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Journal : dinamika dotcom

SECURE KNOWLEDGE MANAGEMENT: CONFIDENTIALITY, TRUST AND PRIVACY Akhriza, Tubagus Mohammad
DINAMIKA DOTCOM DINAMIKA DOTCOM VOL 1 NO 2 TAHUN 2010
Publisher : DINAMIKA DOTCOM

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Abstract

Knowledge is one of the most important assets for an organization or a company. The knowledge in a company is the intellectual capital which could make the company keeps alive and competitive. As an important asset, the company’s knowledge should be managed securely. In this paper, the author limits the discussion of the aspect of secure knowledge management to confidentiality, trust and privacy. For confidentiality, author discusses Role Based Access Control (RBAC) and Usage Control (UCON). Then we move to discuss the trust management and negotiation and stress the topic to trust negotiation (TN). Finally the author discusses about the privacy management. The conclusion is because the knowledge management is an important asset of organization as the intellectual capital, so security has to be incorporated into the knowledge-management lifecycle especially on integrating security strategy with knowledge management and business strategies of an organization.
KLASIFIKASI PENERIMA BANTUAN SOSIAL PROGRAM KELUARGA HARAPAN (PKH) KECAMATAN GONDANGLEGI MENGGUNAKAN METODE NAÏVE BAYES Muhammad Rifat; Tubagus Mohammad Akhriza
Dinamika Dotcom : Jurnal Pengembangan Manajemen Informatika dan Komputer Vol 17 No 2 (2026): DINAMIKA DOTCOM Vol.17 No.2 Tahun 2026
Publisher : Sekolah Tinggi Manajemen Informatika & Komputer PPKIA Pradya Paramita Malang

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Abstract

The Family Hope Program (PKH) is a crucial social assistance initiative for poverty reduction in Indonesia; however, the misallocation of aid remains a primary challenge. This study aims to improve the accuracy of classifying PKH recipients in Gondanglegi District using the Naïve Bayes method, focusing on socioeconomic data. The research employs the Gaussian Naïve Bayes algorithm, utilizing data from 1,000 PKH recipients characterized by 18 attributes grouped into four aspects: health, education, social welfare, and poverty. The process encompasses data preparation, attribute selection, data splitting (using 80:20, 70:30, and 60:40 ratios), and the encoding of categorical variables via One-Hot Encoding. Model evaluation was conducted using a confusion matrix to calculate accuracy, precision, recall, and F1-score. The results indicate that the Gaussian Naïve Bayes model achieved the highest accuracy—96.5%—with an 80:20 data split ratio. The model proved effective in classifying PKH recipients with a high degree of precision, thereby supporting more targeted decision-making by local government authorities. Consequently, implementing this method can enhance the efficiency and effectiveness of social assistance distribution within the PKH program.