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Jurnal Informatika
Published by Universitas Bina Sarana Informatika
ISSN : 23556579     EISSN : 25282247     DOI : https://doi.org/10.31294/ji.v4i2
Core Subject : Science,
Computer Science & IT
Jurnal Informatika respects all researchers Technology and Information field as a part spirit of disseminating science resulting and community service that provides download journal articles for free, both nationally and internationally. The editorial welcomes innovative manuscripts from Technology and Information field. The scopes of this journal are: Expert System Decision Support System Data Mining Artificial Intelligence System Machine Learning Genetic Algorithms Business Intelligence and Knowledge Management Big Data the manuscripts have primary citations and have never been published online or in print. Every manuscript will be checked the plagiarism using Turnitin software. If the manuscript indicated major plagiarism, the manuscript is rejected.
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 316 Documents
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Data Mining Approach to Improve Minimarket Sales using Association Rule Method Harlinda, Harlinda; Satra, Ramdan
Jurnal Informatika Vol 12, No 1 (2025): April
Publisher : Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/inf.v12i1.20835

Abstract

This research aims to provide recommendations for the placement of goods sold by the UMI Faculty of Computer Science mini supermarket. A data mining approach is used to determine the position of sales items between related items. This is done to make it easier for customers to search for items to buy based on the type of item. Another problem is determining the best-selling items and also determining the types of items that will receive promotions. The data mining approach uses association rules with a priori algorithms. Association rule mining is a data analysis technique used to find patterns and relationships in big data. This technique is widely used in business to help optimize marketing and sales strategies. The results of the rule association using an a priori algorithm show that if consumers buy 200 milli of Ultra Milk Slim Chocolate, they also buy 600 milli of LE MINERAL with a support value of 10% and confidence of 60%. This shows that these two items are related when consumers purchase.
Expert System of Error Tracking Automated Weather Observing System Using Certainty Factor Method Based on Android Application M Djibran, Halis; Purba, Joshua; Saadia, Aprilia Ode; Restele, La Ode; Hasria, Hasria
Jurnal Informatika Vol 12, No 1 (2025): April
Publisher : Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/inf.v12i1.22536

Abstract

The limited number of technicians at several BMKG UPT (Task Implementation Units) in Indonesia is the main background of this research. Especially in the field of Aviation Meteorology, which has a significant safety risk for equipment data users. This can be made easier with an expert system. The fault tracking expert system aims to provide information about the symptoms of damage that occur in the Automated Weather Observing System (AWOS) so that it can make it easier for BMKG technicians to repair and handle the equipment. This research stage begins with collecting information data through experts and literature sources regarding AWOS equipment, then calculating the certainty value of the information using the certainty factor method, and produce information that will be displayed through the application. The system uses a Certainty Factor calculation method that presents the calculation of the certainty value of information based on the percentage of information delivery by the source, this method is used in accordance with the type of research that utilizes information from sources or experts in the AWOS field. The resulting system is an android application consisting of several knowledge bases stored in the MySQL database on the server. The results of the data analysis show that the resulting system can be used on the user's smartphone, and users can consult AWOS equipment damage properly. In addition, users can also view the consultation history and damage list. The application user satisfaction questionnaire shows the system has worked and fulfilled the function for users by showing a value of 33.3% Very Good and 66.7% Good.
Optimization of Human Development Index in Indonesia Using Decision Tree C4.5, Support Vector Machine Algorithm, K-Nearest Neighbors, Naïve Bayes, and Extreme Gradient Boosting Ramadhan, Ilham; Budiman, Budiman; Alamsyah, Nur
Jurnal Informatika Vol 12, No 1 (2025): April
Publisher : Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/inf.v12i1.21874

Abstract

The Human Development Index (HDI) is a measure of human development achievement based on quality of life indicators such as Life Expectancy (LE), Mean Years of Schooling (MYS), Expected Years of Schooling (EYS), and Adjusted Per Capita Expenditure (AECE). HDI describes how people access development outcomes through income, health, and education. The determination of development programs implemented by local governments must be based on district/city priorities based on their HDI categories and must be right on target. Therefore, a decision system is needed that can accurately determine the HDI category in each district/city in Indonesia, using machine learning models such as Decision Tree C4.5, Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Naïve Bayes, and Extreme Gradient Boosting (XGBoost). Machine learning models will be used to classify the HDI in Indonesia in 2022 and determine the performance of the most optimal model in classification. This research uses the CRISP-DM method with secondary data from the Central Statistics Agency (BPS) as much as 548 data. The analysis results show that the Decision Tree C4.5 models have an accuracy of 0.86, KNN of 0.95, Naïve Bayes of 0.90, XGBoost of 0.93, and SVM provides the most optimal results with an accuracy of 0.97. UHH, RLS, and HLS variables significantly influence changes in HDI values in Indonesian regions based on the Chi-square, Pearson Correlation, Spearman, and Kendal test results. 
Optimizing Sentiment Analysis on the Linux Desktop Using N-Gram Features Hidayat, Muhamad Taufiq; Kurniawan, Rudi; Suprapti, Tati
Jurnal Informatika Vol 12, No 1 (2025): April
Publisher : Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/inf.v12i1.24773

Abstract

Linux, or GNU/Linux, is a widely used open-source operating system built on the Linux kernel that is available for anyone to use, known for its security and privacy advantages. With advancements in information technology, protecting privacy has become increasingly challenging due to data extraction practices done by major tech companies. This has encouraged some Mastodon users to switch to Linux, with many expressing their opinions on using Linux as their main operating system. This research seeks to analyze the sentiments of Mastodon users toward Linux through sentiment analysis to understand whether the trend is predominantly positive, negative, or neutral. The methodology used includes collecting data with the help of the Mastodon.py library witch then gets manually labelled with the assistance of a linguistic expert as well as a linguistic rule proposed by previous research. The text mining process includes preprocessing steps which includes feature extraction with n-Gram to gain the most optimize result as well as employing feature selection using TF-IDF. The Naïve Bayes algorithm is employed for text classification. The entire process of data analysis is conducted with the help of AI Studio (RapidMiner) software. The results show that the highest-performing model for sentiment analysis is achieved with an n-gram value of 3, revealing user sentiment polarity towards Linux on Mastodon as follows: 42% positive, 28% negative, and 30% neutral. The sentiment analysis model has an accuracy of 63%, with a precision of 70%, recall of 80%, and an f1-score of 74% which shows that this method is able to optimize the sentiment analysis process.
Sentiment Analysis of #Saverafah Hashtag on TikTok Using Naive Bayes and Decision Tree Methods Pirsingki, Nisa; Wandri, Rizky
Jurnal Informatika Vol 12, No 1 (2025): April
Publisher : Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/inf.v12i1.24537

Abstract

Social media facilitates user communication, both in positive, negative and neutral aspects. One of the popular platforms today is Tiktok, where users can create short videos and interact through comments or private messages, as well as follow the latest news, including the major conflict between Palestine and Israel that has been going on since 1948. In this war, many Palestinian civilians, including children and the elderly, became victims, and are currently trying to flee to Rafah to seek protection. This study aims to analyze public sentiment towards the news of Palestinian refugees heading to Rafah, using two classification methods: Naive Bayes and Decision Tree. Before classification, the data goes through a preprocessing process and TF-IDF weighting, and the two methods are compared to determine the best accuracy. The results showed that the Naive Bayes Multinomial method with the application of SMOTE produced an accuracy of 85.43%, a precision of 86.22%, a recall of 85.43%, and an f1-score of 85.53%. Meanwhile, the Decision Tree C4.5 method with the application of SMOTE produced an accuracy of 94.23%, a precision of 94.58%, a recall of 94.23%, and an f1-score of 94.22%. Based on the evaluation results, the best method for sentiment analysis of the hashtag #SaveRafah is Decision Tree C4.5.
Digital Marketing Strategy Optimization Using Support Vector Machine Algorithm AlFauzi, Ihsan; Budiman, Budiman; Alamsyah, Nur
Jurnal Informatika Vol 12, No 1 (2025): April
Publisher : Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/inf.v12i1.22459

Abstract

Information and communication technology (ICT) is essential in rapidly disseminating information. This research discusses the influence of ICT use in marketing promotions through TV, radio, and social media and compares the performance of several classification algorithms in processing the promotion data. The dataset is from Kaggle, with promotional attributes on TV, radio, and social media. The Cross-Industry Standard Process for Data Mining (CRISP-DM) is used. Algorithms tested include Naive Bayes, K-Nearest Neighbor, Support Vector Machine (SVM), Random Forest, and XGBoost. The results showed that SVM had the best performance with 80% accuracy, followed by KNN (79%), Naive Bayes (77%), XGBoost (77%), and Random Forest (76%). SVM provided the most accurate and consistent predictions in marketing promotion classification. This research concludes that the optimal utilisation of ICT and the application of appropriate classification algorithms can increase the effectiveness of marketing promotions in the digital era.
SemetonBug: A Machine Learning Model for Automatic Bug Detection in Python Code Based on Syntactic Analysis Imran, Bahtiar; Riadi, Selamet; Suryadi, Emi; Zulpahmi, M.; Zaeniah, Zaeniah; Wahyudi, Erfan
Jurnal Informatika Vol 12, No 2 (2025): October
Publisher : Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/inf.v12i2.25340

Abstract

Bug detection in Python programming is a crucial aspect of software development. This study develops an automated bug detection system using feature extraction based on Abstract Syntax Tree (AST) and a Random Forest Classifier model. The dataset consists of 100 manually classified bugged files and 100 non-bugged files. The model is trained using structural code features such as the number of functions, classes, variables, conditions, and exception handling. Evaluation results indicate an accuracy of 86.67%, with balanced precision and recall across both classes. Confusion matrix analysis identifies the presence of false positives and false negatives, albeit in relatively low numbers. The accuracy curve suggests a potential overfitting issue, as training accuracy is higher than testing accuracy. This study demonstrates that the combination of AST-based feature extraction and Random Forest can be an effective approach for automated bug detection, with potential improvements through model optimization and a larger dataset.
Performance Evaluation of LSTM and GRU Models for Movie Genre Classification Based on Subtitle Dialogs Using Augmented Data and Cross-Validation Yonita Putri Utami, Ni Luh Putu; Singgih Putri, Desy Purnami; Dwi Rusjayanthi, Ni Kadek
Jurnal Informatika Vol 12, No 2 (2025): October
Publisher : Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/inf.v12i2.25897

Abstract

This study aims to evaluate and compare the performance of Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) models in classifying movie genres based on subtitle dialogs. To address data imbalance across genres, data augmentation was applied to create balanced datasets with 500 and 700 samples per genre, in addition to the original dataset. The classification models were built using Word2Vec for word embedding, followed by LSTM and GRU architectures with a single hidden layer and dropout regularization. Model performance was assessed using accuracy and further validated through 5-fold cross-validation. The best test accuracy was achieved with the dataset containing 700 samples per genre, reaching 91% for LSTM and 92% for GRU. Cross-validation showed stable performance with average accuracies of 0.68 for LSTM and 0.67 for GRU. A paired t-test analysis yielded a p-value of 0.341, indicating no statistically significant difference between the two models. These findings suggest that both LSTM and GRU are effective for genre classification based on subtitle dialogs. The use of data augmentation is a key contribution of this study, enabling improved model performance on underrepresented genres. This research supports the development of automated movie recommendation systems that utilize subtitle-based genre prediction.
Batik Pattern Classification Using Logistic Regression, SVM, and Deep Learning Features Hapsari, Ratih Addina; Yuadi, Imam
Jurnal Informatika Vol 12, No 2 (2025): October
Publisher : Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/inf.v12i2.25855

Abstract

This study presents the integration of deep learning-based feature extraction with conventional machine learning classifiers for automatically categorizing Indonesian batik patterns. The research utilizes five traditional motifs: Alas Alasan, Kokrosono, Semen Sawat Gurdha, Sido Asih, and Sido Mulyo. Feature extraction was conducted using three deep learning models: Inception V3, VGG16, and VGG19, followed by classification through Logistic Regression and Support Vector Machines (SVM), with data processing performed in Orange. Experimental results show that Inception V3 combined with Logistic Regression achieved the highest classification performance, reaching 99.2% classification accuracy and an F1-score of 0.992. These results confirm the effectiveness of deep feature embeddings in improving the automatic classification of batik motifs. The study contributes to developing intelligent classification frameworks, offering a scalable approach to cultural heritage preservation through technology. Future work will focus on enhancing feature extraction methods and expanding the dataset to address motif overlap challenges.
Evaluation of Machine Learning Algorithms for Classifying User Perceptions of a Child Health Monitoring Application Rahmawati, Eka; Wibowo, Adi; Warsito, Budi
Jurnal Informatika Vol 12, No 2 (2025): October
Publisher : Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/inf.v12i2.24639

Abstract

Child growth and development are crucial aspects that every parent should monitor carefully. Proper growth and development foster the creation of a high-quality generation for the nation’s future. Recognizing the importance of monitoring children's growth, the Indonesian Pediatric developed PrimaKu, an application designed to assist parents in tracking the children's growth and development. The application includes health guidelines, growth monitoring tools, and immunization schedules. To maximize the application’s effectiveness, it is essential to evaluate its acceptance by the community, which can be assessed through user perceptions. This study evaluates the performance of machine learning algorithms, including Random Forest, Support Vector Machine, Naive Bayes, and Decision Tree, in classifying user perceptions of the PrimaKu application. The results revealed that the Support Vector Machine model achieved the highest accuracy of 81%, followed by Random Forest at 77%, Decision Tree at 74%, and Naive Bayes at 73%. Precision, recall, and F1-score used to validate the models' performance as the evaluation metrics. The findings underscore the potential of machine learning techniques in effectively classifying user feedback, providing valuable insights for improving application development and enhancing user satisfaction. This study contributes to understanding user acceptance of digital tools for child health monitoring, paving the way for better application usability and community impact

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