cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
Hero Wintolo
Contact Email
herowintolo@stta.ac.id
Phone
-
Journal Mail Official
informatika@stta.ac.id
Editorial Address
-
Location
Kab. bantul,
Daerah istimewa yogyakarta
INDONESIA
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 6 Documents
 1 
Search results for , issue "Vol 12, No 2 (2023): November" : 6 Documents clear
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.  
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.
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.
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.
Arc Circularitie Naive Bayes for Occupational Safety Helmet Detection Rizaldi, Taufiq; Putranto, Hermawan Arief
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.v12i2.1760

Abstract

Occupational Safety and Health (OHS) is an effort to guarantee and protect the safety and health of every worker through efforts to prevent work accidents and work-related diseases. Safety Helmet is one of the components that must exist and be used in accordance with Occupational Safety and Health standards. Detection of safety helmets usage is one of the efforts to support these activities. The application of Arc Circularity Naive Bayes is used to detect whether an object meets the ratio of a circle by utilizing RGB and HSV image filtering and classification using Naïve Bayes. That method is used to detect whether a worker uses a safety helmet or not, it also detects helm color. The average value of accuracy is 50.8, precision is 58.3%, recall is 66.0%, and f1-score is 59.5% which are calculated using the Confusion Matrix

Page 1 of 1 | Total Record : 6

 1 

Filter by Year

2023 2023


Reset
Filter By Issues
All Issue Vol 14, No 2 (2025) Vol 14, No 1 (2025): May Vol 13, No 2 (2024): November Vol 13, No 1 (2024): May Vol 12, No 2 (2023): November Vol 12, No 1 (2023): May Vol 11, No 2 (2022): November Vol 11, No 1 (2022): May Vol 10, No 2 (2021): November Vol 10, No 1 (2021): Mei Vol 10, No 1 (2021): May Vol 9, No 2 (2020): November Vol 9, No 1 (2020): Mei Vol 9, No 1 (2020): May Vol 8, No 2 (2019): November Vol 8, No 1 (2019): Mei Vol 8, No 1 (2019): May Vol 8, No 1 (2019): Mei Vol 7, No 2 (2018): November Vol 7, No 2 (2018): November Vol 7, No 1 (2018): Mei Vol 7, No 1 (2018): May Vol 7, No 1 (2018): Mei Vol 6, No 2 (2017): November Vol 6, No 2 (2017): November Vol 6, No 1 (2017): Mei Vol 6, No 1 (2017): Mei Vol 5, No 2 (2016): November Vol 5, No 2 (2016): November Vol 5, No 1 (2016): Mei Vol 5, No 1 (2016): Mei Vol 4, No 2 (2015): November Vol 4, No 2 (2015): November Vol 4, No 1 (2015): Mei Vol 4, No 1 (2015): Mei Vol 3, No 2 (2014): November Vol 3, No 2 (2014): November Vol 3, No 1 (2014): Mei Vol 3, No 1 (2014): Mei Vol 2, No 2 (2013): November Vol 2, No 2 (2013): November Vol 2, No 1 (2013): Mei Vol 2, No 1 (2013): Mei Vol 1, No 2 (2012): November Vol 1, No 2 (2012): November Vol 1, No 1 (2012): Mei Vol 1, No 1 (2012): Mei More Issue