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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<![CDATA[Carrot Quality Classification Based on Color and Texture Features Using Artificial Neural Network Method ]]>
<![CDATA[Idris, Muh Gimnastiar]]>;
<![CDATA[Fauzi, A. Arfan]]>;
<![CDATA[Syasikirani. N, Adelia]]>;
<![CDATA[Kaswar, Andi Baso]]>
<![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.2025.6.2.1401
<![CDATA[Carrots are popular vegetable plants that are usually consumed by the public. Determination of quality using the visual of human eye is considered to have many shortcomings. In previous studies, the carrot classification process had been carried out using a certain method. However, the level of accuracy resulting from several previous studies is still lacking because the processes and methods used are considered to be inaccurate, so innovation is needed by using processes and methods that are more precise to obtain classification results with a better level of accuracy. Therefore, this research proposes a classification of carrot quality based on color and texture features using an artificial neural network method. The proposed method consists of 6 stages, namely image acquisition, preprocessing, segmentation, morphological operations, feature extraction, and classification using artificial neural networks. In this study, quality is divided into three classes, namely feasible, less feasible, and not feasible using 300 carrot image datasets. The results obtained in the testing process obtained an accuracy of 100%, a misclassification error of 0%, and a computation time of up to 55 seconds. Based on the test results it can be seen that the proposed method can classify the quality of carrots accurately.]]>
<![CDATA[Comparative Analysis of Decision Tree, Random Forest, Svm, and Neural Network Models for Predicting Earthquake Magnitude ]]>
<![CDATA[Turino, Turino]]>;
<![CDATA[Saputro, Rujianto Eko]]>;
<![CDATA[Karyono, Giat]]>
<![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.2025.6.2.2378
<![CDATA[This study conducts a comparative analysis of four machine learning algorithms—Decision Tree, Random Forest, Support Vector Machine (SVM), and Neural Network—to predict earthquake magnitudes using the United States Geological Survey (USGS) earthquake dataset. The analysis evaluates each model's performance based on key metrics: Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and the coefficient of determination (R²). The Random Forest model demonstrated superior performance, achieving the lowest MAE (0.217051), lowest RMSE (0.322398), and highest R² (0.574261), indicating its robustness in capturing complex, non-linear relationships in seismic data. SVM also showed strong performance, with competitive accuracy and robustness. Decision Tree and Neural Network models, while useful, had comparatively higher error rates and lower R² values. The study highlights the potential of ensemble learning and kernel methods in enhancing earthquake magnitude prediction accuracy. Practical implications of the findings include the integration of these models into early warning systems, urban planning, and the insurance industry for better risk assessment and management. Despite the promising results, the study acknowledges limitations such as reliance on historical data and the computational intensity of certain models. Future research is suggested to explore additional data sources, advanced machine learning techniques, and more efficient algorithms to further improve predictive capabilities. By providing a comprehensive evaluation of these models, this research contributes valuable insights into the effectiveness of various machine learning techniques for earthquake prediction, guiding future efforts to develop more accurate and reliable predictive models.]]>
<![CDATA[Performance Evaluation of Transformer Models: Scratch, Bart, and Bert for News Document Summarization ]]>
<![CDATA[Holle, Khadijah Fahmi Hayati]]>;
<![CDATA[Munna, Daurin Nabilatul]]>;
<![CDATA[Ekaputri, Enggarani Wahyu]]>
<![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.2025.6.2.2534
<![CDATA[This study evaluates the performance of three Transformer models: Transformer from Scratch, BART (Bidirectional and Auto-Regressive Transformers), and BERT (Bidirectional Encoder Representations from Transformers) in the task of summarizing news documents. The evaluation results show that BERT excels in understanding the bidirectional context of text, with a ROUGE-1 value of 0.2471, ROUGE-2 of 0.1597, and ROUGE-L of 0.1597. BART shows strong ability in de-noising and producing coherent summaries, with a ROUGE-1 value of 0.5239, ROUGE-2 of 0.3517, and ROUGE-L of 0.3683. Transformer from Scratch, despite requiring large training data and computational resources, produces good performance when trained optimally, with ROUGE-1 scores of 0.7021, ROUGE-2 scores of 0.5652, and ROUGE-L scores of 0.6383. This evaluation provides insight into the strengths and weaknesses of each model in the context of news document summarization.]]>
<![CDATA[Information Retrieval Related to Information Regarding Covid-19 Using Transformers Architecture ]]>
<![CDATA[Wiktasari, Wiktasari]]>;
<![CDATA[Prayitno, Prayitno]]>;
<![CDATA[Kartika, Vinda Setya]]>;
<![CDATA[Lavindi, Eri Eli]]>;
<![CDATA[Ardhana, Naufal Reky]]>;
<![CDATA[Nariswana, Rucirasatti]]>
<![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.2025.6.2.2606
<![CDATA[The spread of the COVID-19 virus has occurred exponentially, necessitating advanced search technologies that provide accurate information. The primary challenge in searching for COVID-19 related information involves the diversity and rapid changes in data, as well as the need to understand specific medical contexts. Unstructured information sources, such as research articles, news reports, and social media discussions, add complexity to retrieving relevant and up-to-date information. As the volume of data and information related to the COVID-19 pandemic increases, there is a pressing need for effective and accurate information retrieval systems. Transformer architecture, known for its capabilities in natural language processing and managing complex contexts, offers great potential to enhance search quality in the healthcare domain. BERT is a deep learning model that performs searches based on specific queries, with search results sorted accordingly. The ranking process uses BERT architecture to compare the performance of transformer encoders, specifically between bi-encoders and cross-encoders. A bi- encoder is an architecture where two separate encoders process two different inputs, such as queries and documents. In contrast, a cross-encoder processes two texts simultaneously using a single encoder, allowing the model to capture contextual interactions between them. Research indicates that cross-encoder performance is significantly better than bi-encoder for cases with relatively small data sets. Evaluation results show that the NDCG score for bi-encoder is 0.89, while for cross-encoder it is 0.9. The mAP score for bi-encoder is 0.7, and for cross-encoder, it is 0.89. Both bi-encoder and cross-encoder achieved an MRR score of 1.0.]]>
<![CDATA[Sentiment Analysis of Cyber Attacks in Bank Syariah Indonesia Using SVM and Indobert Method ]]>
<![CDATA[Apriyadi, Chandra]]>;
<![CDATA[Styawati, Styawati]]>
<![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.2025.6.2.2636
<![CDATA[Bank Syariah Indonesia (BSI) is one of the Islamic banking institutions that operates based on Islamic principles in accordance with Islamic law and has obtained an operational license from the Dewan Syariah Nasional (DSN). The advancement of information technology brings unique risks to the banking industry, including BSI. One example is the ransomware attack experienced by BSI from May 8 to 11, 2023, where 15 million customer data and 1.5 terabytes of internal data were stolen, leading to significant public concern and response across various media platforms. This has the potential to affect public trust in the Islamic banking industry, particularly BSI. This research aims to analyze public sentiment on Twitter regarding the attack to identify the majority sentiment formed, as well as to compare the performance of the SVM and IndoBERT models in classifying sentiments. Additionally, this study reveals the topics present in the negative sentiments based on the classifications of both models through topic modeling using Latent Dirichlet Allocation (LDA). The results indicate that the majority of sentiments are negative, while IndoBERT shows better performance compared to SVM, with an accuracy of 85% and an F1-Score of 82%. The topics present in the negative sentiments classified by SVM include issues related to fund security as well as transfers and withdrawals, whereas the topics present in the negative sentiments classified by IndoBERT are more related to problems with mobile banking and fund withdrawals.]]>
<![CDATA[Identification of Dominant Frequencies in Javanese Vocal Phonemes Using Fast Fourier Transform and Random Forest Classification ]]>
<![CDATA[Muhadi, Muhadi]]>
<![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.2025.6.2.2708
<![CDATA[The majority of speech recognition research currently uses English as the research base, but the results can also be used for another language, including Javanese speech recognition. Previous research stated that there were differences in frequency between English and Dutch. This shows that the frequency of Javanese can also be different. The difference in frequency allows for a new way of recognizing Javanese Speech. By using a dataset of Javanese vowel phonemes, this research aims to identify the dominant frequencies in Javanese speech using the Fast Fourier Transform data extraction an2d the Random Forest Classifier. The feature importance level data will be tested with a deep neural network to determine the accuracy and speed of the process. Choosing a dominant frequency is expected to make the process more effective and efficient in using computing resources.]]>
<![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[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.]]>
