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All Journal SEIKO : Journal of Management & Business INVEST : Jurnal Inovasi Bisnis dan Akuntansi Journal La Multiapp
Adillah, Muhammad Fauzan Nur
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Aerospace Engineering Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Economics, Econometrics & Finance Engineering Social Sciences

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Optimasi Manajemen Pengetahuan di Perpustakaan Kementan: Pendekatan Framework Terpadu Wihayanti, Titik; Kartini, Ani; Adillah, Muhammad Fauzan Nur; Lubis, Muharman
SEIKO : Journal of Management & Business Vol 7, No 1.1 (2024)
Publisher : Program Pascasarjana STIE Amkop Makassar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37531/sejaman.v7i1.6840

Abstract

Perpustakaan Kementan memiliki peran penting dalam mendukung sektor pertanianan melalui penyediaan akses terhadap pengetahuan dan informasi yang relevan. Namun dalam penerapannya berbagai kendala ditemukan seperti keterbatan pemanfaatan teknologi, belum terbentuknya budaya berbagi pengetahuan, dan keterbatasan mekanisme pengelolaan pengetahuan yang terstruktur. Penelitian ini bertujuan untuk mengembangkan sebuah framework terpadu untuk mengoptimalkan manajemen pengetahuan di Perpustakaan Kementan. Metodologi yang digunakan adalah studi literatur yang komprehensif untuk mengidentifikasi kebutuhan pengelolaan pengetahuan di perpustakaan Kementan. Framework yang dikembangkan mengacu pada pada Knowledge Management Framework for University Libraries (de Bem et al. 2016) dan disesuaikan dengan kebutuhan spesifik Perpustakaan Kementan. Hasil penelitian ini menunjukkan bahwa penguatan komunikasi antar unit kerja, pengembangan sistem pengelolaan pengetahuan yang terstruktur, dan integrasi teknologi informasi adalah langkah-langkah kunci untuk mengatasi tantangan yang ada. Penelitian ini memberikan kontribusi signifikan dalam literatur manajemen pengetahuan dan praktis, serta dapat menjadi panduan bagi perpustakaan lain dalam menghadapi tantangan serupa. Kata kunci: framework manajemen pengetahuan, manajemen pengetahuan perpustakaan, perpustakaan Kementan
The Influence of IT Leadership on Business Continuity: Analysis of the Role of Digital Governance in Increasing Company Competitiveness Adillah, Muhammad Fauzan Nur; Fakhrurroja, Hanif
INVEST : Jurnal Inovasi Bisnis dan Akuntansi Vol. 4 No. 2 (2023): INVEST : Jurnal Inovasi Bisnis dan Akuntansi
Publisher : Lembaga Riset dan Inovasi Al-Matani

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55583/invest.v4i2.704

Abstract

This research aims to explore and analyze the influence of leadership in Information Technology (IT) on company business continuity with a focus on the role of Digital Governance in increasing competitiveness. Using qualitative literature study methods, this research details the background to the importance of digital transformation in the modern business context, as well as investigating the key role played by IT leadership in guiding organizations towards digital success. The data and research objects involve analysis of texts and scientific literature that includes various sources and frameworks related to IT leadership and Digital Governance. It is hoped that the results of this research will provide a deeper understanding of how effective IT leaders can influence a company's business continuity through the application of Digital Governance principles, which in turn will increase the company's competitiveness in the digital era.  
Implementation of Machine Learning-Based Classification Model in Employee Recruitment Decision Prediction Adillah, Muhammad Fauzan Nur; Suakanto, Sinung; Utama, Nur Ichsan
Journal La Multiapp Vol. 6 No. 2 (2025): Journal La Multiapp
Publisher : Newinera Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37899/journallamultiapp.v6i2.2050

Abstract

Employees are vital assets for any organization, and accurate recruitment decision-making is crucial for the organization's long-term success. Incorrect decisions can lead to high costs due to re-hiring processes, onboarding, and decreased productivity. This study aims to develop a recruitment decision prediction model using data obtained from the Final Results of the 2024 CPNS Recruitment in the Ministry of Finance. The data includes attributes such as educational background, age, GPA, SKD Score, and SKB Score. To understand the relationships between variables, correlation analysis was conducted using a correlation matrix and heatmap visualization. Additionally, data exploration was performed using histograms to show the influence of attributes on recruitment decisions. This study employs five machine learning algorithms for prediction: Linear Support Vector Machine, Decision Tree (C5.0), Random Forest, k-Nearest Neighbor (k-NN), and Naïve Bayes Classifier. The results indicate that some attributes significantly influence recruitment decisions, and machine learning models can identify candidates who are more suitable for the available positions. Among the five models tested, Naïve Bayes proved to be the most effective, achieving an accuracy of 88% and an AUC of 0.97, demonstrating its strong performance in distinguishing positive and negative classes. The key factors contributing to the model's success include relevant feature selection, data quality, as well as appropriate preprocessing and validation techniques. This model is expected to enhance objectivity, efficiency, and accuracy in employee recruitment processes, thereby assisting organizations in making more precise and fair decisions.
Co-Authors Fakhrurroja, Hanif Kartini, Ani Muharman Lubis Nur Ichsan Utama Sinung Suakanto Wihayanti, Titik