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Tri A. Sundara
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The Indonesian Journal of Computer Science
Published by STMIK Indonesia Padang
ISSN : 25497286     EISSN : 25497286     DOI : https://doi.org/10.33022
Core Subject : Science, Engineering,
Computer Science & IT Electrical & Electronics Engineering Engineering
The Indonesian Journal of Computer Science (IJCS) is a bimonthly peer-reviewed journal published by AI Society and STMIK Indonesia. IJCS editions will be published at the end of February, April, June, August, October and December. The scope of IJCS includes general computer science, information system, information technology, artificial intelligence, big data, industrial revolution 4.0, and general engineering. The articles will be published in English and Bahasa Indonesia.
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 1,170 Documents
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Redesign of Green Coffee Processing Machine using Value Engineering with Ergonomic approach Bramantya Radityatama Nugraha; Safirin, M. Tutuk Safirin
The Indonesian Journal of Computer Science Vol. 13 No. 2 (2024): The Indonesian Journal of Computer Science
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v13i2.3792

Abstract

Green Coffee (Coffea Canephore Var. Robusta) is coffee made from unroasted coffee beans (Coffea spa). Green coffee extract contains higher levels of antioxidants compared to roasted coffee. The current issue is the variability in the quality of green coffee produced, mainly due to manual processing methods. To enhance the economic value of coffee, its quality must be maintained, especially during the production process. Based on this issue, this research is conducted to design a coffee processing machine to improve the quality of the produced coffee. Although coffee machines already exist, their design process often overlooks user comfort. Hence, this research employs anthropometric approaches to produce ergonomic product designs. Additionally, utility engineering is utilized to identify costs. This research results in innovative product designs considering ergonomic aspects and offering economically viable prices compared to similar products currently available in the market.
Enhancing AdaBoost Performance: Comparative Analysis of CPU Parallel Processing on Breast Cancer Classification Ashqi Saeed, Vaman; Zeebaree, Subhi R. M.
The Indonesian Journal of Computer Science Vol. 13 No. 2 (2024): The Indonesian Journal of Computer Science
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v13i2.3793

Abstract

The implementation of time-sharing across processes in a real-time way has the potential to increase the execution efficiency of multiprocessor systems like the one described above. The system is able to carry out tasks that make use of a large number of processors in an effective way as a result of this. The aim of this research is to design a system with two primary goals: to enhance accuracy and to minimise the amount of time necessary with processing. This will be accomplished by integrating the ADABoost model with the decision tree algorithm. Furthermore, the statistics unambiguously demonstrate that the accuracy remains the same regardless of whether or not the central processing unit (CPU) makes use of parallel processing, which suggests that there is no variation in parallelization. As a consequence of this, there is a direct connection between the amount of time that is spent and an increase in the amount of parallel processing that is carried out by the central processing unit pertaining to the breast cancer dataset that is being investigated. This research was carried out using Python, which was the programming language that was used for the coding technique that was carried out during the course of its execution.
Blockchain for Distributed Systems Security in Cloud Computing: A Review of Applications and Challenges Fadhil, Jawaher; Zeebaree, Subhi R. M.
The Indonesian Journal of Computer Science Vol. 13 No. 2 (2024): The Indonesian Journal of Computer Science
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v13i2.3794

Abstract

The blockchain is a technology that utilizes a decentralized and distributed ledger system to enhance security in cloud computing for distributed systems. It has gained significant attention in various applications, including the Internet of Things (IoT) and cloud computing. However, the blockchain has scalability limitations that restrict its ability to handle different types of transactions effectively. On the other hand, cloud computing provides the availability of shared computer system resources on demand, but it faces challenges related to automation, process management, policy, and others. By combining blockchain technology with cloud computing in a unified system, it is possible to improve data integrity, resource management, pricing, fair compensation, and resource allocation. This article examines the applications and challenges of blockchain, emphasizing how it ensures data integrity, transparency, and resistance to tampering. It also explores various use cases to address obstacles like scalability issues and interoperability concerns, providing a comprehensive overview of the intersection between blockchain, distributed systems, and cloud computing security. The integration of cloud computing and blockchain is important for business applications because it offers advantages in terms of privacy, security, and service support. This review provides an extensive and up-to-date summary of the integration of cloud computing and blockchain, highlighting its significance in business contexts.
Perbandingan Algoritma Klasifikasi untuk Prediksi Kelulusan Mahasiswa Teknik Informatika dengan Orange Data Mining Attyyatullatifah, Iqlimah; Kamayani, Mia
The Indonesian Journal of Computer Science Vol. 13 No. 2 (2024): The Indonesian Journal of Computer Science
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v13i2.3796

Abstract

Penyelesaian studi tepat waktu merupakan indikator penting dalam menilai kompetensi lulusan. Meskipun demikian, muncul tantangan karena tidak semua mahasiswa dapat menyelesaikan studi mereka sesuai jadwal yang telah ditentukan. Penelitian ini mengembangkan model prediksi status kelulusan mahasiswa menggunakan empat algoritma klasifikasi: Decision Tree, Naïve Bayes, K-NN, dan SVM. Data penelitian mencakup 500 data mahasiswa angkatan 2018-2020 di Universitas Muhammadiyah Prof. Dr. Hamka, dengan 60% data latihan dan 40% data uji. Analisis dilakukan menggunakan perangkat lunak Orange Data Mining, dengan evaluasi menggunakan K-Fold Cross Validation (k=5), Confusion Matrix, dan ROC. Hasil analisis menunjukkan bahwa model K-NN memiliki performa tertinggi dengan akurasi 92%, recall 90%, dan presisi 92%. Decision Tree menempati posisi kedua dengan akurasi 90%, presisi 87%, dan recall 90%. SVM mencapai akurasi sebesar 84%, dengan presisi 90%, recall 73%. Sementara itu, model Naïve Bayes menunjukkan akurasi 83%, presisi 80%, dan recall 83%.
Skin Cancer Segmentation On Dermoscopy Images Using Fuzzy C-Means Algorithm Aldi, Febri; Sumijan
The Indonesian Journal of Computer Science Vol. 13 No. 2 (2024): The Indonesian Journal of Computer Science
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v13i2.3797

Abstract

Millions of people around the world suffer from skin cancer, a common and sometimes fatal disease. Dermoscopy has become an effective diagnostic technique for skin cancer. Precise segmentation is essential for skin cancer diagnosis. Segmentation allows more precise analysis of dermoscopic images by defining the boundaries of the lesion and separating it from surrounding healthy tissue. Dermoscopy images served as a source of research data, and Fuzzy C-Means (FCM) segmentation techniques were used. FCM is a promising method and has received a lot of attention lately. FCM is able to distinguish the various components within the lesion and effectively separate the lesion from the surrounding area. As a result, the distribution of membership degree values of each pixel in the image for each cluster represents the segmentation results obtained through FCM. The FCM technique for segmenting dermoscopic images is expected to significantly improve the precision and effectiveness of skin cancer diagnosis.
Comparative Analysis of XGBoost Performance for Text Classification with CPU Parallel and Non-Parallel Processing Ahmed Al-Zakhali, Omar; Zeebaree, Subhi; Askar, Shavan
The Indonesian Journal of Computer Science Vol. 13 No. 2 (2024): The Indonesian Journal of Computer Science
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v13i2.3798

Abstract

This paper shows the findings of a study that looks at how CPU parallel processing changes the way Extreme Gradient Boosting (XGBoost) classifies text. XGBoost models can sort news stories into set groups faster and more accurately, with or without CPU parallelism. This is the main goal of the study. The Keras dataset is used to prepare the text so that the TF-IDF (Term Frequency-Inverse Document Frequency) features can be found. These features will then be used to train the XGBoost model. This is used to check out two different kinds of the XGBoost classifier. There is parallelism between one of them and not it in the other. How well the model works can be observed by how accurate it is. This includes both how long it takes to learn and estimate and how well predictions work. The models take very different amounts of time to compute, but they are all pretty close in terms of how accurate they are. Parallel processing on the CPU has made tasks proceed more rapidly, and XGBoost is now better at making the most of that speed to do its task. The purpose of the study is to show that parallel processing can speed up XGBoost models without affecting their accuracy. This is helpful for putting text into categories.
Sistem Rekomendasi Al-Quran Berbasis Topik Putriando, Zakia; Edy Sutanto, Taufik
The Indonesian Journal of Computer Science Vol. 13 No. 2 (2024): The Indonesian Journal of Computer Science
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v13i2.3799

Abstract

Penelitian ini bertujuan untuk mengatasi tantangan kesehatan mental remaja Indonesia, yang merupakan salah satu hambatan dalam mencapai visi "Indonesia Emas" pada tahun 2045, di mana negara diharapkan memasuki masa bonus demografi dengan mayoritas penduduk di usia produktif. Data Kementerian Kesehatan (2023) menunjukkan bahwa 6.1% penduduk usia 15 tahun ke atas mengalami gangguan kesehatan mental, menandakan kebutuhan mendesak untuk intervensi yang efektif. Penelitian ini mengembangkan sistem rekomendasi ayat-ayat Al-Quran, menggunakan konsep similarity dan kategorisasi ayat (topik) berdasarkan Dewan Syariah Nasional MUI. Penelitian ini menyediakan rekomendasi yang akurat dan relevan untuk membantu penggunanya untuk mendapatkan solusi masalah kesehatan mental yang Islami. Evaluasi sistem menunjukkan bahwa mayoritas rekomendasi yang dihasilkan sesuai dengan pandangan para ahli, menandakan keefektifan sistem dalam menyediakan referensi yang jelas dan akurat. Penelitian ini tidak hanya memberikan wawasan baru dalam pengembangan solusi berbasis teks religius untuk isu kesehatan mental, tetapi juga berkontribusi pada upaya lebih luas dalam menghadapi tantangan sosial dan kesehatan yang dihadapi Indonesia.
Implementation of Predicting the Availability of Chicken Eggs on Christmas Day Using Artificial Neural Network Backpropagation Nofianti, Arin; Dwi Suhendra, Christian; Sanglise, Marlinda
The Indonesian Journal of Computer Science Vol. 13 No. 2 (2024): The Indonesian Journal of Computer Science
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v13i2.3800

Abstract

Prediction can be called a science that is used to predict events that are likely to occur in the future based on past events. One of the other prediction methods in circulation is Backpropagation Neural Network. Backpropagation Neural Network (BPNN) is a Neural Network (NN) that is forward in nature and does not have a loop through which signals flow from input neurons to output neurons. This research aims to determine a prediction of egg supply in 2023, especially during Christmas in Manokwari district to meet market and customer needs. By analyzing the availability of egg supplies in the city of Manokwari from January 2018 to December 2022. From the methods used in this research, starting from data collection methods as well as variables and research stages which include the data collection process, data sharing, then training and data testing and validation crosswise, the prediction pattern for the number of egg stocks is 12-16-1, where there are 12 variables in the input layer, then 16 variables in the hidden layer, 1 variable in the output layer, the learning rate value is 0.9 and the value the momentum is 0.1, resulting in a prediction of egg stock in 2023, especially in December, of 131053 eggs. With a MAPE value of 27.4767%. with the results of a feasible prediction model value. With the predicted results, the number of egg stocks in 2023, especially in December (during Christmas celebrations) in Manokwari Regency is 131,053 eggs during December 2023.
Unveiling the Synergistic Relationship between Distributed Systems and Cloud Computing: A Review of Architectural Trends Salih, Sardar; Subhi R. M. Zeebaree
The Indonesian Journal of Computer Science Vol. 13 No. 2 (2024): The Indonesian Journal of Computer Science
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v13i2.3801

Abstract

Cloud providers use distributed systems for scalability, availability, performance, automation, multi-tenancy, and innovation. Distributed cloud computing distributes workload across multiple locations, improving application performance and responsiveness. Significantly potential computational resources are developed in cloud, where large-scale, intricate tasks are performed with the backbone of distribute infrastructure in cloud systems, similar to supercomputing. Cloud computing development has significantly impacted software development and testing, necessitating applications compatible with the cloud, large data users, and high security. Distributed applications hoist on to cloud platforms where increased efficiency, reliability and low costs are favored and further be stored in the cloud for flexibility and scalability. Cloud service models include Software as a Service (SaaS), Platform as a Service (PaaS), and Infrastructure as a Service (IaaS), each offering different application services, programming languages, and hosting environments. The synergistic aspects of Distributed Systems and Cloud Systems with respect to their basic capabilities are discussed and systematically reviewed.
Performance Evaluation of Extra Trees Classifier by using CPU Parallel and Non-Parallel Processing Hussein, Nashwan; R. M. Zeebaree, Subhi
The Indonesian Journal of Computer Science Vol. 13 No. 2 (2024): The Indonesian Journal of Computer Science
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v13i2.3802

Abstract

This research delves into assessing the performance of the Extra Trees Classifier, specifically examining the influence of CPU parallel processing on classification accuracy and computational efficiency. Fashion MNIST, a collection of grayscale images representing clothing items, serves as the foundational dataset for this study. Two variations of the Extra Trees Classifier are implemented: one configured without CPU parallel processing and another utilizing maximum CPU cores for parallel execution. The primary evaluation metrics include accuracy measurement and computational time taken for both training and prediction tasks. The findings reveal notable insights, showcasing that while the Extra Trees Classifier demonstrates commendable accuracy in classifying Fashion MNIST images, the implementation of CPU parallel processing significantly reduces computational time without compromising accuracy levels. This observation underscores the pivotal role of optimizing computational resources for efficient model training and deployment in machine learning applications. The results of this study are very helpful for understanding how to use parallel processing to make machine learning tasks more accurate and more efficient. It also shows how important it is to optimize resources for scalable and effective model development.

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