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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 1,114 Documents
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Detecting Distributed Denial of Service Attacks in Mobile Edge Computing using Modified Extreme Machine Learning Mapunya, Sekgoari; Mthulisi Velempini
The Indonesian Journal of Computer Science Vol. 14 No. 2 (2025): 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.v14i2.4538

Abstract

Mobile Edge Computing (MEC) is a promising technology which enables 5G and reduces latency. By bringing cloud computing capabilities closer to end users, MEC enables latency-sensitive applications to perform more efficiently. However, security attacks pose significant challenges to the objectives of 5G with Distributed Denial of Service (DDoS) attacks being a major threat. These attacks can overwhelm target systems with excessive data preventing access to and disrupting network services. Effective mitigation strategies are required to protect MEC technology. Given the high data volume generated by such attacks, this paper utilizes a modified Firefly Algorithm to select relevant features. These selected features are then used to train a proposed variant of Extreme Learning Machine (ELM), where weights are initialized using Neighbourhood-Based Differential Evolution. MATLAB simulations demonstrate that the proposed modified ELM outperforms traditional approaches, providing an effective solution to DDoS attacks in MEC.
Sistem Presensi Pengenalan Wajah Menggunakan Deep Learning pada Kantor Unit Penyelenggara Pelabuhan Amurang Gupuh, Andi; Rorimpandey, Gladly C.; Moningkey, Efraim R.S
The Indonesian Journal of Computer Science Vol. 14 No. 1 (2025): The Indonesian Journal of Computer Science (IJCS)
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v14i1.4551

Abstract

Identifikasi wajah telah dianggap sebagai domain penelitian yang menarik dalam beberapa tahun terakhir karena memainkan peran biometrik utama biometrik utama dalam beberapa aplikasi termasuk manajemen kehadiran dan sistem kontrol akses. Kehadiran Sistem manajemen kehadiran sangat penting bagi semua organisasi meskipun rumit dan memakan waktu untuk mengelola log kehadiran. Ada banyak teknik identifikasi manusia otomatis seperti biometrik, RFID, pelacakan mata, suara pengenalan. Wajah adalah salah satu biometrik yang paling banyak digunakan untuk otentikasi identitas manusia. Sistem presensi pengenalan wajah sistem presensi pengenalan wajah berdasarkan jaringan syaraf tiruan convolutional neural network. Kami memanfaatkan Deep Learning dengan menggunakan jaringan syaraf tiruan yang telah dilatih sebelumnya dan melatihnya pada data kami. Hasil menunjukkan kinerja yang sangat tinggi dalam dalam hal akurasi prediksi yang tinggi dan waktu pelatihan yang masuk akal.
Optimization Of Raw Material Inventory for Rayon Yarn at CV XYZ Using the EOQ-Lagrange Multiplier Method and Theory Of Constraint Virga, Avril; Donoriyanto, Dwi Sukma
The Indonesian Journal of Computer Science Vol. 14 No. 1 (2025): The Indonesian Journal of Computer Science (IJCS)
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v14i1.4559

Abstract

CV XYZ is a manufacturer of woven sarong in East Java. The problem faced challenges with excess rayon yarn inventory, leading to overcapacity in storage and high costs. Additionally, suppliers' minimum order requirements exacerbate the issue. This study uses the Lagrange Multiplier and Theory of Constraint methods to optimize rayon yarn inventory to minimize costs and storage needs. The results reveal an adjusted storage space requirement of 84 m³, with details of the total storage space and the minimum order quantity, namely rayon yarn R80/2 of 28.32 m³ (147 boxes), R60/2 of 25.80 m³ (134 boxes), and R32/2 of 29.88 m³ (155 boxes). This method also reduces the total inventory cost to IDR 30,640,592, achieving a remarkable 84.61% saving (IDR 168,574,408) compared to the company's current process. The findings demonstrate that combining Lagrange Multiplier and TOC effectively addresses storage limitations, optimizes order quantities, and minimizes inventory costs.
Implementasi Algoritma Priority Scheduling Pada Sistem Informasi Pemesanan Layanan Fotografi dan Videografi Anfaisa, Anfaisa Ibnu Danar Dana; Dedi Gunawan
The Indonesian Journal of Computer Science Vol. 14 No. 2 (2025): 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.v14i2.4583

Abstract

Creative industries in Indonesia are growing rapidly, including photography and videography services. The need for people to capture important moments drives the demand for these services. Agratha Studio, a production house in Surakarta, specializes in photography and videography. However, the ordering process is still done manually, which creates obstacles for customers and studios. This research aims to help Agratha Studio improve services to customers by designing a website-based videography service booking information system. The system is designed with features such as user login and registration, service data management, online ordering, and order management by the admin. This system implements priority scheduling algorithm. The priority scheduling algorithm is a priority-based scheduling algorithm where each scheduling process has a priority number. The development of a website-based videography service ordering information system can help Agratha Studio improve services to customers and increase the efficiency and effectiveness of the ordering process.Regular monitoring of the information system is also conducted to ensure the fulfillment of the objectives underlying the development of this complaint reporting and information system.
A Systematic Review of Challenges in Teaching and Learning Computer Programming Modules Elegbeleye, Femi; Isong, Bassey
The Indonesian Journal of Computer Science Vol. 14 No. 1 (2025): The Indonesian Journal of Computer Science (IJCS)
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v14i1.4592

Abstract

Computer programming has become an essential skill that is needed across many disciplines as it helps foster innovations like machine learning and artificial intelligence. Regardless of its significance, many students studying computer science and other disciplines often grapple with grasping basic programming language concepts, such as understanding logic, syntax, data structure, and data types. These challenges usually lead to very high rates of failure and loss of motivation among the students, therefore producing poor academic outcomes. This study investigates the unique programming challenges the students face, identifying some contributing factors and examining which challenges have more impact on the student. Moreover, it explores whether computing or non-computing students are more affected by these obstacles and reviews interventions to improve learning outcomes. The findings suggest best practices to enhance motivation and engagement in programming education, including introducing adaptive learning tools into the learning management systems, game-based applications, and AI-driven support systems personalized to meet each student's needs.
Dynamic Resource Allocation in Cloud Networks Using Deep Learning : A review Diana Hayder Hussein; Maqdid, Goran; Shavan Askar; Media Ali Ibrahim
The Indonesian Journal of Computer Science Vol. 14 No. 1 (2025): The Indonesian Journal of Computer Science (IJCS)
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v14i1.4597

Abstract

Resource allocation has been a very significant topic for both research and development over the last two decades. Given the increasing volume of data, the proliferation of connected devices, and the demand for seamless service delivery, optimal resource allocation has become a vital factor that influences cloud performance. Recently, deep learning-a subcategory of machine learning-seems to possess a great potential to answer this challenge by enabling predictive, adaptive, and self-organized resource allocation. For the first time, this review embraces all the major milestones achieved in dynamic resource allocation with a discussion on over 25+ peer-reviewed articles published from the year 2000 to 2024. This review has emphasized the use of CNNs, RNNs, and other variants of deep learning approaches. Such a review provides a better view of the potential benefits of the different methodologies by highlighting the pros and cons of each. It also covers the use cases, computational methodologies that discuss algorithmic novelty and challenges in scalability, latency, and energy efficiency. A summary of the development in tech was made by comparison in a table to give a meta-view for the top-ten studies. These findings have important implications for cloud service delivery in applications ranging from industrial automation to consumer-oriented applications. They showcase the vast possibilities of deep learning for changing cloud network operations through advanced optimization and point out several open issues, including the integration of federated and edge learning models that will be necessary to achieve improved decentralization and preservation of network information privacy.
Sistem Rekomendasi Menu untuk Coffee Shop Menggunakan Algoritma Association Rule Mining Rahmatika, Hamni Kamal; Dedi Gunawan
The Indonesian Journal of Computer Science Vol. 14 No. 1 (2025): The Indonesian Journal of Computer Science (IJCS)
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v14i1.4598

Abstract

Coffee shops have evolved beyond being mere places to enjoy coffee; they have become social hubs and spaces for work or relaxation in the modern era. To address the growing competition and constant market changes, coffee shops must develop strategies to enhance customer experiences and operational efficiency. One such strategy is implementing a web-based sales and ordering information system. This study examines the relevance and necessity of adopting information systems in the coffee industry, focusing on small to medium-sized coffee shops, thus addressing a gap in the existing literature. The Waterfall methodology was employed in system development, encompassing requirement analysis, design, implementation, testing, and maintenance phases. The requirement analysis phase identifies the system's functional and non-functional needs, while the design phase includes the creation of Use Case, Activity, Entity Relationship (ER) diagrams, and user interfaces. The system was implemented using HTML, CSS, and JavaScript for the front-end, with PHP and the Laravel Framework for the back-end. System testing was conducted using Black Box Testing and the System Usability Scale (SUS) to ensure optimal performance. The system achieved a SUS score of 75.7, indicating good usability and user acceptance.
Integration of Deep Learning Applications and IoT for Smart Healthcare Diana Hayder Hussein; Yousif Mohammed Ismail; Shavan Askar; Media Ali Ibrahim
The Indonesian Journal of Computer Science Vol. 14 No. 1 (2025): The Indonesian Journal of Computer Science (IJCS)
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v14i1.4611

Abstract

The integration of deep learning (DL) applications with the Internet of Things (IoT) has emerged as a transformative approach for advancing smart healthcare systems. This review synthesizes findings from seven research studies, each exploring the intersection of these technologies in improving healthcare delivery, patient monitoring, and medical decision-making. The paper highlights how IoT devices, including sensors and wearables, generate vast amounts of real-time health data, which DL models leverage for predictive analytics, diagnosis, and personalized treatment recommendations. Key areas explored include: Data Acquisition and Processing: IoT-enabled sensors play a critical role in collecting physiological data, such as heart rate, blood pressure, and glucose levels, which are then processed by DL algorithms to identify patterns and anomalies, Remote Patient Monitoring: The combination of IoT and DL facilitates continuous monitoring of chronic conditions and allows for real-time intervention, reducing hospital readmissions and enhancing patient independence.
Towards a Cashless Transactional Society Through the Adoption of Digital Banking Services Ngandu, Matipa Ricky; Mwansa, Gardner; Ntoyabo, Siwongiwe Queenest
The Indonesian Journal of Computer Science Vol. 14 No. 2 (2025): 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.v14i2.4612

Abstract

This study explores the adoption of digital banking services in a rural South African town in the Eastern Cape, emphasizing its contribution to a cashless society. Employing a quantitative method, it examines digital banking usage, adoption challenges, and information security awareness. Data were collected through surveys and analyzed using descriptive statistics. Findings show that mobile and online banking are widely used for convenience and accessibility, with most users engaging in money transfers, credit payments, and online shopping. However, challenges such as poor digital literacy, infrastructure issues like internet access, cost of access, and cybersecurity concerns persist. Users face risks like phishing, hacking, and social engineering, revealing gaps in security awareness and trust. These issues particularly affect older users and those with limited technological experience. While digital banking improves financial inclusion and economic engagement in rural areas, its full potential is hindered by these barriers. The study underscores the need for digital literacy programmes, infrastructure improvements, and robust cybersecurity measures to build trust and ensure secure adoption. Future research should evaluate interventions, explore emerging technologies like blockchain and AI, and compare regional strategies for inclusive digital financial systems.
Pemantauan Perilaku Reaksi Kimia di dalam Eco Enzyme Obi Januardi, Darius
The Indonesian Journal of Computer Science Vol. 14 No. 1 (2025): The Indonesian Journal of Computer Science (IJCS)
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v14i1.4624

Abstract

Organic waste has become a serious environmental issue, with production reaching 20.23 million tons per year in Indonesia. This research offers an innovative solution through the eco enzyme method and Internet of Things (IoT) technology to monitor chemical reactions during the fermentation process in real-time using sensors and a website. The research compares two samples: a mixture of orange peels, papaya, pineapple, and Javanese sugar; and pineapple peels, water, and molasses (sugarcane syrup). Each used 2.1 kg of organic waste, 700 g of sugar, and 7,000 ml of water. During the 60-day fermentation period, the first sample produced gases such as Methane (CH4), Hydrogen Sulfide (H2S), Ammonia (NH3), Carbon (CO), and Hydrogen (H2), while the second sample produced Methane (CH4), Hydrogen Sulfide (H2S), Ammonia (NH3), and Nitrogen Oxide (NO2). there are also parameters measured such as pH and temperature. research shows that raw materials affect the gas produced, as well as utilizing IoT technology for more efficient and sustainable waste management.

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