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[APPLICATION OF MULTI-CRITERIA PROMETHEE METHOD TO ASSIST CHARACTER SELECTION IN THE ENDLESS RUNNER GAME ]]>
<![CDATA[Nurrahma, Alfina]]>;
<![CDATA[Nugroho, Fresy]]>;
<![CDATA[Buditjahjanto, I.G.P. Asto]]>;
<![CDATA[Pebrianti, Dwi]]>;
<![CDATA[Hammad, Jehad A.H.]]>;
<![CDATA[Fachri, Moch]]>;
<![CDATA[Lestari, Tri Mukti]]>;
<![CDATA[Maharani, Dian]]>;
<![CDATA[Prakasa, Aji Bagas]]>
<![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.2024.5.4.2183
<![CDATA[The endless runner game is one of the most popular game genres, but selecting the optimal character for different map challenges poses a significant problem for players. In this context, this research was conducted to help select characters in the endless runner game using the PROMETHEE method. This selection is recommended based on the weight and difficulty of each map which varies, including the rice field map, road map and alley map. The implementation of calculating character recommendations uses the Preference Ranking Organization Method for Enrichment Evaluation (PROMETHEE) method with the highest score as the best ranking. Rank suitability can be determined by comparing the PROMETHEE method with the TOPSIS method on 15 characters alternatives with 6 criteria. As a result, the PROMETHEE method has significant value, but some still have the same best ranking as the TOPSIS method. Furthermore, usability testing was carried out on 57 respondents using the System Usability Scale (SUS) with an overall score from the evaluation of 78,8. The final score obtained based on the acceptance scale was included in the category suitable for use.]]>
<![CDATA[CLASSIFICATION OF DENTAL CARIES DISEASE IN TOOTH IMAGES USING A COMPARISON OF EFFICIENTNET-B0, MOBILENETV2, RESNET-50, INCEPTIONV3 ARCHITECTURES ]]>
<![CDATA[Wahyuningsih, Wahyuningsih]]>;
<![CDATA[Nugraha, Gibran Satya]]>;
<![CDATA[Dwiyansaputra, Ramaditia]]>
<![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.2024.5.4.2187
<![CDATA[Dental caries is a global metabolic disorder, influenced by complex interactions between the body and microbes, it's caused by prolonged exposure to a low pH environment, leading to demineralized carious lesions. If untreated, it can cause pain and eating difficulties, requiring emergency care and significantly impacting overall quality of life. Diagnosis process can be conducted through physical assessment and analyzing laboratory testing. Image-based artificial intelligence systems, particularly the EfficientNet-B0 model, is suggested as a resolution for classifying dental caries using tooth images. The study's goal is to assess EfficientNet-B0's performance in comparison to other CNN architectures and play a role in advancing medical image classification technology. The original dataset comprising 1554 images was initially collected. After augmentation, the dataset expanded to 6348 images. The data was then divided into three subsets of training, validation, and testing datasets with a distribution ratio of 70:15:15, respectively. From all the evaluated models, the EfficientNet-B0 demonstrated a quite commendable accuracy of 0.98% with overfitting tolerance of less than 2%. Having the same accuracy as the MobileNetV2 (0.98%). Despite its inability to exceed the accuracy achieved by ResNet-50 (0.99%), EfficientNet-B0 accomplished its accuracy level with roughly a quarter of the parameters than ResNet-50 and highger than InceptionV3 (0.97%), highlighting its efficiency in parameter utilization and computational resource management. These findings hold promise for enhancing models and guiding clinical decision-making.]]>
<![CDATA[ANALYSIS OF FACTORS DETERMINING STUDENT SATISFACTION USING DECISION TREE, RANDOM FOREST, SVM, AND NEURAL NETWORKS: A COMPARATIVE STUDY ]]>
<![CDATA[Riyanto, Andi Dwi]]>;
<![CDATA[Wahid, Arif Mu'amar]]>;
<![CDATA[Pratiwi, Aniec Anafisah]]>
<![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.2024.5.4.2188
<![CDATA[Student satisfaction is crucial in higher education, impacting student loyalty, retention rates, and institutional reputation. This study addresses the gap in applying advanced machine learning techniques to predict and understand key determinants of student satisfaction. The primary objective is to analyze and predict the factors determining student satisfaction using four machine learning models: Decision Tree, Random Forest, SVM, and Neural Networks. The dataset comprises 2527 entries with seven relevant features. Data preprocessing involved normalization and exploratory data analysis (EDA) to ensure accurate analysis. The Neural Network model achieved the highest accuracy with an MSE of 0.001399, RMSE of 0.037397, MAE of 0.030773, and R² of 0.998154, followed closely by the SVM model. These results suggest that advanced machine learning models, particularly Neural Networks and SVM, are effective in predicting student satisfaction and identifying key areas for improvement. This study contributes to understanding the determinants of student satisfaction using machine learning models, providing practical implications for educational administrators to develop targeted strategies to enhance student satisfaction by focusing on critical factors such as academic support and financial aid. The findings highlight the importance of using advanced predictive techniques to gain deeper insights into student satisfaction, thereby enabling institutions to implement more effective interventions. Future research should explore additional variables and more sophisticated model architectures to further improve predictive accuracy and expand the applicability of these models in educational settings.]]>
<![CDATA[DETECTION OF HEXYLENE GLYCOL IN THE PERFUMES USING ELECTRONIC NOSE CORRELATED WITH GAS CHROMATOGRAPHY MASS-SPECTROSCOPY ]]>
<![CDATA[Hardoyono, Fajar]]>;
<![CDATA[Windhani, Kikin]]>
<![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.2024.5.4.2197
<![CDATA[Perfume is a cosmetic product that widely used by people to improve their appearance in social interactions. Perfume released specific fragrance from the essential oil. Manufacturers often mix the pure essential oils with hexylene glycol to reduce prices. Utilization of hexylene glycol as the solvent and diluent often reduce the odour profile of the perfumes. This paper investigated the development of an electronic nose (e-nose) based on a metal oxide semiconductor (MOS) gas sensor to detect hexylene glycol in perfumes. E-nose in this study was developed using MOS gas sensors from Figaro and Raspberry series, including TGS 822, TGS 826, TGS 2600, TGS 2620, MQ2, MQ3, MQ8, and MQ135. For the experiment, we collected 10 brands of commercial perfumes from the supermarket around Purwokerto, Central Java. All samples of perfumes were analysed using gas chromatography-mass spectroscopy (GC-MS) to detect the concentration of hexylene glycol in the samples. The concentration of hexylene glycol in the samples identified none (0%), low (1-20%), moderate (21%-50%) and high (more than 50%). Afterward, 10 brands of perfumes were separated into 15 samples, totally created 150 samples. All perfume samples were measured using an e-nose to obtain the responses. Analysis of sensor responses using principal component analysis (PCA) showed that e-nose was highly performed to discriminate the samples based on hexylene glycol concentration. Classification of 150 perfume samples using backpropagation neural networks (BPNN) grouped 150 perfumes in four different classes in which the accuracy of classification reached 96.36% for the training dataset and 92.50% for the testing dataset, respectively.]]>
<![CDATA[ANALYSIS OF WEBQUAL 4.0 AND COGNITIVE WALKTHROUGH METHODS ON CTI GOVIDEO SPARK HIRE ONLINE INTERVIEW APPLICATION ]]>
<![CDATA[Apriyanthi, Ni Putu Eka]]>;
<![CDATA[Gunadi, I Gede Aris]]>;
<![CDATA[Sunarya, I Made Gede]]>
<![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.2024.5.4.2198
<![CDATA[This study aims to analyze the user experience quality of the CTI GoVideo Spark Hire online interview application, focusing on Safaga Indonesia institution. The research methodology employs Webqual 4.0 analysis and Cognitive Walkthrough as frameworks for evaluating application quality. Respondents were selected using Simple Random Sampling, and data were analyzed using reliability and internal consistency tests. The findings from the Webqual 4.0 analysis indicate that the majority of respondents rated the application positively, with 73% considering its quality to be good and 27% rating it as excellent. Internal consistency tests revealed overall high reliability, although the interaction variable showed relatively low values. Analysis using the Cognitive Walkthrough method revealed that 8 respondents were able to complete approximately 92.8% of the total 7 given task scenarios, with an average completion time of around 446 seconds or 7 minutes 26 seconds for all tasks. This evaluation holds significant relevance to Safaga Indonesia's need to enhance recruitment process efficiency.]]>
<![CDATA[DATA MINING ESTIMATE TOURISM INCOME IN TOMOHON CITY USING MULTIPLE LINEAR REGRESSION ALGORITHMS ]]>
<![CDATA[Karamoy, Beauty Leony]]>;
<![CDATA[Rantung, Vivi Peggie]]>;
<![CDATA[Kumajas, Sondy]]>;
<![CDATA[Ratumbuisang, Yosua Fitsgerald]]>
<![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.2024.5.4.2199
<![CDATA[This study aims to estimate the revenue of tourist destinations in Tomohon City using the multiple linear regression algorithm. The problem addressed is how to determine revenue estimates from various tourist spots categorized into nature tourism and cultural experiences. The research method employed is quantitative, using a multiple linear regression approach. Data were collected through interviews with tourist destination owners regarding ticket prices and visitor numbers, and then analyzed using Microsoft Excel. The results show that the independent variables, consisting of ticket prices (X1) and the number of visitors (X2), significantly affect the dependent variable, which is monthly revenue (Y). The regression equation model obtained is Y = -5594 + 250X1 + 2.26X2. Using this model, the next month's revenue prediction for each tourist destination in Tomohon City can be determined, demonstrating that the multiple linear regression approach is effective for estimating tourism revenue.]]>
<![CDATA[DEVELOPMENT OF A WEB-BASED TOULOUR REGIONAL LANGUAGE CORPUS USING THE SYSTEM DEVELOPMENT LIFE CYCLE METHOD ]]>
<![CDATA[Lipan, Kezia]]>;
<![CDATA[Rantung, Vivi P.]]>;
<![CDATA[Rorimpandey, Gladly C.]]>
<![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.2024.5.4.2230
<![CDATA[Language is a tool for communicating with interlocutors Indonesia is a country that uses Indonesian as an official language, but Indonesia is a multilingual country with 719 regional languages in Indonesia, some of which are classified as endangered. One of the Indonesian regional languages is the Toulour regional language which is one of the regional languages of the Minahasa tribe where the use of the Toulour language is currently decreasing due to the increasing use of the Manado Malay language, making the Toulour language increasingly shifting and threatened with extinction. This research aims to build language resources, namely a corpus, for linguistic researchers to create a dictionary that can later be accessed digitally. This research uses the System Development Life Cycle method, a system development stage. The result of this research is a corpus analysis website that shows 6 corpus analysis techniques, namely word frequency, concordance, tokens, collocations, n-grams, and word lists. There is a download-all token feature for users which can later be utilized by the researcher. Users can also carry out their analysis by entering language text and concordances, collocations, and n-grams, users can also search for them with one keyword.]]>
<![CDATA[INTRODUCTION NATIONAL IDENTIFICATION NUMBER AND NAME ON ID CARD USING OCR (OPTICAL CHARACTER RECOGNITION) METHOD ]]>
<![CDATA[Holila, Holila]]>;
<![CDATA[Pratama, Adi Rizky]]>;
<![CDATA[Lestari, Santi Arum Puspita]]>;
<![CDATA[Indra, Jamaludin]]>
<![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.2024.5.4.2242
<![CDATA[This study examines the use of Optical Character Recognition (OCR) methods for the automatic recognition and extraction of text from images of Identity Cards (KTP). The aim is to provide an effective solution to the problems of document forgery and duplication, particularly in the use of KTP as an identity verification tool. Utilizing the Tesseract library, this research involves preprocessing steps such as conversion to grayscale, perspective transformation, and noise reduction to enhance OCR accuracy. Testing was conducted with 50 different KTP images using Python programming, achieving an Optical Character Recognition accuracy rate of 91%. Additionally, tests conducted with a dataset of 50 KTP images containing NIK and name variables showed that all images were successfully detected with an accuracy rate of 90%. This study confirms that the OCR method is effective in reading text from KTP images in real-time, thus it can be implemented for automatic identity verification.]]>
<![CDATA[FACTORS AFFECTING USER'S ACCEPTANCE OF ADOPTING BIOMETRICS TECHNOLOGIES USING THE TAM MODEL ]]>
<![CDATA[Zihan Kalila Gusnan]]>;
<![CDATA[Rio Guntur Utomo]]>
<![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.2024.5.4.2249
<![CDATA[User privacy and security concerns hinder the adoption of biometric authentication technology in Indonesia, especially when consumers are still determining how their biometric data will be stored, accessed and used. This research aims to investigate the variables that influence the adoption of biometric authentication technology in Indonesian society. The Technology Adoption Model is used in this research to estimate the impact of different parameters and investigate the importance of novel elements in adopting biometric authentication. Several factors are examined to see how they affect Actual System Use (ASU), including Perceived Usefulness (PU), Perceived Ease of Use (PEOU), Trust & Security (TS), Perceived Privacy (PP), Attitude towards Using Technology (ATU), and Behavioral Intention to Use (BIU). New theories are presented in this study, focusing on the relationship between PP and BIU, which is supported by respondent data. The results show the rejection of two hypotheses: first, the effect of PU on BIU may not be strong enough to influence user intention, and other variables may be at play; second, the effect of PEOU on BIU implies that perceived ease of use alone may not be sufficient to influence user intention. BIU has a major impact on the adoption of biometric authentication technologies. Furthermore, this research found that compared to PU and PEOU, the original components of TAM BIU are more influenced by variables such as ATU, PP and TS. These results suggest that new considerations such as privacy, trust, and security are more influential in shaping usage intention in biometric authentication.]]>
<![CDATA[A COMPARISON OF THE NAIVE BAYES AND K-NN ALGORITHMS IN PREDICTING THE FRESHNESS OF MILKFISH AT FISH AUCTIONS ]]>
<![CDATA[Setiyowati, Harlis]]>;
<![CDATA[Mayatopani, Hendra]]>;
<![CDATA[Hariyanto, Lilik]]>;
<![CDATA[Harriz, Muhammad Alfathan]]>
<![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.2024.5.4.2277
<![CDATA[This research aims to compare the performance of two machine learning algorithms, Naive Bayes and K-Nearest Neighbors (K-NN), in predicting the freshness of milkfish (Chanos chanos) at fish auctions. Predicting fish freshness is an important aspect to ensure product quality and customer satisfaction. The Naive Bayes algorithm, which is based on Bayes' Theorem with the assumption of independence between features, as well as the K-NN algorithm, which uses an instance-based approach to classify data based on proximity to k nearest neighbors, were implemented and tested. Evaluation is carried out using accuracy and Kappa metrics. The results show that Naive Bayes achieved an accuracy of 73.44% with a Kappa value of 0.594, indicating good performance in predicting the freshness of milkfish. In contrast, K-NN shows an accuracy of 68.75% and a Kappa value of 0.461, which means its performance is lower compared to Naive Bayes. Further analysis revealed that Naive Bayes is more computationally efficient and faster at making predictions, making it better suited for real-time applications at fish auctions. However, Naive Bayes has limitations in assuming feature independence which may not always be true in real-world situations. On the other hand, K-NN although more flexible and capable of capturing complex patterns in data, tends to be slow and requires optimization of parameters such as k values to improve its performance. In conclusion, Naive Bayes is recommended for predicting the freshness of milkfish at fish auctions because of its higher accuracy and reliability. Further research is needed to optimize these two algorithms through parameter adjustments and the use of ensemble methods to improve overall prediction performance.]]>
