Article Per Year (5 Year)
p-Index From 2021 - 2026
0.444
P-Index
This Author published in this journals
All Journal bit-Tech MDP Student Conference
Daniel Johan
Universitas Multi Data Palembang
Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Electrical & Electronics Engineering

Published : 2 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search

 1 
Perancangan Antarmuka Aplikasi Monteer dengan Metode Design Thinking Fellyca Effendi; Kevin Andreas; Calvin Bertnas Valentino; Daniel Johan; Jasen Jonathan; Laurentius Ricardo Wijaya; Muhammad Rizky Pribadi
MDP Student Conference Vol 1 No 1 (2022): The 1st MDP Student Conference 2022
Publisher : Universitas Multi Data Palembang

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (609.236 KB)

Abstract

Abstract: Many people have troubles in fixing their vehicles, calling in mechanics, and maintaining their vehicles. To overcome those problems, We developed Monteer application which will provide features like call a mechanic, guide book, tow, and more. In designing Monteer application, requires a long thinking process. In addition, the error in using the application also happens. So therefore, in designing Monteer UI, "Design Thinking" method is used. Design thinking method is an approach method to solve problems with the main focus on the user and need to go through five steps that is "Empathize", "Define", "Ideate", "Prototype", and "Testing". Therefore, the application will be easy to use. Abstrak: Banyak masyarakat kesulitan dalam memperbaiki kendaraan, memanggil montir, serta merawat kendaraan. Untuk mengatasi kesulitan-kesulitan tersebut, dikembangkan aplikasi Monteer yang akan menyediakan berbagai fitur seperti pemanggilan montir, buku panduan, derek, dan lain sebagainya. Dalam merancang aplikasi Monteer, memerlukan proses pemikiran yang tidak sebentar. Selain itu, kesalahan/kesulitan dalam penggunaan aplikasi juga sering terjadi. Maka dari itu, untuk merancang antarmuka aplikasi Monteer digunakan metode Design Thinking. Metode Design Thinking adalah suatu metode pendekatan untuk memecahkan masalah dengan fokus utama pada pengguna serta perlu melalui lima tahapan, yaitu Empathize, Define, Ideat, Prototype, dan Testing. Dengan demikian, aplikasi akan lebih mudah digunakan.
Machine Learning Classification of Liver Disease Using Clinical Data with SVM and PCA Daniel Johan; Yoannita Yoannita
bit-Tech Vol. 8 No. 3 (2026): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i3.3494

Abstract

Liver disease remains a major global health problem that requires early and accurate diagnosis to prevent severe clinical complications and mortality. In recent years, Support Vector Machine (SVM) combined with Principal Component Analysis (PCA) has been widely applied for liver disease classification. However, existing studies are often limited by small or moderately sized datasets, a lack of systematic comparison among SVM kernel functions, and insufficient discussion of clinical relevance and data representativeness. These limitations restrict model generalizability and hinder practical clinical adoption. To address these gaps, this study evaluates a PCA–SVM classification framework using a large-scale Liver Disease Patient Dataset comprising 30,691 clinical records, thereby improving robustness and population representativeness. The main contribution of this research lies in a systematic and controlled comparison of four SVM kernel functions linear, radial basis function (RBF), polynomial, and sigmoid—under identical preprocessing and dimensionality reduction conditions. PCA is applied to reduce feature redundancy while preserving over 97% of clinically relevant information, supporting efficient learning without increasing model complexity. Experimental results indicate that kernel selection has a substantial impact on diagnostic performance. The RBF kernel consistently outperforms other kernels, achieving an accuracy of 83.63% and an area under the ROC curve of 92.09%, while maintaining strong generalization on unseen data. From a clinical perspective, these findings demonstrate that the proposed PCA–SVM framework has significant potential as a clinical decision support tool for early liver disease screening based on routine laboratory data, offering a balance between predictive performance, computational efficiency, and practical applicability.
Co-Authors Calvin Bertnas Valentino Fellyca Effendi Jasen Jonathan Kevin Andreas Laurentius Ricardo Wijaya Muhammad Rizky Pribadi Yoannita