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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 7 Documents
 1 
Search results for , issue "Vol. 11 No. 1 (2025): April 2025" : 7 Documents clear
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.
Using SVM and KNN for Predicting Customer Response Sentiment of M-PAJAK Application Muhammad Titan Rama Adi Wijaya; Ida Widaningrum; Angga Prasetyo; Dyah Mustikasari
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.v11i1.4528

Abstract

M-Pajak, an application initiated by the Directorate General of Taxes, signifies the modernization of taxation and serves a crucial function. This application facilitates taxpayers in meeting their tax obligations. User satisfaction with this application may be assessed via reviews on the Google Play Store. While this application fulfills client satisfaction, its sustained success is significantly contingent upon user contentment and experience. Sentiment analysis is essential for elucidating user evaluations and interactions with the program. This research analyses the sentiment of M-Pajak application reviews on Google Play using Support Vector Machine (SVM) and K-Nearest Neighbour (KNN), supported by the Term Frequency-inverse Document Frequency (TF-IDF) feature extraction method. A total of 1000 reviews between December 11, 2022 and December 2, 2023 were processed using KNN and SVM. The KNN algorithm yielded 153 positive predictions and 847 negative predictions and achieved 94% of accuracy. Meanwhile, SVM achieved an accuracy of 88.10%, with 325 positive predictions and 675 negative predictions. The results demonstrate the superiority of KNN in sentiment classification of M-Pajak reviews. This study also indicates that negative comments outnumber positive ones in this application. This serves as a signal for the Directorate General of Taxation to enhance user satisfaction with the M-Pajak application through continuous updates.
Region Enhanced Edge-Based Multi-Class Object Proposal for Self-Driving Vehicles Haq, Muhamad Amirul; Huy, Le Nam Quoc; Ridlwan, Muhammad
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.v11i1.4662

Abstract

On-road object detection is a fundamental element for the safety and reliability of autonomous driving systems. A primary challenge is developing object detection algorithms that are both fast and robust. This paper introduces a novel object proposal algorithm, named Region Enhanced Edge-Based (REEB) proposal, designed to accelerate object detection by significantly reducing the number of candidate regions requiring evaluation by a subsequent classification network. REEB leverages edge-map cues to score and rank initial proposals. To further enhance both detection quality and processing speed, the algorithm integrates efficient complementary techniques: image entropy is used to guide proposal generation density in relevant image regions, and road segmentation aids in refining proposal scores by differentiating road from non-road areas. Experimental evaluations on the KITTI dataset demonstrate that REEB achieves an average recall rate of 72.1% across four classes (pedestrian, cyclist, car, and truck) with an average processing time of 15 milliseconds per image. These results indicate strong performance when compared to other traditional, non-deep learning object proposal algorithms.
Performance Comparison of Random Forest, Bagging, and CART Methods in Classifying Recipients of the Family Program in North Aceh Hari Yanni, Meri; Anwar Notodiputro, Khairil; Sartono, Bagus
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.v11i1.5098

Abstract

Machine learning is a method in data mining, it is used to study large data patterns through classification methods including Random Forest, Bagging, and CART. The Random Forest method develops the Bagging technique and Decision Tree components (CART) in decision-making. The difference between RF and Bagging is the selection of random features in forming a decision tree. It is only found in RF. Bagging can improve performance, model stability, and reduce variance by forming many different models. The research aims to see the performance of the Random Forest, Bagging, and CART methods in classifying family recipient programs in North Aceh. The results show that the performance of the RF, Bagging, and CART classification methods using the SMOTE technique for handling unbalanced classes is better than before handling unbalanced data. The classification method is evaluated through each model's accuracy, sensitivity, specificity, precision, F1 score, and AUC values. The results show good performance with accuracy values of 90% Smote-RF and 86% Smote Bagging. The best performance was seen in the Smote-RF model which was obtained by tuning the Grid Search CV model parameters with k = 5 and repeat = 1 for a data set proportion of 90:10. This shows that the model can correctly predict all observations with an accuracy percentage of 90% with an average AUC value of 93.52%. On the other hand, the CART method has a very low accuracy value, so the model is less able to accurately predict all observations. Measurement of the level of importance of predictor variables that have the greatest influence in predicting recipient households is the floor area of the house, the number of household members aged 10 years and over, and the type of work of the head of the household.

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