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[TEXT CLUSTERING IN KARO LANGUAGE USING TF-IDF WEIGHTING AND K-MEANS CLUSTERING ]]>
<![CDATA[Br Sembiring, Trisna Amanda]]>;
<![CDATA[Hasibuan, Muhammad Siddik]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 4 No. 5 (2023): JUTIF Volume 4, Number 5, October 2023 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2023.4.5.1462
<![CDATA[The aim of this research is to see how many presentations there are between dialects and look for clusters. There is also a method used for weighting, namely tf-idf, there are several steps used in this method, namely starting from the tokenizing process, transform cases, stopwords filter and token filter. to search for clusters using the k-means clustering method on rapidminer. The results of this research obtained a tf-idf weighting value, namely ginger dialect 37.5% for the number of word occurrences and 62.5% for the total of all words documented. Furthermore, for the Julu dialect, it was 37.5% for the number of word occurrences and 62.5% for the total of all words documented. The Singaporean Lau dialect accounts for 38% of the number of word occurrences and 62% of the total number of words documented. The singteruh deleng lau dialect accounts for 38% of the number of word occurrences and 62% of the total number of words documented. The Liang Melas dialect accounts for 38% of the number of word occurrences and 62% of the total number of words documented. Based on k-means clustering, it produces cluster 0: 68 items, cluster 1: 3 items, cluster 2: 15 items, cluster 3: 10 items, cluster 4: 4 items with a total sample of 100 items. The conclusion obtained is that the Ginger dialect and the Julu dialect are identical, while the Singaporean Lau dialect, the Teruh Deleng and Liang Melas dialects are also identical.]]>
<![CDATA[APPLICATION OF PROCEDURAL CONTENT GENERATION SYSTEM IN FORMING DUNGEON LEVEL IN DUNGEON DIVER GAME ]]>
<![CDATA[Eka Wahyu Hidayat]]>;
<![CDATA[Euis Nur Fitriani Dewi]]>;
<![CDATA[Insan Saleh Ramadhan]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 5 No. 3 (2024): JUTIF Volume 5, Number 3, June 2024 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2024.5.3.1465
<![CDATA[Developers face numerous challenges in game development, one of which is the lack of games replayability due to the limited variety of levels created. The absence of level variety can lead to player boredom. The Procedural Content Generation (PCG) method provides an effective solution to address this challenge. PCG is applied with a focus on the Cellular Automata method by implementing the Von Neumann Neighborhood rule. The objective of this paper is to apply the Procedural Content Generation System method to create levels in game development. The game development process utilizes Luther's MDLC method. Testing is conducted using tiles of 32x32 units and 64x64 units, with three different test parameters: a fill percentage of 25%, 45%, and 65%. Each fill percentage is tested with three different smooth amount parameters of 2, 4, and 6, with a randomly selected seed. Performance testing results indicate that creating dungeon levels with 32x32 and 64x64 tiles yields short and relatively similar times, around 0.08 to 0.3 seconds. Functional testing reveals that a 25% fill percentage results in nearly empty rooms with no footholds, a 45% fill percentage produces levels with space and footholds, while a 65% fill percentage generates small unconnected rooms. Based on these percentages, a 45% fill percentage is considered the most appropriate for creating dungeon levels because it provides suitable space and footholds for players. Implementing PCG in game level creation not only saves time compared to manual level creation but also offers more efficient variations in dungeon shapes and difficulty levels.]]>
<![CDATA[A ROBUST AND IMPERCEPTIBLE FOR DIGITAL IMAGE ENCRYPTION USING CHACHA20 ]]>
<![CDATA[Nugroho, Widhi Bagus]]>;
<![CDATA[Susanto, Ajib]]>;
<![CDATA[Sari, Christy Atika]]>;
<![CDATA[Rachmawanto, Eko Hari]]>;
<![CDATA[Doheir, Mohamed]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 5 No. 2 (2024): JUTIF Volume 5, Number 2, April 2024 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2024.5.2.1470
<![CDATA[In the current era, data security is mandatory because it protects our personal data from being used by irresponsible people. The objective of this research is to show the robustness of the method we propose to encrypt images using the chacha20 algorithm which is included in the symmetric encryption cryptography technique and uses one key for both encryption and decryption processes. we use the encryption method by reading the bits from a digital image which is processed using the chacha20 algorithm to get the results of the digital image encryption. The results of this study indicate that the Chacha20 algorithm is secure to use when encrypting and decrypting digital images. The average MSE value generated by the chacha20 algorithm is 0.1232. The average PSNR value is 57.4784. The average value of UACI is 49.99%. The average value of NPCR is 99.602%. The test values were acquired by executing encryption and decryption processes on 5 distinct colour digital images with different size. Additionally, this study displays histograms for the original digital image, as well as for the encrypted and decrypted digital images, illustrating the pixel distribution in each. The histogram also serves as material for analysis of the success of the encryption and decryption processes in digital images.]]>
<![CDATA[IMPROVEMENT OF NAIVE BAYES ALGORITHM IN SENTIMENT ANALYSIS OF SHOPEE APPLICATION REVIEWS ON GOOGLE PLAY STORE ]]>
<![CDATA[Elistiana, Khoerotul Melina]]>;
<![CDATA[Bagus Adhi Kusuma]]>;
<![CDATA[Subarkah, Pungkas]]>;
<![CDATA[Awal Rozaq, Hasri Akbar]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 4 No. 6 (2023): JUTIF Volume 4, Number 6, Desember 2023 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2023.4.6.1486
<![CDATA[Reviews of the shopee application on the google play store are included in useful information if processed properly. Old or new users can analyze app reviews to get information that can be used to evaluate services. The activity of analyzing application reviews is not enough just to see the number of stars, it is necessary to see the entire contents of the review comments to be able to know the intent of the review. A sentiment analysis system is a system used to automatically analyze a review to obtain information including sentiment information that is part of an online review. The data is classified using Naive Bayes. A total of 1,000 shopee app user reviews were collected to form the sample dataset. The purpose of this study is to determine the sentiment analysis of shopee application reviews in the Google Play Store using the Naive Bayes algorithm. The stages of this research include, data collection, labeling, pre-processing, sentiment classification, and evaluation. In the pre-processing stage there are 6 stages, namely Cleaning, Case folding, Word Normalizer, Tokenizing, Stopword Removal and Stemming. TF-IDF (Term Frequency - Inverse Document Frequency) method is used for word weighting. The data will be grouped into two categories, namely negative and positive. The data will then be evaluated using accuracy parameter testing. The test results show an accuracy value of 81%, this result shows that shopee application reviews tend to be negative.]]>
<![CDATA[OPTIMIZING RAW MATERIAL INVENTORY MANAGEMENT OF MSME PRODUCT USING EXTREME GRADIENT BOOSTING (XGBOOST) REGRESSOR ALGORITHM: A SALES PREDICTION APPROACH ]]>
<![CDATA[Muhammad Khusni Fikri]]>;
<![CDATA[Farrikh Al Zami]]>;
<![CDATA[Ika Novita Dewi]]>;
<![CDATA[Abu Salam]]>;
<![CDATA[Ifan Rizqa]]>;
<![CDATA[Mila Sartika]]>;
<![CDATA[Diana Aqmala]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 5 No. 2 (2024): JUTIF Volume 5, Number 2, April 2024 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2024.5.2.1487
<![CDATA[Micro, Small and Medium Enterprises or MSMEs have a very important role for the survival of the economic sector in Indonesia. However, as the development of MSMEs, followed by a series of problems that arise. One of them is the problem of sales, business people have difficulty in determining the number of product sales in the future so that there is often an accumulation of raw materials or unsold products. This study aims to help MSMEs optimize raw material management by predicting product sales using the XGBoost Regressor Algorithm. Recently, the algorithm is very famous in the competition because of its reliability and no one has applied it to predict MSME product sales. Based on several other studies, this algorithm is accurate in predicting a value, such as predicting stock prices and the number of accidents in Bali, Indonesia. This research uses historical product sales data and weather data consisting of air temperature and relative humidity in Semarang Indonesia to train and evaluate the performance of the model. The prediction model was performed with predetermined variables and resulted in MAE 3.0752730568649156, MSE 38.25842541629838, and RMSE 6.185339555456788. In the end, it is concluded that the model built with XGBoost Regressor has a low error rate so that it can accurately predict the sales of an MSME product and optimize the management of raw materials for related products.]]>
<![CDATA[INTEGRATION OF ESP32-CAM WITH ANDROID AND IOT BASED ENGLISH-INDONESIAN TRANSLATION APPLICATION USING OCR TECHNOLOGY ]]>
<![CDATA[Nurhaliza, Siti]]>;
<![CDATA[Putri, Kirana Alyssa]]>;
<![CDATA[Iqlimah Attyyatullatifah]]>;
<![CDATA[I’zaaz Akhdan Muhadzdzab]]>;
<![CDATA[Atiqah Meutia Hilda]]>
<![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.2024.5.4.1489
<![CDATA[Language is a constant element in global human interaction, particularly English. This research presents the design and development of an innovative Android and IoT-based translation application, which facilitates seamless English-Indonesian translation. By utilizing Optical Character Recognition (OCR) technology for text input, the app is enhanced by the integration of ESP32-CAM, a versatile microcontroller with a camera module. This unique combination promises accurate and efficient translation, bridging language barriers while exploring the potential of the Internet of Things (IoT) in linguistic applications. This research reveals the intricate process of creating this translator tool, using the Dart programming language and Flutter framework in Android app development, with the support of Visual Studio Code as the software development environment, as well as the Arduino IDE for the ESP32-CAM microcontroller. It shows how OCR technology and ESP32-CAM significantly enhance the translation experience in an increasingly connected world.]]>
<![CDATA[DETECTION OF ACTIONS BISINDO (INDONESIAN SIGN LANGUAGE) INTO TEXT-TO-SPEECH USING LONG SHORT-TERM MEMORY WITH MEDIAPIPE HOLISTICS ]]>
<![CDATA[Agustin, Risda Rosdiana]]>;
<![CDATA[Maulana, Hendra]]>;
<![CDATA[Mandyartha, Eka Prakarsa]]>
<![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.2024.5.4.1492
<![CDATA[Sign language is frequently used by those who have difficulty hearing or speaking to communicate. Because it is a non-verbal language that expresses meaning through hand and body gestures, sign language is an essential form of communication for people who rely on it. The objective of this work is to develop a detection that can understand actions made in Indonesian Sign Language (BISINDO), translate them into text, and use speech recognition (Text- to-Speech) to provide audio responses. In particular at Sekolah Luar Biasa, the main objective is to assist and enhance communication among persons with impairments. Long Short-Term Memory (LSTM) and Mediapipe Holistics are use to achieve its objectives. It is demonstrated how LSTM and Mediapipe Holistics enhance performance and accuracy using two different dataset types. The first dataset landmarks created using the Mediapipe Holistics model, while the second dataset provides original shots devoid of landmarks. Batch size and epoch settings are among the many parameters needed for training and testing processes. Model using the landmark-free dataset only manages to reach an accuracy of approximately 89.33%, the model using the landmark with mediapipe of accuracy of about 96.67%. Furthermore, the landmark-based model exhibits strong F1 scores, recall, and precision. The research successfully recognizes a number of BISINDO acts, such as "saya" (I), "kamu" (you), "ayah" (father), "ibu" (mother), and others present in the dataset. On the basis of the gestures it has identified can also make speech.]]>
<![CDATA[CUSTOMER LOYALTY SEGMENTATION IN ONLINE STORE USING LRFM AND MLRFM IN COMBINATION WITH RM K-MEANS ALGORITHM ]]>
<![CDATA[Utomo, Angelina Caroline]]>;
<![CDATA[Handojo, Andreas]]>;
<![CDATA[Octavia, Tanti]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 5 No. 2 (2024): JUTIF Volume 5, Number 2, April 2024 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2024.5.2.1497
<![CDATA[The rapid development of online business in recent years has driven Store X to embark on a digital transformation. By the end of 2020, Store X relocate their conventional business to online business. The greatest obstacle and key to success for online business operators, such as Store X, is gaining and retaining consumer loyalty in the face of an increasing number of competitors. Therefore, the company must be able to identify the character (behavior) of its clients to provide appropriate treatment. Each customer's behavior is unique, which means they must all be treated differently. However, all this time, Online Store X has provided the same treatment (as much of a discount) to all its customers due to the lack of information regarding their customers’ characteristics. Therefore, in this study, customers of Online Store X were segmented based on their transactional behavior using online transaction history data from March 2021 to March 2023. Two customer analysis models, LRFM and MLRFM, will be combined with RM K-Means to find the best combination through Silhouette Coefficient values. The optimal number of clusters (k) is then determined using the Elbow Method. The results indicate that the optimal number of clusters for both combinations is K=3, with the combination of MLRFM and RM K-Means is the best combination. The finest combination has a silhouette coefficient value of 0.8609. Based on this combination, it is also known that 2,053 customers in cluster 3 are loyal customers, while 2,339 customers in cluster 1 and 2 are lost customers. The results of this study were also implemented on websites built for X Store using Python programming languages and MySQL databases, making it easier for companies to see data visualization.]]>
<![CDATA[CATTLE BODY WEIGHT PREDICTION USING REGRESSION MACHINE LEARNING ]]>
<![CDATA[Anjar Setiawan]]>;
<![CDATA[Utami, Ema]]>;
<![CDATA[Ariatmanto, Dhani]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 5 No. 2 (2024): JUTIF Volume 5, Number 2, April 2024 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2024.5.2.1521
<![CDATA[Increasing efficiency and productivity in the cattle farming industry can have a significant economic impact. Cow health and productivity problems directly impact the quality of the meat and milk produced. In the cattle farming industry, it can help predict cow weight oriented to beef and milk quality. The importance of predicting cow weight for farmers is to monitor animal development. Meanwhile, for traders, knowing the animal's weight makes it easier to calculate the price of the animal meat they buy. This research aims to predict cow weight by increasing the results of smaller MAE values. The methods used are linear Regressor (LR), Random Forest Regressor (RFR), Support Vector Regressor (SVR), K-Neighbors Regressor (KNR), Multi-layer Perceptron Regressor (MLPR), Gradient Boosting Regressor (GBR), Light Gradient boosting (LGB), and extreme gradient boosting regressor (XGBR). Producing cattle weight predictions using the SVR method produces the best values, namely mean absolute error (MAE) of 0.09 kg, mean absolute perception error (MAPE) of 0.02%, root mean square error (RMSE) of 0.08 kg, and R-square of 0.97 compared to with other algorithm methods and the results of statistical correlation analysis showed several significant relationships between morphometric variables and live weight.]]>
<![CDATA[RICE DISEASE RECOGNITION USING TRANSFER LEARNING XCEPTION CONVOLUTIONAL NEURAL NETWORK ]]>
<![CDATA[Muslikh, Ahmad Rofiqul]]>;
<![CDATA[Setiadi, De Rosal Ignatius Moses]]>;
<![CDATA[Ojugo, Arnold Adimabua]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 4 No. 6 (2023): JUTIF Volume 4, Number 6, Desember 2023 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2023.4.6.1529
<![CDATA[As one of the major rice producers, Indonesia faces significant challenges related to plant diseases such as blast, brown spot, tugro, leaf smut, and blight. These diseases threaten food security and result in economic losses, underscoring the importance of early detection and management of rice diseases. Convolutional Neural Network (CNN) has proven effective in detecting diseases in rice plants. Specifically, transfer learning with CNN, particularly the Xception model, has the advantage of efficiently extracting automatic features and performing well even with limited datasets. This study aims to develop the Xception model for rice disease recognition based on leaf images. Through the fine-tuning process, the Xception model achieved accuracies, precisions, recalls, and F1-scores of 0.89, 0.90, 0.89, and 0.89, respectively, on a dataset with a total of 320 images. Additionally, the Xception model outperformed VGG16, MobileNetV2, and EfficientNetV2.]]>
