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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.
Estimation Quality Monitoring Glycerol Esterification Process with IR Sensors Using K Nearest Neighbours Classification Soenandi, Iwan Aang; Liman, Johansah; Harsono, Budi
Metris: Jurnal Sains dan Teknologi Vol. 16 No. 02 (2015): Desember
Publisher : Prodi Teknik Industri, Fakultas Teknik - Universitas Katolik Indonesia Atma Jaya

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

The commercial synthesis of fatty acid esters of glycerol is important because it can be used for other derivative production varieties. This research aims to construct the quality monitoring system for esterification condition faster and more efficient for the production of esterification glycerol. The monitoring systems were based on the measurement parameters from two inputs LED mid IR 3,4 and 5,5 μm sensors that using the data acquisition with computer database via USB 2.0 using Arduino Leonardo microcontroller and classifying the esterification quality condition using the classification method K-Nearest Neighborhood (KNN) The purpose of KNN method is to classify the variations of parameter inputs from the LED mid IR sensors in quality monitoring. In this research the condition of esterification was divided into three conditions: not good, fair,good., these classification was trained and tested in Orange Software for data mining using receiver operating characteristic (ROC) curve that is a graphical plot that illustrates the excellent performance of a classifier system for esterification condition with AUC . In application for quality monitoring, the influence of various parameters such as temperature set in the reactor has relation to the quality of product. By using this system, we obtained the optimum process conditions is 200oC and time needed for the process was 200 minutes.
Hybrid Architecture Model of Genetic Algorithm and Learning Vector Quantization Neural Network for Early Identification of Ear, Nose, and Throat Diseases Hayat, Cynthia; Soenandi, Iwan Aang
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
Pemulihan Kesehatan Dan Fasilitas Pendidikan Pasca Gempa Cianjur Di Wilayah Cugenang Soenandi, Iwan Aang; Peranginangin, Prasasti; Silalahi, Malianti; Mokorowu, Yanny Yeski; Ginting, Meriastuti
Jurdimas (Jurnal Pengabdian Kepada Masyarakat) Royal Vol. 6 No. 3 (2023): Juli 2023
Publisher : STMIK Royal

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33330/jurdimas.v6i3.2274

Abstract

Abstract: The Community Service Program, LDDIKTI 3 in collaboration with DITJENDIKTIRISTEK and Universities launched an Incentive Program for Assignment to Private Universities for the Implementation of Community Service in the Cianjur Earthquake Area based on Key Performance Indicators with a focus on the goal of Recovery for Victims of the Cianjur Earthquake Natural Disaster, West Java. From the results of observations made in the Cianjur and surrounding areas which were affected by the earthquake, many locations in Wangunjaya Village, Cugenang Regency, have not been touched and received assistance. Many people in this area experience health problems due to unbalanced nutritional intake and unclean lifestyle, damage to learning tools in schools and early childhood education, especially at SMPN 2 Cugenang and in early childhood education. The aim of the activity is to improve public health from a physical and mental perspective and to help restore education using digital learning. The activities carried out included conducting free examinations and treatment, teaching Progressive Muscle Relaxation (PMR) therapy to reduce anxiety in the community, distributing healthy food parcels, providing trauma healing for school children, and providing school facilities for schools. Keywords: cianjur earthquake; cugenang; community service; educational facilities; health recovery. Abstrak: Program Pengabdian Masyarakat, LDDIKTI 3 bekerjasama dengan DITJENDIKTIRISTEK dan Perguruan Tinggi mencanangkan Program Insentif Penugasan kepada Perguruan Tinggi Swasta untk Pelaksanaan Pengabdian Kepada Masyarakat di Wilayah Gempa Cianjur berbasis Kinerja Indikator Kinerja Utama dengan fokus tujuan Pemulihan Korban Bencana Alam Gempa Cianjur Jawa Barat. Dari hasil pengamatan yang dilakukan di daerah Cianjur dan sekitarnya yang terkena dampak gempa, lokasi Desa Wangunjaya Kabupaten Cugenang masih banyak yang belum tersentuh dan mendapatkan bantuan. Para masyarakat didaerah ini banyak yang mengalami masalah kesehatan karena asupan gizi yang tidak seimbang serta perilaku hidup yang tidak bersih, kerusakan alat-alat pembelajaran di sekolah dan PAUD khususnya di SMPN 2 Cugenang dan di PAUD. Tujuan kegiatan adalah untuk meningkatkan kesehatan masyarakat dari segi jasmani serta mental dan membantu memulihkan Pendidikan menggunakan pembelajaran digital. Kegiatan yang dilakukan adalah melakukan pemeriksaan dan pengobatan gratis, mengajarkan terapi Progressive Muscle Relaxation (PMR) untuk menurunkan ansietas pada masyarakat,  membagikan parcel makanan sehat, memberikan trauma healing bagi anak sekolah, serta memberikan bantuan fasilitas sekolah bagi sekolah. Hasil dari kegiatan ini menunjukkan bahwa terjadinya peningkatan perasaan bahagia siswa dan masyarakat (+52%), siswa menjadi bisa merasakan pembelajaran digital (+34%) dan masyarakat menjadi lebih merasakan jasmani yang lebih sehat (+17%). Kata kunci: cugenang; fasilitas pendidikan; gempa cianjur; pemulihan kesehatan; pengabdian masyarakat.