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[COMPARISON OF NAIVE BAYES AND RANDOM FOREST METHODS IN SENTIMENT ANALYSIS ON THE GETCONTACT APPLICATION ]]>
<![CDATA[Arisula, Juan Pala]]>;
<![CDATA[Parjito, Parjito]]>
<![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.2024.5.5.2004
<![CDATA[The rapid growth in the use of social media and instant messaging platform apps has significantly changed the way people communicate. One of the most popular apps is GetContact, a platform focused on identifying the phone numbers of irresponsible people and reducing the impact of spam calls. In cases like this, sentiment analysis is important to understand user responses to the service. In performing sentiment analysis, there are two classification methods that will be used, namely the Naive Bayes and Random Forest methods. This research utilizes the SMOTE technique to handle data imbalance, and the results show that the application of SMOTE successfully improves classification accuracy. The Random Forest model performed better than Naive Bayes, with 80% accuracy, 84% precision, 77% recall, and 80% F1 score for positive sentiments, while Naive Bayes achieved 77% accuracy, 79% precision, 79% recall, and 79% F1 score. Although Random Forest is superior in precision, recall , and F1 score for positive sentiments, it performs almost on par with Naive Bayes in classifying negative sentiments, with 76% precision , 84% recall, and 80% F1 score for Random Forest, and 76% precision, 76% recall , and 76% F1 score for Naive Bayes. This shows that both models provide similar results in identifying negative sentiment overall.]]>
<![CDATA[THE PERCEPTIONS OF SEMARANG FIVE STAR HOTEL TOURISTS WITH SUPPORT VECTOR MACHINE ON GOOGLE REVIEWS ]]>
<![CDATA[Aufan, Muhammad Haikal]]>;
<![CDATA[Handayani, Maya Rini]]>;
<![CDATA[Nurjanna, Afifah Basmah]]>;
<![CDATA[Wibowo, Nur Cahyo Hendro]]>;
<![CDATA[Umam, Khotibul]]>
<![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.2024.5.5.2025
<![CDATA[Travelers on the road sometimes need a hotel to rest. In choosing a hotel, they refer to the ratings or reviews written by users through reviews on Google. This is because not all star hotels provide facilities in accordance with user assessments. This study discusses the analysis of the opinions of tourists who have stayed in 5-star hotels in Semarang through a review of commentary data on Google. The 5-star hotels used as the research are Padma, Gumaya, Tentrem, Grand Candi, Ciputra, and PO. The dataset of the six hotels was obtained through a scraping process then followed by data pre-processing. The data was retrieved from Google Maps using the Chrome Instant Data Scrapper extension. Data preprocessing begins with case folding, tokenizing, filtering, and ends with stemming. Support Vector Machine (SVM) is implemented for sentimen classification process. The results from this study are the majority of 5-star hotel reviews in Semarang tend to have positive rather than negative sentimens. Our model was able to produce an accuracy of 0.87 to 0.98. The highest accuracy was achieved by Ciputra Hotel at 0.98 with 543 positive reviews.]]>
<![CDATA[TWITTER (X) SENTIMENT ANALYSIS OF KAMPUS MERDEKA PROGRAM USING SUPPORT VECTOR MACHINE ALGORITHM AND SELECTION FEATURE CHI-SQUARE ]]>
<![CDATA[Sari, Mutiara]]>;
<![CDATA[Syahrullah, Syahrullah]]>;
<![CDATA[Lapatta, Nouval Trezandy]]>;
<![CDATA[Ardiansyah, Rizka]]>
<![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.2024.5.5.2037
<![CDATA[Ministry of Education, Culture, Research and Technology (Kemendikbudristek) has implemented numerous policies aimed at enhancing the quality of education in the country. One of these policies is Kampus Merdeka program. The program includes various initiatives such as Teaching Campus, the Merdeka Student Exchange program, and Internship and Independent Study programs, which have gained significant popularity among students across Indonesia. However, the Kampus Merdeka program has drawn many pros and cons, with some parties supporting the initiative, but also many criticisms related to its implementation, which is considered not optimal in some educational institutions. Social media is where many of these opinions are voiced, one of the most widely used of which is twitter. In light of these circumstances, this study conducted a sentiment analysis of the independent campus program to assess public sentiment towards it. The dataset used in this research consisted of 500 tweets containing the keyword "kampus merdeka" with 250 tweets reflecting positive sentiment and 250 tweets reflecting negative sentiment. The results of the tests carried out obtained the highest increase in results in the 10:90 ratio, namely with an accuracy that increased by 14% from the previous 66% to 80%, precision also increased by 22% from the previous 67% to 89%, recall increased by 16% from the previous 58% to 79%, and the f1-score value which was previously 62% turned into 79% because it also increased by 17%.]]>
<![CDATA[IMPLEMENTATION OF DEEP LEARNING MODELS IN HATE SPEECH DETECTION ON TWITTER USING AN NATURAL LANGUAGE PROCESSING APPROACH ]]>
<![CDATA[Arifin, Muhammad]]>;
<![CDATA[Mahdiana, Deni]]>
<![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.2024.5.5.2043
<![CDATA[In the digital era, the misuse of the freedom to communicate on the internet often leads to problems such as the spread of hate speech, which can harm individuals based on race, religion, and other characteristics. This issue requires effective solutions for content moderation, particularly on social media platforms like Twitter. This research develops a deep learning model utilizing Natural Language Processing (NLP) to detect hate speech and aims to improve existing content moderation mechanisms. The methods used include data collection, preprocessing through techniques such as case folding, tokenization, lemmatization, and model creation using TensorFlow Extended (TFX) involving embedding, dense, and global pooling layers. The model is trained to optimize accuracy by minimizing the loss function and closely monitoring evaluation metrics. The results show that this model achieves a prediction accuracy of 84%, an AUC value of 0.796, and a binary accuracy of 76%. The conclusion of this research is that the use of deep learning and NLP in detecting hate speech offers a highly potential approach to enhancing digital content moderation, providing a solution that is not only efficient but also accurate.]]>
<![CDATA[ANALYSIS OF SIMFAKUM ACCEPTANCE USING THE TAM AND WEBQUAL METHOD ]]>
<![CDATA[Fahril, Muhammad]]>;
<![CDATA[Megawati, Megawati]]>;
<![CDATA[Fronita, Mona]]>;
<![CDATA[Rahmawita, Medyantiwi]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 5 No. 4 (2024): JUTIF Volume 5, Number 4, August 2024 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2024.5.4.2150
<![CDATA[The Faculty of Law, Riau Islamic University implements an Information System that can support and assist students in writing correspondence for student needs and is named the Faculty of Law Management Information System or abbreviated and known as SIMFAKUM. From the results of observations and interviews with several students and SIMFAKUM Admin, it is known that there were complaints or problems experienced by users during the implementation of SIMFAKUM. Based on previous research, TAM and Webqual can be used together to measure information system acceptance. The research was conducted with the aim of obtaining the acceptance level of UIR Law Faculty students towards SIMFAKUM based on the TAM and WEBQUAL methods as well as producing recommendations to the UIR Law Faculty with the results of the analysis of student acceptance levels towards SIMFAKUM. For sampling, Simple Random Sampling was used for students at the Faculty of Law, Islamic University of Riau as SIMFAKUM users with a total of 95 respondents. Data processing techniques use Structural Equation Model (SEM) and Partial Least Squres (PLS) with Smart-PLS 3.0 software. There are 6 hypotheses in this research and 5 hypotheses are accepted while 1 hypothesis is rejected, therefore SIMFAKUM needs to improve and improve features to support student needs in correspondence matters.]]>
<![CDATA[IMPLEMENTATION OF THE FORWARD CHAINING METHOD FOR DETECTING SCHOOL READINESS IN CHILDREN ]]>
<![CDATA[Rorimpandey, Gladly C.]]>;
<![CDATA[Mantik, Felitia Theona Geofani]]>;
<![CDATA[Hasibuan, Alfiansyah]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 5 No. 4 (2024): JUTIF Volume 5, Number 4, August 2024 - SENIKO ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2024.5.4.2221
<![CDATA[Primary school education is the education of children aged 7 to 13 years as education at the basic level which is developed in accordance with educational units, regional potential and socio-culture. School readiness for children is no less important because in fact school readiness for children is very important for children because many children are found to be still not ready. attended school when but was already in elementary school. To overcome this problem, this research provides a solution by building an expert system for detecting school readiness in children using the forward chaining method, which is a technique in an expert system that begins with gathering information starting from collecting premises which is followed by a conclusion or derived information (then). From the results of testing using functional testing and usability testing, it is known that in implementing the forward chaining method, 24 symptoms of school readiness were identified based on knowledge obtained directly from psychologists. The results obtained in testing using usability testing based on the results obtained are assessed as final and then the average value is calculated. The final conclusion of the results determined through the SUS Score assessment is 84%. This shows that this system easy and useful in assessing children's school readiness. The implication of the results of this test is that the school readiness detection expert system can be a useful tool for parents, teachers to evaluate children's readiness to enter elementary school.]]>
<![CDATA[THE PERFORMANCE ANALYSIS OF REACTIVE AND PROACTIVE ROUTING PROTOCOLS FOR V2V COMMUNICATION IN DYNAMIC TRAFFIC SIMULATION ]]>
<![CDATA[Bintoro, Ketut Bayu Yogha]]>;
<![CDATA[Syahputra, Ade]]>;
<![CDATA[Rismanto, Akmal Hadi]]>;
<![CDATA[Marchenko, Michael]]>
<![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.2024.5.5.2237
<![CDATA[The research problem addressed in this study arises from the urgent need to enhance Vehicle-to-Vehicle (V2V) communication in dynamic traffic scenarios. V2V communication is a critical component of intelligent transportation systems aimed at improving traffic safety and efficiency. However, existing routing protocols exhibit varying performance under different traffic conditions, such as free flow, steady flow, and congestion. Consequently, a comprehensive comparison is necessary to evaluate the effectiveness of three routing protocols—AODV, LA-AODV, and DSDV—in dynamic V2V scenarios. This research aims to address this problem by simulating realistic traffic conditions and evaluating the Quality of Service (QoS) of each protocol using metrics such as Packet Delivery Ratio (PDR), Packet Loss Ratio (PLR), Throughput, End-to-End Delay, and Jitter. The findings indicate that LA-AODV demonstrates superior performance in terms of PDR (up to 4% at 500 seconds), PLR (reaching 95.33% at 500 seconds), and Throughput (reaching 84.81 Kbps at 800 seconds). This makes it an excellent choice for applications prioritizing reliable data transfer. Conversely, AODV exhibits the lowest latency and jitter, with latency (reaching 7.40E+10 ns) and jitter (reaching 1E+10 ns) at 300 and 400 seconds, respectively. AODV is well-suited for real-time V2V communication due to its minimal delay and jitter. DSDV, while minimizing control overhead, performs less favorably in other metrics. Consequently, AODV emerges as the preferred option for real-time V2V communication. LA-AODV excels in scenarios emphasizing data delivery and high throughput. DSDV may find relevance in security-sensitive applications where minimizing control traffic is crucial.]]>
<![CDATA[FACIAL PHOTO AUTHENTICITY DETECTION USING FACE RECOGNITION AND LIVENESS DETECTION ]]>
<![CDATA[Achmad, Bimo Vallentino]]>;
<![CDATA[Supatman, Supatman]]>
<![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.2024.5.5.2328
<![CDATA[Facial recognition has been widely adopted by many systems as authentication. However, relying on facial photos for authentication is insufficient, as these can be manipulated using printed or digital photos. One method that can be used to prevent this is to integrate face recognition with liveness detection. In this research, face recognition and liveness detection are implemented using a Convolutional Neural Network (CNN) because CNN has the ability to process and extract features from photos effectively. There are two types of datasets used, namely CelebA-Spoof for liveness detection and lfw-deepfunneled for face recognition. The face recognition model achieved good accuracy and loss results of 0.9153 and 0.0514, very promising. Meanwhile, the liveness detection accuracy and loss were 0.8633 and 0.7166.]]>
<![CDATA[PAPAYA TYPE CLASSIFICATION USING YOLOv8 ]]>
<![CDATA[Verdiansyah, Egi]]>;
<![CDATA[Nurdiyansyah, Firman]]>;
<![CDATA[Istiadi, Istiadi]]>
<![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.2024.5.5.2336
<![CDATA[Papaya (Carica papaya L) is a fruit that is easily found in subtropical and tropical regions, including Indonesia. With many varieties of papaya, the manual method used in distinguishing papaya types by humans depends on individual knowledge which can cause inaccuracies in the classification process. The manual classification process also takes a very long time if production is done on a large scale. Therefore, a technology for sorting automation is needed, especially in the industrial world. This research aims to classify papaya classes based on their type. The classification is divided into four classes, namely bangkok papaya, california papaya, hawai papaya, and red lady papaya. The classification process in this study uses the YOLOv8 model, where the total dataset is 1200 papaya images with a training data division of 88% (1050 images), 8% validation data (100 images), and 4% test data (50 images). The dataset is separated according to papaya fruit class. Data training was conducted with 300 epochs. The results show that bangkok papaya has a mAP value of 96%, california papaya 97%, hawai papaya 95%, and red lady papaya has 95% mAP. The average class has a precision value of 99.6%, and recall 100.0%. It can be concluded that the YOLOv8 classification model is able to achieve a high level of accuracy.]]>
<![CDATA[CATARACT CLASSIFICATION USING CONVOLUTIONAL NEURAL NETWORK (CNN) INCEPTION RESNETV2 ]]>
<![CDATA[Zulfa, M. Mauludin]]>;
<![CDATA[Sri Kusuma Aditya, Christian]]>
<![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.2024.5.5.2340
<![CDATA[The eye is a human sensory device that functions as an organ of vision. Referring to data from the World Health Organization (WHO) in 2018, cataracts are responsible for 48% of blindness cases in the world and are the main cause in Indonesia. People still find it difficult to distinguish cataract eyes from normal eyes, so they often do not realize the indications of cataract disease. It is important to conduct early detection of cataract disease before blindness occurs. As technology develops, cataract identification becomes easier and simpler with digital image processing classification. This research develops a cataract image classification model using Convolutional Neural Network (CNN) with Inception-ResnetV2 architecture to identify cataract eyes with normal eyes. The proposed model consists of two parts of Inception-ResnetV2 architecture as the base model, and the head model in the form of Fully Connected Layers consisting of global average polling, 2 dense relu layers of 128 and 256 neurons, 2 batch normalization layers, 2 layers of dropout parameter 0.5, and softmax activation function for the output layer. To improve model training, the Stochastic Gradient Descent (SGD) optimization function is used. The dataset consists of 2,192 eye fundus images with 2 main classes of cataract and normal taken from the public data provider site Kaggle. Learning rate tests on the optimization function were carried out with parameters 0.1, 0.01, and 0.001, the results of the proposed model compiled with Stochastic Gradient Descent (SGD) learning rate 0.01 gave a final accuracy of 96%.]]>
