Jurnal Informatika first publication in 2014 (ISSN: e. 2528-2247 p. 2355-6579) is scientific journal research in Informatics Engineering, Informatics Management, and Information Systems, published by Universitas Bina Sarana Informatika which the articles were never published online or in print. The publication is scheduled twice a year (April and October). The Editor welcomes submissions of manuscripts that relate to the field. Jurnal Informatika has been accredited by SINTA 3 (No. Surat Keterangan : 177/E/KPT/2024 Judul Surat Keterangan : Peringkat Akreditasi Jurnal Ilmiah Periode II Tahun 2024 Tanggal Surat Keterangan : 13 November 2024) and indexed by Google Scholar, Academic Resource Index, PKP Index, One Search, SINTA, Base, Scilit. Jurnal Informatika, previously published through the link https://ejournal.bsi.ac.id/ejurnal/index.php/ji
Implementation of Image Data Security Using the AES-256 Algorithm in the Work Accident Recording System
This study aims to implement an image data security mechanism in a web-based work accident recording system in an industrial environment by applying the Advanced Encryption Standard (AES) 256-bit cryptographic algorithm in Cipher Block Chaining (CBC) mode. The problem faced is the lack of an adequate protection mechanism for sensitive work accident photo files so that they have the potential to be accessed, copied, or modified by unauthorized parties. This study uses the Software-Oriented Prototyping method which allows system development to be carried out iteratively based on user needs and evaluation results at each development stage. The encryption process is carried out when the image file is uploaded into the system by generating a random Initialization Vector (IV) of 16 bytes, then the image data is encrypted using the AES-256-CBC algorithm and stored in ciphertext form with the file extension .enc. The decryption process is carried out when the file will be displayed again using the appropriate secret key and IV without storing the file in plaintext form on the server. The test results show that the encryption process has an average execution time of around 0.002–0.008 seconds, while the decryption process takes around 0.00004–0.001 seconds. In addition, the size of the encrypted file relatively follows the size of the original image file with an additional size of around 16–32 bytes due to the padding process and the use of initialization vectors. The results of the study indicate that the application of the AES-256-CBC algorithm is able to maintain the confidentiality and integrity of image data without having a significant impact on system performance. Thus, the developed system can improve the security of digital file storage and support a more structured, secure, and efficient management of work accident data.
Copyright (c) 2026 Dzulfiqar Alang Setiawan, Bambang Agus Herlambang, Ramadhan Renaldy (Author) https://creativecommons.org/licenses/by-sa/4.0
Digital Marketing Strategy Optimization Using Support Vector Machine Algorithm
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.
Information and communication technology
Copyright (c) 2025 Ihsan AlFauzi, Budiman Budiman, Nur Alamsyah (Author) https://creativecommons.org/licenses/by-sa/4.0
Sentiment Analysis of #Saverafah Hashtag on TikTok Using Naive Bayes and Decision Tree Methods
Social media facilitates user communication, both in positive, negative and neutral aspects. Tiktok is a popular platform that allows users to stay up to date on the latest news, including the major conflict between Palestine and Israel. In this war, many Palestinian civilians, including children and the elderly, became victims, and are currently trying to flee to Rafah to seek protection. The objective of this study is to evaluate public sentiment regarding the news of Palestinian refugees en route to Rafah. To achieve this purpose, we will examine 2982 comments on TikTok relating to the hashtag #SaveRafah, which will be the data to be trained. Prior to classification, the data will undergo a preprocessing process and TF-IDF weighting. The two classification methods will be compared to ascertain the most accurate approach. Because the data at the labeling stage has a larger percentage of positive data 90.7%, this study will employ the technique SMOTE to address class imbalance in the data set. 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.
Copyright (c) 2025 Nisa Pirsingki, Rizky Wandri (Author) https://creativecommons.org/licenses/by-sa/4.0
Optimizing Sentiment Analysis on the Linux Desktop Using N-Gram Features
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 which 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 optimized 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.
Copyright (c) 2025 Muhamad Taufiq Hidayat, Rudi Kurniawan, Tati Suprapti (Author) https://creativecommons.org/licenses/by-sa/4.0
Expert System of Error Tracking Automated Weather Observing System Using Certainty Factor Method Based on Android Application
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.
Automated Weather Observing System (AWOS)
Copyright (c) 2025 Halis M Djibran, Joshua Purba, Aprilia Ode Saadia, La Ode Restele, Hasria (Author) https://creativecommons.org/licenses/by-sa/4.0
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
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.
Copyright (c) 2025 Ilham Ramadhan, Budiman, Nur Alamsyah (Author) https://creativecommons.org/licenses/by-sa/4.0
Data Mining Approach to Improve Minimarket Sales using Association Rule Method
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.
Copyright (c) 2025 Harlinda Lahuddin, Ramdan Satra (Author) https://creativecommons.org/licenses/by-sa/4.0
Predicting Stock Price Movements with Technical, Fundamental, and Sentiment Analysis Using the LSTM Model
The challenge of minimizing risk and maximizing profit is what traders in the stock market have been endeavoring to solve for years. Stock prices typically exhibit the characteristic of volatility, influenced by various factors and necessitate a substantial amount of data to identify patterns in price movements. Considering the significant data requirements and the rapid advancement of big data and artificial intelligence, the LSTM (Long-Short Term Memory) model stands as a suitable approach for utilization in Deep Learning. The independent variables employed encompass technical indicator variables, currency exchange rates, interest rates, the Jakarta Composite Index (IHSG), and sentiment data extracted from Twitter tweets. The results indicate that sentiment analysis using the IndoBERT model achieved an accuracy of 0.69, while LSTM analysis produced the model with the smallest error for the fourth (4th) combination of variables, comprising closing price, technical indicators, IHSG, exchange rate, and Twitter sentiment, as well as the twelfth (12th) combination of variables, encompassing closing price, technical indicators, and IHSG. These combinations yielded average RMSE errors of 1.765E-04 and 1.978E-04, respectively. Hyperparameter optimization is done to six hyperparameter, number of unit layer, dropout rate, learning rate, batch size, optimizer, and timestamps. Following hyperparameter optimization, the best-identified model was the fourth (4th) combination of variables, yielding a minimal error of 7.580E-05 and an RMSE of 332.66 in the evaluation of test data.
Copyright (c) 2025 Muhammad Ighfar Saputra, Erna Nurmawati, Rayhan Abyasa (Author) https://creativecommons.org/licenses/by-sa/4.0
Comparative Analysis of Naïve Bayes Variants for Predicting Stunting-Risk Families
Stunting is a chronic nutritional condition that adversely affects children’s physical growth and cognitive development, highlighting the need for effective early detection, particularly at the household level. This study proposes a comparative analysis of three Naïve Bayes variants Gaussian, Multinomial, and Bernoulli to identify families at risk of stunting using machine learning techniques. The dataset used in this study consists of family-level records obtained from the National Population and Family Planning Agency (BKKBN) of Southeast Sulawesi Province, comprising demographic, socioeconomic, and health-related attributes. Data preprocessing involved handling missing values, removing irrelevant attributes, and transforming categorical variables. The dataset was divided into training and testing sets using an 80:20 ratio. The main contribution of this study lies in evaluating the effectiveness of different Naïve Bayes variants for family-based stunting risk prediction, which has been rarely explored in previous studies. Model performance was evaluated using accuracy, precision, recall, and F1-score. The results indicate that the Bernoulli Naïve Bayes model achieved the best performance, with an accuracy of 88% and balanced evaluation metrics across both classes. These findings suggest that the Bernoulli Naïve Bayes model is the most suitable approach for predicting family-level stunting risk and can support data-driven early intervention strategies.
Copyright (c) 2026 Alwas Muis, Zila Razilu, Umy Ramadhani Senga, Hutri Wulandari, Reva Andriyani (Author) https://creativecommons.org/licenses/by-sa/4.0
Comparison of Logistic Regression and Random Forest Methods in Predicting Vehicle Tax Payment Compliance
Motor vehicle tax is a major source of Regional Original Income (PAD). However, the level of motor vehicle tax payment compliance in North Aceh Regency is still suboptimal, particularly related to late payments. A data-driven approach is needed to predict and understand taxpayer compliance patterns more accurately. This study aims to compare the performance of the Logistic Regression and Random Forest methods in predicting motor vehicle tax payment compliance, as well as to identify factors that influence taxpayer compliance behavior at the North Aceh Samsat (Sat). This study uses secondary data in the form of motor vehicle tax payment transactions at the North Aceh Samsat for the 2022–2024 period, totaling 100,000 observations. The response variable is the tax payment compliance status (compliant and non-compliant), while the predictor variables include vehicle age, type of ownership, vehicle type, and vehicle brand. The data is divided into 70% training data and 30% testing data. The performance evaluation model is conducted using accuracy, precision, recall, and Area Under Curve (AUC) metrics. The analysis results show that Random Forest has better predictive performance than Logistic Regression, with higher accuracy and AUC values. Vehicle age and type of ownership are the most influential variables in predicting tax payment compliance, while vehicle brand has a relatively smaller influence. Logistic Regression provides a clear interpretation of the variable relationship, but has lower discrimination ability than Random Forest. Random Forest has proven to be more effective as a prediction model for motor vehicle tax payment compliance at the North Aceh Samsat. The application of machine learning-based predictive models has the potential to support more targeted policy making in an effort to improve motor vehicle tax payment compliance, especially in reducing late payments.
Tax; Motor Vehicle Tax; Logistic Regression; Random Forest; Classification
Copyright (c) 2026 Khairul Fuadi, Taufiq, Arnawan Hasibuan, Dahlan Abdullah, Nurdin Nurdin (Author) https://creativecommons.org/licenses/by-sa/4.0
Performance Evaluation of RESTful API in Sales Target Monitoring System for Direct Sales and Sales Canvassers
In an increasingly competitive digital era, manual sales target monitoring often leads to delayed information and inefficiency in decision-making. This research aims to develop a web and mobile-based sales target monitoring system integrated with RESTful API to enhance the efficiency of monitoring the performance of direct sales and sales canvassers. The system is developed using the Laravel framework for the back-end and Flutter for the mobile application, with Agile methodology applied in the development process. Testing is conducted using the Black Box Testing method to ensure the accuracy of system functionalities, including user authentication, sales data management, and sales target monitoring. Additionally, load testing is performed using Apache JMeter with scenarios of 500, 750, and 1000 users. The test results show that the system has stable performance with an average response time of 758 ms for 500 users, 762 ms for 750 users, and 880 ms for 1000 users, all below the threshold of 900 ms. The error rate is recorded at 0.00%, and the system throughput exceeds the set target, indicating the system's reliability in handling simultaneous user requests. The conclusion of this research shows that the implementation of RESTful API in the sales monitoring system can improve operational efficiency, enable real-time data exchange, and support faster, data-driven decision-making. As a recommendation, further development could include broader integration with mobile applications and the implementation of AI-based analytics for sales strategy optimization.
Copyright (c) 2025 Suyud Widiono, Restian Dwi Friwaldi, Afwan Anggara (Author) https://creativecommons.org/licenses/by-sa/4.0
Comparative Optimization of EfficientNetB3, MobileNetV2, and ResNet50 for Waste Classification
Waste management is an important challenge in protecting the environment and public health. Improperly managed waste can cause pollution and hinder the recycling process. This study aims to classify waste based on images by optimizing three deep learning architectures, namely EfficientNetB3, MobileNetV2, and ResNet50, to determine the model with the best performance. The dataset comes from the Kaggle platform, consisting of 4,650 images in six categories: battery, glass, metal, organic, paper, and plastic. The research stages include preprocessing, data augmentation, model development, and evaluation using accuracy, precision, recall, and F1-score metrics. The results show that EfficientNetB3 with the Adam optimizer achieved the best performance with 93% accuracy, followed by ResNet50 with 91%, while MobileNetV2 ranged from 70–73% depending on the optimizer. Variations in optimizers were found to affect model performance, while data augmentation improved generalization capabilities, especially in classes with limited samples. This research confirms the potential of deep learning methods in supporting automatic waste classification systems and provides a basis for the development of technology-based waste management systems in the future.
Copyright (c) 2025 Sarifah Agustiani, Haryani, Agus Junaidi, Rizky Rachma Putri, Meutia Raissa Emiliana (Author) https://creativecommons.org/licenses/by-sa/4.0
Pose Analysis and Classification in Shooting Sport Using Convolutional Neural Network and Long Short-Term Memory
Shooting sport requires high accuracy and speed, making training evaluation essential for athlete performance improvement. Conventional evaluation methods are often limited, thus the application of Artificial Intelligence (AI) and Computer Vision provides an effective alternative. This research aims to analyze and classify shooting sport poses using Deep Learning methods. A dataset consisting of several thousand pose images was collected from both field recordings and publicly available sources, followed by preprocessing for coordinate extraction. Convolutional Neural Network (CNN) was employed to extract coordinate data from shooting pose images, while Long Short-Term Memory (LSTM) was applied for pose classification. Experimental results demonstrated 94% accuracy, 95% Percentage of Correct Keypoints (PCK), and 4 mm Mean Per Joint Position Error (MPJPE), with training conducted at a learning rate of 0.0001 over 150 epochs on 5% test data, involving a total of 596,642 parameters. These results indicate that the proposed CNN–LSTM model provides a reliable approach for pose analysis and classification in shooting sport. The contribution of this study lies in presenting a novel dataset and framework for AI-based shooting sport evaluation, which can enhance training feedback and broaden AI applications in sports.
Copyright (c) 2025 Bahar Sobari, Moedjiono, M. Asep Rizkiawan (Author) https://creativecommons.org/licenses/by-sa/4.0
Comparative study of DistilBERT and ELECTRA-Small Models in Spam Email Classification
Spam email detection is one of the challenging tasks in cybersecurity due to the variability of spam content. These characteristics make it harder to identify spam, therefore researchers create different spam detection methods. Among these, Natural Language Processing (NLP) and machine learning techniques have shown outstanding results in classifying emails as spam or non-spam. Transformer-based models, such as BERT, have demonstrated pinpoint accuracy in text classification tasks. However, the computational requirements and resources are not practical in resource-limited environments. To mitigate this, smaller and more lightweight models, such as the DistilBERT and ELECTRA-Small, have been developed. This paper presents a comparative study of the DistilBERT and ELECTRA-Small models for spam email classification. The objective is to evaluate the performance and computational efficiency of these two compact transformer architectures. Both DistilBERT and ELECTRA-Small models were fine-tuned on an email dataset comprising 5728 samples. Our experimental results on the primary test set indicate that both models achieved an accuracy of almost 99%. However, when evaluated on a separate external validation set containing 10,000 emails, the ELECTRA-Small model achieved an accuracy of 86.53%, outperforming DistilBERT's 83.68%. Furthermore, ELECTRA-Small demonstrated superior computational efficiency with a training time of 00:02:00, compared to DistilBERT's 00:04:46. This study represents one of the few studies to directly compare the performance and computational efficiency of these two models in the context of spam email detection, highlighting their potential as lightweight and effective solutions for real-world applications.
Copyright (c) 2025 Ferdy Agusman (Author) https://creativecommons.org/licenses/by-sa/4.0
Academic Information System on The Prince Jayakarta Junior High School Student Assessment System
Information technology has supported the development of quality school services throughout the world. However, there are still many schools that have not used it optimally, especially in Indonesia, for example at Prince Jayakarta Bekasi Middle School. As in general, schools only use Ms. Word and Ms. applications . Excel. This resulted in different assessment formats and errors when filling in grades into the report card format. The academic information system application developed in this study uses the PHP, HTML, and MySQL programming languages and is named SIAP, which means eighteen academic information systems. The purpose of making this application is so that students / parents of students can receive school assessment information in a precise, fast and accurate manner. Teachers can also use the facility to process student scores so that they are well integrated and summarized as data for the Principal to make policies. This application can be opened on any browser platform, making it easier for users to access it anywhere and anytime.
Copyright (c) 2025 Fesa Asy Syifa Nurul Haq, Vika Febri Muliati, Abdu Rahman, Muhammad Zarlis, Ucuk Darusalam, Nurhayati Siregar (Author) https://creativecommons.org/licenses/by-sa/4.0
Batik Pattern Classification Using Logistic Regression, SVM, and Deep Learning Features
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.
Copyright (c) 2025 Ratih Addina Hapsari, Imam Yuadi (Author) https://creativecommons.org/licenses/by-sa/4.0
Evaluation of Machine Learning Algorithms for Classifying User Perceptions of a Child Health Monitoring Application
Supporting children’s early development requires consistent attention, ensuring their growth aligns with health standards. PrimaKu is one of the mobile applications developed by the Indonesian Pediatric Society. That application was created to assist parents in recording developmental milestones, monitoring immunization schedules, and accessing practical health information. This study investigates user perceptions of the application by analyzing publicly available reviews and ratings from the Google Play Store. Four supervised machine learning algorithms were applied to classify the sentiment expressed in the reviews: Support Vector Machine (SVM), Random Forest, Decision Tree, and Naive Bayes. Among the models tested, SVM achieved the highest classification accuracy (81%), followed by Random Forest (77%), Decision Tree (74%), and Naive Bayes (73%). Precision, recall, and F1-score were also used to evaluate the performance of each model. The results highlight the relevance of machine learning in capturing and interpreting user sentiment toward digital health tools. Further exploration of deep learning architectures is encouraged to enhance classification accuracy and understanding of features.
Copyright (c) 2025 Eka Rahmawati, Adi Wibowo, Budi Warsito (Author) https://creativecommons.org/licenses/by-sa/4.0
Performance Evaluation of LSTM and GRU Models for Movie Genre Classification Based on Subtitle Dialogs Using Augmented Data and Cross-Validation
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.
Copyright (c) 2025 Ni Luh Putu Yonita Putri Utami, Desy Purnami Singgih Putri, Ni Kadek Dwi Rusjayanthi (Author) https://creativecommons.org/licenses/by-sa/4.0
SemetonBug: A Machine Learning Model for Automatic Bug Detection in Python Code Based on Syntactic Analysis
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.
Copyright (c) 2025 Bahtiar Imran, Selamet Riadi, Emi Suryadi, M. Zulpahmi, Zaeniah, Erfan Wahyudi (Author) https://creativecommons.org/licenses/by-sa/4.0
Opinion Mining on Spotify Music App Reviews Using Bidirectional LSTM and BERT
The increasing number of user reviews on digital music platforms such as Spotify highlights the importance of sentiment analysis to better understand user perceptions. This study aims to develop a sentiment classification model for Spotify user reviews using a Bidirectional Long Short-Term Memory (BiLSTM) approach combined with BERT embeddings. The dataset consists of multilingual user reviews collected from the Google Play Store. Preprocessing steps include text cleaning, tokenization, and padding. BERT is utilized to generate contextual word embeddings, which are then processed by the BiLSTM model to classify sentiments as either positive or negative. The model’s performance is evaluated using a confusion matrix with accuracy, precision, recall, and F1-score metrics. The results show that the BiLSTM-BERT model achieves an F1-score of 0.8852, a recall of 0.9396, a precision of 0.8375, and an accuracy of 0.8374. These findings demonstrate the model’s effectiveness in handling multilingual sentiment analysis tasks, offering valuable insights for developers in enhancing user experience through data-driven decision-making.
Copyright (c) 2025 Primandani Arsi, Reza Arief Firmanda, Iphang Prayoga, Pungkas Subarkah (Author) https://creativecommons.org/licenses/by-sa/4.0
Introduction to Numbers in Mobile-Based For Early Childhood
Child age early in the current era This faced on Lots existing technology environment around , among them is cellphone , television , And computer . Application multimedia capable interesting attention And interest student in the learning process Because served in form picture as well as animation , proven by 80% from 26 respondent agreed matter the . Wrong One ability Which currently develop moment age early is ability speak . Child age early demanded For can quick learn And understand numbers . But problem Which happen moment this , Still Lots child age early Which Still difficult in do learning For know numbers . No only it , still Lots also parents who still difficulty in do teaching because that child not enough interested with the learning process . By Because it , build it system application introduction letter And number For child age Early with utilise Visual. Keyword — Child Early Age , Introduction numbers , Visual.
Copyright (c) 2026 Vika Febri Muliati, Fesa Asy Syifa Nurul Haq, Muhammad Zarlis, Ucuk Darusalam, Abdu Rahman (Author) https://creativecommons.org/licenses/by-sa/4.0
Semi-Supervised Bullying Detection in Narrative Student Counselling Reports Using a Hybrid CNN-LSTM with Pseudo-Labelling
Bullying incidents in schools are often documented in narrative student counselling reports containing informal language, emotional expressions, and contextual dependencies, which pose challenges for automated text classification, particularly under limited labeled data conditions. This study aims to develop a bullying detection model for narrative student counselling reports using a Hybrid CNN-LSTM architecture combined with a pseudo-labelling-based semi-supervised learning approach. The proposed model is trained through a two-stage process, consisting of pre-training on approximately 70,000 publicly available abusive-language texts and fine-tuning using 1,000 anonymized student counselling reports validated by guidance counsellors. Pseudo-labelling is employed to expand the training data while preserving domain relevance and adhering to ethical considerations. Experimental results show that the proposed model achieves an accuracy of 0.8698, a recall of 0.8570, and an F1-score of 0.7951. Although the precision value (0.7415) is relatively lower, higher recall is prioritized to reduce the risk of overlooking potential bullying cases in the school counselling context. Comparative analysis with Logistic Regression and Linear SVM indicates that the Hybrid CNN-LSTM model demonstrates more stable performance when processing longer narrative inputs that require contextual interpretation. This study contributes empirical evidence on the effectiveness of semi-supervised deep learning for bullying detection in low-resource, narrative student counselling data, a setting that remains underexplored in prior work.
Copyright (c) 2026 Suwarno, Muthia Andini, Mangapul Siahaan (Author) https://creativecommons.org/licenses/by-sa/4.0
Mapping Building Outdoor to Analysis Noise Leq with Matlab and 3DField
Noise Level Standards are the maximum limit of noise levels that are permitted to be released into the environment from businesses or activities so that they do not cause harm to human health and environmental comfort. Sound Level Meter is a tool used to measure noise, unwanted sounds, or those that can cause pain in the ears. Sound Level Meters are usually used in work environments such as the aviation industry, malls, campuses and so on. This time the Sound Level Meter was used to measure noise in the Animal Health Building environment at Gadjah Mada University which was carried out on Friday 17 February 2017 from 13.00 WIB to 17.00 WIB. Sound Level Meter is used to measure noise between 30-130 dB in dB units from frequencies between 20-20,000Hz. Research was also carried out during the Practicum Class for the Analysis and Instrumentation Course. The research is carried out by holding the Sound Level Meter parallel to the practitioner's head and the results will be obtained after the Sound Level Meter sounds by waiting for an interval of 5 seconds when the dB value in the Sound Level Meter is stable, then the resulting dB value is written on a sheet of paper that has been made in a table. The aim of the practicum is so that students can understand the sound phenomena that occur in our environment, understand the level of noise that occurs in our environment and understand how the Sound Level Meter works clearly.
Copyright (c) 2026 Risky Via Feriyanti (Author) https://creativecommons.org/licenses/by-sa/4.0
Accuracy Comparison of Support Vector Machine and K-Nearest Neighbors in Face Recognition for Library User Identification
Traditional library book lending systems that rely on membership cards or personal IDs are prone to misuse due to human error. To address this, this study developed a web-based book lending application using face recognition enabling automatic user verification without physical cards, improving security, and reducing human errors. In this research 10 university students took roles as the application’s users. The goal is that the application is able to identify every library user who is going to borrow or return books based on their real time face image. The face recognition itself has been developed using dlib’s face detection, cropping, and feature extraction functions and Support Vector Machine (SVM) classification model. The K-Nearest Neighbors (KNN) model was also tested to for classification accuracy comparison. Model validation tests show that the dlib works well in detecting face location within an image, cropping the face area, and extracting face features while the two classification models are able to well classify student IDs too. The SVM model results in 91% accuracy, 90% precision, 91% recall, and 91% F1-score, which is however slightly better than KNN’s 89% accuracy, 89% precision, 88% recall and 88% F1-score. The SVM has been then chosen for the application. Following the completion of application development, a system test has been conducted with black box method and returns with system accuracy of 90%. This finding confirms that implementing dlib and an SVM model for user identification for an application can be a promising method.
Copyright (c) 2026 Ellyza Hardianty, Mohammad Nasucha (Author) https://creativecommons.org/licenses/by-sa/4.0
Implementation of DSS for the Selection of MSME Financing Based on Financial Performance and Growth Potential
Many businesses have difficulty choosing a funding scheme that suits their financial condition and their company's growth potential. The Analytical Hierarchy Process (AHP) method and the Order of Preference by Similarity to Ideal Solution (TOPSIS) technique are combined to create the right Decision Support System (DSS). This research collected data through surveys and interviews with experts in 30 relevant agency-assisted MSMEs, but the data that will be displayed in this journal are as many as 5 MSME data that we have selected. Then, using AHP to determine how important financial criteria and business prospects are, and using the TOPSIS method to compare alternative financing such as people's business loans, fintech loans, and venture capital. Research outputs include a prototype of a system that can generate scientific publications, policy inputs, and objective financing advice for financial institutions. It is hoped that with this DSS, the financing selection process will be more measurable and accurate, and will support the sustainable growth of MSMEs. This resualt shows that the Analytical Hierarchy Process (AHP) method is able to produce a consistent criterion weight with a Consistency Ratio (CR) value of < 0.1, where business duration (0.22) and income (0.17) are the most dominant factors in assessing the feasibility of MSME financing. Furthermore, the TOPSIS method was used to produce an objective MSME ranking, with Krh Kreasikus Production obtaining the highest preference value (Ci = 0.6911) and Nainay Salted Egg Producer obtaining the lowest value (Ci = 0.2259), demonstrating the ability of this method in distinguishing at-risk MSMEs and MSMEs with better growth potential. Overall, the AHP-TOPSIS-based Decision Support System model has proven to be effective in helping financial institutions evaluate MSMEs in a more systematic, transparent, and data-based manner compared to conventional methods.
Copyright (c) 2026 Willy Eka Septian, Maya Permata Sari, Rinaldi Adam, Muhammad Ibrahim, Silvia Maharani, Lena Magdalena (Author) https://creativecommons.org/licenses/by-sa/4.0
Implementation of IndoBERT Model in Predicting Anxiety Disorders from Comments on Social Media
The development of social media as a means of emotional expression has opened up new opportunities in the early detection of mental health disorders, particularly anxiety disorders, which are still rarely analyzed using Indonesian-language computational approaches. To implement and evaluate IndoBERT model in detecting indications of anxiety disorders based on Indonesian-language social media comments. The method of study used an experimental quantitative approach with a total of 6,075 comments collected from Twitter, Instagram, and TikTok, which were classified into two categories: anxiety and normal. Pre-processing processes were carried out through text cleaning, slang normalization, and stopword removal before the IndoBERT model was trained using fine-tuning techniques for three epochs. Model performance was tested using accuracy, precision, recall, and F1- score metrics, and evaluated through confusion matrix analysis and k-fold cross-validation to ensure consistency of results. The results show that IndoBERT achieved 99.67% accuracy, 0.98 precision, 0.96 recall, and 0.97 F1-score in the anxiety class, with very low classification errors. This performance demonstrates that the model is able to effectively recognize linguistic patterns of anxiety despite data imbalance between classes. These findings confirm IndoBERT’s potential as a basis for developing a text-based early detection system for anxiety disorders in Indonesia. It is recommended for future studies to expand the data sources, add other psychological disorder categories, and compare performance with other algorithms to improve the model’s reliability.
Copyright (c) 2026 Rekha Inaya Putri, Lathifah Alfat (Author) https://creativecommons.org/licenses/by-sa/4.0