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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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Numerical Analysis of Flow Velocity and Performance Test of Cross-Flow Turbine (500 W) Kyaw, Sandar; Swe, War War Min; Soe, Aung Kyaw; Latt, Aung Ko
The Indonesian Journal of Computer Science Vol. 13 No. 3 (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.v13i3.4175

Abstract

The purpose of this study is to analyze the flow velocity and assess the performance of a cross-flow turbine through numerical simulations and performance tests. Using computational fluid dynamics (CFD), the flow patterns and velocity distributions around the turbine blades to gain insights into the turbine's hydrodynamic behavior. In the experimental phase, constructed a prototype of the cross-flow turbine and tested it in a water flume, measuring key performance metrics such as power output and efficiency.The test were performed under design flow condition. The numerical results were validated with performance tests. The numerical analysis revealed critical areas of flow separation and turbulence, which were consistent with the observed experimental data.
Identifikasi Kanker Darah pada Gambar Apusan Darah Perifer (PBS) Menggunakan Ekstraksi Fitur HSV Nozomi, Irohito; Aldi, Febri
The Indonesian Journal of Computer Science Vol. 13 No. 4 (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.v13i4.4177

Abstract

Blood cancer is a category of diseases that have an impact on the development and operation of blood cells. Due to the complexity and diversity of these diseases, proper diagnosis is required before starting treatment. Medical imaging techniques have undergone significant advances in recent years, especially in peripheral blood smear (PBS) image processing. The aim of this study was to uncover how important the extraction of PBS image features is for the diagnosis of blood cancer. Feature extraction is essential to detect anomalies in blood cells in terms of blood cancer detection. The method used is feature extraction based on hue and saturation values (HSV) and uses Machine Support Vector Machine (SVM) machine learning algorithms in classifying malignant and benign PBS images. PBS image data used in this study was 100 images, consisting of 50 malignant PBS images, and 50 benign PBS images. Through the application of HSV feature extraction techniques and PBS image analysis, SVM algorithms can uncover latent indicators of blood cancer and facilitate timely and precise diagnosis. With the SVM technique, classification accuracy can be achieved by 92%. These results demonstrate the potential effectiveness of this feature extraction method. Extraction of HSV features may alter the diagnosis of blood cancer with additional research and application in clinical settings.
Perbandingan Penggunaan Daya Listrik Antara Baremetal Dengan Docker Container Pada Raspberry Pi 4B Hasvid Dwi Putranto; Muhammad Koprawi, S.Kom., M.Eng.
The Indonesian Journal of Computer Science Vol. 13 No. 4 (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.v13i4.4178

Abstract

In today's development, many services are starting to use Container base services where it is easier to do development and there is also a lot of implementation documentation. But there are also those who still use Baremetal where installing the service is installed directly on the Operating System (OS) and some people are used to using Baremetal in this case the application on the Edge Server or Internet of Things (IoT) such as on the Raspberry Pi, electricity usage is a limitation in making an efficient system so that in this study, will compare electricity usage in Input/output operations per second (IOPS) and Floating point operations per second (Flops). And the results show that Baremetal's electricity usage is smaller than that of Docker Container where the difference is 1.06 GigaFlops/Watt on HPLinpack and on IOPS/Watt, Baremetal is far superior to Docker Container where in IOPS read/Watt superior to 3246.12 and in IOPS write/Watt superior to 1857.51 it is because Docker Container uses several services to run its Container so that it consumes more power.
Application of SMOTE Method on Topic Based Question Classification Using Naïve Bayes Algorithm Orvalamarva; Oktariani Nurul Pratiwi; Faqih Hamami
The Indonesian Journal of Computer Science Vol. 13 No. 4 (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.v13i4.4179

Abstract

In today's digital era, the utilization of technology in education is essential to support the learning process. This research discusses the classification of junior high school mathematics questions using the Naïve Bayes method. The use of an automated system in question classification helps reduce time and effort in grouping questions based on topics. The Naïve Bayes method was chosen because of its simplicity and ability to process data. The results showed that Naïve Bayes with SMOTE and Math symbols achieved 69% accuracy, while without SMOTE, the accuracy was lower. Cross-validation showed that the classification without symbols attained an accuracy of 89.35%, slightly superior to the classification using symbols, which was 88.79%. This result indicates that Naïve Bayes with SMOTE is more effective. Although the difference in accuracy with or without symbols is slight 0.56%, the performance is relatively equivalent, with an accuracy of 89%.
Detection of DDoS Attack Based on Deep Neural Network with various Number of Features Jaber, Suhad Shakir; Kadhim, Rasim Azeez
The Indonesian Journal of Computer Science Vol. 13 No. 4 (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.v13i4.4180

Abstract

Distributed Denial of Service (DDoS) attacks have become an effective threat to the reliability and availability of the services of the internet in last decades. The effectiveness of utilizing Deep Neural Networks (DNNs) for DDoS attack detection is investigated in this paper. We implemented a reliable detection system that analyzes network traffic data to spot any DDoS activities. A multi-layer perceptron model trained on a dataset containing five different forms of DDoS attacks and normal traffic is used in this method. Also, three cases of varous number of features was investigated to extract the optimal number of features that can be used for detection of DDoS attacks. To improve accuracy, a great deal of testing was done on the model's architecture using various hyperparameters and training procedures. With a 96.5% detection rate, the DNN results showed a high degree of accuracy. This demonstration highlights the ability of deep neural networks to identify DDoS attacks in the midst of regular traffic. The six-category classification enhances detection granularity and facilitates the application of more specialized and successful mitigation techniques. Given the great precision attained, DNNs have the potential to be an essential part of real-time detection systems, providing a major advancement over conventional techniques.
Implementasi Komunikasi LoRaWAN dan Platform Antares pada Monitoring Supply Pupuk Tanaman Selada Hidroponik Berbasis Website Mujab, Rachmat Syaiful; Hadi Supriyanto; Wahyudi Purnomo
The Indonesian Journal of Computer Science Vol. 13 No. 4 (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.v13i4.4182

Abstract

Menurut data Badan Pusat Statistik (BPS), konsumsi selada hijau di Indonesia meningkat dari 94,8 gram per orang per minggu pada tahun 2021 menjadi 100,0 gram per orang per minggu pada tahun 2022. Penggunaan Internet of Things (IoT) di sektor pertanian dapat memaksimalkan produksi pangan untuk memenuhi permintaan sayur mayur yang terus meningkat. Dalam Penelitian ini, sistem pemantauan diusulkan berbasis Teknologi LoRaWAN, dengan menggunakan platform gateway milik telkom yaitu Antares. Sistem ini bekerja dengan baik secara keseluruhan. Nilai analog TDS yang memiliki keakuratan 95,25 jika dibandingkan dengan TDS Meter. Pengujian Sensor ultrasonik masing masing memiliki keakuratan di 97,88% dan 98,1%. Untuk nilai SNR memiliki Rata Rata sebesar -5,19dB, dan nilai RSSI memiliki Rata Rata sebesar -113dBm. Sistem ini dapat bekerja selama 24 jam tanpa ada masalah pengiriman Data. Untuk Pengiriman ke website melalui API tidak ada delay signifikan dan semua bekerja dengan baik.
Potret Implementasi e-Government pada Layanan Publik Pengaduan Masyarakat di Indonesia Yunita, Novi Prisma; Selina, Refdian Shella; Anggriawan, Galih
The Indonesian Journal of Computer Science Vol. 13 No. 4 (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.v13i4.4184

Abstract

Public complaints are a form of public services provided by governments. Its function is to facilitate the public in submitting complaints related to public services. Public complaint channels: SMS, telephone number, email, form, and website. Based on website searches, various implementations of complaints were found in government websites. They are vary in term of number and implementation. For example, one website provides 2 channel, other website provides none. Two website provide 2 channel, but differs in attributes. Apart of this diversity, the portrait of implementing public complaints is unexplored. This research aims to map the implementation of public complaints in provincial governments in Indonesia. Official website is used as primary data. Documentation and website analysis is conducted to each websites. Keywords, such as complaints, complaints, public services, and public complaints, are used in website content analysis. The content analysis  is classified into the Gartner stage model: presence, interaction, transaction, and transformation. The results show that of the 38 provincial governments, 6 provincial governments are at the presence stage, 23 at the interaction stage, 4 at the transaction stage, and 5 provincial governments at the transformation stage.
Tingkat Kematangan Tata Kelola Data Lembaga Pemerintah Yang Bertugas Di Bidang Pangan Abhimata Ar Rasyiid; Betty Purwandari; Andi Kurnianto
The Indonesian Journal of Computer Science Vol. 13 No. 4 (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.v13i4.4191

Abstract

In order to support the national food security program, Sistem Informasi Pangan dan Gizi (SIPG) was built. This system was built by one of the government institutions tasked with food. In supporting its function, this system integrates data from internal and external institutions. Currently, not all data can be integrated into the system. The absence of policies related to reference codes and mechanisms for how data is obtained, processed, and displayed is one of the causes. To overcome this, the implementation of data governance is needed. Before doing this, it is necessary to assess the current level of data governance maturity. The assessment will provide an overview of whether the current data governance has been carried out correctly. The assessment was carried out using the Stanford Data Governance Maturity Model. Assessment results show that the current level of maturity is at level 2. While the expectation to be achieved is at level 4. To achieve the expected level of maturity, 48 recommendations are given.
Pemodelan Topik dan Analisis Sentimen pada Teks Ulasan Pengguna Aplikasi Perbankan Seluler di Indonesia Moh Hasan Basri
The Indonesian Journal of Computer Science Vol. 13 No. 4 (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.v13i4.4200

Abstract

Bank di Indonesia telah meluncurkan perbankan seluler (mobile banking) guna memberikan pengalaman layanan terbaik bagi nasabah. Bank harus meningkatkan efektivitas aplikasi perbankan seluler mereka untuk memberikan peningkatan nilai perbankan seluler. Dalam upaya menemukan ruang untuk perbaikan bagi perbankan, penelitian ini dilakukan untuk mengetahui topik yang dibicarakan dan diharapkan serta mengetahui sentimen ulasan pengguna layanan perbankan seluler di Indonesia berdasarkan ulasan Google Play aplikasi perbankan seluler yang dimiliki oleh BNI, BCA, dan Mandiri. Algoritma klasifikasi sentimen logistic regression, naïve bayes, dan support vector machine digunakan dalam penelitian ini. Setiap algoritma dijalankan dengan pemodelan k-fold cross validation. Pemodelan topik menggunakan LDA (Latent Dirichlet Allocation). Algoritma logisitc regression memiliki akurasi tertinggi yaitu 96.88%. Menggunakan model tersebut dapat diketahui ulasan didominasi sentimen negatif yaitu 62,22% sedangkan sentimen positif sebesar 37,78%. Pemodelan topik sentimen positif memiliki nilai koheren tertinggi 0,649 dengan jumlah 19 topik, sentimen negatif memiliki nilai koheren tertinggi 0,440 dengan jumlah 18 topik.
Performance of Cloud Computing Resources Allocations SGLA Model Compared to ARIMA Sekwatlakwatla, Sello Prince; Malele, Vusumuzi
The Indonesian Journal of Computer Science Vol. 13 No. 4 (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.v13i4.4205

Abstract

Solutions for cloud computing are growing in popularity as a means for businesses to streamline operations, save expenses and increase productivity. The benefit of cloud services is that they let customers access on-demand apps and services from a shared pool of programmable computer resources and store data offsite. Cloud computing resource allocation requires sophisticated tools and methodologies for optimal utilization. These problems include load balancing, efficient resource management and compliance with legal and regulatory requirements. The majority of businesses are switching to cloud services and advising their customers to use internet services. However, effective resource allocation is critical for improving performance and lowering costs in this area. Due to unpredictable network traffic in cloud computing, resource allocation is challenging, which causes customers to complain about application timeouts, delayed system times and higher bandwidth use during peak hours. This entails allocating resources to various users and programs, such as memory, processing power, storage, and network bandwidth. In this regard, this study compares the performance ensemble method, which is stepwise Gaussian Linear Autoregressive (SGLA), and the individual method which is autoregressive integrated moving average (ARIMA). The Matlab tool is used for simulation and evaluation of the results. The results show SGLA prediction accuracy increased to an average of 98.9%, and ARIMA prediction showed an accuracy of 75.5%. In this regard, the ensemble method performed better than individual methods using the same datasets. The study recommends the ensemble method for the prediction and allocation of resources in cloud computing.

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