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All Journal Abdimas UNWAHAS : Jurnal Pengabdian Masyarakat UNWAHAS Community Development Journal: Jurnal Pengabdian Masyarakat Jurnal Pengabdian kepada Masyarakat Nusantara Jurnal Bingkai Ekonomi (JBE) Jurnal Info Sains : Informatika dan Sains Jurnal Informasi Sains dan Teknologi (ISAINTEK) Jurnal Algoritma Jurnal Cakrawala Informasi (JCI) Jurnal Ilmiah Ekonomika & Sains Jurnal Pengabdian Kepada Masyarakat Ngudi Waluyo Empowerment : Jurnal Pengabdian Kepada Masyarakat Prosiding Seminar Nasional Ilmu Teknik Journal of New Trends in Sciences Proceeding of The International Conference on Electrical Engineering and Informatics Jurnal Ilmu Komputer Programming and Algorithm Fundamentals
Marsiska Ariesta Putri
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Agriculture, Biological Sciences & Forestry Arts Humanities Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice Library & Information Science Medicine & Pharmacology Public Health Social Sciences Other

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IMPLEMENTASI NUERAL NETWORK BACKPROPAGATION UNTUK MEMPREDIKSI KURS VALUTA ASING Marsiska Ariesta Putri; Iwan Setiawan Wibisono
Multimatrix Vol. 2 No. 1 (2019)
Publisher : Universitas Ngudi Waluyo

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Abstract - Technology neural network system has been implemented in various applications, especially in terms of forecasting (forecasting), including backpropagation that can be applied to predict foreign exchange rates. There are two steps being taken in this backpropagation method of training and testing phases. In this network backpropagation algorithm is given a pair of pattern - a pattern that consists of the input pattern and desired pattern. When a pattern is given  to the network, weights - weights modified to minimize the differences in the pattern of output and the desired pattern. This exercise is performed over and over - re-issued so that all the patterns the network can meet the desired pattern. The next stage is the testing stage. This stage begins by using the best weights obtained from the training phase to process the input data to produce the appropriate output. It is used to test whether the ANN can work well is that it can predict the pattern of data that has been drilled with a small error rate. From the test results using data from the monthly period in the training process the network can recognize input patterns are provided so entirely in accordance with the target. While testing with the use of data daily and weekly period that does not comply with the given target, it is because the network requires more data to identify patterns provided. As more data are trained, the better the network will recognize the pattern - a pattern so that the results more accurate predictions, but will be impacted by slowing the process of training. Keywords:  Foreign currency exchange rate prediction, Neural Network, Bacpropagation
Aplikasi Augmented Reality Untuk Pengenalan Perangkat Jaringan Komputer Marsiska Ariesta Putri; Iwan Setiawan Wibisono
Multimatrix Vol. 2 No. 2 (2020): Inovasi Teknologi Pada Masa Pademik Covid-19
Publisher : Universitas Ngudi Waluyo

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Augmented Reality merupakan penggabungan benda maya dua dimensi atau tiga dimensi menjadi lingkungan tiga dimensi nyata, kemudian memproyeksikan benda-benda maya tersebut secara real time. Tujuan dari penelitian ini adalah untuk menambah pengetahuan dan meningkatkan pengetahuan dan pemahaman siswa-siswi SMK Hidayah tentang pengenalan berbagai jenis perangkat jaringan dengan aplikasi Augmented Reality yang menampilkan objek 3D dan video tutorial materi pada modul sehingga tercipta suasana baru. dalam pengertian jaringan komputer. Penelitian dibatasi pada perancangan aplikasi yang dibangun untuk pengenalan materi berupa objek 3D berbasis jaringan komputer android smartphone. Model penelitian ini adalah metode waterfall atau alur kehidupan klasik (Classic Life Cycle). Produk aplikasi android ini telah melalui tahapan uji kelayakan produk yang dilakukan oleh dosen dan guru telah dinyatakan layak pakai, selanjutnya produk tersebut diujicobakan kepada siswa-siswi SMK Hidayah kelas XI dan dinyatakan “baik”. Berdasarkan data diatas maka dapat disimpulkan bahwa aplikasi berbasis android berupa Perancangan Visualisasi 3D Pengenalan Jaringan Komputer dengan menggunakan sistem operasi android berbasis Augmented Reality, dapat digunakan oleh guru dan siswa sebagai pemahaman tentang materi pengenalan komputer jaringan
Algoritma Neural Network Menggunakan Model Particle Swarm Optimization Untuk Prediksi Penyakit Kanker Payudara Marsiska Ariesta Putri; Iwan Setiawan Wibsiono
Multimatrix Vol. 4 No. 1 (2022): Kemajuan Teknologi Informatika Pada Era Dunia Digital
Publisher : Universitas Ngudi Waluyo

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ABSTRACT Breast cancer is one of the causes of cancer deaths in women worldwide. One technique to diagnose breast cancer: mammography. In this study developed a system to classify the "Breast Cancer" using Backpropagation neural network optimized with Particle Swarm Optimization for classifying tumors of the symptoms that cause breast cancer. The main objective of this study was to develop a more cost effective and easy to use system to support doctors. For the problem of diagnosis of breast cancer tumor symptoms, the experimental results show that the neural network based model of particle swarm optimization achieved a high degree of accuracy. Dataset used in this study were breast cancer database from the University of Wisconsin Machine Learning (UCI) Repository. Keywords: Breast Cancer, Backpropagation, Particle Swarm Optimization, Accuracy  
Sistem Pengenalan Retina Menggunakan Self Organizing Map Untuk Mendeteksi Retinopati Diabetika: Marsiska Ariesta P, Iwan Setiawan W, Sri Mujiyono Marsiska Ariesta Putri
Multimatrix Vol. 5 No. 1 (2023): Jurnal Multimatrix Juli 2023
Publisher : Universitas Ngudi Waluyo

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Eyes are important human sense. Diseases that damage many function of eye is diabetic retinopathy. Diabetic retinopathy is a microvascular complication that can occur in patients with diabetic and attacking vision function. Clinical symptoms of this disease is the emergence of mikroaneurisma which is swelling of blood vessels are microscopic and can be seen as reddish dots on the retina. The retina recognition process was done by taking the retina image data were processed using the Laplacian operator. Then do the feature extraction using Principal Component Analysis (PCA). PCA results of binary data is used as an input to the process of Neural Networks Self Organizing Map (SOM). The training process in order to make a decision about whether diabetic retinopathy or not . Results obtained with feature extraction Principal Component Analysis (PCA) with the variables, learning rate (a) = 0.6 , reduction of alpha (δ) = 0.5, threshold = 0.02 similarity and distance = 1x10-15, has produced recognition rate by 85% for the best possible, and 50% for the worst possible. Keyword : Retina Recognition, Principal Component Analysis, Self Organizing Map, Diabetic Retinopathy
A Hybrid Data Structure and Algorithmic Approach for Efficient Memory Management and Query Processing in High Performance Software Systems Zulfikar Zulfikar; Febri Adi Prasetya; Marsiska Ariesta Putri
Programming and Algorithm Fundamentals Vol. 1 No. 1 (2026): January: Programming and Algorithm Fundamentals
Publisher : Asosiasi Pengelola Jurnal Informatika dan Komputer Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.66472/paf.v1i1.20

Abstract

In high-performance computing (HPC) environments, the need to balance memory efficiency and query performance is crucial for ensuring optimal system performance. Traditional data structures, such as B-trees and hash tables, often prioritize either memory usage or query speed, leading to suboptimal performance in memory-constrained systems. This paper proposes a hybrid data structure that combines the strengths of multiple traditional data structures to optimize both memory usage and query processing speed. The proposed hybrid structure integrates cache-conscious algorithms, dynamic memory allocation, and compression techniques for intermediate query results. The approach is evaluated through extensive benchmarking tests comparing it to standard data structures like B-trees and hash tables under various workloads. Results show that the hybrid data structure reduces memory overhead by up to 30% while maintaining query processing speeds up to 1.5 times faster than conventional methods. Furthermore, the hybrid structure demonstrates robust performance across different types of queries, including both point and range queries, ensuring versatility and efficiency. The findings indicate that this hybrid approach provides a promising solution for HPC systems, where both memory efficiency and query speed are essential. Future research can explore extending the hybrid structure to distributed systems and emerging technologies, further improving its scalability and adaptability to new computational paradigms.
Analisis Fisika Gelombang Tsunami untuk Desain Sistem Peringatan Dini Berbasis Komputasi Cepat Marsiska Ariesta Putri; Ninik Dwi Atmin
Journal of New Trends in Sciences Vol. 2 No. 4 (2024): November : Journal of New Trends in Sciences
Publisher : CV. Aksara Global Akademia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59031/jnts.v2i4.764

Abstract

The increasing frequency and severity of tsunamis in coastal areas underscore the urgent need for efficient Tsunami Early Warning Systems (TEWS). This research aims to optimize TEWS by integrating fast computational tsunami wave modeling to enhance prediction speed and accuracy. The study utilizes numerical simulations employing finite volume methods, along with GPU acceleration, to model tsunami wave propagation and its impact on coastal areas. Machine learning techniques, such as regression trees, are incorporated to analyze large datasets of pre-computed tsunami simulations for accurate forecasting. The results reveal that by applying rapid computational methods, detection time can be reduced by up to 7 minutes, particularly for near-field tsunamis. This significant time-saving enables more effective evacuation procedures and better disaster mitigation efforts. In comparison to conventional systems, the fast computation model also provides more accurate predictions, including tsunami heights and arrival times. The implications of these findings suggest that fast computational methods can substantially improve the current TEWS, allowing for quicker and more reliable tsunami warnings. Moreover, the integration of advanced machine learning techniques ensures the system's adaptability and robustness in predicting tsunami behaviors based on varying data inputs. The potential for implementing this model in tsunami-prone regions worldwide is considerable, offering an improved approach to tsunami disaster preparedness and response. By reducing detection time and enhancing prediction accuracy, the optimized TEWS can significantly minimize loss of life and infrastructure damage, making it a valuable tool for global disaster management strategies.  
Edukasi Data Science Untuk Meningkatkan Strategi Bisnis UMKM Di Kelurahan Gedawang Kecamatan Banyumanik Marsiska Ariesta Putri; Aji Priyambodo; Prihati; Ika Susanti; Ihda Nor Rohmah
Ngudi Waluyo Empowerment: Jurnal Pengabdian Kepada Masyarakat Vol. 5 No. 1 (2026): Ngudi Waluyo Empowerment: Jurnal Pengabdian Kepada Masyarakat
Publisher : Fakultas Komputer dan Pendidikan Universitas Ngudi Waluyo

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Usaha Mikro, Kecil, dan Menengah (UMKM) memiliki peran penting dalam mendorong pertumbuhan ekonomi masyarakat. Namun, banyak pelaku UMKM masih menghadapi kendala dalam pengambilan keputusan bisnis berbasis data, sehingga strategi usaha yang diterapkan sering kali kurang optimal. Kegiatan pengabdian kepada masyarakat ini bertujuan untuk memberikan edukasi mengenai data science sebagai pendekatan strategis dalam meningkatkan daya saing dan efektivitas bisnis UMKM di Kelurahan Gedawang, Kecamatan Banyumanik. Metode pelaksanaan meliputi penyuluhan, pelatihan interaktif, serta pendampingan dalam memahami konsep dasar data science, pengolahan data sederhana, dan pemanfaatan data untuk analisis perilaku konsumen serta tren pasar. Kegiatan ini diikuti oleh pelaku UMKM dari berbagai sektor usaha. Hasil kegiatan menunjukkan adanya peningkatan pemahaman peserta terkait pentingnya pengelolaan data dalam mendukung strategi bisnis, seperti penentuan target pasar, pengelolaan stok, dan evaluasi penjualan. Edukasi data science ini diharapkan dapat mendorong transformasi digital UMKM sehingga mampu meningkatkan produktivitas, daya saing, dan keberlanjutan usaha di era ekonomi digital.
Co-Authors Achmad Badjuri Aji Priyambodo Alfa Durri Khayya Andreas Tigor Oktaga, Andreas Aniqotunnafiah Dheavita Reza Artamevia Dimas Adi Wicaksono Febri Adi Prasetya Grace Ivana Heru Yulianto Heses, Martinus Apun Ihda Nor Rohmah Ika Susanti Ika Susanti Ika Susanti Iwan Setiawan Wibisono Iwan Setiawan Wibisono Iwan Setiawan Wibsiono jefri Heridiansyah Kasidi Kristiawan Nurdianto Kristiawan Nurdianto Lestari, Utami Puji Martinus Apun Heses Martinus Apun Heses Martinus Apunheses Ninik Dwi Atmin Nor Rohmah, Ihda Prihati Prihati Prihati Prihati Prihati Raden Mohamad Herdian Bhakti Rismawati , Rizky RITA ANDINI Riyono Safari, Teti Siska Narulita Siti Helmyati WAHYUDI Zulfikar