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Agus Junaidi
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Jurnal Informatika
Published by Universitas Bina Sarana Informatika
ISSN : 23556579     EISSN : 25282247     DOI : https://doi.org/10.31294/informatika
Core Subject : Science,
Computer Science & IT
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 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, and Big Data.
Arjuna Subject : Ilmu Komputer (semua kategori) - Kecerdasan Buatan
Articles 18 Documents
 1   2 
Academic Information System on The Prince Jayakarta Junior High School Student Assessment System Fesa Asy Syifa Nurul Haq; Vika Febri Muliati; Abdu Rahman; Muhammad Zarlis; Ucuk Darusalam; Nurhayati Siregar
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/

Abstract

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.
Comparative study of DistilBERT and ELECTRA-Small Models in Spam Email Classification Ferdy Agusman
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/

Abstract

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.
Pose Analysis and Classification in Shooting Sport Using Convolutional Neural Network and Long Short-Term Memory Bahar Sobari; Moedjiono Moedjiono; M. Asep Rizkiawan
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/

Abstract

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. 
Comparative Optimization of EfficientNetB3, MobileNetV2, and ResNet50 for Waste Classification Sarifah Agustiani; Haryani Haryani; Agus Junaidi; Rizky Rachma Putri; Meutia Raissa Emiliana
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/

Abstract

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.
Performance Evaluation of RESTful API in Sales Target Monitoring System for Direct Sales and Sales Canvassers Suyud Widiono; Restian Dwi Friwaldi; Afwan Anggara
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/

Abstract

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.   
Predicting Stock Price Movements with Technical, Fundamental, and Sentiment Analysis Using the LSTM Model Muhammad Ighfar Saputra; Erna Nurmawati; Rayhan Abyasa
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/informatika.v12i1.12248

Abstract

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. 
Data Mining Approach to Improve Minimarket Sales using Association Rule Method Harlinda Lahuddin; Ramdan Satra
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/informatika.v12i1.12249

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.
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 Ilham Ramadhan; Budiman Budiman; Nur Alamsyah
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/informatika.v12i1.12253

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. 

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