cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
Masduki
Contact Email
lppi@ums.ac.id
Phone
-
Journal Mail Official
khif@ums.ac.id
Editorial Address
Program Studi Teknik Informatika, Fakultas Komunikasi dan Informatika, Universitas Muhammadiyah Surakarta Gedung J Lantai 1 Sayap Barat Jl. A. Yani No 1, Pabelan 57162, Surakarta Indonesia
Location
Kota surakarta,
Jawa tengah
INDONESIA
Khazanah Informatika : Jurnal Ilmu Komputer dan Informatika
Published by Universitas Muhammadiyah Surakarta
ISSN : 2621038X     EISSN : 2477698X     DOI : -
Core Subject : Science, Engineering,
Computer Science & IT Control & Systems Engineering
Khazanah Informatika : Jurnal Ilmu Komputer dan Informatika, an Indonesian national journal, publishes high-quality research papers in the broad field of Informatics and Computer Science, which encompasses software engineering, information system development, computer systems, computer network, algorithms and computation, and social impact of information and telecommunication technology
Arjuna Subject : Ilmu Komputer (semua kategori) - Sistem Informasi
Articles 23 Documents
 1   2   3 
Developing a Dyscalculia Identification Game Using The Rapid Game Development (RGD) Model Kurniawan, Ady Purna; Roedavan, Rickman; Pudjoatmodjo, Bambang; Sazilah Salam
Khazanah Informatika : Jurnal Ilmu Komputer dan Informatika Vol. 10 No. 2 (2024): Oktober 2024
Publisher : Universitas Muhammadiyah Surakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23917/khif.v10i2.2113

Abstract

Dyscalculia, a common learning difficulty impacting arithmetic comprehension, poses challenges across diverse intelligence levels. Often misconstrued as a lack of intelligence or effort in mathematics, dyscalculia's accurate identification remains elusive. This study focuses on developing a 3D digital game incorporating basic arithmetic instruments for dyscalculia identification. Leveraging the Rapid Game Development (RGD) method and Unity Game Engine, the game integrates interactive features to assess arithmetic skills. Through standardized assessments and game participation, children both with and without dyscalculia were evaluated, revealing a significant correlation between the game and standard assessments. This innovative, interactive game holds promise for early dyscalculia detection and intervention, enhancing educators' and professionals' capacity to address this learning difficulty effectively. Future research should validate its efficacy across varied populations and explore its integration into educational settings. In conclusion, this study presents a compelling tool in the form of a 3D digital game utilizing basic arithmetic instruments, fostering timely support and improved outcomes in dyscalculia identification and intervention.
Empowering Early Education: Developing a Hijaiyah Game for Preschoolers Yulianto, Ade Rizki; Hendri, Ainayah Syifa; Sudaryanto, Aninditawidagda Pandam; Hanif, Muhammad Iqbal
Khazanah Informatika : Jurnal Ilmu Komputer dan Informatika Vol. 10 No. 1 (2024): April 2024
Publisher : Universitas Muhammadiyah Surakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23917/khif.v10i1.2286

Abstract

Early education is very important to create a basis for learning and holistic development in preschool children. So we need interesting and interactive learning media to maximize children's learning potential. This research aims to help children achieve learning goals and dig deeper about how to develop interactive hijaiyah games that are creative and in accordance with the characteristics of preschool-aged children so that they can achieve learning goals, where educational games are designed to improve students' ability to think critically and increase their concentration. The system was tested using Black Box and the System Usability Scale (SUS) Testing. The results of system testing show that each button and feature in the educational game application for learning hijaiyah letters runs well and is suitable for preschool-age children with the SUS.
The Implementation of Machine Learning for Software Effort Estimation: A Literature Review Hariyanti, Eva; Paradista , Mirtha Aini; Goyayi, Maria Lauda Joel; Arthalia, Arthalia; Shabirina, Detria Azka; Nurjanah, Endang; Husna, Oktavia Intifada; Yahrani, Fakhrana Almas Syah
Khazanah Informatika : Jurnal Ilmu Komputer dan Informatika Vol. 10 No. 1 (2024): April 2024
Publisher : Universitas Muhammadiyah Surakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23917/khif.v10i1.2803

Abstract

Effort estimation is pivotal for the triumph of software development endeavors. The appropriate forecasting approach is vital for aligning software project effort estimation outcomes. This process aids in efficiently distributing resources, charting project strategies, and facilitating informed choices in IT Project Management. Machine learning, a facet of artificial intelligence (AI), is dedicated to crafting algorithms and models that empower computers to enhance their performance based on data and facilitate predictions or decision-making. This study discusses the implementation of machine learning in software development effort estimation. We collected 558 relevant papers on software effort estimation and machine learning techniques. After a quality review process, we identified 40 articles for in-depth review. We categorized machine learning techniques into supervised, unsupervised, and reinforcement learning. The results indicate that using ensemble techniques in supervised and unsupervised learning can improve the accuracy of software effort estimation. Artificial Neural Networks, Regression, K-Nearest Neighbors, Decision Trees, Random Forest, and Bootstrap Aggregation are the most commonly used methods. Ensemble techniques also aid in selecting relevant features and preprocessing data to enhance model performance. This study provides insights into implementing machine learning techniques to estimate software effort and highlights the advantages of ensemble technique.
A Systematic Literature Review: Impact of Generative AI as Technology to Learning in Higher Education Suprobo, Winanti; Sucipto Basuki; Nurasiah
Khazanah Informatika : Jurnal Ilmu Komputer dan Informatika Vol. 10 No. 2 (2024): Oktober 2024
Publisher : Universitas Muhammadiyah Surakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23917/khif.v10i2.3056

Abstract

The development of generative Artificial Intelligence (AI) technology cannot be avoided because developments have entered all domains of life, including the world of education. Universities are preparing themselves to implement generative AI to support learning to be more efficient and effective without having to fear the threats it poses. The method used in this paper is a systematic literature review by exploring existing research through predetermined keywords. Extraction and selection of previous research results that are relevant to the topic are carried out and irrelevant topics are removed from the discussion. The papers produced from previous studies used as sources for the systematic literature review were 28 selected study papers, with range from 2004 to 2023. The most papers published in 2023 being 23 papers. The authors of the papers used were 24 authors from academic background, while the rest from industry and general publics. Generative AI, especially ChatGPT usage capabilities of learning in higher education, can create sophisticated text as humans do, as a virtual tutor and answer questions quickly, edit videos, help with manufacturing design, and act as language generation, language translation, and summarization. The advantages of Generative AI as a choice that cannot be underestimated and avoided and can be a responsive friend with accurate judgment. In order not to harm students, there needs to be strict and clear regulations so that the use of generative AI does not harm students, lecturers, and universities. Generative AI also has negative impacts, including decreasing knowledge productivity, ethics, and law becoming challenges, cheating, and hampering skill development, producing biased information, and low levels of privacy. In the future, universities should prepare clear regulations and policies for data security and privacy so that data remains safe and generative use has a positive and beneficial impact on higher education.
An Innovative Approach for Treating Chronic Vaginitis Based on AI-Driven Drug Repurposing Daungsupawong, Hinpetch; Wiwanitkit, Viroj
Khazanah Informatika : Jurnal Ilmu Komputer dan Informatika Vol. 10 No. 1 (2024): April 2024
Publisher : Universitas Muhammadiyah Surakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23917/khif.v10i1.3201

Abstract

This study evaluates the effectiveness of ChatGPT, an AI language model, in assisting healthcare practitioners in selecting drugs for treating chronic vaginitis, which is still an important medical problem. A panel of experts assessed ChatGPT’s recommendations for ten fictional clinical scenarios related to this condition. The study aims to determine if ChatGPT can provide accurate and relevant guidance on pharmaceutical options for managing chronic vaginitis.The authors use the set of question as input to ChatGPT system to derive the output then the output was further validated by expert panel. The results show that ChatGPT consistently offers valuable suggestions for potential drug repurposing, supported by scientific evidence. Despite limitations, such as the need for more clinical data and the inability to modify treatment, ChatGPT shows promise as a tool for drug repurposing in the treatment of chronic vaginitis. The present study is a novel approach in applying the AI based technique for drug repurposing in clinical medicine. Future research should focus on refining the model’s capabilities, incorporating more comprehensive clinical data, and enabling customization of treatment plans to enhance its effectiveness in assisting healthcare practitioners.By addressing these issues, ChatGPT could become a valuable resource for managing chronic vaginitis in females.  
Automatic Categorization of Mental Health Frame in Indonesian X (Twitter) Text using Classification and Topic Detection Techniques Basuki, Setio; Indrabayu, Rizky; Effendy, Nico Ardia
Khazanah Informatika : Jurnal Ilmu Komputer dan Informatika Vol. 10 No. 2 (2024): Oktober 2024
Publisher : Universitas Muhammadiyah Surakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23917/khif.v10i2.3328

Abstract

This paper aims to develop a machine learning model to detect mental health frames in Indonesian-language tweets on the X (Twitter) platform. This research is motivated by the gap in automatically detecting mental health frames, despite the importance of mental health issues in Indonesia. This paper addresses the problem by applying classification and topic detection methods across various mental health frames through multiple stages. First, this paper examines various mental health frames, resulting in 7 main labels: Awareness, Classification, Feelings and Problematization, Accessibility and Funding, Stigma, Service, Youth, and an additional label named Others. Second, it focuses on constructing a dataset of Indonesian tweets, totaling 29,068 data, by filtering tweets using the keywords "mental health" and "kesehatan mental". Third, this paper conducts data preprocessing and manual labeling of a random selection of 3,828 tweets, chosen due to the impracticality of labeling all data. Finally, the fourth stage involves conducting classification experiments using classical text features, non-contextual and contextual word embeddings, and performing topic detection experiments with three different algorithms. The experiments show that the BERT-based method achieved the highest accuracy, with 81% in the 'Others' vs. 'non-Others' classification, 80% in the seven main label classifications, and 92% in the seven main labels classification when using GPT-4-powered data augmentation. Topic detection experiments indicate that the Latent Dirichlet Allocation (LDA) and Latent Semantic Indexing (LSI) algorithms are more effective than the Hierarchical Dirichlet Process (HDP) in generating relevant keywords representing the characteristics of each main label.
Investigating the Use of Digital Consumption for Responsible Ride-Hailing Services in Indonesia Younger Generations Asfarian, Auzi; Sukmasetya, Pristi; Ramadhan, Dean Apriana; Athoetan, Salma; Hasbi Hutauruk1, Dzakiyyah; Firdaus Anavyanto, Arazka; Annisa; Noris Mohd Norowi
Khazanah Informatika : Jurnal Ilmu Komputer dan Informatika Vol. 10 No. 2 (2024): Oktober 2024
Publisher : Universitas Muhammadiyah Surakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23917/khif.v10i2.3367

Abstract

This study thoroughly examined responsible consumption implemented in Indonesia's ride-hailing services. We employed a mixed-methods approach that integrated quantitative data from the Socially Responsible Purchase and Disposal (SRPD) questionnaire with qualitative insights derived from think-aloud protocols and interviews. Our objective was to examine data and identify discrepancies between attitudes and behaviors among young individuals aged 18-29 to provide directions for future research. The results of our study uncovered substantial disparities between the attitudes that participants expressed and their subsequent actions. Although individuals acknowledge the importance of responsible consumption, the testing shows that they rarely use features related to responsible consumption, such as carbon funds, electric vehicle options, and tipping. Several key factors they stated are increased cost, lack of awareness of the features of responsible consumption, lack of perceived trust, and transparency of funds. Based on the interview, the personal connection between participants and ride-hailing drivers encourages them to tip more. Our research highlights the critical gap between attitudes and actions in responsible ride-hailing services in Indonesia's younger generation, which can be solved by implementing proper digital nudges. This research emphasizes the importance of further research to bridge this gap and encourage more accountable digital consumption.
Object Detection of BISINDO Sign Language Letters Using Residual Network Eriyadi, Maulidina Norick; Ilyas, Ridwan; Abdillah, Gunawan; Hadiana, Asep Id
Khazanah Informatika : Jurnal Ilmu Komputer dan Informatika Vol. 10 No. 1 (2024): April 2024
Publisher : Universitas Muhammadiyah Surakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23917/khif.v10i1.3670

Abstract

Indonesian Sign Language or BISINDO is an alternative language used by people who suffer from disabilities, especially those who have hearing impairments. This language grew and developed from the deaf community, so its use is based on the visual aspect. This research aims to apply Residual Networks to detect objects in the context of Bisindo Letter Sign Language, with the hope of increasing accuracy and efficiency in letter recognition. Object detection goes through 2 stages, namely feature extraction and model training. ResNet is a type of Convolutional Neural Network (CNN) architecture that utilizes models that have been previously trained, so it can save the time required in the model development process. In this research, Residual Network (ResNet) was used for feature extraction to recognize important aspects in the Bisindo letter sign image, such as hand position, finger shape characteristics, and direction of movement. The research results show that the new dataset used as training data and test data has a fairly good ability to detect with a division of 70% train set, 20% valid set and 10% test set with size 640x640 with 300 epochs for the training model.
Flood Prediction Using Machine Learning Model Integrated with Geographical Information System Perdana Putra, Muhammad Ricky; Rama Ashari; Muhirin; Azib Widad Zuhaily Imam; Kusrini
Khazanah Informatika : Jurnal Ilmu Komputer dan Informatika Vol. 10 No. 2 (2024): Oktober 2024
Publisher : Universitas Muhammadiyah Surakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23917/khif.v10i2.3723

Abstract

Flooding in Indonesia is still a frequent natural disaster compared to other types of disasters. In addition, the number of flood events also shows an increase every year. This research aims to develop a flood prediction model as a preventive measure as an early warning system and flood risk mitigation management that may occur based on Geographical Information System (GIS). It is expected that areas that have the potential to experience flooding can be more proactive in making preparations before flooding. This prediction model uses a classification type machine learning (ML) algorithm with training data involving rainfall within 12 months. The model evaluation results use two techniques: confusion matrix and K-Fold cross validation and each fold is calculated for accuracy. The K-Nearest Neighbors (KNN) model with a value of K = 31 gets the highest accuracy value of 88.89%, Decision Tree (DT) of 72.22%, and Naive Bayes of 78%. The average accuracy using K-Fold resulted in 89.09% for KNN, 77.12% for DT, and 86.59% for Naive Bayes. By considering these results, this research chose the KNN method to be applied in the prediction model. The code was rewritten in the Flask framework to be used as an API and integrated with Laravel as a Backend platform and Frontend using Bootstrap, JQuery, Axios, and LeafletJS as map visualisation. With this research, it is hoped that it can be one of the solutions in predicting as well as early warning of floods so that it can provide sufficient time for affected residents to make preparations for flooding.
Incorporating Learning Analytics and Business Intelligence into Higher Education E-Learning Laksitowening, Kusuma Ayu; Fahrudin, Tora; Insani, Rokhmatul; Umar, Ubaidilah
Khazanah Informatika : Jurnal Ilmu Komputer dan Informatika Vol. 10 No. 2 (2024): Oktober 2024
Publisher : Universitas Muhammadiyah Surakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23917/khif.v10i2.4142

Abstract

Business Intelligence (BI) represents a pivotal advancement in leveraging information technology to enhance organizational performance. BI tools serve as crucial aids in decision-making processes by furnishing requisite insights. In higher education institutions, BI can contribute to leaders and managers in providing perspectives related to academics, learning, and management. Central to BI development is the meticulous gathering of requirements, a process pivotal in identifying organizational informational and knowledge needs. This involves employing various methods such as interviews, observation, and analysis, including leveraging learning analytics to discern data utility for enhanced learning processes. Various studies show that learning analytics contributes to improving the learning and education process. On the other hand, learning analytics requires activity data that is integrated, subject oriented, and time series which are aligned with the characteristics of the data warehouse (DWH) as the main component of BI. This research endeavors to develop BI utilizing academic and e-learning data, exemplified through a case study of Telkom University's Academic Systems and Learning Management Systems (LMS). This study aims to provide actionable insights into the intersection of BI and learning analytics, ultimately enhancing educational processes and organizational decision-making capabilities. By integrating learning analytics into BI development, the resultant BI systems can cater not only to current managerial demands but also anticipate future analytical needs. The implementation of the multidimensional schema was successfully executed. This process involved mapping data from the academic information system and the LMS as data sources to the data warehouse, the Extract, Transform, and Load (ETL) process, and development of the prototype. The testing on the prototype indicated that the prototype meets the intended requirements and provides valuable insights through its comprehensive reporting capabilities. This demonstrates the effectiveness of the implemented multidimensional schema, ETL process, and the overall design of the reporting dashboard.

Page 1 of 3 | Total Record : 23

 1   2   3 

Filter by Year

2024 2025


Reset
Filter By Issues
All Issue Vol. 11 No. 1 (2025): April 2025 Vol. 10 No. 2 (2024): Oktober 2024 Vol. 10 No. 1 (2024): April 2024