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All Journal Techno.Com: Jurnal Teknologi Informasi IIJSE
Ken Ditha Tania
Universitas Sriwijaya, Palembang, Indonesia
Computer Science & IT Economics, Econometrics & Finance Engineering

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The Influence of Knowledge Management on Employee Performance and Innovative Work Behavior at PT. PBT Site Bukit Asam Vensi Yeka Isdiani; Ken Ditha Tania
Indonesian Interdisciplinary Journal of Sharia Economics (IIJSE) Vol 8 No 1 (2025): Sharia Economics
Publisher : Universitas KH. Abdul Chalim Mojokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31538/iijse.v8i1.5976

Abstract

This research aims to analyze the influence of knowledge management on employee performance and innovative work behavior at PT. PBT Site Bukit Asam. A quantitative approach using the census method was used, involving 30 respondents from various departments. Data was collected through a questionnaire with a Likert scale and analyzed using Partial Least Square (PLS) with software SmartPLS version 4.0. The research results show that knowledge management positive and significant effect on employee performance (t = 14.114, p = 0.000) and innovative work behavior (t = 9.465, p = 0.000). Knowledge management facilitates the creation, storage, transfer, and application of knowledge so that employees can work more effectively, efficiently, and innovatively. PT. PBT Site Bukit Asam needs to realize the importance of knowledge management and investing resources to develop and implement the system of knowledge management effective way to improve the quality of human resources and company competitiveness. This research shows the importance of knowledge management for PT. PBT Site Bukit Asam in improving the quality of human resources and competitiveness. It is recommended that companies invest sufficient resources to develop and implement the system sustainably.
Analisis Komparatif Algoritma Machine Learning untuk Prediksi Kekambuhan Kanker Payudara Berdasarkan Karakteristik Tumor Chairunnisa Desti Arzety; Vanya Dwi Nabila; Aprilia Herawati; Allsela Meiriza; Ken Ditha Tania
Techno.Com Vol. 25 No. 2 (2026): May 2026
Publisher : LPPM Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/tc.v25i2.15976

Abstract

Kanker payudara merupakan salah satu penyebab kematian tertinggi pada perempuan di dunia, di mana tantangan utamanya terletak pada risiko kekambuhan (recurrence). Penelitian ini bertujuan untuk membangun model prediksi kekambuhan kanker payudara dengan membandingkan tiga algoritma machine learning, yaitu Logistic Regression, Decision Tree, dan Random Forest, berdasarkan karakteristik tumor dari dataset METABRIC. Tahapan penelitian meliputi pra-pemrosesan data, seleksi fitur klinis, dan pembagian data dengan rasio 80:20. Hasil evaluasi menunjukkan bahwa Logistic Regression memiliki performa terbaik dalam hal akurasi (0,661) dan ROC-AUC (0,689), sementara Random Forest menunjukkan keunggulan pada metrik recall (0,544) yang krusial untuk deteksi pasien berisiko. Analisis feature importance mengidentifikasi bahwa jumlah mutasi genetik (Mutation Count), Nottingham Prognostic Index (NPI), dan ukuran tumor merupakan faktor paling dominan dalam memprediksi kekambuhan. Penelitian ini menyimpulkan bahwa karakteristik biologis tumor memiliki pengaruh signifikan terhadap risiko kekambuhan dan penggunaan machine learning berpotensi besar menjadi sistem pendukung keputusan klinis untuk stratifikasi risiko pasien secara objektif.   Kata kunci - Kanker Payudara, Kekambuhan, Machine Learning, METABRIC, Karakteristik Tumor
Co-Authors Allsela Meiriza Aprilia Herawati Chairunnisa Desti Arzety Vanya Dwi Nabila Vensi Yeka Isdiani