Article Per Year (5 Year)
p-Index From 2021 - 2026
0.444
P-Index
This Author published in this journals
All Journal Jurnal Sisfotek Global Jurnal Sistem Informasi, Manajemen dan Teknologi Informasi
Abdul Hadi
STMIK Palangkaraya
Computer Science & IT Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering

Published : 2 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search

 1 
Tata Kelola Data Center Berbasis ISO 27001 dan ISO 20000 pada DISKOMINFOSANTIK Kalimantan Tengah Herkules Herkules; Christia Putra; Abdul Hadi
Jurnal Sistem Informasi, Manajemen dan Teknologi Informasi Vol. 1 No. 2 (2023): Juli
Publisher : STMIK Palangkaraya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33020/jsimtek.v1i2.429

Abstract

Data Center yang dikelola dan dimanfaatkan dengan baik dapat menghasilkan informasi yang merupakan salah satu sumber daya strategis bagi suatu organisasi. Standar acuan yang tepat dalam pengelolaan data dengan memperhitungkan perangkat keras dan perangkat lunak pendukung dapat menjadi salah satu kunci sukses untuk menghasilkan dan mendistribusikan informasi yang berkualitas sesuai dengan kebutuhan pelaku organisasi. Manajemen pengelolaan pusat data Dinas Komunikasi, Informatika, Persandian dan Statistik (DISKOMINFOSANTIK) Provinsi Kalimantan Tengah melakukan penyusunan manajemen pengelolaan data center yang berfungsi sebagai acuan dalam pengelolaan data center. Tahapan yang dilakukan yaitu studi pustaka, pengumpulan data, proses analisis, pemetaan kontrol ISO 270001 dan ISO 20000, dan perencanaan kebijakan dan prosedur. Tujuan penelitian ini menyusun dokumen tata kelola data center berbasis ISO 270001 dan ISO 20000. Berdasarkan standar ISO tersebut pendekatan yang dilakukan dibagi menjadi tiga bagian utama yaitu  pengelolaan layanan, pengelolaan layanan informasi, pengelolaan kapasitas. Ketiga aspek tersebut peneliti berhasil merinci menjadi 17 standar pengelolaan.
Prediction of UFC Lightweight Winners Using Ensemble Machine Learning Praja Anugerah Pratama; Veny Cahya Hardita; Abdul Hadi
JURNAL SISFOTEK GLOBAL Vol 16, No 1 (2026): JURNAL SISFOTEK GLOBAL
Publisher : Institut Teknologi dan Bisnis Bina Sarana Global

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.38101/sisfotek.v16i2.16311

Abstract

The Ultimate Fighting Championship (UFC) lightweight division presents significant prediction challenges due to factors including knockout variability, injuries, and fluctuating fighter momentum. This study develops an intelligent prediction system for UFC lightweight fight outcomes using ensemble machine learning, deployed as a web-based platform. Historical data from UFCStats.com comprising 6,000 fights and 675 fighters were collected and preprocessed. Feature engineering generated 63 differential attributes, including stance compatibility, recent performance metrics (last five fights), win streak differential, age difference, reach difference, and striking/takedown statistics. Multiple models, including XGBoost, LightGBM, and Logistic Regression, were optimized using Bayesian hyperparameter tuning, with Synthetic Minority Over-sampling Technique (SMOTE) applied to address class imbalance. The soft voting ensemble classifier achieved 79.25% accuracy and 88.67% ROC-AUC on time-based test data, representing a 13.7% to 14.2% improvement over previous state-of-the-art approaches. The primary contributions of this study include: (1) development of 63 domain-specific engineered features with quality adjustments and temporal weighting, (2) achievement of state-of-the-art prediction accuracy through optimized ensemble architecture, and (3) deployment as an accessible web application providing real-time predictions with confidence scores and market odds comparison—transforming academic findings into a practical decision-support tool. Validation against betting market odds demonstrated 76% agreement with market favorites and 82.1% accuracy in consensus cases, confirming alignment with domain expertise while identifying value betting opportunities.
Co-Authors Christia Putra Herkules Herkules Praja Anugerah Pratama Veny Cahya Hardita