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Compiler
Published by Institut Teknologi Dirgantara Adisutjipto
ISSN : 22523839     EISSN : 25492403     DOI : 10.28989/compiler
Core Subject : Science,
Computer Science & IT
Jurnal "COMPILER" dengan ISSN Cetak : 2252-3839 dan ISSN On Line 2549-2403 adalah jurnal yang diterbitkan oleh Departement Informatika Sekolah Tinggi Teknologi Adisutjipto Yogyakarta. Jurnal ini memuat artikel yang merupakan hasil-hasil penelitian dengan bidang kajian Struktur Diskrit, Ilmu Komputasi , Algoritma dan Kompleksitas, Bahasa Pemrograman, Sistem Cerdas, Rekayasa Perangkat Lunak, Manajemen Informasi, Dasar-dasar Pengembangan Perangkat Lunak, Interaksi Manusia-Komputer, Pengembangan Berbasis Platform, Arsitektur dan Organisasi Komputer, Sistem Operasi, Dasar-dasar Sistem,Penjaminan dan Keamanan Informasi, Grafis dan Visualisasi, Komputasi Paralel dan Terdistribusi, Jaringan dan Komunikasi, Desain, Animasi dan Simulasi Pesawat Terbang. Compiler terbit setiap bulan Mei dan November.
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Articles 7 Documents
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Search results for , issue "Vol 13, No 1 (2024): May" : 7 Documents clear
Optimising Bcrypt Parameters: Finding the Optimal Number of Rounds for Enhanced Security and Performance Listiawan, Indra; Zaidir, Zaidir; Winardi, Sugeng; Diqi, Mohammad
Compiler Vol 13, No 1 (2024): May
Publisher : Institut Teknologi Dirgantara Adisutjipto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28989/compiler.v13i1.2111

Abstract

Recent advancements in the field of information security have underscored the imperative to fine-tune Bcrypt parameters, particularly focusing on the optimal number of rounds as the objective of research. The method of research is a Brute Force Search method to find the optimal value of bcrypt rounds. The primary focal point of optimization lies in the number of Bcrypt rounds due to its direct impact on security levels. Elevating the number of rounds serves to fortify the security of the Bcrypt algorithm, rendering it more resilient against brute-force attacks. The execution of the Bcrypt rounds in the experimental method mirrors real-world scenarios, specifically in the evaluation of Bcrypt parameters with a focus on entropy assessment of the hash. The selection of the number of rounds should consider the specific needs of the system, where security takes precedence or faster performance is a crucial factor.
Impact of Wolf Thresholding on Background Subtraction for Human Motion Detection Pambudi, Elindra Ambar; Nurhidayat, Muhammad Ivan
Compiler Vol 13, No 1 (2024): May
Publisher : Institut Teknologi Dirgantara Adisutjipto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28989/compiler.v13i1.2116

Abstract

Series of motion detection based on background subtraction there is an image segmentation stage. Thresholding is a common technique used for the segmentation process. There are two types that can be used in thresholding techniques namely local and global. This research intends to implement local adaptive wolf thresholding as the threshold value of the background subtraction method to detect motion objects. The proposed method consists of the reading frame, background and foreground initialization of each frame, preprocessing, background subtraction, wolf thresholding, providing a bounding box, and running frame sequentially. Based on MSE and PSNR obtained on four videos, it has shown that wolf thresholding has succeeded in outperforming of global threshold.
A Comparative Analysis between K-Means and Agglomerative Clustering Techniques in Maritime Skill Certification Setyawan, Deny Adi; Purwatiningsih, Agustina
Compiler Vol 13, No 1 (2024): May
Publisher : Institut Teknologi Dirgantara Adisutjipto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28989/compiler.v13i1.2158

Abstract

The maritime industry must constantly adjust seafarer training to meet evolving operational demands and ensure compliance with new regulations. This study addresses the challenge of assessing the relevance of Certificate of Proficiency (COP) services by categorizing them to determine which qualifications are essential for marine professionals. The goal is to identify obsolete or misaligned training programs that need updates or enhancements to better serve industry needs. To this end, the study employed two clustering algorithms, K-Means and Agglomerative Clustering, on data from 2021 to 2023. K-Means was chosen for its efficiency in processing large datasets and creating clear, non-overlapping groups. Agglomerative Clustering was selected for its ability to offer a detailed, hierarchical view of data, which helps in understanding the complex structure of certification demands more comprehensively. The analysis identified three main clusters; notably, Cluster 2 indicated a high demand for critical certifications, while Cluster 1, containing the majority of certifications, received little interest, suggesting they may be less relevant. This insight encourages training providers to consider refining their offerings. Although comprehensive, the study's three-year timeframe suggests extending this period in future research for a more detailed trend analysis and forecasting in maritime training adaptations.
Classification and Evaluation of Sleep Disorders Using Random Forest Algorithm in Health and Lifestyle Dataset Widyastuty, Wiwiek; Azis, Mochammad Abdul
Compiler Vol 13, No 1 (2024): May
Publisher : Institut Teknologi Dirgantara Adisutjipto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28989/compiler.v13i1.2184

Abstract

Sleep is a fundamental aspect of human life, accounting for approximately one-third of our existence and playing a crucial role in the restoration of physical health and overall quality of life. However, poor sleep quality can interfere with these critical restorative processes, leading to disorders such as apnoea and insomnia. These conditions not only impair daily performance but also have long-term health consequences. Furthermore, the challenges imposed by modern lifestyles have increased the prevalence of these sleep disorders, emphasizing the need for effective diagnostic tools. This research aims to harness the capabilities of Machine Learning (ML), specifically the Random Forest algorithm, to detect and analyse patterns indicative of sleep disorders in collected data sets. Random Forest is particularly suited for this task due to its ability to manage complex data sets by building multiple decision trees, thus creating a comprehensive and robust model for classifying sleep disorders. The findings of the study are promising, showing that the Random Forest algorithm can achieve a high level of accuracy in sleep disorder detection. The model demonstrated a test accuracy rate of 97.33%, with a precision of 96%, and a recall rate of 100%. Additionally, it achieved an F1-Score of 98% and a Kappa Score of 0.945, validating the reliability of this algorithm in producing precise classifications. This research offers significant insights into the patterns of sleep disorders and contributes to the development of targeted interventions aimed at improving sleep quality. Ultimately, this could significantly enhance the quality of life for individuals suffering from sleep disorders.
Implementation of the Psychological Scale Depression Anxiety Stress Scale 21 (Dass-21) in the Expert System for Diagnosing Mental Health Disorder Mola, Sebastianus Adi Santoso; Melly, Margaretha Delima; Yublina Pandie, Emerensye Sofia
Compiler Vol 13, No 1 (2024): May
Publisher : Institut Teknologi Dirgantara Adisutjipto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28989/compiler.v13i1.1938

Abstract

Mental health is something that needs to be considered properly because if the mental is disturbed then the body also feels the impact. Mental health disorders including depression, stress, and anxiety can affect anyone, especially students. Due to the lack of awareness of mental health in students and the minimal number of clinical psychologists in Indonesia, students are reluctant to see a psychologist. The existence of an expert system for early detection of mental health disorders using the Depression Anxiety Stress Scale (DASS-21) with 21 symptoms can help students analyse the level of mental health disorders which are divided into depression, stress, and anxiety. The results of the study based on 100 student data of Nusa Cendana University obtained the system can diagnose mental health disorders including depression, stress, and anxiety with an accuracy rate of expert and system results of 100% which shows that the implementation of the DASS-21 instrument into the system is correct. Findings from the diagnosis results show that most students (70%) suffer from anxiety in the moderate to severe category. However, special attention needs to be paid to students who suffer from moderate to severe depression (37%) and severe to moderate stress (36%).
Morphological Study of The Liliba River Utilizing Remote Sensing System Anmuni, Avilla Martha; Loden, Onisius; Rafael, Jusuf Wilson Meynerd
Compiler Vol 13, No 1 (2024): May
Publisher : Institut Teknologi Dirgantara Adisutjipto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28989/compiler.v13i1.2114

Abstract

The branch of natural sciences called river morphology focuses on the study of the characteristics and dynamics of rivers, including their structure, classification, and changes on spatial and temporal scales. Two main factors influence river configurations. The first is natural factors, such as floods and landslides, and the second is human factors, such as human activities that alter river morphology. Cyclone Seroja caused landslides on the banks of the Liliba River in Kupang, East Nusa Tenggara in April 2022. This mainly occurred at Naimata Bridge in Liliba Village, Oebobo District. River geometry, especially the channel and bed elevation, can be significantly influenced by landslides occurring on the riverbanks. Therefore, a study of the morphology of the Liliba River was conducted using remote sensing systems. To conduct a detailed analysis, this study incorporated these photos. In this review, changes in the river channel were examined by extracting the river's course from Landsat image data. From 2009 to 2022, the Liliba River experienced an average shift of 1.60 meters westward and eastward. The research results indicate the need for reforestation along the riverbanks, and residents should be encouraged to reduce the disposal of plastic waste into the river. Future research should also further investigate the geological characteristics of the Liliba River, such as rock types, and conduct hydrological analyses to comprehensively understand the factors influencing changes in the riverbed.
Recommendation System for Clustering to Allocate Classes for New Students Using The K-Means Method Ariyanto, Yuri; Sabilla, Wilda Imama; As Sidiq, Zidan Shabira
Compiler Vol 13, No 1 (2024): May
Publisher : Institut Teknologi Dirgantara Adisutjipto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28989/compiler.v13i1.1962

Abstract

SMAN 1 Durenan has a plan to organize the allocation of classes for new students using a system to achieve practical and efficient student grouping. The reason for implementing this class allocation system is SMAN 1 Durenan aims to create a new system to process student data for class allocation according to specific needs. This research involves the development of a Recommendation System for Clustering to Allocate Classes for New Students using the K-Means method. The system processes data of newly enrolled students at SMAN 1 Durenan based on specific attributes. The results of this student data processing serve as considerations and references for SMAN 1 Durenan to perform class allocation as needed. The analysis in this research utilizes the K-Means method to obtain data clusters that maximize the similarity of characteristics within each group and maximize the differences between the collections created. The developed recommendation system website provides information about the student data clustering results from the K-Means process at SMAN 1 Durenan.

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