Article Per Year (5 Year)
p-Index From 2021 - 2026
0.444
P-Index
This Author published in this journals
All Journal Journal of Information Systems and Informatics SMART : Jurnal Teknologi Informasi dan Komputer
Haryoko
Universitas Amikom Yogyakarta
Automotive Engineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering Languange, Linguistic, Communication & Media Library & Information Science

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

Found 2 Documents
Search

 1 
Implementation of a Finite State Machine for Character Animation Transitions in Unity-Based 3D Games Muhammad Fairul Filza; Ahmad Zaid Rahman; Nadea Cipta Laksmita; Haryoko; Jedidta Adoni Saputra
SMART : Jurnal Teknologi Informasi dan Komputer Vol. 4 No. 2 (2025): July - December
Publisher : Gayaku Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58222/sj.v4i2.1808

Abstract

A responsive character animation system with smooth transitions is a crucial factor in creating a high-quality and immersive gameplay experience. A common issue that frequently arises is animation that appears rigid or unnatural when a character switches from one action to another. This study aims to implement a Finite State Machine (FSM) to manage player character animation transitions in the game Legacy of the Sunstone. The method employed is a Hierarchical Finite State Machine (HFSM) with Moore Machine characteristics, in which the output in the form of animation is determined by the currently active state at a given time. The implementation was carried out using the Unity Engine and applied the State Pattern to establish a structured, modular, and maintainable code architecture. The CrossFadeInFixedTime technique was utilized to achieve smooth animation blending between states with configurable transition durations. The developed FSM system consists of two main categories: Locomotion states, including Idle, Movement, Jump, Falling, and Crouch, and Combat states, including Aim, Melee, and Takedown. System testing can be conducted using Black Box Testing through an iterative approach across 13 test scenarios that cover all functional requirements of the system.
Explainable AI for Water Quality Classification Using Ensemble Stacking Windha MP Dhuhita; Hastari Utama; Hartatik; Bayu Setiaji; Haryoko
Journal of Information System and Informatics Vol 8 No 3 (2026): June
Publisher : Asosiasi Doktor Sistem Informasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63158/journalisi.v8i3.1601

Abstract

This study proposes a robust and interpretable machine learning framework for water quality classification using a publicly available water quality dataset containing 7,996 samples and 20 physicochemical features with an imbalanced class distribution (88.59% majority and 11.41% minority). The study addresses the critical issue of biased classification toward the majority class, which can lead to risk-prone misclassification of unsafe water. An ensemble stacking model combining XGBoost, LightGBM, and CatBoost with a Random Forest meta-learner (passthrough) was developed using an anti-leakage pipeline integrating RobustScaler and SMOTE within stratified 80:20 train–test cross-validation, while hyperparameter tuning was optimized using F1-score to improve minority-class performance; SHAP was further applied for global and local explainability. The proposed model achieved an F1-score of 0.8563 for the minority class and a ROC-AUC of 0.9846, indicating strong discriminative performance, while SHAP analysis identified ammonia as the most influential feature and revealed that False Negative errors were mainly caused by complex feature interactions. The study contributes an integrated framework combining stacking ensemble learning, anti-leakage evaluation, and SHAP-based global–local interpretation to support more reliable and transparent water quality classification; however, the findings are currently limited to a single dataset and and require multi-dataset validation.
Co-Authors Ahmad Zaid Rahman Bayu Setiaji Hartatik Jedidta Adoni Saputra Muhammad Fairul Filza Nadea Cipta Laksmita Utama, Hastari Windha MP Dhuhita