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All Journal International Journal of Electronics and Communications Systems Prosiding SEMNAS INOTEK (Seminar Nasional Inovasi Teknologi)
Triyoga, Khavid Wasi
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Mathematics Mechanical Engineering Transportation

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Pembangunan Ontology Berbasis Metode Methontology Untuk Domain Tuberculosis Triyoga, Khavid Wasi; Cahyani, Denis Eka; Sihwi, Sari Widya
Prosiding SEMNAS INOTEK (Seminar Nasional Inovasi Teknologi) Vol. 3 No. 1 (2019): PROSIDING SEMNAS INOTEK Ke-III Tahun 2019
Publisher : Universitas Nusantara PGRI Kediri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29407/inotek.v3i1.512

Abstract

Aplikasi knowledge base untuk penyakit tuberculosis seperti repository telah banyak dikembangkan. Pada pengaplikasiannya, metode yang digunakan kebanyakan menggunakan metode konvensional yang memiliki banyak keterbatasan dalam representasi pengetahuan. Ontology adalah ide baru dalam penerapan pengetahuan yang lebih jelas dan kompleks dibandingkan menggunakan metode konvensional. Selain itu ontology juga memiliki kelebihan dalam semantic basic query dibandingan dengan metode konvensional. Hasil dari penelitian ini adalah sebuah ontology yang mampu menjadi dasar penerapan knowledge penyakit tuberculosis.
Enhancing Academic Staff Performance Prediction in Higher Education: A Data-Driven Hybrid Machine Learning Approach Triyoga, Khavid Wasi; Laksono , Pringgo Widyo; Damayanti, Retno Wulan
International Journal of Electronics and Communications Systems Vol. 5 No. 2 (2025): International Journal of Electronics and Communications System
Publisher : Universitas Islam Negeri Raden Intan Lampung, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24042/ijecs.v5i2.28670

Abstract

Enhancing the performance of academic staff is a key factor in maintaining institutional productivity and service quality in higher education. This study aims to develop a data-driven hybrid model capable of enhancing performance management effectiveness through the integration of predictive intelligence and evidence-based managerial recommendations. This model combines K-Means Clustering, Data Envelopment Analysis (DEA), Exploratory Factor Analysis (EFA), and Random Forest to analyze digital attendance data, service satisfaction surveys, and performance records from 2022 to 2024. This research was conducted at the Faculty of Teacher Training and Education, Sebelas Maret University (FKIP UNS) as a representative case study. The test results show that the predictive model achieved 92 percent accuracy and an F1-score of 0.90 in classifying low performance risk. A strong negative correlation was found between attendance tardiness and service satisfaction levels. DEA analysis identified 32 percent inefficiency in resource utilization, while EFA revealed three dominant latent factors: compliance with SOPs (0.82), academic productivity (0.89), and psychosocial well-being (0.93). Intervention cluster management (SOP training and workload reduction) resulted in a 28 percent increase in SOP compliance. These findings indicate that the integration of hybrid machine learning with efficiency and factor analysis can be an effective framework for data-driven improvement in academic staff performance.
Co-Authors Denis Eka Cahyani Laksono , Pringgo Widyo Retno Wulan Damayanti Sari Widya Sihwi, Sari Widya