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Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi)
Published by Ikatan Ahli Informatika Indonesia
ISSN : 25800760     EISSN : 25800760     DOI : https://doi.org/10.29207/resti.v2i3.606
Core Subject : Science, Engineering,
Computer Science & IT Engineering
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) dimaksudkan sebagai media kajian ilmiah hasil penelitian, pemikiran dan kajian analisis-kritis mengenai penelitian Rekayasa Sistem, Teknik Informatika/Teknologi Informasi, Manajemen Informatika dan Sistem Informasi. Sebagai bagian dari semangat menyebarluaskan ilmu pengetahuan hasil dari penelitian dan pemikiran untuk pengabdian pada Masyarakat luas dan sebagai sumber referensi akademisi di bidang Teknologi dan Informasi. Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) menerima artikel ilmiah dengan lingkup penelitian pada: Rekayasa Perangkat Lunak Rekayasa Perangkat Keras Keamanan Informasi Rekayasa Sistem Sistem Pakar Sistem Penunjang Keputusan Data Mining Sistem Kecerdasan Buatan/Artificial Intelligent System Jaringan Komputer Teknik Komputer Pengolahan Citra Algoritma Genetik Sistem Informasi Business Intelligence and Knowledge Management Database System Big Data Internet of Things Enterprise Computing Machine Learning Topik kajian lainnya yang relevan
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 1,046 Documents
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NLP-Based Intent Classification Model for Academic Curriculum Chatbots in Universities Study Programs Najma Rafifah Putri Syallya; Anindya Apriliyanti Pravitasari; Afrida Helen
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 9 No 1 (2025): February 2025
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v9i1.6276

Abstract

Chatbots are increasingly prevalent in various fields, including academic fields. Universities often rely on lecturers and staff for information access, which can lead to delays, limited availability outside working hours, and the risk of missed questions. This study aims to develop a chatbot model capable of addressing questions about the curriculum through intent classification, reducing reliance on manual responses, and providing a solution that ensures quick, accurate information retrieval. The research focuses on optimizing the IndoBERT model for intent classification and addresses challenges that arose due to imbalance data, which could have impacted model performance. Data was collected through an open poll on common curriculum-related questions asked by students. To address data imbalance, we tried oversampling techniques, such as SMOTE, B-SMOTE, ADASYN, and Data Augmentation. Data augmentation was chosen and successfully addressed the imbalance problem while maintaining data semantics effectively. We achieved the best model with hyperparameters batch size of 8, learning rate of 0.00001, 15 epochs, and 64 neurons in the hidden layer, resulting in 98.7% accuracy on the test data. Evaluation metrics further demonstrate the model's robustness across multiple intents. This research demonstrates the advantages of the IndoBERT model in intent classification for academic chatbots, achieving excellent performance.
Implementation of Generative Language Models (GLM) in Cyber Exercise Secure Coding using Prompt Engineering Sidabutar, Jeckson; Osdie, Alfido
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 9 No 2 (2025): April 2025
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v9i2.6012

Abstract

With the advancement of technology, the need for secure software is becoming increasingly urgent due to the rise in vulnerabilities in applications. In 2022, the National Cyber and Encryption Agency (BSSN) recorded 2,348 cases of web defacement, with one of the main causes being the lack of attention to secure coding practices during software development. This study explores the utilization of Generative Language Models (GLMs), such as ChatGPT, in secure coding training to enhance developers' skills. GLMs were implemented in a cybersecurity platform designed specifically for secure coding training, also serving as learning assistants that users can interact with during the cyber exercise. The study results show that the cyber exercise using GLMs significantly improved users' secure coding skills, as evidenced by comparing pre-test and post-test scores, indicating an increase in knowledge and proficiency in secure coding practices.
Large Language Model-Based Extraction of Logic Rules from Technical Standards for Automatic Compliance Checking Nugroho, Rizky; Krisnadhi, Adila; Saptawijaya, Ari
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 9 No 2 (2025): April 2025
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v9i2.6285

Abstract

In this research, we design logic rules as a representation of technical standards documents related to ship design, which will be used in automatic compliance checking. We present a novel design of logic rules based on a general pattern of technical standards’ clauses that can be produced automatically from text using a large language model (LLM). We also present a method to extract said logic rules from text. First, we design data structures to represent the technical standards and logic rules used to process the data. Second, the representation of technical standards is produced manually and tested to ensure that it can give the same conclusion as human judgment regarding compliance. Third, a variation of prompting methods, namely pipeline method and few-shot prompting, is given to LLM to instruct it to extract logic rules from text following the design. Evaluation against the logic rules produced shows that the pipeline method gives an accuracy score of 0.57, a precision of 0.49, and a recall of 0.62. On the other hand, logic rules extracted using few-shot prompting have an accuracy score of 0.33, precision of 0.43, and recall of 0.5. These results show that LLM is able to extract a logic rule representation of technical standards. Furthermore, the representation resulting from the prompting technique that utilizes the pipeline method has a better performance compared to the representation resulting from few-shot prompting.
Enhancing Problem-Solving Reliability with Expert Systems and Krulik-Rudnick Indicators Sari, Lita; Jufriadif Ma'am; Addini Yusmar; Khairiyah Khadijah; Sri Wahyuni; Naufal Ibnu Salam
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 9 No 2 (2025): April 2025
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v9i2.6333

Abstract

Problem-solving is one of the skills needed in the 21st century, but there is a significant gap between the ideal conditions and the reality of students' problem-solving skills. One method that can improve students' problem-solving skills is Krulik and Rudnick, but implementing this method with an expert system to improve problem-solving skills is still limited. This research aims to build an expert system to determine the level of problem-solving using Krulik and Rudnick's problem-solving indicators processed using the forward chaining and certainty factor algorithms. The study had five stages: data analysis, rule generation, certainty measurement, prediction, and testing. The data was processed by developing 5 Krulik and Rudnick problem-solving indicators into 35 statements. Each statement was categorized using Forward Chaining by producing three rules: low, medium, and high. The problem-solving level obtained is calculated using the Certainty Factor for a confidence value. The system's prediction results were evaluated using a confusion matrix, resulting in an accuracy of 80%, a precision of 92%, and a recall of 85%, indicating the system's reliable performance in measuring the level of problem-solving. This research can be used as a reference to support problem-solving in various more advanced educational and professional environments.
Impact of XBRL Technology Implementation on Information Asymmetry in Indonesia’s Capital Market Wahyudi, Aldika; Anggraini, Dyah
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 9 No 1 (2025): February 2025
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v9i1.5908

Abstract

This research aims to examine whether the publication of financial statements in XBRL format could reduce the level of information asymmetry, measured by the bid-ask spread, in Indonesia’s capital market over eight years of its implementation. Furthermore, this research examines differences in the level of information asymmetry in two observation periods, which are the early stage and the advanced stage of the XBRL implementation. The population in this study are listed companies on the IDX80 index which were sampled using the purposive method. The analytical instrument used is a panel data regression test using a random effect model and the non-parametric Wilcoxon test of statistical differences. Consistent with similar studies, the results show that the publication of financial reports in XBRL format could reduce the level of information asymmetry by providing accurate, integrated, and universally accessible reporting. The difference test further reveals that the level of information asymmetry is lower in the advanced stages compared to the early stages. This suggest that XBRL implementation becomes more effective over time due to the positive developments in institutional readiness and stakeholder facilitation.
Ant Colony Optimization for Jakarta Historical Tours: A Comparative Analysis of GPS and Map Image Approaches Bodhi, Gabriel; Charleen; Fitrianah, Devi
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 9 No 1 (2025): February 2025
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v9i1.5968

Abstract

The Traveling Salesman Problem (TSP) is a problem that represents a difficult combinatorial optimization problem starting from practical problems. The ant colony optimization (ACO) algorithm is implemented in several topics, particularly in solving combinatorial optimization problems. ACO is inspired by the behavior of ants in searching for the shortest path between a food source and their nest. In this research, ACO is used to find the best path or traveling salesman problem for museums and historical sites in Jakarta capital city of Indonesia. This research employs an approach based on the location's coordinates or latitude and longitude, while another method depends on coordinate data obtained from a supplied map image. After implementing both models, it can be concluded that the ACO model is not very good at solving TSP using actual coordinates. Meanwhile, the algorithm can quickly find near-optimal paths when using coordinates from a map image. The algorithm generates the optimal path in 11 seconds, reducing the initial distance from 17.938 to 4.430, using 4.731 ants and 75 trips with a distance power of 1. Statistical random variation was also performed, which proved that the algorithm is flexible and reliable when tested under various conditions.
Measuring Factors of Trust in the Use of E-Government: A Multi-Factor Analysis of the E-Government in Indonesia Altino, Iqbal Caraka; Sudarto, Reska Nugroho; Sensuse, Dana Indra; Lusa, Sofian; Putro , Prasetyo Adi Wibowo; Indriasari , Sofiyanti; Brillianto, Bramanti
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 9 No 2 (2025): April 2025
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v9i2.6016

Abstract

The implementation of dynamic records management applications within the Indonesian government remains relatively limited, with a lack of comprehensive integration between authorised institutions at both the central and regional levels. This research examines the impact of technical aspects, government agency variables, citizen variables, and risk indicators on trust in e-government. Furthermore, this study seeks to establish the effect of social factors and the advantages of trust in e-government. Finally, this research shows how trust in e-government influences satisfaction, willingness to use, and acceptance of e-government. The study examined 117 respondents using the integrated dynamic archival information system - SRIKANDI. Technical and risk factors were found to positively influence trust in e-government, with effects on satisfaction, intention to use, and adoption of e-government. Those who trusted SRIKANDI were more likely to utilize and implement the program. The findings indicate that for civil servants, trust in the government is also a factor influencing the utilisation of e-government services.
Application of VGG16 in Automated Detection of Bone Fractures in X-Ray Images Adhyaksa, Resky; Purnama, Bedy
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 9 No 1 (2025): February 2025
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v9i1.6101

Abstract

The purpose of this research is to determine whether or not a deep learning model called VGG16 can automatically identify bone fractures in X-ray pictures. The dataset, sourced from Kaggle, includes 10,522 images of human hand and foot bones, which underwent preprocessing steps such as normalization and resizing to 224x224 pixels to enhance data quality. The study utilizes the VGG16 architecture, pre-trained on ImageNet, as a base model, with transfer learning applied to adapt the model for fracture detection by fine-tuning its weights. This architecture consists of five blocks of convolutional and max-pooling layers to effectively extract and enhance information from the images for precise classification. The training and testing phases utilized an 80:20 split of the data, employing binary cross-entropy as the loss function and the Adam optimizer for efficient weight updates. The model achieved high performance, with an accuracy of 99.25%, precision of 98.62%, recall of 98.88%, and an F1-score of 99.16% over 25 epochs with a batch size of 128. Experimental results indicate that smaller batch sizes generally enhance accuracy and reduce loss values, with batch sizes of 128 and 16 yielding optimal performance. The study's findings underscore the potential of VGG16 in improving diagnostic accuracy and reliability in medical imaging, providing a robust tool for fracture detection. Future research should continue exploring hyperparameter optimization to further enhance model performance while balancing computational efficiency.
Hand Sign Recognition of Indonesian Sign Language System SIBI Using Inception V3 Image Embedding and Random Forest Sari, Mayang; Jamzuri, Eko Rudiawan
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 9 No 2 (2025): April 2025
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v9i2.6156

Abstract

This paper presents a sign language recognition system for the Indonesian Sign Language System SIBI using image embeddings combined with a Random Forest classifier. A dataset comprising 5280 images across 24 classes of SIBI alphabet symbols was utilized. Image features were extracted using the Inception V3 image embedding, and classification was performed using Random Forest algorithms. Model evaluation conducted through K-Fold cross-validation demonstrated that the proposed model achieved an accuracy of 59.00%, an F1-Score of 58.80%, a precision of 58.80%, and a recall of 59.00%. While the performance indicates room for improvement, this study lays the groundwork for enhancing sign language recognition systems to support the preservation and broader adoption of SIBI in Indonesia.
Comparison of Sugarcane Drought Stress Based on Climatology Data using Machine Learning Regression Model in East Java Aries Suharso; Yeni Herdiyeni; Suria Darma Tarigan; Yandra Arkeman
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 9 No 2 (2025): April 2025
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v9i2.6159

Abstract

Crop Water Stress Index (CWSI), derived from vegetation features (NDVI) and canopy thermal temperature (LST), is an effective method to evaluate sugarcane sensitivity to drought using satellite data. However, obtaining CWSI values ​​is complicated. This study introduces a novel approach to estimate CWSI using climatological data, including average air temperature, humidity, rainfall, sunshine duration, and wind speed features obtained from the local weather station BMKG Malang City, East Java, for the period 2021-2023. Before estimating CWSI, we analyzed sugarcane water stress phenology, examined the strength of the correlation between climatological features and CWSI, and looked at the potential for adding lag features. Our proposed prediction model uses climatological features with additional Lag features in a machine learning regression approach and 5-fold cross-validation of the training-testing data split with the help of optimization using hyperparameters. Different machine learning regression models are implemented and compared. The evaluation results showed that the prediction performance of the SVR model achieved the best accuracy with R2 = 90.45% and MAPE = 9.55%, which outperformed other models. These findings indicate that climatological features with lag effects can effectively predict water stress conditions in rainfed sugarcane if using an appropriate prediction model. The main contribution of this study is the utilization of local climatological data, which is easier to obtain and collect than sophisticated satellite data, to estimate CWSI. The application of the results shows that climatological data with lag effects can accurately estimate water stress conditions in rainfed sugarcane. In drought-prone areas, this strategy can help sugarcane farmers make better choices about land management and irrigation.

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