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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
 92   93   94   95   96 
METODE STATISTIK DAN MACHINE LEARNING UNTUK PREDIKSI HARGA BAHAN POKOK DI JAWA TIMUR Dzulfiqar, Achmad Fakhri; Ferry Astika Saputra; Iwan Syarif
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.v14i1.4625

Abstract

Price fluctuations of basic commodities impact economic stability and community welfare. This study compares predictive methods based on statistical approaches (Simple Moving Average, Linear Regression) and machine learning techniques (Support Vector Regression, Long Short-Term Memory) using data from SISKAPERBAPO, which records daily prices of 76 basic commodities across 119 central markets in 38 districts/cities in East Java. The study supports the role of Regional Inflation Control Teams (TPID) in maintaining stable and low inflation through coordinated policies. Evaluation based on Root Mean Square Error (RMSE) and Squared Correlation indicates that SVR performs best of 4 commodities (rice, sugar, chicken meat, chicken eggs), while LSTM excels for 3 commodities (cooking oil, beef, garlic). These findings recommend SVR and LSTM as the most efffective methods for price prediction and provide a reference for TPID and policymakers in developing for price control.
Comprehensive Review of Advanced Machine Learning Strategies for Resource Allocation in Fog Computing Systems Abdulwahab, Sara; Ibrahim, Media; Askar, Shavan; Hussien, Diana
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.v14i1.4632

Abstract

This paper targets the development of advanced machine learning strategies for fog computing systems and is designed to further enhance current mechanisms related to resource allocation. Fog computing represents the extension of cloud facilities to network edges with increased data processing, allowing minimal latency for applications that need real-time processing. This is a review underlining deep learning as one of the basic tools through which neural networks predict the resource usage and optimization of resource allocation with its dynamic adaptation to modifications within the network conditions. The paper reviews techniques such as Convolutional Neural Networks, Recurrent Neural Networks, and Generative Adversarial Networks that are explored for their roles in enhancing efficiency, privacy, and responsiveness within the realm of distributed environments. These findings reveal that deep learning significantly enhances operational performance, reduces latency, and strengthens security in fog networks. By processing data locally and autonomously managing resources, these strategies ensure efficient handling of diverse and dynamic demands. It concludes that the integration of machine learning into fog computing forms a scalable and robust framework toward meeting modern challenges imposed by digital ecosystems, enabling smarter real-time decision-making systems at the edge.
Analisis Kepuasan Pengguna Aplikasi Lazada Berbasis Mobile Melalui Pendekatan End-User Computing Satisfaction Sanjaya, M Rudi Sanjaya; Bayu Wijaya Putra; Annisa Khoiriah
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.v14i1.4634

Abstract

The process of conducting business online using the internet and network-connected devices, such as the Lazada platform, is known as e-commerce, or electronic commerce. There is a decrease in the quantity of users on Lazada. The degree of consumer happiness is one of the many variables that may be causing this reduction. The degree to which a customer's expectations and actual product usage experience align is a key indicator of customer happiness. The purpose of this study is to assess users' satisfaction levels with the Lazada application. This study employs a single technique, End-User Computing happiness (EUCS), to measure customer happiness. EUCS includes multiple characteristics, including content, correctness, format, timeliness, and ease of use. Based on the responses from 115 participants in the research questionnaire, these five factors fell into the satisfied group.
C vs Rust: Manual vs Automatic Spatial and Temporal Memory Safety Syalim, Amril; Sheradhien, Dewangga Putra
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.v14i2.4640

Abstract

The C programming language is commonly used for creating high-performance and low-level applications such as device drivers and operating systems due to its efficiency. However, despite its performance capabilities, C is known for its vulnerabilities and unsafe coding practices. Rust is presented as an alternative to C, with a focus on improved safety without compromising performance. Rust employs ownership and borrowing concepts to manage memory usage, ensuring that the memory cannot be manipulated freely without adhering to specific rules designed to prevent security attacks. The memory restrictions are implemented either at compile time or runtime without requiring the programmer's direct involvement; however, the programmer must adhere to a strict coding standard. In contrast, C programs can be secured by manually implementing similar restrictions on memory access and adding checks for unpredictable runtime behavior. While this approach offers some protection against attacks, it requires the developer to have detailed knowledge of memory management and programming best practices. This research focuses on evaluating memory safety issues in terms of spatial and temporal safety, comparing Rust's security mechanisms (or lack thereof) to C. Spatial safety involves securing vulnerable memory locations, while temporal safety ensures safe access to memory at different times. These concepts are frequently exploited by attackers to access data or inject attack payload. Our analysis demonstrates that Rust offers stronger guarantees for memory safety compared to manual security implementations in C. However, C remains a viable option for performance-critical applications, as it can still be secured through careful coding practices.
Understanding the Adoption of Healthy Mobile Diet Applications Among Adults Rima Zakiah Putri; Betty Purwandari; Ni Wayan Trisnawaty
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.v14i2.4644

Abstract

Non-communicable diseases (NCDs) account for 53% of premature deaths globally, posing significant challenges to healthcare systems. In Indonesia, the rising prevalence of obesity and overweight among adults highlights the urgent need for innovative interventions to promote healthier lifestyles. Mobile diet applications have emerged as a promising solution, offering accessible tools for health monitoring and behaviour change. However, adoption rates in Indonesia remain low due to a limited understanding of the factors influencing user acceptance. This study aims to analyze the determinants of mobile diet application adoption among Indonesian adults using the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2) framework, extended with five external factors: trust, perceived health threat, health consciousness, health conditions, and body image. Data were collected through an online survey with 218 respondents who had used diet applications such as Cronometer, FastEasy, and MyFitnessPal within the past six months. Partial Least Squares Structural Equation Modeling (PLS-SEM) was employed for analysis. The findings reveal that effort expectancy, social influence, price value, health conditions, and body image significantly influence adoption. In contrast, performance expectancy, hedonic motivation, trust, perceived health threat, and health consciousness were not significant predictors. These results underscore the importance of intuitive interface design, community-driven features, and personalization based on health data to enhance user engagement and adoption. This study contributes to understanding user behaviour in health technology adoption in a developing country. It offers practical recommendations for application developers and policymakers to optimize the use of mobile diet applications as part of broader efforts to address NCD challenges in Indonesia.
A Determination of Sample Size for Plant Leaves in Deep Learning Models for Predicting Late Blight in Irish Potatoes: An experimentation methodology in Kigezi –Uganda Turihohabwe, Jack
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.v14i2.4647

Abstract

Determining the sample size of Deep learning models still remains a challenges in the Artificial Intelligence world. This is because most of the developers of deep learning models utilize available data collected from public datasets sites such PlantVillage or Kaggle. This study proposes using the acreage method putting into consideration of the machine learning dataset condition. The main objective of this research is to experiment the methods that can be used to determine the appropriate sample size for a Deep learning model. This study used the experimental and statistical methodologies and incorporated the boundaries of the Machine learning condition. The average sample estimation of the measurements in the piece of land (plot) was (1x4X10) cm. The measurement of the leaves was 3.5-5cm in length and 1.5-3 cm in width. The experiments were done between (2:00-4:00) am to have a good lighting condition. The optimal leaning rate of the deep learning architectures involved in the study used a learning rate of 0.0001. The study covered an acreage of 28000.25 acres and the Dataset 2145 Irish potato leaves was obtained and got 9,660 images after augmentation. This was purposively collected from ten sub-counties due to time and financial constraints in this study. This study proposed a methodology for obtaining the sample size using the acreage methodology and purposive sampling and there use the Machine learning condition for  sample sizes  for creation of deep learning models from potato leaf images targeted at preventing late blight based on leaf images. Future research may extend this study to further more validate the acreage methodology putting into account the Machine learning condition and also developing the Deep learning condition. 
Strategi Manajemen Pengetahuan untuk Mengoptimalkan Operasi Help Desk: Tinjauan Literatur Sistematis Lathiful Alamsyah; Fachri Munandar; Dana Indra Sensuse; Sofian Lusa; Nadya Safitri; Damayanti Elisabeth
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.v14i1.4648

Abstract

In today's digital era, effective knowledge management (KM) is vital for businesses, especially in public sector help desks where user interaction is critical. This systematic literature review (SLR) explores factors influencing KM implementation and identifies strategies to optimize help desk operations. Using PRISMA criteria and the PICO model, 25 studies from 2019 to 2024 were selected after screening 5,490 publications. Key factors impacting KM include organizational culture, leadership support, and technological infrastructure. Recommended strategies involve fostering a knowledge-sharing culture, developing knowledge bases, and utilizing AI for knowledge capture. The findings contribute theoretically by consolidating a framework for KM in help desks and practically by guiding public sector organizations. However, reliance on secondary data limits the study, as it may not fully reflect real-world KM practices. Future research could empirically validate these findings and explore emerging technologies like AI to enhance KM effectiveness.
Analisis Sentimen Publik Terhadap Penangkapan Ikan Ilegal oleh Kapal Vietnam di Twitter Menggunakan Metode GRAFT Ardiansyah, Rama; Inayah, Nur; Liebenlito, Muhaza
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.v14i2.4650

Abstract

Penangkapan ikan ilegal oleh kapal asing, khususnya dari Vietnam, telah menjadi masalah yang cukup serius di Indonesia. Kegiatan ini tidak hanya merugikan perekonomian nasional, tetapi juga mengancam sumber daya laut. Di era digital, media sosial, khususnya Twitter, telah menjadi wadah penting bagi masyarakat untuk menyuarakan pendapat dan reaksi terhadap berbagai isu terkini. Penelitian ini menggunakan metode GRAFT untuk menganalisis sentimen publik di Twitter terkait penangkapan ikan ilegal oleh Vietnam. Analisis ini bertujuan untuk menggali pandangan masyarakat dan mendapatkan wawasan yang lebih mendalam mengenai isu-isu tersebut. Temuan penelitian ini diharapkan dapat menjadi dasar untuk merumuskan kebijakan yang lebih efektif dalam menanggulangi IUU Fishing di perairan Indonesia.
Generic Enterprise Architecture Model Design for Operational Management in Indonesian Ports: A Case Study of PT Integrasi Logistik Cipta Solusi (ILCS) Daniel , Muhammad; Purwandari, Betty; Satria, Riri; Putro, Prasetyo
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.v14i1.4651

Abstract

Ports serve as the backbone of trade and Logistics in Indonesia, supporting integration and efficiency through digital transformation. However, the absence of a generic Enterprise Architecture framework has led to system and process fragmentation, hindering cross-Stakeholder integration within the maritime sector. This study AIms to design a generic Enterprise Architecture model for port operational management in Indonesia using the TOGAF framework. The methodology employed includes needs analysis, interviews, document reviews, and observations. The scope of the research covers the Preliminary phase through to the Technology Architecture phase within the TOGAF ADM cycle. The results of the study include an Enterprise Architecture design focused on integration standards, process efficiency, and system interoperability. This model is expected to serve as a strategic guide for PT Integrasi Logistik Cipta Solusi (ILCS) in aligning information technology with the business needs of national ports.
Implementation of Data Mining to Analyze Consumer Purchasing Patterns at CV XYZ Using the Apriori Algorithm Sulistyo Wibowo, Arief; 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.v14i1.4652

Abstract

CV XYZ is a retail store located on Jl. Tidar, Surabaya, East Java, specializing in the trade of industrial and household chemical products. To remain competitive amid rapid technological advancements and increasing competition in the area, CV XYZ has adopted online sales through the Shopee platform. This study aims to analyze consumer purchase patterns using the Apriori algorithm based on sales transaction data to support effective and efficient marketing strategies. Based on the analysis, five purchasing patterns were identified: (1) if Silicon Oil is purchased, Counterdust will also be purchased with a support value of 0.391 and a confidence value of 0.818; (2) if Cocamidopropyl Betain is purchased, Counterdust will also be purchased with a support value of 0.391 and a confidence value of 0.857; (3) if Car Shampoo is purchased, Counterdust will also be purchased with a support value of 0.359 and a confidence value of 0.868; (4) if Talc is purchased, Counterdust will also be purchased with a support value of 0.348 and a confidence value of 0.842; and (5) if Linear Alkylbenzene Sulfonate is purchased, Counterdust will also be purchased with a support value of 0.359 and a confidence value of 0.892. These findings indicate that data mining techniques using the Apriori algorithm provide an effective approach for identifying consumer purchasing patterns of chemical products sold online through the Shopee platform. This insight can help businesses optimize their marketing strategies and decision-making processes.

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