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All Journal Jurnal Buana Informatika JOURNAL OF APPLIED INFORMATICS AND COMPUTING Infotekmesin Switch: Jurnal Sains dan Teknologi Informasi Advance Sustainable Science, Engineering and Technology (ASSET)
Egia Rosi Subhiyakto
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Chemistry Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Industrial & Manufacturing Engineering Mechanical Engineering

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Teknologi dan Teknik Sistem Komputasi Pervasif dalam Sistem Layanan Kesehatan: Studi Literatur Sistematis Danang Wahyu Utomo; Egia Rosi Subhiyakto
Jurnal Buana Informatika Vol. 7 No. 3 (2016): Jurnal Buana Informatika Volume 7 Nomor 3 Juli 2016
Publisher : Universitas Atma Jaya Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24002/jbi.v7i3.657

Abstract

Abstract. Technology of pervasive computing can be applied in daily activities such as sport, education, game and public interest such as public health. In healthcare system, the issues about high cost and errors in reviewing of patient record are still a major topic for healthcare provider (hospital). The technology of pervasive computing was developed to address these issues. This study will discuss the technology to support healthcare system. The main purpose is that users need to know the technology and its characteristics in order to prevent fatal actions in its use. The integration of different kinds of technology such as mobile devices, wireless networks, sensors, and wearable technologies is able to give better healthcare service than the technology itself.  Keywords: Technology, Pervasive Healthcare System, Systematic Literature Review. Abstrak. Teknologi komputasi pervasif dapat diterapkan dalam aktifitas manusia mulai dari kebutuhan pribadi seperti olahraga, belajar, permainan dan kepentingan umum seperti kesehatan umum. Dalam sistem layanan kesehatan,isu tentang biaya yang tinggi, adanya kesalahan dalam review data pasien masih menjadi topik utama bagi penyedia layanan kesehatan (rumah sakit). Teknologi komputasi pervasif dikembangkan untuk mengatasi masalah tersebut. Dalam makalah ini akan dibahas mengenai teknologi dan karakteristiknya dalam mendukung sistem layanan kesehatan. Tujuan utama adalah pengguna harus mengetahui teknologi dan karakteristiknya agar tidak terjadi tindakan fatal dalam penggunaanya. Integrasi antar teknologi seperti mobile device, wireless network, sensor, dan wearable technologies mampu memberikan layanan kesehatan yang lebih baik dibanding teknologi itu sendiri.         Kata Kunci: Teknologi, Sistem Layanan Kesehatan Pervasif, Studi Literatur Sistematis.
Pengembangan Sistem Modul Komisi Dinamis pada Modul Penjualan ERP - Odoo12 Danang Wahyu Utomo; Defri Kurniawan; Egia Rosi Subhiyakto
Infotekmesin Vol 12 No 2 (2021): Infotekmesin: Juli 2021
Publisher : P3M Politeknik Negeri Cilacap

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35970/infotekmesin.v12i2.729

Abstract

The improvement of the sales system not only focuses on the advantage result of the sales transaction but also can use another parameter to improve it. One of a parameter used is commission. Giving commissions to the salesperson can improve their work performance and have an impact on increasing sales targets. Based on the study literature, the problem faced by the company is the discrepancy of commission. It canbe affected by several factors such as the commission system are not integrated with the main system, improper formula, or there are many systems used in the company so it the staff are difficult to integrate the system. For example, the company using Odoo ERP to support sales transaction and use commission information system separately. The salesperson must integrate sales data into both of the systems. It can affect the time delay of decision commission. Based on the problem above, we propose a prototype commission system that integrates with Odoo12. The salesperson does not need to integrate data manually into the system because it automatically integrates into the system. This study uses a prototyping model as a software development method. The results show that the commission system can implement on the Odoo12 ERP to decide commission to the salesperson. 70% of respondent agree that system has able to use in order to setting up commission module on Odoo
Penggunaan Algoritma Naïve Bayes dengan Polarity Textblob untuk Analisis Sentimen pada Acara ASEAN CUP 2024 U-16 di Media Sosial Twitter Arya Erlangga; Yani Parti Astuti; Etika Kartikadarma; Sindhu Rakasiwi; Egia Rosi Subhiyakto
Switch : Jurnal Sains dan Teknologi Informasi Vol. 3 No. 1 (2025): Januari : Switch: Jurnal Sains dan Teknologi Informasi
Publisher : Asosiasi Riset Teknik Elektro dan Informatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62951/switch.v3i1.357

Abstract

Football is a popular sport in the world and is enjoyed by people of all ages. The Indonesia U-16 national team played in the ASEAN CUP 2024 event in this field. Twitter users gave their support through #timnasday during the event. This provided many forms of support for the Indonesian national team which made it difficult to identify positive, neutral, and negative sentiments. This requires the use of lexicon-based textblob to perform automatic labeling. In the labeling results using textblob from a total of 1138 user tweet data resulted in positive sentiment values of 50.9% or 579 positive data, neutral 33.7% or 384 neutral data, and negative 15.4% or 175 negative data. In the test results using one of the machine learning from the naïve bayes classifier, namely gaussian naïve bayes with the division of test data and training data of 0.3 and 0.7, the accuracy value is 98.53%
Stroke Risk Classification Using the Ensemble Learning Method of XGBoost and Random Forest Gullam Almuzadid; Egia Rosi Subhiyakto
Journal of Applied Informatics and Computing Vol. 9 No. 3 (2025): June 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i3.9528

Abstract

Stroke is a leading cause of global death and disability. This study proposes a stroke risk classification model using ensemble learning that combines Random Forest and XGBoost algorithms. A Kaggle dataset with 5110 samples (249 stroke, 4861 non-stroke) presented significant class imbalance. To address this, a comprehensive preprocessing pipeline was implemented, including feature encoding, feature scaling, feature selection using ANOVA F-test, outlier handling with Z-Score and IQR methods, and missing value imputation using MICE. The SMOTE-ENN approach was applied to handle class imbalance, resulting in a more balanced sample distribution. The dataset was split into 80% training and 20% testing data (hold-out test) to ensure objective evaluation. Hyperparameter optimization was performed using Bayesian optimization, while model evaluation employed stratified K-fold cross-validation to prevent overfitting. Validation on the hold-out test set demonstrated exceptional ensemble model performance with an AUC of 0.99, 98% accuracy, 98% precision, and 98% recall. Feature importance analysis identified average glucose level and age as the strongest stroke risk predictors. The proposed approach significantly improved predictive accuracy compared to previous research, demonstrating the effectiveness of ensemble learning and preprocessing methods in developing reliable, high-performing machine learning models for early stroke risk assessment.
Performance Comparison of Random Forest, SVM, and XGBoost Algorithms with SMOTE for Stunting Prediction Maulana As'an Hamid; Egia Rosi Subhiyakto
Journal of Applied Informatics and Computing Vol. 9 No. 4 (2025): August 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i4.9701

Abstract

Stunting is a growth and development disorder caused by malnutrition, recurrent infections, and lack of psychosocial stimulation in which a child’s length or height is shorter than the growth standard for their age. With a prevalence of 21.5% in Indonesia by 2023, stunting is a global health problem that requires technology-based detection approaches for early intervention. This study evaluates the performance of three machine learning algorithms: Random Forest (RF), Support Vector Machine (SVM) and eXtreme Gradient Boosting (XGBoost) in predicting childhood stunting, and applying the SMOTE technique to handle data imbalance.  The evaluation results show that XGBoost with SMOTE achieves the best performance with 87.83% accuracy, 85.75% precision, 91.59% recall, and 88.57% F1-score, superior to RF (84.56%) and SVM (68.59%). These results show that the combination of XGBoost and SMOTE is the best solution for an accurate and effective machine learning-based stunting detection system, so it can be used in public health programs to accelerate stunting risk identification.
Implementing Long Short Term Memory (LSTM) in Chatbots for Multi Usaha Raya Raharjo, Ilham Dwi; Egia Rosi Subhiyakto
Advance Sustainable Science Engineering and Technology Vol. 6 No. 4 (2024): August-October
Publisher : Science and Technology Research Centre Universitas PGRI Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26877/asset.v6i4.934

Abstract

The furniture industry is an important sector in Indonesia that supports the economy and provides quality furniture. An in-depth understanding of the furniture business is essential for industry players to improve operational efficiency and customer satisfaction. This research aims to develop a chatbot for Multi Usaha Raya furniture company to improve customer service and operational efficiency. In its development, the Machine Learning Model Development Life Cycle (MDLC) and deep learning approach using the Flask platform are employed. LSTM, a type of recurrent neural network (RNN) architecture capable of handling long-term dependencies, is utilized in this chatbot model. The model training results show an accuracy of 99%, validation accuracy of 96%, loss of 0.1%, and validation loss of 0.2% after 200 epochs, demonstrating the effectiveness of the LSTM algorithm for developing a chatbot in this company.
Co-Authors Arya Erlangga Defri Kurniawan Etika Kartikadarma Gullam Almuzadid Maulana As'an Hamid Raharjo, Ilham Dwi Sindhu Rakasiwi Utomo, Danang Wahyu Yani Parti Astuti