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IJID (International Journal on Informatics for Development)
Published by Universitas Islam Negeri Sunan Kalijaga Yogyakarta
ISSN : 22527834     EISSN : 25497448     DOI : -
Core Subject : Science,
Computer Science & IT
One important point in the accreditation of higher education study programs is the availability of a journal that holds the results of research of many investigators. Since the year 2012, Informatics Department has English language. Journal called IJID International Journal on Informatics for Development. IJID Issues accommodate a variety of issues, the latest from the world of science and technology. One of the requirements of a quality journal if the journal is said to focus on one area of science and sustainability of IJID. We accept the scientific literature from the readers. And hopefully these journals can be useful for the development of IT in the world. Informatics Department Faculty of Science and Technology State Islamic University Sunan Kalijaga.
Arjuna Subject : -
Articles 217 Documents
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Analysis of Factors Affecting the Students’ Acceptance Level of E-Commerce Applications in Yogyakarta Using Modified UTAUT 2 Candra, Dori Gusti Alex; Nuruzzaman, Muhammad Taufiq; 'Uyun, Shofwatul; Sugiantoro, Bambang; Pratiwi, Millati
IJID (International Journal on Informatics for Development) Vol. 12 No. 1 (2023): IJID June
Publisher : Faculty of Science and Technology, UIN Sunan Kalijaga Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14421/ijid.2023.3990

Abstract

Yogyakarta is listed as the region with the highest number of residents engaging in e-commerce transactions. A total of 10.2% of the population are active e-commerce sellers, while 16.7% belong to the buyer category. Research by IDN Times showed that e-commerce application users have been dominated by students, with a percentage of 44.2%.  The purpose of this study is to analyze the factors that influence students’ level of acceptance of e-commerce applications in Yogyakarta using the modified UTAUT 2. This is quantitative research with multiple linear regression models using SPSS software version 25 with a sample size of 303 people. Data analysis in this study was conducted in a few steps, including descriptive analysis, validity test, reliability test, classical assumption test and hypothesis testing. The results of this study indicate that the student’s level of acceptance of e-commerce applications is within good criteria. The variables that have a positive effect on the behaviour intention (BI) are performance expectancy (PE), effort expectancy (EE), social influence (SI), facilitating conditions (FC), hedonic motivation (HM), habit (HB), price value (PV), perceived risk (PR), perceived security (PS), and trust (TR) are variables that negatively affect the variable behaviour intention (BI). All independent variables affect the dependent variable or behaviour intention (BI) with a total of 63.3% and the difference with a total of 36.7% is caused by other factors not examined by the researcher.
Optimisation of Residual Network Using Data Augmentation and Ensemble Deep Learning for Butterfly Image Classification Diniati Ruaika; Shofwatul Uyun
IJID (International Journal on Informatics for Development) Vol. 12 No. 2 (2023): IJID December
Publisher : Faculty of Science and Technology, UIN Sunan Kalijaga Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14421/ijid.2023.4038

Abstract

Image classification is a fundamental task in vision recognition that aims to understand and categorize an image under a specific label. Image classification needs to produce a quick, economical, and reliable result. Convolutional Neural Networks (CNN) have proven effective for image analysis. However, problems arise due to factors such as the model’s quality, unbalanced training data, overfitting, and layers’ complexity. ResNet50 is a transfer learning-based convolutional neural network model frequently used in many areas, including Lepidopterology. Studies have shown that ResNet50 performs with lower accuracy than other models for classifying butterflies. Therefore, this study aims to optimise the accuracy of ResNet50 using an augmentation approach and ensemble deep learning for butterfly image classification. This study used a public dataset of butterflies from Kaggle. The dataset contains 75 different butterfly species, 9.285 training images, 375 testing images, and 375 validation images. A sequence of transformation functions was applied. The ensemble deep learning was constructed by incorporating ResNet50 with CNN. To measure ResNet50 optimisation, the experimental results of the original dataset and the proposed methods were compared and analysed using evaluation metrics. The research revealed that the proposed method provided better performance with an accuracy of 95%.
The WASPAS Method in Determining BSM Recipients Objectively Tundo, Tundo; Wijonarko, Panji; Raffiudin, Muhammad
IJID (International Journal on Informatics for Development) Vol. 12 No. 1 (2023): IJID June
Publisher : Faculty of Science and Technology, UIN Sunan Kalijaga Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14421/ijid.2023.4089

Abstract

This research was conducted due to complaints from several parents regarding the determination of BSM at SDN Karanganyar 02 which still contains subjectivity in its selection so that some students are less fortunate. SDN Karanganyar 02, once a year always carries out activities related to determining the selection of BSM recipients. With this activity, it is hoped that students who are underprivileged but have fairly good achievements can receive this BSM so that the activities they carry out do not feel burdened with financial needs. The fact is that in institutions there are still many students who do not get BSM, even though according to the requirements these students should be eligible to get BSM. So in the selection that occurs there is a very irrational subjectivity. To solve this problem, the researcher tries to make a solution through an application that applies the Weight Aggregated Sum Product Assessment (WASPAS) method, which is a method of determining with predetermined criteria. The criteria in question are activities, achievements, report cards, parental income, home conditions, and parental dependents. After analyzing and implementing the WASPAS Decision Support System, it was found that the results were detrimental to students where the criteria scores and final determination were lower than some other students, but the SD carried out an assessment by obtaining BSM. To prevent this incident from recurring, WASPAS is very capable of answering objective determinations with the results obtained at 79.88% and the previous subjective determination at 20.12%.
Design and Development of an Edugame Arabic for Learning Media Yudha Riwanto; Inggrid Yanuar Risca Pratiwi; Asri Wulan Septiana; Fauzia Anis Sekar Ningrum; Ajie Kusuma Wardhana
IJID (International Journal on Informatics for Development) Vol. 12 No. 2 (2023): IJID December
Publisher : Faculty of Science and Technology, UIN Sunan Kalijaga Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14421/ijid.2023.4297

Abstract

Learning media provides significant advantages to students by improving their learning experience through the use of multimedia applications, resulting in a more engaging and fascinating learning environment while reducing the monotony associated with traditional manual learning techniques. Digital learning material, provides a platform for interesting learning activities, encouraging a delightful and cost-effective learning experience. The impact of learning media is especially noticeable in the subject of the Arabic language. Arabic is traditionally regarded as a difficult language, and many students dislike this language course. However, the Edugame Arabic was created to overcome this issue. Using the GDLC process, which includes phases of initialization, pre-production, production, testing, and publishing. This game-learning application was evaluated through a testing phase that included groups of school students who were actively involved in Arabic language lessons. Edugame Arabic has successfully been installed and runs smoothly on various Android smartphones. Moreover, the game's offline capability allows users to continue their learning without an internet connection. The questionnaire responds, with users strongly agreeing that the app has an appealing design, an intriguing game premise, good material delivery, and considerable aid in learning Arabic. Furthermore, users generally acknowledged that the Edugame is simple to use and helps with vocabulary learning.
Anomaly-Based Intrusion Detection System for the Internet of Medical Things Franklin, Eichie; Pranggono, Bernardi
IJID (International Journal on Informatics for Development) Vol. 12 No. 2 (2023): IJID December
Publisher : Faculty of Science and Technology, UIN Sunan Kalijaga Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14421/ijid.2023.4308

Abstract

The use of the Internet of Things (IoT) in the health sector, known as the Internet of Medical Things (IoMT), allows for personalized and convenient (e)-health services for patients. However, there are concerns about security and privacy as unethical hackers can compromise these network systems with malware. We proposed using hyperparameter-optimized Machine and Deep Learning models to address these concerns to build more robust security solutions. We used a representative Anomaly Intrusion Detection System (AIDS) dataset to train six state-of-the-art Machine Learning (ML) and Deep Learning (DL) architectures, with the Synthetic Minority Oversampling Technique (SMOTE) algorithm used to handle class imbalance in the training dataset. Our hyperparameter optimization using the Random search algorithm accurately classified normal cases for all six models, with Random Forest (RF) and K-Nearest Neighbors (KNN) performing the best in accuracy. The attention-based Convolutional Neural Network and Long Short-Term Memory (CNN-LSTM) model was the second-best performer, while the hybrid CNN-LSTM model performed the worst. However, there was no single best model in classifying all attack labels, as each model performed differently in terms of different metrics.
Design of a Web-Based E-Commerce Sales System for the Economic Empowerment of Tambak Fish Farmers Siburian, Dian Prima Trendi; Hartiyani, Selvi Dwi; Wicaksono, Ardy; Gustina, Sapriani
IJID (International Journal on Informatics for Development) Vol. 13 No. 1 (2024): IJID June
Publisher : Faculty of Science and Technology, UIN Sunan Kalijaga Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14421/ijid.2024.4440

Abstract

This research addresses the economic challenges of fish farmers in Argomulyo Village, Cangkringan District, by developing a web-based e-commerce sales system. The primary issue identified is the limited market access experienced by these farmers. To address this, the study employs a qualitative research methodology using the Waterfall software development model and gathers data through observation, interviews, literature reviews, and questionnaires. The e-commerce platform aims to enhance economic opportunities for local fish farmers by providing a digital marketplace to overcome limited market access. Quantitative data was collected from 15 respondents using a questionnaire with 10 statements to evaluate the system. The analysis results show that the validity test (R Calculated > R Table) confirms all statements are valid, and reliability is tested with a Cronbach's Alpha of 0.958, exceeding the reference value of 0.6, indicating high reliability. The e-commerce system has proven effective in broadening market reach, boosting sales, and increasing farmers' income. The study results highlight the e-commerce system's positive impact on fish farmers' economic empowerment, demonstrating its potential to foster sustainable growth and market expansion in the digital era. This research provides valuable insights into the use of technology to enhance and advance the lives of farmers in rural communities.
Analysis of Public Sentiment Towards POLRI's Performance using Naive Bayes and K-Nearest Neighbors Handika, Yusuf; Hanif, Isa Faqihuddin; Hasan, Firman Noor
IJID (International Journal on Informatics for Development) Vol. 13 No. 1 (2024): IJID June
Publisher : Faculty of Science and Technology, UIN Sunan Kalijaga Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14421/ijid.2024.4500

Abstract

Using Twitter as a platform for sharing information includes tracking public perceptions of the performance of the Indonesian National Police (POLRI). Public sentiment assists as a gauge for evaluating POLRI's operational capabilities and supports decision-making processes to enhance the organization's reputation. However, raw public opinion data often requires careful analysis for decision-making. Hence, conducting sentiment analysis of Twitter data is crucial. This analytical process involves extracting and classifying opinions into neutral, positive, and negative sentiments. This study employs two distinct sentiment analysis methods: the Naive Bayes algorithm and the K-Nearest Neighbors. Analysis of 1285 tweets reveals prevailing satisfaction with POLRI's performance, indicated by many positive sentiments. However, there is also a notable number of negative feelings. The assessment from confusion matrix results demonstrate that the Naive Bayes algorithm achieves 99.03% accuracy, while the K-Nearest Neighbors algorithm achieves 95.33% accuracy. By leveraging insights from public opinion data, POLRI can make more accurate and timely decisions, enabling it to better fulfill the community's needs and expectations. This strategic use of data enhances service quality and bolsters POLRI's favorable image among the public fosters more harmonious relationships and enhances public trust in law enforcement agencies.
Sentiment Analysis of TIMNAS Indonesia's Participation in the Asian Cup U23 2024 on X Using Naive Bayes and SVM Fathurrohman, Sewin; Afandi, Irfan Ricky; Hasan, Firman Noor
IJID (International Journal on Informatics for Development) Vol. 13 No. 1 (2024): IJID June
Publisher : Faculty of Science and Technology, UIN Sunan Kalijaga Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14421/ijid.2024.4504

Abstract

This study aims to analyze the sentiment of the Indonesian public regarding the participation of the Indonesian National Team in the 2024 U-23 Asian Cup through the social media platform X. Sentiment analysis is crucial for understanding public perception and its impact on support for the national team. The research methodology involves collecting user comments on X related to the team's performance during the tournament, followed by data cleaning. The dataset is manually labeled, with 80% used as training data for algorithmic model training and the remaining 20% as test data, classified using Naive Bayes and Support Vector Machine algorithms. The analysis results indicate that the SVM algorithm achieves a higher % accuracy rate of 95% compared to Naive Bayes, which achieves 87%. The majority of the 3367 opinions analyzed express positive or satisfactory sentiments towards the national team's participation. However, there are fewer negative sentiments, highlighting areas requiring team management's attention. This study provides valuable insights into public perception of the Indonesian National Team. Furthermore, these findings can inform policymakers and team managers' decision-making to enhance the team's quality and performance in the future.
Development of Geographic Information Systems in Mapping Village-Owned Enterprises in Sleman Regency Ramadhan, Imam; Arfiani, Ika
IJID (International Journal on Informatics for Development) Vol. 13 No. 1 (2024): IJID June
Publisher : Faculty of Science and Technology, UIN Sunan Kalijaga Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14421/ijid.2024.4513

Abstract

The population increase in the Special Region of Yogyakarta poses challenges, including developing Village-owned Enterprises or BUM Desa in Sleman Regency to enhance rural community welfare. BUM Desa data management currently relies on manual spreadsheets and lacks a dynamic data storage system, hindering access to accurate information. This study employed the Scrum methodology, gathering data through literature reviews, interviews, and observations to assess the current state of BUM Desa. A product backlog guided the development of a web-based GIS application through sprint planning, resulting in an application that maps BUM Desa locations in the Sleman Regency based on coordinates and provides detailed development classifications. This application enhances data management and decision-making for BUM Desa development, simplifies government data management, and improves public access to BUM Desa locations. Black box testing confirmed its functionality, with 100% validity. End-user computing Satisfaction (EUCS) surveys indicated high user satisfaction, emphasizing the application's usability and alignment with user expectations in providing accurate and accessible BUM Desa information.
Leveraging Ontology-Driven Machine Learning for Public Policy Analysis: A Systematic Review of Social Media Applications Kero, ADMAS; Demissie, Dawit; kekeba, Kula
IJID (International Journal on Informatics for Development) Vol. 13 No. 2 (2024): IJID December
Publisher : Faculty of Science and Technology, UIN Sunan Kalijaga Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14421/ijid.2024.4176

Abstract

As social media platforms increasingly serve, machine learning techniques are formulated with particular ontologies, which furnish invaluable resources. This qualitative literature review investigates the incorporation of ontology-driven machine learning methodologies for analysing public policy utilizing social media data. This review encompasses findings from scholarly research published between 2019 and 2024 that apply ontologies to enhance models' interpretation, precision, and flexibility across diverse sectors, including health, environment, economy, and culture. An integrated methodology is adopted to identify, select, and evaluate pertinent studies by scrutinizing elements such as genre ontology, machine learning, existing literature, and evaluation metrics. The findings indicate that the ontology-centric framework facilitates the extraction process and semantic analysis, ultimately contributing to a more nuanced comprehension of unstructured data. Nonetheless, obstacles persist in ontology development concerning capacity enhancement, data integrity, and ethical considerations. The review concludes with a discourse on the ramifications for policymakers and researchers who may leverage these insights to guide decision-making, and scholars are now urged to confront limitations and investigate novel platforms, metrics, and ethical frameworks. The review underscores the potential of ontology-driven machine learning as a formidable strategy in the advancement of policy research and social analysis.

Page 20 of 22 | Total Record : 217

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