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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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The Feasibility Study of a Solar Power Plant at Building B, Universitas Multimedia Nusantara Saputri, Fahmy
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.v13i3.3977

Abstract

The use of environmentally friendly energy resources is becoming increasingly important in reducing the carbon footprint of buildings. One approach is to integrate solar power generation into the building's energy system. This research aims to reduce the carbon footprint by replacing conventional electricity sources with environmentally friendly renewable energy sources and integrating energy sources without disrupting the productivity of activities in Building B of Universitas Multimedia Nusantara (UMN). This study encompasses several key parameters, including the limited use of solar panels in Building B of UMN and simulations conducted using PVsyst software with shadow-free assumptions and solar panel orientation facing north. Additionally, the research considers the potential production of clean energy from available utilization areas, including the roof and fifth-floor balcony of Building B. This feasibility study also includes an analysis of electricity usage over three months, with electricity consumption sourced from PLN. The analysis results show that the use of solar panels can yield long-term economic benefits, with capital costs recoverable within 20 years. However, further review is needed regarding potential system losses, costs, and the addition of energy storage options. Cost calculations need to be adjusted to consider building owner profitability for more accurate results. This study only considers profits without accounting for repair and maintenance costs over the 20-year system operation period.
Enhancing Agricultural Efficiency: Deep Learning-Based Soil Crack Detection for Water Irrigation Myint, Khin Moe; Aye, Maung; Hla, Tin Tin
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.v13i3.3979

Abstract

The escalating demand for agricultural precision and environmental monitoring underscores the necessity for effective soil crack detection methods. This study explores the feasibility of employing a Raspberry Pi-powered camera system and deep learning image recognition to detect soil cracks and control agricultural irrigation. The purpose is to develop a soil crack detection system using deep learning techniques, sustain plant growth process, increase productivity, and optimize water irrigation practice. Our approach leverages TensorFlow to craft a convolutional neural network tailored specifically for execution on a Raspberry Pi 3B+. A dataset comprises manually captured images and is trained with the InceptionV3 model categorized into crack or nocrack classes. The accuracy is achieved ranging from 97% to 99%. These results underscore deep learning image recognition models on Raspberry Pi for cost-effective soil crack monitoring and controlling the plants watering system.
Fashion Design Classification Based on Machine Learning and Deep Learning Algorithms: A Review Shushi, Ahmed; Adnan M. Abdulazeez
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.v13i3.3980

Abstract

Integration of machine learning algorithms in fashion design classification brought transformational change by allowing the automated analysis, categorization, and prediction of fashion items based on different attributes of the item. This paper reviews the state-of-the-art in fashion design classification through machine learning techniques. Review of literature, methodology, and challenges in this area indicate that an array of algorithms and methods, stretching from traditional machine learning algorithms to convolutional neural networks and further to transfer learning approaches, is being tried and tested. In this paper, I will discuss performance comparison among several machine learning algorithms, pinning their strengths, limitations, and possible applications in the fashion industry. We further elaborate on crucial challenges, such as touching on the issue of data variability, interpretability, and others on ethical consideration issues, all pointing to the need for fairness and sustainability with respect to the representation of reality in algorithmic decision-making. This paper aims to inform researchers, practitioners, and stakeholders of the opportunities and challenges brought about by the use of machine learning in a fast-paced world like fashion, hence demystifying current directions in landscape classification of fashion design.
Deteksi hate speech pada kolom komentar TikTok dengan menggunakan SVM Ariska, Amelia; Kamayani, Mia
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.v13i3.3982

Abstract

The TikTok application provides numerous features, including the comment section for users to interact with each other. Users can exchange their opinions openly through the comment section. However, as the interaction or exchange of opinions among users increases, the use of hate speech, consciously or unconsciously, remains prevalent. Hate speech refers to actions by an individual or group that can incite criminal acts, thereby harming others. This study aims to identify the use of hate speech in TikTok comment sections using the SVM algorithm and to compare two libraries used in the labeling process to observe the performance of the SVM algorithm model. The labeling process employs a lexicon-based approach. The dictionaries used in this study are the Inset lexicon and VaderSentiment. The SVM algorithm is used as the model to test the evaluation results. The results obtained using the Inset lexicon labeling show an accuracy of 82%, while the second labeling method using VaderSentiment yields an accuracy of 96.21%.
Analysis and Design of E-Commerce Adoption as a Form of Digital Marketing for MSMEs: A Literature Review Amalia F, Endang
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.v13i3.3983

Abstract

Information technology is increasingly advanced and developing according to society's needs. Progress and development of information technology occurs in all sectors, especially trade and business. This makes business actors continue to innovate services related to the use of information technology, so that they can be known by the wider community, especially in transactions and buying and selling especially MSMEs. Besides that, the growth of the digital world and the internet, in particular, has opened up new avenues for MSMEs to offer their products and services. Digital marketing is a viable marketing strategy in the current economic climate. This paper was written using a literature review method or literature review with a qualitative approach. Based on the 7 research both international and national, obtained from various sources via Google Scholar and other online media sources from 2020-2023 with the keywords e-commerce adoption and MSME digital marketing. It can be seen that Branding, Social Media, Content and Caption Marketing, E-mail Marketing, Video Marketing, SEO, Web Design, App Development, SEM have have an important role in digital marketing to increase online sales.
Integration of Machine Learning with Fog Computing for Health Care Systems Challenges and Issues: A Review Abdulazeez, Ali Hussein; Abdulazeez, Adnan Mohsin
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.v13i3.3986

Abstract

Fog computing, a distributed cloud computing model, extends the traditional cloud paradigm to the network's edge, reducing latency and alleviating congestion. It addresses challenges in classical cloud architectures exacerbated by real-time IoT applications, which produce massive amounts of data that traditional cloud computing struggles to process due to limited bandwidth and high propagation delays. Fog computing is crucial in latency-sensitive applications like health monitoring and surveillance, where it processes vast volumes of data, minimizing delays and boosting performance. This technology brings computation, storage, monitoring, and services closer to the end-user, enhancing real-time decision-making capabilities. This paper presents the challenges of IoT applications. It also demonstrates the role of two emerging technologies fog computing and machine learning in health care scenarios.
Penerapan Deep Learning pada Sistem Klasifikasi Tumor Otak Berbasis Citra CT Scan Bela Agustina; Auliya Rahman Isnain
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.v13i3.3989

Abstract

Brain tumor detection and classification is an important challenge in the medical field that requires a fast and accurate solution. In this study, we propose the application of Deep Learning to a CT scan image-based brain tumor classification system. We use DenseNet as the base model and train it using CT scan image dataset to distinguish between positive class (brain tumor) and negative class (no brain tumor). In addition, we conducted a series of experiments with varying number of epochs to understand the development of the model's performance during the training process. The evaluation results show that our model achieved the highest accuracy of 0.92 at epoch 100, with precision, recall, and F1 score stabilizing at high values. Although there are fluctuations in performance at some stages of training, the model still shows stable performance overall. These findings suggest that the application of Deep Learning can be an effective tool in supporting the diagnosis of complex brain diseases.
Skin Disease Recognition Based on Deep Learning Algorithms: A Review Darweesh, Ahwaz; Mohsin, Adnan
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.v13i3.3991

Abstract

The sharp increase in cases of melanoma and other skin cancers worldwide highlights the urgent need for improved diagnostic methods. Because skin lesions vary widely and access to dermatological knowledge is limited in resource-poor areas, traditional methods - which rely on visual inspection and clinical experience - have difficulty identifying diseases accurately. This situation requires innovative approaches to improve accessibility and diagnostic accuracy. To address these issues, this work uses deep learning (DL) and convolutional neural networks (CNNs). This paper is trying to transform skin cancer diagnosis through the use of large databases of dermoscopic images and advanced artificial intelligence algorithms. In order to evaluate the effectiveness of CNNs and DL in identifying skin diseases, we conducted a comprehensive analysis of the literature, focusing on the accuracy of skin cancer type classification. Our approach focused on model architectures, data preparation methods, and performance indicators while examining existing research using AI algorithms to diagnose skin cancer. With the ultimate goal of improving patient outcomes through early detection and accurate classification of skin conditions, this approach not only underscores the great potential of DL and CNN in transcending traditional diagnostic limitations, but also highlights the continued development of AI-based tools in pathology. Dermatology. Diagnosis.
Evaluasi Kerentanan Keamanan Pada Perangkat Iot: Studi Kasus Pada Smart home Cahyo Utomo, Ihsan
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.v13i3.3994

Abstract

The presence of the Internet of things (IoT) has changed the way of interacting with electronic devices in everyday environments. One implementation of IoT is a smart home, where the automation of various devices in the home is connected online to increase comfort and efficiency. However, in implementing a smart home there are challenges on the security side. Because smart home applications are connected to the internet, they are vulnerable to security attacks such as access rights attacks, viruses and malware. This research discusses the need for security evaluation on smart home devices, this is because the characteristics of smart homes are heterogeneous, dynamic and connected to the internet network, so they have potential vulnerabilities from cyber attacks. The aim of this research is to evaluate security vulnerabilities in IoT devices in the context of a smart home. Through case studies covering various devices commonly found in smart homes, one of which is the potential vulnerabilities and security risks associated with the use of IoT devices. The research methods used include penetration testing, vulnerability analysis, and evaluation of implemented security policies. The results of this research can provide an overview of potential security risks to IoT devices in smart homes and help develop stronger protection models to face the growing security challenges in IoT applications.
Credit Card Fraud Detection Based on Machine Learning Classification Algorithm Naman, Bareq Mardan; Adnan Mohsin Abdulazeez
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.v13i3.3996

Abstract

Credit risk analysis is a critical process in the financial industry, as it helps lenders assess the likelihood of borrowers defaulting on their loans. With the advent of machine learning algorithms, there has been a growing interest in leveraging these techniques for more accurate and efficient credit risk prediction. Traditional credit risk models often rely on manual processes and limited data sources, resulting in potential biases and inaccuracies. Additionally, the rapid growth of credit card usage and the increasing complexity of financial transactions have made it challenging to accurately assess credit risk using conventional methods. This review paper aims to provide a comprehensive overview of machine learning algorithms used for credit risk prediction in the context of credit card lending. It explores classification techniques and their applications in credit risk analysis. The paper also discusses the challenges and limitations associated with these algorithms, including data quality, overfitting, and interpretability.

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