Jurnal Teknik Informatika (JUTIF)
Jurnal Teknik Informatika (JUTIF) is an Indonesian national journal, publishes high-quality research papers in the broad field of Informatics, Information Systems and Computer Science, which encompasses software engineering, information system development, computer systems, computer network, algorithms and computation, and social impact of information and telecommunication technology. Jurnal Teknik Informatika (JUTIF) is published by Informatics Department, Universitas Jenderal Soedirman twice a year, in June and December. All submissions are double-blind reviewed by peer reviewers. All papers must be submitted in BAHASA INDONESIA. JUTIF has P-ISSN : 2723-3863 and E-ISSN : 2723-3871. The journal accepts scientific research articles, review articles, and final project reports from the following fields : Computer systems organization : Computer architecture, embedded system, real-time computing 1. Networks : Network architecture, network protocol, network components, network performance evaluation, network service 2. Security : Cryptography, security services, intrusion detection system, hardware security, network security, information security, application security 3. Software organization : Interpreter, Middleware, Virtual machine, Operating system, Software quality 4. Software notations and tools : Programming paradigm, Programming language, Domain-specific language, Modeling language, Software framework, Integrated development environment 5. Software development : Software development process, Requirements analysis, Software design, Software construction, Software deployment, Software maintenance, Programming team, Open-source model 6. Theory of computation : Model of computation, Computational complexity 7. Algorithms : Algorithm design, Analysis of algorithms 8. Mathematics of computing : Discrete mathematics, Mathematical software, Information theory 9. Information systems : Database management system, Information storage systems, Enterprise information system, Social information systems, Geographic information system, Decision support system, Process control system, Multimedia information system, Data mining, Digital library, Computing platform, Digital marketing, World Wide Web, Information retrieval Human-computer interaction, Interaction design, Social computing, Ubiquitous computing, Visualization, Accessibility 10. Concurrency : Concurrent computing, Parallel computing, Distributed computing 11. Artificial intelligence : Natural language processing, Knowledge representation and reasoning, Computer vision, Automated planning and scheduling, Search methodology, Control method, Philosophy of artificial intelligence, Distributed artificial intelligence 12. Machine learning : Supervised learning, Unsupervised learning, Reinforcement learning, Multi-task learning 13. Graphics : Animation, Rendering, Image manipulation, Graphics processing unit, Mixed reality, Virtual reality, Image compression, Solid modeling 14. Applied computing : E-commerce, Enterprise software, Electronic publishing, Cyberwarfare, Electronic voting, Video game, Word processing, Operations research, Educational technology, Document management.
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962 Documents
<![CDATA[CLASSIFICATION OF FAMILY HOPE PROGRAM RECIPIENTS USING NAIVE BAYES AND C4.5 METHODS ]]>
<![CDATA[Fauzi, Farras Ahmad]]>;
<![CDATA[Rohana, Tatang]]>;
<![CDATA[Juwita, Ayu Ratna]]>;
<![CDATA[Wahiddin, Deden]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 5 No. 5 (2024): JUTIF Volume 5, Number 5, Oktober 2024 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2024.5.5.3697
<![CDATA[Receiving PKH assistance in Rawamerta District does not always go well, so there are people who are not entitled to receive assistance. This is because there is still no system that can facilitate the process of classifying PKH assistance recipients. The application of data mining can facilitate classification with high speed and accuracy. The purpose of this study is to classify PKH assistance recipients using the Naïve Bayes and C4.5 methods to determine the eligibility of PKH for people facing social welfare problems. The data used is PKH data in Rawamerta District, Karawang Regency in 2023, totaling 1834 data. The results of naive bayes accuracy of 98.89%, precision 98.25%, recall 98.51%, F1-score 98.89%, and AUC 1.00 are included in the excellent classification because they are in the range of 0.90-1.00, while the C4.5 algorithm produces Accuracy values of 99.26%, Precision 99.25%, Recall 99.25%, F1-score 99.25% and AUC 0.99 are included in the excellent classification because they are in the range of 0.90-1.00. The C4.5 algorithm is superior to Naive Bayes, because the accuracy produced is higher.]]>
<![CDATA[Sentiment Analysis of Shoe Product Reviews on Indonesian E-Commerce Platform Using Lexicon Based and Support Vector Machine ]]>
<![CDATA[Muttakin, Fitriani]]>;
<![CDATA[Andrika, Nadila]]>;
<![CDATA[Salsabila, Salsabila]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 6 No. 2 (2025): JUTIF Volume 6, Number 2, April 2025 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2025.6.2.3800
<![CDATA[The rapid development of e-commerce has encouraged people, especially young people, to switch from offline shopping to online platforms such as Shopee that offer fashion products, including shoes, at affordable prices and a wide selection. This phenomenon creates great opportunities for sellers, but also poses challenges related to analyzing product quality contained in customer reviews. The large number of scattered and unstructured reviews makes it difficult for potential buyers to accurately assess products. Therefore, this study aims to analyze the sentiment of 10,323 shoe product reviews on Shopee using the Support Vector Machine (SVM) algorithm and the Lexicon-Based method. SVM was chosen because of its advantage in achieving high accuracy in text classification, with accuracy results reaching 92.62%. The Lexicon-Based method is used to detect specific sentiment words, which provides deeper insight into consumer opinions on shoe products. The analysis results show that shoe product reviews are dominated by positive sentiments, reflecting a high level of customer satisfaction. The findings not only provide guidance for sellers in designing more effective marketing strategies, but also help potential buyers in making better decisions based on objective sentiment analysis. In addition, this study contributes to the literature related to sentiment analysis with SVM in the e-commerce domain, especially for fashion shoes. Thus, the combined use of SVM and Lexicon-Based methods shows great potential in providing valuable insights into consumer preferences as well as increasing customer confidence in choosing shoe products in the e-commerce.]]>
<![CDATA[Accelerating Classification For Iot Attack Detection Using Decision Tree Model With Gini Impurity Tree-Based Feature Selection Technique ]]>
<![CDATA[Dzaki, Muhammad Hafizh]]>;
<![CDATA[Nugraha, Adhitya]]>;
<![CDATA[Luthfiarta, Ardytha]]>;
<![CDATA[Riyanto, Azizu Ahmad Rozaki]]>;
<![CDATA[Novandian, Yohanes Deny]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 6 No. 5 (2025): JUTIF Volume 6, Number 5, Oktober 2025 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2025.6.5.3817
<![CDATA[The Internet of Things (IoT) continues to expand rapidly, with the number of connected devices expected to reach billions in the near future. However, it makes IoT devices prime target for cyber-attack. Therefore, an effective Intrusion Detection System (IDS) is required to detect these attacks swiftly and accurately. This study aims to build a machine learning-based IDS to effectively detect attack on IoT network using the CIC IoT 2023 dataset. The dataset contains over 46 million data rows with 48 features, covering 33 attack types and 1 benign class. To address the dataset's complexity and enhance processing efficiency, feature selection technique was applied. Six feature selection techniques from three categories – Filter-based, Wrapper-based, and Hybrid methods – were evaluated to produce the best feature subset. Each subset was tested using a Decision Tree algorithm. Then, the model performance calculated based on accuracy, computational time, as well as macro-precision, -recall, and -F1-score. The results demonstrate that the three best feature selection from each category – Mutual Information, Genetic Algorithm, and Gini Impurity Tree-based – improved training time by average different 55 seconds from 148 seconds, which speed up by 63.06% without sacrificing accuracy. The Gini Impurity Tree-based algorithm proved to be the most efficient, producing the smallest feature subset, which is 10 features, faster processing times, which is 40 seconds, and shallower tree’s depth, which is 64 level from 73 level. In conclusion, feature selection not only enhances computational efficiency but also simplifies tree’s shape without sacrificing the accuracy of detection.]]>
<![CDATA[Comparison ff Sentiment Labeling Using Textblob, Vader, and Flair in Public Opinion Analysis Post-2024 Presidential Inauguration with IndoBERT ]]>
<![CDATA[Kusnawi, Kusnawi]]>;
<![CDATA[Anam, Khoerul]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 6 No. 2 (2025): JUTIF Volume 6, Number 2, April 2025 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2025.6.2.4015
<![CDATA[The results of the 2024 Indonesian presidential election decided that Prabowo Subianto and Gibran Rakabuming Raka became the elected pair of Indonesian presidential and vice-presidential candidates in 2024. The pair's election triggered various public reactions, especially on social media platforms. Some social media platforms provided diverse opinions, indicating a wide variety of views on this issue. This research aims to analyze public opinion after the election of the 2024 Indonesian president by comparing sentiment using TextBlob, VADER (Valence Aware Dictionary and sEntiment Reasoner), and Flair. Training and testing are done with the IndoBERT model to determine the most effective sentiment labeling. This research starts by collecting text data from social media X, YouTube, and Instagram, then preprocessing, translating, and labeling data using three libraries, training, and testing using IndoBERT. The results of training and testing data show that Flair has an accuracy of 81.29%, TextBlob has an accuracy of 73.35%, and VADER has an accuracy of 74.86%. From the accuracy results obtained, it can be concluded that labeling using Flair provides the greatest accuracy of the others because the Flair labeling process uses deep learning and contextual embedding techniques.]]>
<![CDATA[SENTIMENT ANALYSIS OF COMMENTS ON TOURIST ATTRACTIONS IN LAMPUNG PROVINCE USING THE NAIVE BAYES METHOD ]]>
<![CDATA[Wirahudha, Muhammad Arif]]>;
<![CDATA[Damayanti, Damayanti]]>;
<![CDATA[Megawaty, Dyah Ayu]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 6 No. 2 (2025): JUTIF Volume 6, Number 2, April 2025 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2025.6.2.4037
<![CDATA[Lampung Province is a province that has so much natural beauty, this also makes Lampung Province one of the tourist destinations that are visited by many domestic and foreign tourists so that there is a problem, namely the many negative comments that are not in accordance with reality affect the number of tourist visits to Lampung Province because they are not in accordance with reality so that they affect public opinion about tourism in Lampung Province which results in tourist attractions being deserted. The method used to analyze sentiment analysis is the naive bayes algorithm by crawling data using python. The stages of the naive bayes algorithm in the study using preprocessing consist of five processes, namely cleansing, tokenization, case folding, stopword removal, and stemming. Lampung Province tourist attractions are Pahawang, Way Kambas, Krui Beach / West Coast, Mutun Beach and Kiluan Bay. The results of a fairly high level of accuracy in positive comments on Pahawang Beach. In this study, it was concluded that the impact of comments can affect the number of visitors coming to tourist attractions.]]>
<![CDATA[Integration of BERT and SVM in Sentiment Analysis of Twitter/X Regarding Constitutional Court Decision No. 60/PUU-XXII/2024 ]]>
<![CDATA[Irianti , Artia]]>;
<![CDATA[Halimah, Halimah]]>;
<![CDATA[Sutedi, Sutedi]]>;
<![CDATA[Agariana, Melda]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 6 No. 2 (2025): JUTIF Volume 6, Number 2, April 2025 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2025.6.2.4068
<![CDATA[This research analyzes public sentiment towards the Indonesian Constitutional Court's decision No. 60/PUU- XXII/2024 by utilizing natural language processing techniques using the BERT (Bidirectional Encoder Representations from Transformers) model and the Support Vector Machine model (SVM). The research methodology includes four stages: data preprocessing, data labeling using BERT, embedding extraction, and SVM model training. The data is taken from the Twitter platform, where various public opinions are reflected in three sentiment categories: positive, neutral, and negative. The preprocessing process results in the removal of approximately 23% of duplicate data, and sentiment labeling shows a dominance of the positive category. Evaluation results from the SVM model training demonstrated varying performance: negative sentiment achieved a Precision of 0.57, Recall of 0.36, and F1-score of 0.44; neutral sentiment had a Precision of 0.81, Recall of 0.62, and F1-score of 0.70; while positive sentiment recorded a Precision of 0.98, Recall of 1.00, and F1-score of 0.99. The model's overall accuracy reached 0.97. These findings indicate that the integration of BERT and SVM is effective for sentiment classification, but improvements are needed in the negative and neutral categories to achieve more balanced performance.]]>
<![CDATA[Convolutional Neural Network for COVID-19 Detection Using InceptionV3 Transfer Learning ]]>
<![CDATA[Pratama, Dhimas Rama Anthony Navy]]>;
<![CDATA[Azhar, Yufis]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 6 No. 2 (2025): JUTIF Volume 6, Number 2, April 2025 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2025.6.2.4094
<![CDATA[The COVID-19 pandemic has underscored the need for rapid and accurate diagnostic methods. Although Reverse Transcription Polymerase Chain Reaction (RT-PCR) is the gold standard for detecting COVID-19, it presents limitations such as high costs, lengthy processing times, and the requirement for specialized personnel. Medical imaging, particularly lung X-rays, offers a viable alternative for COVID-19 detection. This study evaluates five Convolutional Neural Network (CNN) models: a handcrafted CNN, VGG-16, VGG-19, ResNet50, and InceptionV3, with the aim of enhancing classification accuracy between COVID-19 and normal lung images. The dataset, obtained from Kaggle, comprises 13,808 X-ray images, which were balanced using random oversampling to address class imbalance. Data augmentation techniques were applied to improve model generalization and mitigate overfitting. After training the models for 100 epochs, the results revealed that both VGG-19 and InceptionV3 achieved the highest accuracy, each attaining 100%, outperforming the other models. VGG-16 and CNN Handcraft also demonstrated strong performance with an accuracy of 99% and 97%, whereas ResNet50 exhibited the lowest accuracy at 78%. These findings suggest that more complex CNN architectures, such as VGG- 19 and InceptionV3, are highly effective in detecting COVID-19 from X-ray images. Future research should explore additional CNN models and employ further model tuning to optimize performance.]]>
<![CDATA[Gastroesophageal Reflux Disease Early Detection using XGBoost Method Classifier ]]>
<![CDATA[Wisesty , Untari Novia]]>;
<![CDATA[Delfina, Haura Adzkia]]>;
<![CDATA[Kurniawan, Isman]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 6 No. 2 (2025): JUTIF Volume 6, Number 2, April 2025 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2025.6.2.4143
<![CDATA[Gastroesophageal reflux disease (GERD) is a clinical condition that occurs when the gastric content within the stomach rises into the esophagus. If left untreated, GERD can result in complications such as esophageal inflammation, ulcers, and even cancer. In this study, the early detection of GERD is performed using the GERD dataset obtained from the Harvard Dataverse online repository and processed with the XGBoost machine learning model. The SMOTE technique was implemented as a solution to address the data imbalance present in the dataset. In addition, this study applied Principal Component Analysis (PCA) and Pearson Correlation to select the most relevant attributes, with the aim of improving computational efficiency. The results demonstrated that feature selection through Pearson correlation and feature extraction using principal component analysis (PCA) yielded the optimal model performance when utilizing 16 attributes and 16 principal components, respectively. The XGBoost model with PCA achieves a macro average F1-score of 0.9615, while the XGBoost model with Pearson Correlation attains a value of 0.9809. Subsequently, the XGBoost model based on the original dataset yielded a macro F1-score value of 0.9568. The findings of this research indicate that the XGBoost model with the Pearson Correlation-based feature selection method has a better f1-score value than the feature extraction method with PCA or based on the original dataset with a difference in value of 0.0194 and 0.0241 respectively in enhancing the performance of the XGBoost model for early detection of GERD in this study.]]>
<![CDATA[Development of a Convolutional Neural Network Method for Classifying Ripeness Levels of Servo Variety Tomatoes ]]>
<![CDATA[Rosalina, Rosalina]]>;
<![CDATA[Husodo, Ario Yudo]]>;
<![CDATA[Wijaya, I Gede Pasek Suta]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 6 No. 2 (2025): JUTIF Volume 6, Number 2, April 2025 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2025.6.2.4168
<![CDATA[The distribution of tomatoes in Indonesia is huge, making it an important commodity in the agricultural sector. However, manual classification of tomato ripeness can lead to human error and decrease supply chain efficiency. Therefore, an automated system capable of classifying tomatoes quickly and accurately is needed, in order to reduce the potential for human error and improve supply chain efficiency. This research aims to develop the Convolutional Neural Network (CNN) method to improve the accuracy of tomato ripeness detection through modifications to the architecture, such as reducing several layers, adding batch normalization, and adding dropouts. The dataset used in this study consists of 500 images taken by the researcher himself which are divided into 5 classes, namely unriped, half-riped, riped, half-rotten, and rotten, with each class containing 100 images. There are 3 proposed CNN models, namely the standard model, as well as the addition of batch normalization and dropout in the architecture. The results showed that the proposed model 3 with the addition of dropout on several layers of its architecture is the optimal model with a parameter of 2.4 million and using a batch size of 16 resulting in an accuracy of 98%, as well as precision, recall, and F1-score values of 98%. With these results, the proposed CNN model is effective in identifying the ripeness level of tomato fruit. This research is expected to be applied in the agricultural industry to improve the efficiency of sorting and distributing tomato fruits according to the desired quality standards.]]>
<![CDATA[Enhanced Identity Recognition Through the Development of a Convolutional Neural Network Using Indonesian Palmprints ]]>
<![CDATA[Aprilla, Diah Mitha]]>;
<![CDATA[Husodo, Ario Yudo]]>;
<![CDATA[Wijaya, I Gede Pasek Suta]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 6 No. 2 (2025): JUTIF Volume 6, Number 2, April 2025 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2025.6.2.4169
<![CDATA[The use of palmprint as an identification system has gained significant attention due to its potential in biometric authentication. However, existing models often face challenges related to computational complexity and the ability to scale with larger datasets. This research aims to develop an efficient Convolutional Neural Network (CNN) model for palmprint identity recognition, specifically tailored to address these challenges. A novel contribution of this study is the creation of an original palmprint dataset consisting of 700 images from 50 Indonesian college students, which serves as a foundation for future research in Southeast Asia. The dataset includes different scenarios with varying input sizes (32x32, 64x64, 96x96 pixels) and the number of classes (30, 40, 50) to assess the model's scalability and performance. Three CNN architectures were designed with varying layers, activation functions, and dropout strategies to capture the unique features of palmprints and improve model generalization. The results show that the best-performing model, Model 3, which incorporates dropout layers, achieved 95% accuracy, 96% precision, 95% recall, and 95% F1-score on 50 classes with 1.2 million parameters. Model 1 achieved 98% accuracy, 99% precision, 98% recall, and 98% F1-score on 40 classes with 1.7 million parameters. These findings demonstrate that the proposed CNN models not only achieve high accuracy but also maintain computational efficiency, offering promising solutions for real-time palmprint authentication systems. This research contributes to the advancement of biometric authentication systems, with significant implications for real- world applications in Southeast Asia.]]>
