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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
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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 23 Documents
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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.
Optimization of Sentiment Analysis of Government Regulation in Lieu of Law on Job Creation Using KNN, Random Forest, and PSO Lestari, Sri; Tupari; Yan Aditiya Pratama; Suhendro
Khazanah Informatika : Jurnal Ilmu Komputer dan Informatika Vol. 11 No. 1 (2025): April 2025
Publisher : Universitas Muhammadiyah Surakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23917/khif.v11i1.3197

Abstract

Twitter is one of the social media used by the public to convey their views regarding the government's policy of issuing a Government Regulations in Lieu of Laws (Bahasa: Peraturan Pemerintah Pengganti Undang-undang (Perpu)). The public's pros and cons of this policy are material for sentiment analysis. The purpose of this study was to analyze Twitter users' opinions regarding the Job Creation Perpu using the K-Nearest Neighbors (KNN), Random Forest (RF), and Particle Swarm Optimization (PSO) methods. The data was 3.128 tweets from Twitter social media users regarding the Government Regulation in Lieu of Law on Job Creation. Based on 3.128 data, 1.599 sentiments were positive, 1.473 sentiments were negative and 53 sentiments were neutral. The results showed that PSO feature optimized Twitter social media sentiment analysis against this regulation. KNN and RF algorithms for sentiment analysis was carried out before and after optimization with PSO. Experimental results using RapidMiner 9.10 showed that PSO feature succeeded in increasing classification accuracy in both algorithms. Before optimization, the KNN accuracy value reached 80.40%, then increased significantly to 85.23% after optimization with PSO was applied. Meanwhile, Random Forest accuracy value before optimization was 77.21% and increased to 80.53% after PSO was applied. This result indicated that the PSO-based KNN algorithm had better performance in conducting sentiment analysis of the Government Regulation in Lieu of Law on Job Creation on Twitter compared to the Random Forest algorithm in the context of this study. It concluded that Random Forest algorithm based on PSO is the best classifier for sentiment analysis and a potential and effective algorithm for classifying and analyzing sentiment on the same topic.
Sentiment Analysis on Social Media Using Long Short-Term Memory and Word2Vec Feature Expansion Methods with Adam Optimization Khoirunnisa, Sanabila; Setiawan, Erwin Budi
Khazanah Informatika : Jurnal Ilmu Komputer dan Informatika Vol. 11 No. 1 (2025): April 2025
Publisher : Universitas Muhammadiyah Surakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23917/khif.v11i1.3957

Abstract

Twitter is one of Indonesia's most popular social media, so it has many users. The intensity of Twitter use can be used to carry out sentiment analysis related to topics being widely discussed, especially regarding the 2024 Indonesian presidential election. To understand public views, public opinion is divided from text data into positive and negative polarities to measure public sentiment. The classification model uses Long Short-Term Memory (LSTM) for feature extraction, utilizing TF-IDF. In addition, this model also combines Word2Vec based on the Indonews corpus, which contains 142,545 articles for feature expansion. This model is further optimized using the Adam optimization technique to improve accuracy. By using a dataset of 37,391 data, the results of this research obtained an accuracy score of 83.04% and an f1 score of 82.62%. This is an increase in accuracy of 11.22%; for the f1 score, it was a 10.92% increase from the baseline. This indicates that the classification model using Long Short-Term Memory (LSTM) with the application of TF-IDF as feature extraction, Word2Vec as feature expansion, and Adam optimization successfully produced optimal sentiment predictions regarding the 2024 Indonesian Presidential Election.
Twitter Social Media-Based Sentiment Analysis Using Bi-LSTM Method With Genetic Algorithms Optimization Prahasto, Girindra Syukran; Setiawan, Erwin Budi
Khazanah Informatika : Jurnal Ilmu Komputer dan Informatika Vol. 11 No. 1 (2025): April 2025
Publisher : Universitas Muhammadiyah Surakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23917/khif.v11i1.3959

Abstract

Advances in information technology, particularly social media platforms such as Twitter, can be used to explore public sentiment around the much-discussed 2024 Indonesian Presidential Election. Using sentiment analysis as part of text mining, we focus on distinguishing positive and negative polarity using Natural Language Processing (NLP) techniques with to detect the accuracy of tweet polarity regarding the 2024 Indonesian Presidential Election. Specifically, we implement the Bidirectional Long Short-term Memory (Bi-LSTM) method, an enhanced version of LSTM, for sentiment analysis. The text is preprocessed, TF-IDF is used for word importance weighting, and Word2Vec is used for efficient learning of high-quality words. To optimize the accuracy of the model, we used Genetic Algorithm (GA), a heuristic approach rooted in the principles of genetics and natural selection. GA operates on a chromosome-based population, aligned with Darwinian evolutionary concepts. This research aims to compare the accuracy of the Bi-LSTM model with various feature extraction methods, including TF-IDF and Word2Vec, in measuring the polarity of election-related tweets. This research highlights the comparison and improvement of the accuracy of each scenario in the built model. The accuracy score results in this research was 83%, where the accuracy score increases from the baseline by 7.98%.
Enhancing Retail Store Layout for Impulsive Buying Using Market Basket Analysis and the Apriori Algorithm Widiyanesti, Sri; Hakim, Muhammad Naufal; Syamsiyah, Nur; Dirohmat, Yogia Mugi
Khazanah Informatika : Jurnal Ilmu Komputer dan Informatika Vol. 11 No. 1 (2025): April 2025
Publisher : Universitas Muhammadiyah Surakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23917/khif.v11i1.4228

Abstract

Retail serves as a crucial element in connecting product to end-customer. Accordingly, product assortment and placement are key factors in enhancing a store’s attractiveness and promote convenience shopping. Therefore, customizing retail store layout must abide with customer behaviour. Market basket analysis (MBA) and association rule is the common framework to understand customer behaviour through historical transaction data. Yet, it can be extended to inform store layout improvement based on buying patterns. The current study aims to unveil customer buying pattern through MBA and association rules, then, use the collected insights to propose a new store layout design. We employed the Apriori algorithm to extract itemset relationships from the historical transaction data of a local convenience store brand. Furthermore, we integrate leverage metric to strengthen rule validation, offering more reliable interpretation compared to prior studies. Our findings suggest five solid rules that became the foundation of the proposed store layout, including a notably strong relationship between snack and drink products. The proposed framework can be adopted by retail businesses to improve store layout design tailored to their customer buying pattern.

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