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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.
Arjuna Subject : -
Articles 423 Documents
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Minimization Implementation of Fuzzy Logic to Optimize a Cashew Nut Production using Simpleks-Duality Theory Asriningtias, Yuli; Utami, Wahyu Sri
Compiler Vol 12, No 2 (2023): November
Publisher : Institut Teknologi Dirgantara Adisutjipto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28989/compiler.v12i2.1836

Abstract

Production control of cashew nuts greatly affects the profits earned by the company. When cashew nut raw materials exceed production needs, it results in storing cashews for an extended period, causing the cashew products to lose freshness, and the quality of processed cashews deteriorates. If the raw materials are damaged, the manufacturer must acquire additional costs to procure raw materials again. Another issue is that cashew nut raw materials are seasonal products and are not always available.To solve these problems, a calculation method is needed to control optimal production inventory to minimize the company's expenditures. The method used is to formulate the problem into a Fuzzy Linear Programming mathematical model using a combination of methods: the Simplex algorithm and Duality Theory. The case implementation focuses on the uncertainty of cashew nut production quantities outside the harvest season. Moreover, the calculation of an optimal production quantity to minimize resulting losses is needed. The output generated is an optimal production prediction using the combination of the Simplex Algorithm and Duality Theory in solving Fuzzy Linear Programming.  
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.
Implementation of the Decission Tree Algorithm to Determine Credit Worthiness Abdussomad, Abdussomad; Kurniawan, Ilham; Wibowo, Agung
Compiler Vol 12, No 2 (2023): November
Publisher : Institut Teknologi Dirgantara Adisutjipto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28989/compiler.v12i2.1911

Abstract

Credit is a loan from a bank that needs to be repaid with interest. In practice, problematic credit or bad credit often occurs due to less thorough credit analysis in the credit granting process, or from bad customers. This research aims to predict creditworthiness using the Decision Tree Classification Algorithm and find a solution for determining it. This research uses the CRISP-DM (Cross-Industry Standard Process for Data Mining) method. This research method tests the effects of using the decision tree, Support Vector Machine, and Naïve Bayes model with the Decision Tree Classification Algorithm. The decision tree classification algorithm accurately analyzed problem loans and non-problem debtors at 93.49%. The decision tree algorithm test results are better than the support vector machine by 3.45%, and naïve bayes by 13.03%. The results of our study were also 4.16% better than the previous study. This research has also implemented the selected model in the form of website application deployment.
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.
Combatting Heart Diseases: Advanced Predictions Using Optimized DNN Architecture Azis, Mochammad Abdul; Sumarna, Sumarna
Compiler Vol 12, No 2 (2023): November
Publisher : Institut Teknologi Dirgantara Adisutjipto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28989/compiler.v12i2.1915

Abstract

Heart disease has become a global health issue and is recorded as one of the primary causes of death in many countries. In this modern era, with rapid technological advancements and shifting lifestyles, numerous factors contribute to the increasing prevalence of heart diseases. These range from dietary habits, lack of physical activity, stress, to genetic factors. Given the complexity of this ailment, information technology plays a crucial role in providing innovative solutions. One of them is predicting the risk of heart disease, enabling more targeted early prevention and treatment interventions.Correct data analysis is pivotal in making predictions. However, a common challenge often encountered is the imbalance in data classes, which can result in a predictive model being biased. This is certainly detrimental, especially in the context of predicting strokes, where prediction accuracy can mean the difference between life and death.In this research, our focus was on developing a Deep Neural Network (DNN) Architecture model. This model aims to offer more accurate predictions by considering data complexities. By optimizing several key parameters, such as the type of optimizer, learning rate, and the number of epochs, we strived to achieve the model's best performance. Specifically, we selected Adagrad as the optimizer, set the learning rate at 0.01, and employed a total of 100 epochs in its training.The results obtained from this research are quite promising. The optimized DNN model displayed an accuracy score of 0.92, precision of 0.92, recall of 0.95, and an f-measure of 0.93. This indicates that with the right approach and meticulous optimization, technology can be a highly valuable tool in combatting heart diseases.
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.
Optimization of production planning using integer linear programming method (case study of bakpia menik) Astuti, Marni; Prabowo, Ardian; Sullyarta, Esa Rengganis; Zabidi, Yasrin
Compiler Vol 12, No 2 (2023): November
Publisher : Institut Teknologi Dirgantara Adisutjipto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28989/compiler.v12i2.1919

Abstract

Bakpia Menik is a Small and Medium Enterprise (UKM) which operates in the field of bakpia production. Bakpia is one of the local cakes which is the main souvenir from Yogyakarta, so there is a big potential for increasing production. However, SMEs still lack production management planning to optimize resource use. So it is difficult to capture this big opportunity to maximize profits for SMEs. Seeing these problems, this research designed an integer linear programming mathematical model to optimize profits by using existing resources. Integer Linear Programming (ILP) is a mathematical model for maximizing profit and minimizing cost based on a mathematical model involving integer variables represented in a linear relationship. This research produces an integer linear programming model that presents the variables, resources, and constraints of Bakpia Menik. The model output shows that the optimal production amount by maximizing resource use is 31232 units and the maximum profit is IDR 33,431,750. From the model output, it can also show the advantages and disadvantages of Bakpia resources, so that resource availability planning can be carried out which can minimize holding costs.
Forward Chaining Method in a Web-Based Bipolar Disorder Expert System Aritonang, Roselina; Prasetyaningrum, Putri Taqwa
Compiler Vol 12, No 2 (2023): November
Publisher : Institut Teknologi Dirgantara Adisutjipto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28989/compiler.v12i2.1694

Abstract

Bipolar disorder is a psychiatric disorder. At Argodadi Pinilih Disability Secretariat, Sedayu District, Yogyakarta Region, around 60% of people with disabilities are affected by psychiatric disorders and one of them is bipolar, one of them is depression, and suicide if initial treatment is not given to the sufferer. In addition, the cost of doing consultation with the medical specialist is not cheap.An expert system is an artificial intelligence system that is useful for diagnosing an error and as a decision-maker with the knowledge rules applied by an intelligent system that can solve problems like an expert. In making an expert system, the forward chaining method is used which aims to be able to diagnose bipolar disorder with accurate results and its utilization can be used by experts and laypeople to make an initial diagnosis of bipolar disorder.The results of this study are in the form of an expert system program that is used to diagnose bipolar disorder which can provide information related to the disease and can provide information on initial treatment of the disease. The information obtained by the consultant from the expert system is in the form of a percentage.
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%).

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