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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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Deep learning with Bayesian Hyperparameter Optimization for Precise Electrocardiogram Signals Delineation Darmawahyuni, Annisa; Sari, Winda Kurnia; Afifah, Nurul; Siti Nurmaini; Jordan Marcelino; Rendy Isdwanta
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.v9i2.6171

Abstract

Electrocardiography (ECG) serves as an essential risk-stratification tool to observe further treatment for cardiac abnormalities. The cardiac abnormalities are indicated by the intervals and amplitude locations in the ECG waveform. ECG delineation plays a crucial role in identifying the critical points necessary for observing cardiac abnormalities based on the characteristics and features of the waveform. In this study, we propose a deep learning approach combined with Bayesian Hyperparameter Optimization (BHO) for hyperparameter tuning to delineate the ECG signal. BHO is an optimization method utilized to determine the optimal values of an objective function. BHO allows for efficient and faster parameter search compared to conventional tuning methods, such as grid search. This method focuses on the most promising search areas in the parameter space, iteratively builds a probability model of the objective function, and then uses that model to select new points to test. The used hyperparameters of BHO contain learning rate, batch size, epoch, and total of long short-term memory layers. The study resulted in the development of 40 models, with the best model achieving a 99.285 accuracy, 94.5% sensitivity, 99.6% specificity, and 94.05% precision. The ECG delineation-based deep learning with BHO shows its excellence for localization and position of the onset, peak, and offset of ECG waveforms. The proposed model can be applied in medical applications for ECG delineation.
Enhanced Heart Disease Diagnosis Using Machine Learning Algorithms: A Comparison of Feature Selection Hirmayanti; Ema Utami
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.v9i2.6175

Abstract

Heart disease or cardiovascular disease is one of the leading causes of death in the world. Based on WHO data, in 2019, as many as 17.9 million people died from cardiovascular disease. If early prevention is not carried out immediately, of course, the victims will increase every year. Therefore, with the increasingly rapid development of technology, especially in the health sector, it is hoped that it can help medical personnel in treating patients suffering from various diseases, especially heart disease. So in this study, it will be more focused on the selection of relevant features or attributes to increase the accuracy value of the Machine Learning algorithm. The algorithms used include Random Forest and SVM. Meanwhile, for feature selection, several feature selection techniques are used, including information gain (IG), Chi-square (Chi2) and correlation feature selection (CFS). The use of these three techniques aims to obtain the main features so that they can minimize irrelevant features that can slow down the machine process. Based on the results of the experiment with a comparison of 70:30, it shows that CFS-SVM is superior by using nine features, which obtain the highest accuracy of 92.19%, while CFS-RF obtains the best value with eight features of 91.88%. By using feature selection and hyperparameter techniques, SVM obtained an increase of 10.88%, and RF obtained an increase of 9.47%. Based on the performance of the model using the selected relevant features, it shows that the proposed CFS-SVM shows good and efficient performance in diagnosing heart disease.
Comparison of Transfer Learning Model Performance for Breast Cancer Type Classification in Mammogram Images Cahya Bagus Sanjaya; Muhammad Imron Rosadi; Moch. Lutfi; Lukman Hakim
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.v9i1.6177

Abstract

Globally, breast cancer is the type of cancer that most women suffer from. Early detection of breast cancer is very important because there is a big chance of cure. Mammography screening makes it possible to detect breast cancer early. The study of computer-assisted breast cancer diagnosis is gaining increasing attention. Breast cancer comes in two forms: benign cancer and malignant cancer. advances in deep learning (DL) technology and its use to overcome obstacles in medical imaging, and classification using a number of transfer learning models to identify the type of breast cancer (malignant, benign, or normal). This work conducted a thorough comparison analysis of eight prevalent pre-trained CNN algorithms (VGG16, ResNet50, AlexNet, MobileNetV2, ShuffleNet, EfficientNet-b0, EfficientNet-b1, and EfficientNet-b2) for breast cancer classification. In this study, we permonData is divided into training, testing, and validation. Using the publicly accessible mini-DDSM dataset, we assess the proposed architecture. were used to measure the classification accuracy (Acc). For genBased on test results, the best accuracy was obtained using EfficientNetb2 with an accuracy value of 94% for training data and 98% for test data on mammogram images.
Advancing Hate Speech Detection in Indonesian Language Using Graph Neural Networks and TF-IDF Syaikha Amirah Zikrina; Fitriyani
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.v9i1.6179

Abstract

Most of the hate speech and abusive content on social media, particularly in the Indonesian language, presents significant challenges for content moderation systems. Previous research has applied machine learning models such as Recurrent Neural Networks (RNN), Support Vector Machines (SVM), and Convolutional Neural Networks (CNN) to address this issue. However, these approaches are limited in their ability to capture the relational and contextual nuances inherent in the data, resulting in suboptimal performance. This study introduces an approach by combining Graph Neural Networks (GNN) with Term Frequency-Inverse Document Frequency (TF-IDF) for feature extraction to improve hate speech detection on Twitter (platform X). The dataset consists of 13,169 Indonesian tweets, manually labeled for hate speech and abusive categories. Preprocessing steps include text cleaning, stemming, stop-word removal, and normalization. The GNN model achieved superior results, with accuracy scores of 92.90% for Abusive and 89.78% for Hate Speech, significantly outperforming the RNN model, which achieved accuracy of 86.09% and 86.15%, respectively. This study highlights the advantage of graph-based approaches in capturing complex relationships within text data. Future research can explore expanding datasets to include regional dialects and integrating advanced feature extraction techniques like Word2Vec or BERT. This study establishes a robust framework for improving hate speech detection, offering a valuable contribution to safer digital environments.
Comparative Performance of ResNet Architectures for Toraja Carving Image Classification with Data Augmentation Herman; Akbar, Muhammand; Nasir, Haidawati; Herdianti; Azis, Huzain; Hayati, Lilis Nur
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 9 No 4 (2025): August 2025
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v9i4.6181

Abstract

The complexity of the motifs and large number of different patterns make the classification of Toraja carvings challenging. The objective of this study is to develop a Convolutional Neural Network automatic classification model using a comparative analysis of the performance of three ResNet architectures. Data augmentation techniques were used to enrich the diversity of the training samples and improve the robustness of the model. The experimental results showed that ResNet101V2 had the highest validation accuracy, which was greater than 97%, followed by ResNet50V2 with more than 96%, and finally, ResNet152V2 with more than 94.74%. These test results indicate that the ResNet101V2 architecture has a better classification performance for complex motifs, with a good balance between precision and recall. However, the confusion matrix and per-class performance metrics indicated that motifs with high similarity, such as Paqdon-Bolu and Paqtedong, remained challenging. This study demonstrated that deeper CNN architectures and data augmentation techniques are effective in improving the classification accuracy of complex carving patterns. Further research should explore hybrid or advanced augmentation methods to improve the overall robustness and accuracy of the model.
Application of Formal Concept Analysis and Clustering Algorithms to Analyze Customer Segments Budaya, I Gede Bintang Arya; Dharmendra, I Komang; Triandini, Evi
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.v9i2.6184

Abstract

Business development cannot be separated from relationships with customers. Understanding customer characteristics is important both for maintaining sales and even for targeting new customers with appropriate strategies. The complexity of customer data makes manual analysis of the customer segments difficult, so applying machine learning to segment the customer can be the solution. This research implements K-Means and GMM algorithms for performing clustering based on the Transaction data transformed to the Recency, Frequency, and Monetary (RFM) data model, then implements Formal Concept Analysis (FCA) as an approach to analyzing the customer segment after the class labeling. Both K-Means and GMM algorithms recommended the optimal number of clusters as the customer segment is four. The FCA implementation in this study further analyzes customer segment characteristics by constructing a concept lattice that categorizes segments using combinations of High and Low values across the RFM attributes based on the median values, which are High Recency (HR), Low Recency (LR), High Frequency (HF), Low Frequency (LF), High Monetary (HM), and Low Monetary (LM). This characteristic can determine the customer category; for example, a customer that has HM and HR can be considered a loyal customer and can be the target for a specific marketing program. Overall, this study demonstrates that using the RFM data model, combined with clustering algorithms and FCA, is a potential approach for understanding MSME customer segment behavior. However, special consideration is necessary when determining the FCA concept lattice, as it forms the foundation of the core analytical insights.
Comparative Analysis of Machine Learning Algorithms for Predicting Patient Admission in Emergency Departments Using EHR Data Chamid, Ahmad Abdul; Nindyasari, Ratih; Ghozali, Muhammad Imam
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.v9i2.6188

Abstract

Every patient who is rushed to the Emergency Department needs fast treatment to determine whether the patient should be inpatient or outpatient. However, the existing fact is that deciding whether an inpatient or outpatient must wait for the diagnosis made by the existing doctor, so if there are many patients, it generally takes quite a long time. So, to predict patient admissions to the emergency unit, a machine learning model that can be fast and accurate is needed. Therefore, this study developed a machine learning and neural network model to determine patient care in Emergency Departments. This study uses publicly available electronic health record (EHR) data, which is 3,309. The model development process uses machine learning methods (SVM, Decision Tree, KNN, AdaBoost, MLPClassifier) and neural networks. The model that has been obtained is then evaluated for its performance using a confusion matrix and several matrices such as accuracy, precision, recall, and F1-Score. The results of the model performance evaluation were compared, and the best model was obtained, namely the MLPClassifier model with an accuracy value = 0.736 and an F1-Score value = 0.635, and the Neural Network model obtained an accuracy value = 0.724 and an F1-Score value = 0.640. The best models obtained in this study, namely the MLPClassifier and Neural Network models, were proven to be able to outperform other models.
Multi-Process Data Mining with Clustering and Support Vector Machine for Corporate Recruitment Zain, Ruri Hartika; Randy Permana; Sarjon Defit
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.v9i2.6197

Abstract

Having an efficient and accurate recruitment process is very important for a company to attract candidates with professionalism, a high level of loyalty, and motivation. However, the current selection method often faces problems due to the subjectivity of assessing prospective employees and the long process of deciding on the best candidate. Therefore, this research aims to optimize the recruitment process by applying data mining techniques to improve efficiency and accuracy in candidate selection. The method used in this research utilizes a multi-process Data Mining approach, which is a combination of clustering and classification algorithms sequentially. In the initial stage, the K-Means algorithm is applied to cluster candidates based on administrative selection data, such as document completeness and reference support. Next, a classification model was built using a Support Vector Machine (SVM) to categorize the best candidates based on the results of psychological tests, medical tests, and interviews. The experimental results show that the SVM model produces high evaluation scores, with an AUC of 87%, Classification Accuracy (CA) of 90%, F1-score of 89%, Precision of 91%, and Recall of 90%. With these results, it can be concluded that this model is able to improve accuracy in the employee selection process and help companies make more measurable and data-based recruitment decisions.
Hybrid Gradient Descent Grey Wolf Optimizer for Machine Learning Performance Enhancement Puteri Baharie, Sri Rossa Aisyah; Sugiyarto Surono; Aris Thobirin
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.v9i1.6203

Abstract

Advancements in machine learning have enabled the development of more accurate and efficient health prediction models. This study aims to improve diabetes prediction performance using the Support Vector Machine (SVM) model optimized with the Hybrid Gradient Descent Gray Wolf Optimizer (HGD-GWO) method. SVM is a robust machine learning algorithm for classification and regression. Still, its performance depends significantly on selecting appropriate hyperparameters such as regularization (C), kernel coefficient (γ), and polynomial kernel degree (d). The HGD-GWO method synergizes gradient descent for local optimization and the Gray Wolf Optimizer for global solution exploration. Using the Pima Indians Diabetes dataset, the process includes normalization, hyperparameter optimization, data division, and performance evaluation using accuracy, precision, recall, and F1-score metrics. The optimized SVM achieved an accuracy of 81.17%, with precision, recall, and F1-score values of 75.00%, 57.45%, and 65.06%, respectively, at a data ratio of 80%:20%. These findings highlight the potential of HGD-GWO in enhancing predictive models, particularly for early diabetes detection.
Customer Satisfaction Evaluation in Online Food Delivery Services: A Systematic Literature Review Adimas Fiqri Ramdhansya; Shella Maria Vernanda; Indra Budi; Prabu Kresna Putra; Aris Budi Santoso
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.v9i2.6205

Abstract

The rapid growth of online food delivery services has heightened the need for effective customer satisfaction measurement. This systematic literature review examines 476 papers, selecting 15 key studies to identify prevailing evaluation approaches. Findings reveal that sentiment analysis and PLS-SEM are the most frequently used analytical methods, each appearing in six studies. Satisfaction measurement relies on sentiment polarity scores in five studies and SERVQUAL frameworks in three studies. Data collection primarily involves surveys in seven studies and user-generated content in six studies, but limited demographic diversity reduces generalizability. Three key future research directions emerge. Advanced analytical techniques appear in 5 of 11 future works in the analysis methods domain. Expanding evaluation metrics is mentioned in 6 of 12 proposals in the evaluation domain. Exploring demographic context is highlighted in 10 of 25 recommendations in the dataset’s domain, with dataset development receiving twice the attention of methodological advancements. These results provide researchers with a structured framework for customer satisfaction evaluation while guiding food delivery platforms in refining service quality. By systematically mapping current methodologies and future priorities, this study bridges gaps between academia and industry, ensuring more effective customer satisfaction assessments.

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