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All Journal TEKNIK INFORMATIKA JIK Jurnal Ilmu Komputer JOIN (Jurnal Online Informatika)
Syopiansyah Jaya Putra
UIN Syarif Hidayatullah
Computer Science & IT

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Algoritma Cellular Automata (CA) Dan Backtracking Untuk Simulasi Pencarian Jalan Pada Maze Putra, Syopiansyah Jaya; Durachman, Yusuf; Huda, M. Qamarul
JIK: Jurnal Ilmu Komputer Vol 6, No 2 (2008)
Publisher : Lembaga Penerbitan Universitas Esa Unggul

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47007/komp.v6i2.464

Abstract

Human being wishes to finish problems and gain advantages as much as possible with efficiency of resources.The research discusses and compares the use of algorithms: back-tracking and cellular automata (CA) to look for the best way and solution. The main objective of this research is to learn the characteristic of CA and backtracking methos with their implementation at seeking walke at maze. It was found that CA method is more effective than backtracking method.Keywords:  Cellular Automata (CA), Finite Automata (FA), Backtracking
The process capability model for governance of the Election Organizer Ethics Court system Putra, Syopiansyah Jaya
JOIN (Jurnal Online Informatika) Vol 3 No 2 (2018)
Publisher : Department of Informatics, UIN Sunan Gunung Djati Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15575/join.v3i2.266

Abstract

The capability level assesment for governance of the Election Organizer Ethics Court Information System [SIPEPP] is necessary to ensure strategic planning allignment, value delivery, risk management, resources management and performance measurement. SIPEPP implementation has special problems in optimizing human resources and weak supervision management, so it is important to asess capability levels to provide solutions to these problems. The purpose of this paper is to assess the current and expected capability level conditions, gap analysis and recommendations for SIPEPP good governance. This research method uses the Process Assessment Model (PAM) from Control Objectives for Information and Related Technology (COBIT 5) which consists of stages of initiation, planning the assessment, briefing, data collection, data validation, process attribute level, and result and recommendation. The results of this study indicate the level of optimization of resources and performance monitoring processes are level 2 (Managed Process) which means that the process has been recorded, measured and in accordance with the objectives. The process of managing human resources, assets and operations are at level 1 (Performed Process), meaning that both processes have been applied to SIPEPP governance. Recommendations related human resources are the selection of appropriate human resources by involving management, while related assets require a priority list of implementation systems, and related supervision requires detailed monitoring schedules. This study result can be taken into consideration for improving good governance of SIPEPP implementation.
A Comparative Analysis of Random Forest, XGBoost, and LightGBM Algorithms for Emotion Classification in Reddit Comments Anggraini, Nenny; Putra, Syopiansyah Jaya; Wardhani, Luh Kesuma; Arif, Farid Dhiya Ul; Hakiem, Nashrul; Shofi, Imam Marzuki
JURNAL TEKNIK INFORMATIKA Vol. 17 No. 1: JURNAL TEKNIK INFORMATIKA
Publisher : Department of Informatics, Universitas Islam Negeri Syarif Hidayatullah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15408/jti.v17i1.38651

Abstract

This research aims to compare the performance of three classification algorithms, namely Random Forest, XGBoost, and LightGBM, in classifying emotions in Reddit comments. Emotion classification in Reddit comments is a complex classification problem due to its numerous variations and ambiguities. This research utilizes the GoEmotions Fine-Grained dataset, filtered down to 7,325 Reddit comments with 5 different basic emotion labels. In this study, data preprocessing steps, feature extraction using CountVectorizer and TF-IDF, and hyperparameter tuning using GridSearchCV for each algorithm are conducted. Subsequently, model evaluation is performed using Cross-Validation and confusion matrix. The results of the study indicate that Random Forest outperforms the XGBoost and LightGBM algorithm with an accuracy of 75.38% compared to XGBoost with 69.05% accuracy and LightGBM with 66.63% accuracy.
Co-Authors Arif, Farid Dhiya Ul Luh Kesuma Wardhani M. Qamarul Huda Nashrul Hakiem Nenny Anggraini, Nenny Shofi, Imam Marzuki Yusuf Abdurachman