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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 10 Documents
 1 
Search results for , issue "Vol. 10 No. 1 (2024): April 2024" : 10 Documents clear
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.
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.  
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.
Effect of Chatbot-Assisted Learning on Students’ Learning Motivation and Its Pedagogical Approaches Septiyanti, Nisa Dwi; Luthfi, Muhammad Irfan; Darmawansah, Darmawansah
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.v10i1.4246

Abstract

Abstract- The use of chatbots in the learning process has been increasingly investigated and applied. While many studies have discussed the chatbot's ability to motivate students' interest in learning, few have examined whether students' perception of learning affects the effectiveness of chatbots and the pedagogical approach taken by chatbots as conversational agents during the learning process. There is a need for new analysis to capture the effects of Chatbot-Assisted Learning (Chatbot-AL) and student-chatbot conversations. In an eight-week semester, 48 first-year undergraduate students participated in a chatbot-assisted learning environment integrated into an engineering course. Data were collected through questionnaires on students' learning motivation and discourse in chatbot conversations. Statistical non-parametric analysis and Epistemic Network Analysis (ENA) were used to explore the research questions. The results showed that students with high learning perception had better learning motivation using chatbot-AL than students with low learning perception. Additionally, most of the questions asked by students were aimed at receiving emotional support through casual conversation with the chatbot. Finally, the implications, limitations, and conclusions were discussed.
Approach Integration Design Sprints to Design Thinking in Learning Management System Sakattaku Nida, Siti Nabilah; El Akbar, R Reza; Rahmatulloh, Alam
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.v10i1.4833

Abstract

The Ministry of Education and Culture has introduced a new phase of its independent learning policy called the Movement Organization Program (POP). As a participant in POP, the Sakata Innovation Center Foundation offers a Saung Coding training program that combines hybrid learning methods, including utilizing the Sakattaku Learning Management System (LMS) platform. However, feedback from teachers and school principals indicates that 45% of the respondents (20 out of 45) faced difficulties and discomfort while using the LMS. The user interface (UI) and user experience (UX) are crucial in enhancing the system's functionality and overall user satisfaction. Hence, this research aims to analyze and develop a plan to implement improvements in the UI/UX of the sakattaku.com LMS. The study also involves testing the system and recommending design enhancements. By prioritizing UI and UX, the research combines the compatibility of Design Thinking and Design Sprint methodologies. The final findings indicate that 9 of 15 expert users completed the assigned scenario tasks successfully. The User Experience Questionnaire (UEQ), administered to a sample of 45 individuals, yielded positive impressions across all assessment aspects, with particular improvement noted in the clarity aspect, which transformed from a below-average rating to an excellent rating. Additionally, the novelty aspect exhibited the highest positive difference, with a value of 75.6%.
Enhanced Image Classification by Eliminating Outliers with the Combination of Feature Selection and K-means Techniques Sevani, Nina; Cuvianto, Lukas; Octaviany, Jessica
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.v10i1.4834

Abstract

Accurate image classification will yield valuable information to support decision-making. Support Vector Machine (SVM) is a widely used technique to achieve high classification accuracy. However, data outliers can reduce the SVM’s accuracy. To resolve this problem, the K-Means clustering method is used to eliminate the outliers by checking the proximity between data and clustering the data. Nevertheless, one of the challenges of using K-Means is the sensitivity of the initial centroid selection which is done randomly. Therefore, this study combines the use of K-Means, feature extraction with VGG-16 deep learning architecture, and feature selection using the Chi2 technique to get better classification accuracy. The combination of these methods is empirically proven to increase the accuracy of three image dataset about 20%. The results demonstrate that using these methods in conjunction can also reduce the amount of time needed for image classification. Nevertheless, label information is not taken into consideration in this study. Therefore, in the future, this research can still be developed by applying other standards and adding information labels in the feature selection process.
Automated Course Timetabling Optimization Using Tabu-Simulated Annealing Hyper-Heuristics Algorithm Muklason, Ahmad; Marom, Ahsanul; Premananda, I Gusti Agung
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.v10i1.4835

Abstract

The topic of solving Timetabling Problems is an interesting area of study. These problems are commonly encountered in many institutions, particularly in the educational sector, including universities. One of the challenges faced by universities is the Course Timetabling Problem, which needs to be addressed regularly in every semester, taking into consideration the available resources. Solving this problem requires a significant amount of time and resources to create the optimal schedule that adheres to the predefined constraints, including both hard and soft constraints. As a problem of computational complexity, University Course Timetabling is NP-hard, meaning that there are no exact conventional algorithms that can solve it in polynomial time. Several methods and algorithms have been proposed to optimize course timetabling in order to achieve the optimal results. In this study, a new hybrid algorithm based on Hyper-Heuristics is developed to solve the course timetabling problem using the Socha Dataset. This algorithm combines the strengths of Simulated Annealing and Tabu Search to balance the exploitation and exploration phases and streamline the search process. The results show that the developed algorithm is competitive, ranking second out of ten previous algorithms, and finding the best solution in six datasets.
Prediction of Presidential Election Results using Sentiment Analysis with Pre and Post Candidate Registration Data Firdaus, Asno Azzawagama; Yudhana, Anton; Riadi, Imam
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.v10i1.4836

Abstract

Social-media is a solution for politicians as a campaign tool because it can save costs compared to conventional campaigns. The 2024 Indonesian presidential election has attracted public attention, especially among social media users. Twitter, as one of the most widely used social media platforms in Indonesia, has become an effective campaign platform. Sentiment analysis is one approach that can be used to measure public opinion on Indonesian presidential candidates based on Twitter data. The data was collected before the declaration of candidates in March 2023 and shortly after the registration of presidential and vice-presidential candidates in November 2023. The data obtained amounted to 15,000 in March 2023 collection and 11,569 in November 2023 collection and used manual labeling by linguists. After removing duplicated tweets, the data changed to 10,569 data with each candidate having 3,523 data for March 2023 and 4,893 data, with each candidate pair having 1,631 data for November 2023. The sentiment analysis classification model is determined using the Naïve Bayes and Support Vector Machine (SVM) methods with Term Frequency-Inverse Document Frequency (TF-IDF) feature extraction. Based on the data, the highest percentage of positive sentiment for the data obtained in March 2023 is for Ganjar Pranowo data by 77.94% and the highest percentage of negative sentiment is for Anies Baswedan data by 31.39%. Meanwhile, for the data obtained in November 2023, the highest positive sentiment was obtained for the candidate pair Ganjar Pranowo - Mahfud MD by 69.16%, and the highest negative sentiment was found in the data Prabowo Subianto - Gibran Rakabuming Raka by 52.12%. Words that frequently appeared in the positive sentiment for Ganjar Pranowo - Mahfud MD included "strong", "corruption", "support", "appreciation", and others. This research achieved the highest accuracy for SVM method which is 86% and Naive Bayes method which is 79%.
Implementing Bayes’ Theorem Method in Expert System to Determine Infant Disease Muslimah, Virasanty
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.v10i1.4837

Abstract

The infant’s immune system requires parental attention and is recommended to be checked regularly by health professionals. These diseases suffered by infants are Acute Respiratory Infections (ARI), Diarrhea, Acute Pharyngitis, Scabies, and Allergic Contact Dermatitis (ACD). Treatment can be provided at the public health center (Puskesmas), although there is still a general shortage of specialist doctors and no system to help diagnose diseases suffered by infants. Bayes’ Theorem is a rule that uses probability to make the best decision based on available information. This study makes a diagnosis of the disease suffered by the baby with the aim that the disease can be treated early by using the Bayes’ Theorem method. Based on the scenario that babies who experience symptoms of cold cough, itchy, and runny nose are then calculated using the Bayes’ Theorem method, it is concluded that the baby is suffering from Scabies. Bayes’ Theorem method, which was tested on 30 data, was found to have an accuracy value of 0.89 or 89%. The infant disease expert system using the Bayes’ Theorem method makes it easier for parents to find out the disease in their baby so that they can take action on the symptoms that appear.

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