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All Journal Jurnal Aplikasi Bisnis dan Manajemen (JABM) E-Journal bit-Tech
Johan, Daniel
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Computer Science & IT Decision Sciences, Operations Research & Management

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Persepsi Petani Terhadap Digitalisasi Pertanian untuk Mendukung Kemandirian Petani Johan, Daniel; Maarif, M. Syamsul; Zulbainarni, Nimmi
Jurnal Aplikasi Bisnis dan Manajemen Vol. 8 No. 1 (2022): JABM Vol. 8 No. 1, Januari 2022
Publisher : School of Business, Bogor Agricultural University (SB-IPB)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.17358/jabm.8.1.203

Abstract

The importance of the role of farmers in the agribusiness system is not directly proportional to the welfare of the farmers themselves, this is because the bargaining power of farmers in the system is very low. If we look at the high capacity of rice production in Sambas Regency, it is hoped that it can be maximized by the implementation of agricultural digitization in this district, as well as being an example for the application of agricultural digitization in other districts. This study aims to to identify existing conditions that occur in agriculture in the Sambas Regency area, then analyze farmers' perceptions of agricultural digitization, and formulate a model for developing agricultural digitization to support farmers' self-reliance in Sambas. The scope of this research is only farmers in Sambas Regency, West Kalimantan as a contributor to 20% of food production, especially rice in West Kalimantan Province. This research includes causality research (cause and effect). The method used in this study is the Structural Equation Modeling (SEM) method and the analysis of the development of agricultural digitization models through the synthesis of SEM analysis and Focus Group Discussion (FGD) in order to advance the analysis. The results of the study describe farmers' perceptions, farmer characteristics, and the role of assistants farming has a significant influence on agricultural digitization, but for farmers' perceptions the effect is negative. Keywords: agricultural digitalization, assistants farming, farmer characteristics, farmers perception, FGD, SEM
Machine Learning Classification of Liver Disease Using Clinical Data with SVM and PCA Johan, Daniel; 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 M. Syamsul Maarif Nimmi Zulbainarni Yoannita, Yoannita