Garuda - Garba Rujukan Digital

Heart Disease Prediction based on Physiological Parameters Using Ensemble Classifier and Parameter Optimization Agung Muliawan; Achmad Rizal; Sugondo Hadiyoso
Journal of Applied Engineering and Technological Science (JAETS) Vol. 5 No. 1 (2023): Journal of Applied Engineering and Technological Science (JAETS)
Publisher : Yayasan Riset dan Pengembangan Intelektual (YRPI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37385/jaets.v5i1.2169

This study describes the prediction of heart disease using ensemble classifiers with parameter optimization. As input, a public dataset was taken from UCI machine learning repository, which refers to the dataset at UCI Machine learning. The dataset consists of 13 variables that are considered to influence heart disease. Particle swarm optimization (PSO) was used for feature selection and principal component analysis (PCA) for feature extraction to reduce the features' dimensions. The application of parameter optimization on several machine learning methods such as SVM (Radial Basis Function), Deep learning, and Ensemble Classifier (bagging and boosting) to get the highest accuracy comparison. The results of this study using PSO dimensionality reduction in the public dataset of heart disease resulted in the slightest accuracy compared to PCA. In contrast, the highest accuracy was obtained from optimizing Deep Learning parameters with an accuracy of 84.47% and optimization of SVM RBF parameters with an accuracy of 83.56%. The highest accuracy in the ensemble classifier using bagging on SVM of 83.51%, with a difference of 0.5% from SVM without using bagging.

Meningkatkan Literasi Teknologi melalui Webinar Pintu Gerbang Menuju Digital Hermansyah, Masud; Andita Prasetyo , Nur; Wahid, Abdul; Afreyna Fauziah, Difari; Muliawan, Agung
JURNAL PENGABDIAN MASYARAKAT (JPM) Vol 3 No 2 (2023)
Publisher : Institut Teknologi dan Sains Mandala

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

In the ever-evolving digital era, technology has become a major driving force in social, economic and educational change. Information and communication technologies (ICTs) have changed the way people work, communicate, and learn. As technology advances, it is important for individuals to have sufficient technological literacy to be able to participate actively in a digital society. The aim of this webinar is to provide an in-depth understanding of digital technology and teach practical skills in using it wisely. This webinar presents a series of topics related to digital technology, including digital transformation of Internet of Things (IoT) Technology in the Industrial World, Information Security Culture, and Computer and Network Security. By using the Zoom Video Communications application, webinar participants can easily participate from their respective locations, thus enabling broad participation and more flexibility for students to learn about technology. This webinar succeeded in increasing high school and vocational students' interest in the field of technology, as well as opening their insights about various career opportunities in the digital era. In addition, students also become more aware of the importance of ethics and responsibility in using technology, and are aware of its impact on society.

Penerapan Metode Analytical Hierarchy Process (AHP) Pada Penilaian Pegawai Teladan Muliawan, Agung; Sabilirrasyad, Iqbal; Fauziah, Difari Afreyna
Journal of Digital Literacy and Volunteering Vol. 2 No. 2 (2024): July
Publisher : Puslitbang Akademi Relawan TIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57119/litdig.v2i2.76

One of the factors supporting the success of a business place is productive employees who have maintained and improved qualification standards. The company's appreciation for exemplary employees can be given by giving gifts or awards. Employee performance assessment can be done to determine employees who are qualified and highly dedicated to the company. However, many companies experience difficulties in evaluating employee performance because the calculations are still manual so that they are less effective and objective, one of which is SMK Visi Global Jember. The research will apply the Analytical Hierarchy Process (AHP) method in determining the best employees at SMK Visi Global Jember so that the selection process is right on target with the needs of the criteria given. The required criteria include honesty, loyalty, commitment, discipline and cooperation which will be processed to produce the highest rank for determining recommendations for exemplary employees. The results of this study produce a Consistency Ratio (CR) value of 0.083 so that the value of giving preferences is consistent and can be used in determining exemplary employees at SMK Visi Global Jember

Community Service on Health Issue Stunting in Jelbuk Village Iqbal Sabilirrasyad; Agung Muliawan; M Faiz Firdausi; Nur Andita Prasetyo; Ferry Wiranto
RECORD: Journal of Loyality and Community Development Vol. 1 No. 1 (2024): January - April 2024
Publisher : Medikun Publisher

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

Stunting is a condition that disrupts a child's growth and development, leading to a discrepancy between their height and age. Indonesia is among the countries still grappling with this issue. Based on observational data collected during field visits to Jelbuk Village, Jember City, the researchers found that a majority of toddlers that live in the village are categorized as experiencing stunting. Consequently, the researchers organized counseling sessions and motor skills screening activities for children. The primary objective of these activities is to educate parents about stunting and how important to monitor children's development as they grow. The screening of children's development involves the use of the Pre-Developmental Screening Questionnaire (KPSP), consisting of 9-10 questions assessing a child's developmental milestones. From the screening activities using the Developmental Pre-Screening Questionnaire, the following results were obtained: out of 25 toddlers who attended the counseling, 6 were identified with developmental deviations, 4 were considered doubtful, and 15 were developing appropriately

Handling Stunting as a Management Community Service Agung Muliawan; Difari Afreyna Fauziah; Ahmad Nurdianyah; Arini Shufia Dwi Sukmawati; Muhammad Rijalus Sholihin
RECORD: Journal of Loyality and Community Development Vol. 1 No. 1 (2024): January - April 2024
Publisher : Medikun Publisher

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

Stunting is a condition of chronic malnutrition caused by insufficient nutritional intake over a long period of time due to inadequate food supply to meet nutritional needs. Stunting is a problem that is difficult to solve if the factors causing stunting in each region cannot be controlled. Basically, the layer that interacts most intensively with patients diagnosed with stunting directly is the posyandu cadres who are the first counselors for mothers and children at the lowest level. The method used in this Collaborative Real Work Lecture (KKN) student service activity is counseling and training to improve the skills and role of the targets, namely posyandu and RDS cadres in the prevention and early detection program of stunting in children and toddlers. This activity aims to directly increase the role of posyandu cadres who are very close to the community in resolving stunting problems and indirectly to motivate the community to participate in paying attention to the growth and development of their children so that their growth and development can be optimal. It is hoped that the knowledge of Lampeji Village cadres and RDS members regarding stunting prevention will increase and the knowledge gained can be applied to the Lampeji Village community so that they can contribute to parenting and assisting children's growth and development

Membangun Sistem Rekomendasi Hotel dengan Content Based Filtering Menggunakan K-Nearest Neighbor dan Haversine Formula Muliawan, Agung; Badriyah, Tessy; Syarif, Iwan
Technomedia Journal Vol 7 No 2 October (2022): TMJ (Technomedia Journal)
Publisher : Pandawan Incorporation, Alphabet Incubator Universitas Raharja

Peningkatakan pertumbuhan industri hotel pada tiap tahunnya dan preferensi konsumen yang bervariasi dalam kebutuhan layanan hotel mengakibatkan konsumen lebih konsumtif dalam memilih hotel. Kurangnya pilihan kriteria bobot pada penyedia layanan hotel mengakibatkan konsumen mengalami kesulitan dalam memilih hotel yang sesuai dengan preferensinya, sehingga diperlukan sebuah sistem rekomendasi hotel sebagai pilihan alternatif dalam memilih hotel. Dalam penelitian ini digunakan permodelan Case Based Reasoning (CBR) untuk memberikan pembelajaran kepada sistem. Pilihan dari user pada pilihan hotel secara otomatis akan disimpan ke dalam database dan dijadikan sebagai data training sehingga sistem akan mendapatkan informasi secara berkelanjutan. Pada penelitian ini diberikan tiga jenis kebutuhan antara lain Kebutuhan Prioritas (KP), Kebutuhan Umum (KU) dan Kebutuhan Tambahan (KT) dan atribut yang digunakan terdapat enam yaitu: fasilitas, lokasi, harga, tipe kamar, bintang dan skor yang sangat mempegaruhi hasil rekomendasi. Untuk setiap nilai bobot yang ada, dilakukan uji validitas bobot kepentingan menggunakan pairwise comparison matrix (PCM) sehingga nilai bobot menjadi valid dengan rentang nilai 0-1. Selain itu penerapan content based filtering menggunakan metode haversine formula dan K-Nearest Neighbor (KNN) dalam menentukan nilai terdekat dengan data training. Dari eksperimen, didapatkan hasil pengukuran performansi yang memuaskan berupa rata-rata kemiripan (similarity) sebesar 84.50% Kata kunci : Case Based Reasoning, Content Based Filtering, Haversine Formula, K-Nearest

Detection of Diabetes in Pregnant Women Using Machine Learning as an Effort Towards Golden Indonesia 2045 Muliawan, Agung; Rohim, Muhamat Abdul; Fauziah, Difari Afreyna; Yusuf, Hamzah Fansuri
Journal of Informatics Development Vol. 3 No. 1 (2024): Oktober 2024
Publisher : Institut Teknologi dan Bisnis Widya Gama Lumajang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30741/jid.v3i1.1418

One of the goals of the Golden Indonesia 2045 program is to utilize health technology to enhance public health, with diabetes being a prominent concern. This research aims to employ ensemble classifier optimization techniques in machine learning for the early detection of diabetes among pregnant women. The study uses physiological data, including variables such as glucose levels, number of pregnancies, skin thickness, blood pressure, insulin levels, body weight, family history, and age. By combining multiple models, ensemble classifiers can enhance prediction accuracy, stability, and overall model performance. This research utilizes an open Kaggle dataset on pregnant women to train and test machine learning models, specifically Support Vector Machine (SVM) and Deep Learning, incorporating ensemble techniques such as bagging and boosting. Experimental results indicate that the ensemble classifier approach significantly enhances diabetes classification, with SVM using bagging achieving the highest accuracy at 76.95%. These findings suggest that ensemble classifier methods could be a valuable tool for early diabetes detection, providing timely intervention and improved risk management during pregnancy, which supports the objectives of improving public health under the Golden Indonesia 2045 initiative.

Implementation Of Arima Model In The Analysis Of City Temperature Averag Rohim, Muhamat Abdul; Muliawan, Agung; Wiranto, Ferry
Journal of Informatics Development Vol. 3 No. 1 (2024): Oktober 2024
Publisher : Institut Teknologi dan Bisnis Widya Gama Lumajang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30741/jid.v3i1.1419

This study analyzes the daily average temperature data of Delhi city from 2013 to 2017 using the Autoregressive Integrated Moving Average (ARIMA) model to model and predict temperature trends. The temperature data processed in this study is non-stationary, so differentiation is applied to achieve stationarity. Two ARIMA models were evaluated: ARIMA (1,1,1) and ARIMA (1,1,1)(1,0,1). The ARIMA (1,1,1) model is effective in capturing short-term patterns, while the ARIMA (1,1,1)(1,0,1) model performs better in handling seasonal components. The findings show that the ARIMA (1,1,1)(1,0,1) model provides more accurate prediction results by accounting for seasonal fluctuations in temperature data. This research is expected to serve as a reference for preventive measures related to temperature changes, as temperature variations can affect public health, infrastructure, and quality of life in rapidly growing cities like Delhi. Understanding temperature trends and making accurate predictions helps in city planning, resource management, and developing adaptation strategies for climate change, which is crucial for mitigating negative impacts and planning for a more sustainable future.

IMPLEMENTATION OF MACHINE LEARNING ON EMPLOYEE ATTRITION BASED ON PERFORMANCE PARAMETERS USING PARTICLE SWARM OPTIMIZATION AND ENSEMBLE CLASSIFER METHODS Fauziah, Difari Afreyna; Muliawan, Agung; Dimyati, Muhaimin
Jurnal Teknik Informatika (Jutif) Vol. 5 No. 6 (2024): JUTIF Volume 5, Number 6, Desember 2024
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2024.5.6.3442

This research aims to apply machine learning to predict the start of employee attrition by considering performance parameters and other related factors in the company environment. Employee attrition refers to employee turnover in an organization for various reasons such as resignation, moving, retirement, and so on. This research uses a dataset originating from the IBM HR Analytics Employee Attrition dataset available on Kaggle (https://www.kaggle.com/) which consists of 35 attributes. Particle Swarm Optimization (PSO) method is a dimension reduction method to improve the efficiency and performance of machine learning models by reducing unnecessary data. The machine learning approaches used in the early prediction of employee attrition in this research are Support Vector Machine, Deep Learning and Neural Network methods. This research will combine the dimensionality reduction process with machine learning to obtain employee attrition prediction results that are optimized using the Ensemble method, namely Bagging and Boosting to increase the accuracy value of the prediction results. The results of this research show that applying dimensionality reduction using the PSO method can improve the accuracy of results on the IBM HR Analytics Employee Attrition dataset. The best accuracy in attrition prediction was obtained by the Deep Learning method with an accuracy value of 86.94%, a precision value of 88.90%, and a recall value of 96.40% after combining it with PSO and optimizing with Bagging.

Penerapan Metode Analytical Hierarchy Process (AHP) Pada Penilaian Pegawai Teladan Muliawan, Agung; Sabilirrasyad, Iqbal; Fauziah, Difari Afreyna
Journal of Digital Literacy and Volunteering Vol. 2 No. 2 (2024): July
Publisher : Puslitbang Akademi Relawan TIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57119/litdig.v2i2.76

One of the factors supporting the success of a business place is productive employees who have maintained and improved qualification standards. The company's appreciation for exemplary employees can be given by giving gifts or awards. Employee performance assessment can be done to determine employees who are qualified and highly dedicated to the company. However, many companies experience difficulties in evaluating employee performance because the calculations are still manual so that they are less effective and objective, one of which is SMK Visi Global Jember. The research will apply the Analytical Hierarchy Process (AHP) method in determining the best employees at SMK Visi Global Jember so that the selection process is right on target with the needs of the criteria given. The required criteria include honesty, loyalty, commitment, discipline and cooperation which will be processed to produce the highest rank for determining recommendations for exemplary employees. The results of this study produce a Consistency Ratio (CR) value of 0.083 so that the value of giving preferences is consistent and can be used in determining exemplary employees at SMK Visi Global Jember

Title

Found 10 Documents
Search

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Title Search

Found 10 Documents Search

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Title

Found 10 Documents
Search