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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Teknik dan Ilmu Komputer Journal of Information Systems Engineering and Business Intelligence Jurnal Sistem dan Manajemen Industri Jurdimas (Jurnal Pengabdian Kepada Masyarakat) Royal ComTech: Computer, Mathematics and Engineering Applications Jurnal Teori dan Aplikasi Fisika Jurnal Ilmiah Teknik Industri Patria : Jurnal Pengabdian Kepada Masyarakat Jurnal ABDIMAS (Pengabdian kepada Masyarakat) UBJ Journal of Innovation and Community Engagement Metris: Jurnal Sains dan Teknologi
Iwan Aang Soenandi
Universitas Kristen Krida Wacana
Arts Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice Materials Science & Nanotechnology Mathematics Physics Public Health Social Sciences Transportation Other

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Hybrid Architecture Model of Genetic Algorithm and Learning Vector Quantization Neural Network for Early Identification of Ear, Nose, and Throat Diseases Cynthia Hayat; Iwan Aang Soenandi
Journal of Information Systems Engineering and Business Intelligence Vol. 10 No. 1 (2024): February
Publisher : Universitas Airlangga

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20473/jisebi.10.1.1-12

Abstract

Background: In 2020, the World Health Organization (WHO) estimated that 466 million people worldwide are affected by hearing loss, with 34 million of them being children. Indonesia is identified as one of the four Asian countries with a high prevalence of hearing loss, specifically at 4.6%. Previous research was conducted to identify diseases related to the Ear, Nose, and Throat, utilizing the certainty factor method with a test accuracy rate of 46.54%. The novelty of this research lies in the combination of two methods, the use of genetic algorithms for optimization and learning vector quantization to improve the level of accuracy for early identification of Ear, Nose, and Throat diseases. Objective: This research aims to produce a hybrid model between the genetic algorithm and the learning vector quantization neural network to be able to identify Ear, Nose, and Throat diseases with mild symptoms to improve accuracy. Methods: Implementing a 90:10 ratio means that 90% (186 data) of the data from the initial sequence is assigned for training purposes, while the remaining 10% (21 data) is allocated for testing. The procedural stages of genetic algorithm-learning vector quantization are population initialization, crossover, mutation, evaluation, selection elitism, and learning vector quantization training. Results The optimum hybrid genetic algorithm-learning vector quantization model for early identification of Ear, Nose, and Throat diseases was obtained with an accuracy of 82.12%. The parameter values with the population size 10, cr 0.9, mr 0.1, maximum epoch of 5000, error goal of 0.01, and learning rate (alpha) of 0.5. Better accuracy was obtained compared to backpropagation (64%), certainty factor 46.54%), and radial basic function (72%). Conclusion: Experiments in this research, successed identifying models by combining genetic algorithm-learning vector quantization to perform the early identification of Ear, Nose, and Throat diseases. For further research, it's very challenging to develop a model that automatically adapts the bandwidth parameters of the weighting functions during trainin   Keywords: Early Identification, Ear-Nose-Throat Diseases, Genetic Algorithm, Learning Vector Quantization
Pemeriksaan Cepat Struktur Bangunan Terdampak Gempa Bumi Cianjur Wijaya, Usman; Soenandi, Iwan Aang
Patria : Jurnal Pengabdian Kepada Masyarakat Vol 6, No 2: September 2024
Publisher : Universitas Katolik Soegijapranata

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24167/patria.v6i2.11081

Abstract

Di Indonesia saat akhir November 2022, terjadi gempa berkekuatan 5,6 SR di Kecamatan Cugenang, Kabupaten Cianjur, Jawa Barat. Oleh karena itu, beberapa bangunan di kawasan tersebut terkena dampak negatif, beberapa di antaranya mengalami kerusakan struktural yang parah atau bahkan runtuh total. Oleh karena itu, struktur konstruksi, terutama perumahan dan bangunan umum seperti masjid dan sekolah, harus diperiksa secara singkat dan bersifat tidak merusak. Tujuan dari pengamatan ini akan menilai kerusakan akibat gempa Cianjur, mengurangi dampaknya jika terjadi gempa susulan, dan menilai kelayakan bangunan dalam jangka panjang. Metode pengamatan dan pengujian yang diterapkan pada kegiatan pengabdian masyarakat ini menunjukkan bahwa bentuk-bentuk kerusakan bangunan yang khas, seperti reruntuhan dan retakan pada dinding, rangka atap, serta detail perkuatan dan sistem struktur yang diterapkan pada bangunan, tetap harus mengikuti peraturan terkait di Indonesia. Sebaliknya, struktur dengan kondisi struktur dan material yang baik yang dirancang oleh insinyur struktur hanya mengalami kerusakan kecil pada bagian non-struktural, seperti dinding.Kata kunci: penilaian singkat, kerusakan structural, bangunan umum, pengujian tidak merusak
Peningkatan Kesiapan Literasi Digital Dalam Menunjang MBKM Kampus Mengajar di Wilayah Desa Tajur Halang SD, SMP Sinar Kasih dan Masyarakat Sekitar Soenandi, Iwan Aang; Angin, Prasasti Perangin; Anu, Benisius
Jurnal Pengabdian kepada Masyarakat UBJ Vol. 4 No. 3 (2021): Special Issue (December 2021)
Publisher : Lembaga Penelitian Pengabdian kepada Masyarakat dan Publikasi Universitas Bhayangkara Jakarta Raya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31599/g2jzcm37

Abstract

Digital learning is a necessity in the industrial era 4.0. Students must be equipped with various skills based on information technology. Digital learning infrastructure and the ability of Human Resources (HR) for SD and SMP Sinar Kasih need attention in creating schools based on digital learning. In addition, the people of Kampung Cina where the Sinar Kasih Elementary and Middle School are located have the same needs, where the students who attend school there are free of charge for the school fees, because their vision is to serve the underprivileged communities around the Tajurhalang neighborhood and village. Problem solving is carried out through training to increase the capacity of SD and SMP Sinar Kasih human resources to carry out digital learning. The material presented is divided into two, namely creative pedagogy and digital literacy. Second, through the fulfillment of digital learning equipment, including the provision of computers, LCD projectors, training modules, increasing internet capacity, and providing video tutorials for teachers and students. This will also support the effectiveness of the implementation of the Independent Learning Campus Merdeka BKP Teaching Assistance which has been carried out by 25 Ukrida students in Sinar Kasih Elementary and Middle School.
Deep learning model for detection acute cardiogenic pulmonary edema in cases of preeclampsia Hayat, Cynthia; Soenandi, Iwan Aang
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 4: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v13.i4.pp4806-4812

Abstract

The physiological changes during the pregnancy period increase the risk of developing pulmonary edema and acute respiratory failure. This condition falls under critical medical emergencies associated with maternal mortality. This study utilized a convolutional neural networks (CNN) architectural model employing chest Xray dataset images. CNN utilizes the convolution process by moving a convolutional kernel of a certain size across an image, allowing the computer to derive new representative information from the multiplication of portions of the image with the utilized filter.To simplify, the vanishing gradient issue occurs when information dissipates before reaching its destination due to the lengthy path between input and output layers. This study was developed model for detection acute cardiogenic pulmonary Edema in pre-eclampsia cases using chest Xray images, implemented using PyTorch, Keras, and MxNet. The validated model achieved its optimum with accuracy 90.65% and binary cross-entropy loss (BCELoss) value of 0.4538. It exhibited an improved sensitivity value of 83.514% using a 5% dataset and a specificity value of 57.273%. This indicates an increase in sensitivity value by 83.514% using a 5% data set and a specificity value of 57.273%. The research results demonstrate an improvement in accuracy compared to several similar studies that also utilized CNN models.
Fatigue analysis and design of a motorcycle online driver measurement tool using real-time sensors Soenandi, Iwan Aang; Oktavera, Isnia; Lusiana, Vera; Widodo, Lamto; Harsono, Budi
Jurnal Sistem dan Manajemen Industri Vol. 7 No. 2 (2023): December
Publisher : Universitas Serang Raya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30656/jsmi.v7i2.7500

Abstract

Work fatigue is an important aspect and is very influential in determining the level of accidents, especially motorbike accidents. According to WHO, almost 30% of all deaths due to road accidents involve two- and three-wheel­ed motorized vehicles, such as motorbikes, mopeds, scooters and electric bicycles (e-bikes), and the number continues to increase. Motor­cycles dominate road deaths in many low- and middle-income countries, where nine out of ten traffic accident deaths occur among motorcyclists, as in Indonesia. However, until now, in Indonesia, there has been no monitor­ing system capable of identifying fatigue in motorbike drivers in the transportation sector. This research aims to determine fatigue patterns based on driver working hours and create a sensor system to monitor fatigue measurements in real-time to reduce the number of accidents. The research began with processing questionnaire data with Pearson correlation, which showed a close relationship between driver fatigue and driving time and a close relationship between fatigue and increased heart rate and sweating levels. From calibration tests with an error of 3% and direct measurements of working conditions, it was found that two-wheeled vehicle driver fatigue occurs after 2-3 hours of work. With a measurement system using the Box Whiskers analysis method, respondents' working conditions can also be de­ter­mined, which are divided into 4 zones, namely zone 1 (initial condition or good condition), zone 2 a declining condition, zone 3 a tired condition and zone 4 is a resting condition. Hopefully, this research will identify fati­gue zones correctly and reduce the number of accidents because it can iden­tify tired drivers so they do not have to force themselves to continue working and driving their motorbikes. As a conclusion from this research, a measure­ment system using two sensors, such as ECG and GSR can identify work fatigue zones well and is expected to reduce the number of accidents due to work fatigue.
Co-Authors Angin, Prasasti Perangin Antika Ningrum Sundoro Anu, Benisius Budi Harsono Budi Harsono BUDI HARSONO Budi Harsono Budi Marpaung Budi Marpaung Cynthia Hayat Cynthia Hayat Doddy Budiawan Eilen Celsia Florensa Rosani Purba Harya, Eka Mulyo Isnia Oktavera Johansah Liman Johansah Liman Johansah Liman Joice Joice Joice, Joice lamto widodo Lusiana, Vera Malvin, Raymond Marcelle Marcelle Marpaung, Budi Meriastuti Ginting Novita Yulianti Oktavera, Isnia Prasasti Peranginangin Raymond Malvin Ronaldo Josua Ondang Silalahi, Malianti Tanra, Ivan Vera Lusiana Wilson Kosasih Yanny Yeski Mokorowu