi
Device Vol 14 No 1 (2024): Mei
Publisher : Fakultas Teknik dan Ilmu Komputer (FASTIKOM) UNSIQ

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32699/device.v14i1.6701

Abstract

Identifikasi bangunan daerah rawan longsor adalah suatu hal yang penting dalam mitigasi bencana alam. Longsor dapat terjadi di mana saja dan kapan saja, dan dapat menyebabkan kerugian yang besar baik dari segi manusia maupun materiil (fadli dkk, 2023). Oleh karena itu, penting untuk mengidentifikasi bangunan yang berada di daerah rawan longsor agar dapat mengambil tindakan pencegahan yang tepat. Metode yang dapat digunakan untuk identifikasi bangunan di daerah rawan longsor adalah artificial neural network (ANN). ANN adalah suatu model komputasi yang terinspirasi dari sistem saraf biologis yang terdiri dari sejumlah unit pemrosesan sederhana yang disebut neuron. ANN dapat belajar dari data dan menyesuaikan bobot koneksi antar neuron untuk menghasilkan keluaran yang diinginkan. ANN memiliki kemampuan untuk mengenali pola, mengklasifikasikan data, dan memprediksi hasil. Berdasarkan analisis data klasifikasi dinding sederhana di Kecamatan Kalirejo, Kecamatan Kokap, Kabupaten Kulon Progo, Yogyakarta, menggunakan Artificial Neural Network (ANN), presentase prediksi setiap data dari pemodelan ANN menunjukkan bahwa indikator Bangunan Tidak Aman mencapai 100%, dengan 89% prediksi Bangunan Aman, dan 82,7% prediksi Bangunan Aman berdasarkan History Accuracy. Hasil tersebut diukur dengan merujuk pada kurva model validasi yang semakin meningkat dan stabil, mencapai nilai akurasi rata-rata di atas 80%, yakni sebesar 88%.
Kolaborasi Jaringan Saraf Tiruan (JST) Dalam Identifikasi Prioritas Penanganan Pemeliharaan Jalan Kabupaten Sari, Sely Novita; Pratama, Bagus Gilang; ircham, ircham
Device Vol 14 No 1 (2024): Mei
Publisher : Fakultas Teknik dan Ilmu Komputer (FASTIKOM) UNSIQ

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32699/device.v14i1.6702

Abstract

Pemeliharaan jalan kabupaten menjadi elemen krusial dalam pembangunan infrastruktur dan pertumbuhan ekonomi di daerah. Kendati begitu, keterbatasan anggaran dan sumber daya manusia menyebabkan banyak jalan kabupaten mengalami kerusakan yang memerlukan penanganan pemeliharaan yang efektif. Dalam rangka mengidentifikasi prioritas pemeliharaan, digunakan metode Jaringan Saraf Tiruan (JST), sebuah teknologi kecerdasan buatan yang mampu mempelajari pola dari data dan mengklasifikasikan informasi baru. JST dapat memproses data kompleks, non-linear, dan tidak pasti, sehingga cocok untuk estimasi biaya, peramalan, klasifikasi, dan optimasi. Hasil analisis data menggunakan JST menunjukkan tingkat akurasi prediksi Prioritas Mutlak Penting sebesar 100%, sementara untuk Prediksi Prioritas Sangat Penting, Prioritas Cukup Penting, dan Prioritas Sedikit Penting masing-masing mencapai 66,7%. Prediksi Prioritas Tidak Penting juga mencapai 100%, dengan menggunakan History Accuracy sebagai acuan. Dengan demikian, pemodelan ini memberikan presentase prediksi untuk setiap kategori prioritas pemeliharaan jalan kabupaten, memberikan dasar informasi yang berguna untuk pengambilan keputusan.
Co-Authors Afredo Tubur, Hasi Albani Musyafa, Albani Alfinur Insaniyati Umi Sa'adah Alwarizi, Fahrol Amir Machmud Amir Machmud, Amir Andary, Fauziah Andrea Sumarah Asih Andri Daeng Salimung Anggi Hermawan Ardian, Oggi Heical Ariza Tiara Ramadhanti Astuti Umasugi Avon Budiono Bagus Gilang Pratama, Bagus Gilang Bere, Gracensia Bismoko Rahadrian Suseno Cengiz, Korhan 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 Fajar, Muhammad Rizky Fandanu Firdyan Syah Faturrahman Jahrun Trumpi Filipus Alfriyadi Junaidi Filipus Alfriyadi Junaidi Fitri Nugraheni Fitri Nugraheni Grito, Mortalesel Hadi Riswanto, Teguh Hafid, Anggun Abdul Ilham Mopio Ilham Mopio Ircham Iwan Tri Riyadi Yanto Iwan Tri Riyadi Yanto, Iwan Tri Riyadi Jesika Dekrita Uan Joko Prasojo Kartika, Erawati Kota, Reynaldus Sean Kristin Yunita Mahbubi, Khairul Mokoagow, Fahrul Nurfajri Muhammad Hanif Jufri Mustafa Mat Deris Musyafa', Albani Mutiara Pasande Surugallang Nawawi, Ilham nico siliansyah Norhalina Senan Oggi Heical Ardian Oggi Heicqal Ardian Oni Yuliani Ozyurt, Basak Putri Jea, Maria Carvallo Putri, Silfia Dwi Rahmad Junaidi, Rahmad Rahmatullah Gafar kahar Ramadhani, Fauziah Ramadhani, Nanda ratih dwi indrajad ratih Ratna Kartikasari Rd. Rohmat Saedudin Ricko Rivaldo Ruben Do’o Ridayati Ridayati Ridwan, Khalid S Riswan 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 Sarwidi, Sarwidi Setya Winarno sianturi, faldi daud suiyoso Sogar, Aris Umbu Soru Sulton, Adam Syach Reza Fachlevi Syamsul Arifin Tedy Kurniawan Topac, Tuna triwuryanto Triwuryanto Triwuryanto Triwuryanto Veronica Diana Anis Anggorowati wahyu anisa dwi bekti Wijanarko, Yulius Yobel, Felix Zulkahhar Ariga