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[CLASSIFICATION MODELS FOR ACADEMIC PERFORMANCE: A COMPARATIVE STUDY OF NAÏVE BAYES AND RANDOM FOREST ALGORITHMS IN ANALYZING UNIVERSITY OF LAMPUNG STUDENT GRADES ]]>
<![CDATA[Kurniasari, Dian]]>;
<![CDATA[Hidayah, Rekti Nurul]]>;
<![CDATA[Notiragayu, Notiragayu]]>;
<![CDATA[Warsono, Warsono]]>;
<![CDATA[Nisa, Rizki Khoirun]]>
<![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.2024.5.5.2066
<![CDATA[At the university, students are provided with a comprehensive assessment of their academic achievements for each course completed at the end of every semester. This study aimed to compare the effectiveness of two classification methods, the Naïve Bayes and the Random Forest methods, in classifying student learning outcomes. The research process is segmented into various stages: data selection, data preparation, model building and testing, and model evaluation. The findings indicated that the Naïve Bayes and Random Forest approaches exhibited superior accuracy levels when employing data splitting strategies, in contrast to k-fold cross-validation. Based on the examination, the Random Forest approach demonstrated superiority in identifying the scores of University of Lampung students, achieving an accuracy percentage of 99.38%. Notably, both techniques showed a substantial performance improvement using Gradient Boosting. The Naïve Bayes method attained an accuracy rate of 99.89%, while the Random Forest method reached 99.45%. The results demonstrate that employing the Random Forest classification method consistently leads to superior performance in identifying and classifying student grades. Furthermore, using Gradient Boosting in the boosting process has demonstrated its efficacy in enhancing the classification methods' accuracy. These findings significantly contribute to the comprehension and advancement of evaluation systems for assessing student learning outcomes in the university environment.]]>
<![CDATA[SENTIMENT ANALYSIS CLASSIFICATION IN WOMEN'S E-COMMERCE REVIEWS WITH MACHINE LEARNING APPROACH ]]>
<![CDATA[Afan Firdaus, Alfiki Diastama]]>;
<![CDATA[Rahmawan, Rizki Dwi]]>;
<![CDATA[Mahendra, Yuzzar Rizky]]>;
<![CDATA[Cahyono, Hasan Dwi]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 5 No. 6 (2024): JUTIF Volume 5, Number 6, Desember 2024 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2024.5.6.2392
<![CDATA[User reviews on e-commerce are one of the important elements in e-commerce. User reviews can help potential buyers make decisions based on the experiences and opinions of other people, for example women's e-commerce reviews. In providing positive, neutral or negative sentiment reviews, understanding customer perceptions is challenging. Classifying sentiment reviews will solve this problem, several classification techniques have been carried out, but there is still room for development in the use of simple machine learning techniques and sampling to overcome data class imbalance. Classification techniques used in this paper include Naive Bayes, SVM, and KNN. These algorithms will be compared to determine the most accurate model. Several preprocessing techniques are also carried out such to balance the dataset using ROS and SMOTE. It was obtained that the SVM method with ROS had the highest accuracy of around 0.94 for accuracy value, 0.93 for precision value, 0.94 for recall, and 0.92 for F1-score value. This research shows that the use of sampling techniques such as ROS and SMOTE can be effective in balancing imbalanced datasets, thereby improving model classification performance. These findings can be a reference for developing more efficient and accurate sentiment classification models, especially in the case of imbalanced data.]]>
<![CDATA[STOCK PREDICTION PERFORMANCE OPTIMIZATION: ENHANCING COVARIANCE MATRIX WITH KNN ]]>
<![CDATA[Saputra , Iskandar Abdul Azis]]>;
<![CDATA[Sidiq, Muhammad Rais]]>;
<![CDATA[Guritno, Sangaji Suryo]]>;
<![CDATA[Cahyono, Hasan Dwi]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 5 No. 6 (2024): JUTIF Volume 5, Number 6, Desember 2024 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2024.5.6.2399
<![CDATA[Stock price prediction is a fundamental yet complex challenge in quantitative finance. With the increasing availability of data and advancements in machine learning techniques, various models have been developed to capture intricate patterns in stock price movements. While complex neural network models such as Recurrent Neural Networks (RNNs), Graph Neural Networks (GNNs), and Transformers have shown potential in handling stock market data, they often face optimization difficulties and performance limitations, especially when data is scarce. This paper explores the use of simpler and more accessible prediction methods, specifically Linear Regression (LR) and K-Nearest Neighbors (KNN), alongside more advanced models like Temporal Spatial Transformer (TST) and a Multi-Layer Perceptron (MLP) model called Stockmixer. The NASDAQ dataset is utilized in this study, providing a comprehensive view of stock market dynamics with high variability. Results indicate that KNN, among the evaluated models, exhibits superior and more stable performance in predicting validation data compared to MLP. KNN achieved a low Mean Squared Error (MSE) at 100 epochs, and demonstrated positive Information Coefficient (IC) and Return Information Coefficient (RIC) values. Additionally, it showed high Precision at 10 (P@10) and Sharpe Ratio (SR), making it a robust choice for stock price prediction tasks. In contrast, MLP, despite its sophistication, revealed some weaknesses, particularly in the alignment between predictions and actual values. These findings offer valuable insights into the effectiveness of various models for stock price prediction and suggest that simpler models like KNN can provide competitive results compared to more complex models.]]>
<![CDATA[CLASSIFICATION OF COAL MINE PILLAR STABILITY USING EXTREME LEARNING MACHINE AND PARTICLE SWARM OPTIMIZATION ADAPTIVE WEIGHT DELAY VELOCITY ]]>
<![CDATA[Farhana, Nadhilah]]>;
<![CDATA[Hertono, Gatot Fatwanto]]>;
<![CDATA[Handari, Bevina Desjwiandra]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 5 No. 6 (2024): JUTIF Volume 5, Number 6, Desember 2024 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2024.5.6.2403
<![CDATA[In underground mining, pillars are prime structural parts for supporting the overburden. Precise prediction of pillar stability is necessary because pillar failure might cause catastrophic events that could endanger mining personnel and equipment. This research aims to classify the stability of underground coal mining pillars using the Extreme Learning Machine model with Particle Swarm Optimization Adaptive Weight Delay Velocity that used to optimize the model's input weights and bias. Extreme Learning Machine is a model for training artificial neural networks using a single-layer feedforward Network architecture. Performance comparison is presented between the proposed model and the Particle Swarm Optimization-Extreme Learning Machine. The dataset originated from South African coal mining with two pillar stabilities: failed and intact. The pillar stability of the dataset expanded into five categories: failed upper, failed lower, intact upper, intact lower slender, intact lower not-slender. Out of the five pillar stability categories, the failed lower category is the most dangerous pillar category, with the rest are non-dangerous pillar category. The expanded categories are according to the Probability of Failure of the pillar and the type of pillar (slender, intermediate, and squat). The results showed that the AUC 91,4%; 74,3%; 72,6%, and G-mean 82,2% were all at least 10% higher in the proposed model. The proposed model successfully classified 91.24% of non-dangerous pillar stability category, but only 36% of the most dangerous pillar stability category. The results of this study are expected could give assistance to provide information as a consideration in predicting pillar.]]>
<![CDATA[WORD EMBEDDING OPTIMIZATION IN SENTIMENT ANALYSIS OF REVIEWS ON MYTELKOMSEL APP USING LONG SHORT-TERM MEMORY AND SYNTHETIC MINORITY OVER-SAMPLING TECHNIQUE ]]>
<![CDATA[Haziq, Muhammad Raffif]]>;
<![CDATA[Sibaroni, Yuliant]]>;
<![CDATA[Prasetyowati, Sri Suryani]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 5 No. 6 (2024): JUTIF Volume 5, Number 6, Desember 2024 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2024.5.6.2498
<![CDATA[Telkomsel is one of the internet service provider companies that has a mobile-based application called MyTelkomsel which functions to facilitate users in conducting online services independently. Users of the application certainly have their own responses about the application, so that users can provide responses to the application. Therefore, sentiment analysis can be one of the solutions to find out public sentiment towards the application. In this research, the author builds a system for sentiment analysis using word embedding Word2vec, GloVe, FastText to get word representation in vector form with classification using Long Short-Term Memory (LSTM) combined with Synthetic Minority Over-sampling Technique (SMOTE) which can handle data imbalance. The data used comes from user reviews of the MyTelkomsel application found on the Google Play Store. This study compares the performance of several word embedding in LSTM and LSTM-SMOTE classifiers. The results showed the results show that the performance of three-word embedding on the LSTM model is superior compared to the LSTM-SMOTE model. Overall, it was found that the combination of FastText and LSTM gave the best performance compared to the other five combinations with an accuracy value of 89.11%.]]>
<![CDATA[COMBINATIONS OF FEATURE EXTRACTIONS AND MACHINE LEARNING ALGORITHMS FOR SKIN CANCER CLASSIFICATION ]]>
<![CDATA[Asfar, A. Muh. Fitrah]]>;
<![CDATA[Hasnawi, Mardiyyah]]>;
<![CDATA[Darwis, Herdianti]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 5 No. 6 (2024): JUTIF Volume 5, Number 6, Desember 2024 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2024.5.6.2514
<![CDATA[One of the most common causes of death worldwide is skin cancer and its incidence is increasing. To achieve optimal treatment and improve clinical outcomes for patients, precision skin cancer detection and classification approaches are required, which can be achieved through the application of feature extraction and machine learning algorithms. The development of such algorithms to identify important patterns from skin cancer image datasets enables early detection and more accurate classification and more effective treatment. Although previous studies have tried to detect skin cancer using feature extraction techniques such as HFF, HOG, and GLCM, some weaknesses still need to be improved. This research aims to combine various feature extraction methods such as Gray Level Co-occurrence Matrix, Histogram Oriented Gradients, and Local Binary Patterns and machine learning algorithms such as Support Vector Machine, Random Forest, and Gaussian Naïve Bayes in the classification process between Melanoma and Nevus skin cancers. In this research, the number of datasets used is 17,397 derived from the ISIC Dataset. The results showed that the Histogram Oriented Gradients method with Support Vector Machine algorithm achieved the highest accuracy of 92%. The combination of Gray Level Co-occurrence Matrix and Local Binary Patterns with Random Forest algorithm also achieved an accuracy of 92%, the combination of Gray Level Co-occurrence Matrix, Histogram Oriented Gradients, and Local Binary Patterns with Random Forest algorithm also resulted in an accuracy of 92%. These findings suggest that the combination of various feature extraction methods and machine learning algorithms can improve accuracy in skin cancer classification, which in turn can contribute to early detection and more effective treatment.]]>
<![CDATA[HATE SPEECH DETECTION USING GLOVE WORD EMBEDDING AND GATED RECURRENT UNIT ]]>
<![CDATA[Ardana, Aulia Riefqi]]>;
<![CDATA[Sibaroni, Yuliant]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 5 No. 6 (2024): JUTIF Volume 5, Number 6, Desember 2024 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2024.5.6.2557
<![CDATA[Social media has become a tool that makes it easier for people to exchange information. The freedom to share information has opened the door for increased incidents of hate speech on social media. Hate speech detection is an interesting topic because with the increasing use of social media, hate speech can quickly spread and trigger significant negative impacts, discrimination, and social conflict. This research aims to see the effect of GRU method, GloVe word embedding and word modifier algorithm in detecting hate speech. GRU and GloVe are used in this research for the hate speech detection system, where deep learning with a Gated Recurrent Unit (GRU) and Word Embedding with the Global Vector model (GloVe) converts words in text into numerical vectors that represent the meaning and context of the words. GRU is chosen due to its ability to capture long-term dependencies in textual data with higher computational efficiency compared to Long Short-Term Memory (LSTM). Gated Recurrent Unit (GRU) model processes the sequence of words to understand the sentence structure. GRU model processes the sequence of words to understand the sentence structure. The evaluation results for the classification of hate speech using GRU and GloVe are 90.7% accuracy and 91% F1 score. With the combination of informal word modifier algorithms there is an increase with a value of 92.8% F1 and 92.4% accuracy. in conclusion, the use of informal word modifier algorithms can increase the evaluation value in detecting hate speech.]]>
<![CDATA[STOCK PRICE PREDICTION USING THE LONG SHORT-TERM MEMORY METHOD ]]>
<![CDATA[Sahroni, Muhammad]]>;
<![CDATA[Firman Arif, Mochammad]]>;
<![CDATA[Misdram, Muhammad]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 5 No. 6 (2024): JUTIF Volume 5, Number 6, Desember 2024 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2024.5.6.2615
<![CDATA[Stocks are a highly risky investment instrument if not handled correctly. Therefore, accurately predicting stock prices is crucial to supporting better investment decisions. Today, more young people in the current generation know the importance of investing in stocks. Hence, understanding prediction methods early on is essential to reduce potential losses for prospective investors. With accurate prediction methods, the results will be more reliable. The data used consists of daily stock prices of Bank Syariah Indonesia from May 2019 to May 2024, totaling 1,215 data points. The research method employs LSTM (Long Short-Term Memory), which includes data collection, preprocessing, LSTM model formation, and model evaluation. The LSTM model is implemented using the Python programming language, and model evaluation is conducted using the Mean Absolute Percentage Error (MAPE) and Root Mean Squared Error (RMSE) metrics. The results show that the LSTM model can provide accurate predictions with a MAPE error value of only 1.72% and an RMSE of 53.49. This research indicates that the LSTM method is effective in predicting stock prices with an accuracy level of 98.28% and can be one of the bases when starting stock investment.]]>
<![CDATA[ANALYSIS THE IMPACT OF REFACTORING FROM MONOLITHIC APPLICATIONS TO MICROSERVICES ON RESPONSE TIME USING THE MDA AND SCA APPROACHES ]]>
<![CDATA[Yusri, Shidqi Fadhlurrahman]]>;
<![CDATA[Suwawi, Dawam Dwi Jatmiko]]>;
<![CDATA[Adrian, Monterico]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 5 No. 6 (2024): JUTIF Volume 5, Number 6, Desember 2024 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2024.5.6.2617
<![CDATA[This study investigates the impact of refactoring from a monolithic to a microservices architecture on application response time. Monolithic architecture, initially chosen for ease of development, faces scalability challenges as the application grows. Microservices offer a solution by enabling independent service deployment and enhanced scalability. This research uses Meta-Data Aided (MDA) and Static Code Analysis (SCA) methodologies to facilitate the refactoring process, applying them to the inventory-application project from a collaborative software development platform (GitHub). The refactoring involves decomposing the monolithic application, containerizing it with Docker, and evaluating performance using JMeter. Results show that microservices significantly reduce response time, particularly in API interaction tasks. While microservices improve scalability and flexibility, they require careful management of service communication. This research enhances understanding of the benefits of microservices in terms of response time and offers practical guidance for developers considering refactoring.]]>
<![CDATA[COMPARATIVE ANALYSIS OF CONTRAST ENHANCEMENT METHODS FOR CLASSIFICATION OF PEKALONGAN BATIK MOTIFS USING CONVOLUTIONAL NEURAL NETWORK ]]>
<![CDATA[Kurniawan, Muhammad Bayu]]>;
<![CDATA[Utami, Ema]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 5 No. 6 (2024): JUTIF Volume 5, Number 6, Desember 2024 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2024.5.6.2621
<![CDATA[Batik artists in Pekalongan have freedom in determining motifs, creating a diversity of distinctive batik motifs. However, this diversity often makes it difficult for people to recognize the different motifs, as visual identification requires in-depth knowledge. The lack of understanding about Pekalongan batik is a challenge in recognizing these motifs. To overcome this challenge, an efficient and accurate method of motif identification is needed. This study aims to analyze the efficacy of contrast enhancement methods in improving the classification results of Pekalongan batik motifs using convolutional neural networks (CNN) with ResNet50 architecture. The dataset of 480 images was collected directly from Museum Batik Pekalongan and split into three distinct categories: 15% for validation, 15% for testing, and 70% for training. Two contrast enhancement methods: contrast limited adaptive histogram equalization (CLAHE) and histogram equalization (HE), were applied to create additional datasets. The Adam optimizer was used to train the model over 50 epochs at a learning rate of 0.001. The test results show that the original dataset contrast-enhanced with CLAHE reaches the best accuracy of 83%, followed by the original dataset contrast-enhanced with HE at 81%, and the original dataset at 76%. This finding shows that the application of contrast enhancement methods, especially CLAHE, can increase the model's accuracy in classifying batik motifs.]]>
