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INDONESIA
TEKNIK INFORMATIKA
Published by Universitas Islam Negeri Syarif Hidayatullah Jakarta
ISSN : 19799160     EISSN : 25497901     DOI : -
Core Subject : Science,
Computer Science & IT
Jurnal Teknik Informatika merupakan wadah bagi insan peneliti, dosen, praktisi, mahasiswa dan masyarakat ilmiah lainnya untuk mempublikasikan artikel hasil penelitian, rekayasa dan kajian di bidang Teknologi Informasi. Jurnal Teknik Informatika diterbitkan 2 (dua) kali dalam setahun.
Arjuna Subject : -
Articles 262 Documents
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ANALISIS QUALITY OF SERVICE JARINGAN WIRELESS SUKANET WiFi DI FAKULTAS SAINS DAN TEKNOLOGI UIN SUNAN KALIJAGA Bambang Sugiantoro; Yuha Bani Mahardhika
JURNAL TEKNIK INFORMATIKA Vol 10, No 2 (2017): Jurnal Teknik Informatika
Publisher : Department of Informatics, Universitas Islam Negeri Syarif Hidayatullah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15408/jti.v10i2.7027

Abstract

ABSTRAK Performa layanan jaringan Internet pada UIN Sunan Kalijaga Fakultas Sains dan Teknologi masih belum maksimal, yaitu memiliki tingkat kualitas delay sebesar 159 milidetik menurut TIPHON Bagus. Besar Throughput sebesar 9.0 MBps dan presentase Throughput sebesar 50 % dikategorikan menurut standarisasi TIPHON sedang. Dan memiliki nilai packet loss ratio sebesar 36 % dikategorikan menurut standarisasi TIPHON adalah jelek. ABSTRACT Internet service network performance in Islamic State University of Sunan Kalijaga environment in the faculty of science and technology area is still not maximal. It has a delay quality level of 159 milliseconds according to good TIPHON. Large throughput of 9.0 Mbps and throughput percentage of 50% are categorized according to standardized of normal TIPHON and it has a value of packet loss ratio of 36% categorized according to TIPHON standardization is bad.How to Cite : Sugiantoro, B. Mahardhika, Y.B . (2017). ANALISIS QUALITY OF SERVICE JARINGAN WIRELESS SUKANET WiFi DI FAKULTAS SAINS DAN TEKNOLOGI UIN SUNAN KALIJAGA. Jurnal Teknik Informatika, 10(2), 191-201. doi:10.15408/jti.v10i2.7027Permalink/DOI: http://dx.doi.org/10.15408/jti.v10i2.7027
SVM Optimization with Grid Search Cross Validation for Improving Accuracy of Schizophrenia Classification Based on EEG Signal Masdar Desiawan; Achmad Solichin
JURNAL TEKNIK INFORMATIKA Vol 17, No 1: JURNAL TEKNIK INFORMATIKA
Publisher : Department of Informatics, Universitas Islam Negeri Syarif Hidayatullah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15408/jti.v17i1.37422

Abstract

The advantage of the Support Vector Machine (SVM) is that it can solve classification and regression problems both linearly and non-linearly. SVM also has high accuracy and a relatively low error rate. However, SVM also has weaknesses, namely the difficulty of determining optimal parameter values, even though setting exact parameter values affects the accuracy of SVM classification. Therefore, to overcome the weaknesses of SVM, optimizing and finding optimal parameter values is necessary. The aim of this research is SVM optimization to find optimal parameter values using the Grid Search Cross-Validation method to increase accuracy in schizophrenia classification. Experiments show that optimization parameters always find a nearly optimal combination of parameters within a specific range. The results of this study show that the level of accuracy obtained by SVM with the grid search cross-validation method in the schizophrenia classification increased by 9.5% with the best parameters, namely C = 1000, gamma = scale, and kernel = RBF, the best parameters were applied to the SVM algorithm and obtained an accuracy of 99.75%, previously without optimizing the accuracy reached 90.25%. The optimal parameters of the SVM obtained by the grid search cross-validation method with a high degree of accuracy can be used as a model to overcome the classification of schizophrenia.
IoT Based Early Flood Detection System with Arduino and Ultrasonic Sensors in Flood-Prone Areas Muhammad Darwis; Hafiizh Asrofil Al Banna; Setiawan Restu Aji; Dinda Khoirunnisa; Nakia Natassa
JURNAL TEKNIK INFORMATIKA Vol 16, No 2 (2023): JURNAL TEKNIK INFORMATIKA
Publisher : Department of Informatics, Universitas Islam Negeri Syarif Hidayatullah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15408/jti.v16i2.32161

Abstract

IoT is one of the focuses of application development carried out by various developers today. The aim is to enable various devices and work independently to meet the various needs of their users. The flood early warning system is one of the much-needed IoT-based applications, enabling users to quickly obtain water level information in an area. This application can help people to be more aware of flood disasters, especially during the rainy season. This research develops a flood early warning system application by utilizing Arduino and ultrasonic sensors installed in flood-prone areas. The sensor is used to measure the water level at a time based on the distance from the water surface to the sensor. When the distance between the water surface and the sensor is less than or equal to the set threshold, the sensor will send data and alerts to the user via email. This research applies the IoT design and development method. In addition, this research also used the C and Python programming language for application prototypes and the MySQL database to store the data. the application in this study was tested using the blackbox method and the results showed that all application functions could run properly.
Evaluation of Website Performance and Usability Using GTMetrix, Usability Testing, and System Usability Scale (SUS) Methods Toto Andri Puspito
JURNAL TEKNIK INFORMATIKA Vol 17, No 2: JURNAL TEKNIK INFORMATIKA
Publisher : Department of Informatics, Universitas Islam Negeri Syarif Hidayatullah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15408/jti.v17i2.38530

Abstract

This study was conducted to measure the performance of IAIN Metro's website in terms of performance and user perception to ensure that the campus website can adequately support visitors' needs. This study aims to determine things that need to be improved to improve the performance of the IAIN Metro website. To get comprehensive results from the performance of the website, this research uses GTMetrix to analyse the technical performance of the IAIN Metro website, and then, to test user perceptions, researchers use Usability Testing and System Usability Scale methods. In usability testing, several aspects will be measured to determine usability problems, namely learnability and efficiency, while the System Usability Scale questionnaire will be used to test the satisfaction level. Based on the test results using GTMetrix, after testing, several aspects of the access speed of the IAIN Metro website need to be improved. Although, in general, from the test results, Usability Testing and System Usability Scale users still consider the performance of the website to be acceptable, the results of the first task on Time Based Efficiency testing show that initial access to the main page metrouniv.ac.id, takes a relatively long time compared to other tasks. This is also evident from the GTMetrix score on the performance aspect, which has a low presentation of 25%. Therefore, optimisation is needed on the main page to improve website performance.
Integration of Design Sprint Method into Mobile Development Application Life Cycle to Create MobilePQI Application Prototype Fenty Eka Muzayyana Agustin; Nuriyah Thahir; Ade Rina Farida; Kania Mayastika
JURNAL TEKNIK INFORMATIKA Vol 17, No 1: JURNAL TEKNIK INFORMATIKA
Publisher : Department of Informatics, Universitas Islam Negeri Syarif Hidayatullah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15408/jti.v17i1.37818

Abstract

This study aims to create a mobile-based learning application that can be used to support blended learning. Blended Learning is carried out synchronously, either online using Zoom or Google Meet, or offline in the classroom. asynchronous learning is implemented using MobilePQI Apps. MobilePQI Apps was developed using the Kotlin programming tool and  MADLC methodology (Mobile Application Development Life Cycle). MADLC consists of seven stages: Identification; Design; Development; Prototyping; Testing; Deployment; and Maintenance. We use design sprint method and figma to create design prototype, and Kotlin development kit. The testing method used heuristics evaluation which tests 10 usability principles. The number of questions asked was 115, with 5 respondents consisting of 3 students and 3 lecturers. The results of the heuristics evaluation score were 89% of respondents answered YES. That it can be concluded that the 10 usability  principles of the prototype was acceptable. The SUS results show a score of 74, which means the application's user interface is in the Good and acceptable category.
Hoax News Detection Using Passive Aggressive Classifier And TfidfVectorizer Maulana Fajar Lazuardi; Renaldy Hiunarto; Kareena Futri Ramadhani; Noviandi Noviandi; Riya Widayanti; Muhamad Hadi Arfian
JURNAL TEKNIK INFORMATIKA Vol 16, No 2 (2023): JURNAL TEKNIK INFORMATIKA
Publisher : Department of Informatics, Universitas Islam Negeri Syarif Hidayatullah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15408/jti.v16i2.34084

Abstract

Indonesia is one of the countries with the highest number of social media users. Million social media users in Indonesia reached 167 million in January 2023. These users are spread, across various social media, including Twitter with 24 million users. The high number of social media users on Twitter makes the information validation process even more neglected. Moreover, the trend of news interest read by social media users is only adjusted to their individual tastes. This phenomenon is evidenced by the large number of fake news (hoaxes) circulating in society which are spread through social media. Therefore, an accurate machine learning model is needed to classify "real" and "hoax" news. This study uses the TfidfVectorizer algorithm and Passive Aggressive Classifier for datasets that are shared through the Kaggle site. The contents of the dataset were sourced via social media Twitter over a span of 5 years, namely 2015-2020. At the preprocessing stage to making the Confusion Matrix, the machine learning model shows that it can work well as expected, namely getting Accuracy, Precision, and Recall scores of 82.44%, 80.66%, and 82.44%. In addition, the results of the confusion matrix show that in the dataset used, there is more "real" news than "hoaxes", that is, the model is able to predict 1059 real news and 211 hoax news, with actual conditions 1106 real news and 164 hoax news.
A Backpropagation Artificial Neural Network Approach for Loan Status Prediction Edwin Setiawan Nugraha; Gabrielle Jovanie Sitepu
JURNAL TEKNIK INFORMATIKA Vol 15, No 2 (2022): JURNAL TEKNIK INFORMATIKA
Publisher : Department of Informatics, Universitas Islam Negeri Syarif Hidayatullah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15408/jti.v15i2.27006

Abstract

Providing credit has become a main source of profit for financial and non-financial institutions. However, this transaction might lead into credit risk. This risk occurred if debtors unable to complete their obligations that will led loss for creditors.  It is necessity for company to create assessment in distinguishing eligible or non-eligible prospective customer. Artificial Neural Network (ANN) is introduced in solving this typical classification case. Furthermore, one of learning algorithm in ANN namely Backpropagation is able to minimizing error of output in order to receive accurate result. This research aims to form models that capable in classifying the loan status of applicants by utilizing historical data. The method developed in this research is Backpropagation with activation function is a sigmoid function. In addition, this research formed two data model for analyzed; with first data model is every variable given in dataset and for the second data model is the variables that influenced the loan acceptance. Backpropagation shows high performance with more or less data variables. The results of this research show that the both data model has highest accuracy of prediction is 94.37% while the lowest accuracy prediction is 80.28%.
Optimizing the Learning Rate Hyperparameter for Hybrid BiLSTM-FFNN Model in a Tourism Recommendation System Aufa Ab'dil Mustofa; Erwin Budi Setiawan
JURNAL TEKNIK INFORMATIKA Vol 17, No 2: JURNAL TEKNIK INFORMATIKA
Publisher : Department of Informatics, Universitas Islam Negeri Syarif Hidayatullah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15408/jti.v17i2.40250

Abstract

Indonesia, with its abundant natural resources, is rich in captivating tourist attractions. Tourism, a vital economic sector, can be significantly influenced by digitalization through social media. However, the overwhelming amount of information available can confuse tourists when selecting suitable destinations. This research aims to develop a tourism recommendation system employing content-based filtering (CBF) and hybrid Bidirectional Long Short-Term Memory Feed-Forward Neural Network (BiLSTM-FFNN) model to assist tourists in making informed choices. The dataset comprises 9,504 rating matrices obtained from tweet data and reputable web sources. In various experiments, the hybrid BiLSTM-FFNN model demonstrated superior performance, achieving an accuracy of 93.36% following optimization with the Stochastic Gradient Descent (SGD) algorithm at a learning rate of about 0.193. The accuracy, after applying Synthetic Minority Over-sampling Technique (SMOTE) and fine-tuning the learning rate hyperparameter, showed a 14.3% improvement over the baseline model. This research contributes by developing a recommendation system method that integrates CBF and hybrid deep learning with high accuracy and provides a detailed analysis of optimization techniques and hyperparameter tuning.
Comparison of Criteria Weight Determination Using MEREC and CRITIC Methods in Choosing The Best Student Accommodation with the MOORA Method Case Study: Coventry University M. Thosin Yuhaililul Hilmi; Ulla Delfana Rosiani; Ely Setyo Astuti
JURNAL TEKNIK INFORMATIKA Vol 17, No 2: JURNAL TEKNIK INFORMATIKA
Publisher : Department of Informatics, Universitas Islam Negeri Syarif Hidayatullah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15408/jti.v17i2.41097

Abstract

One of the challenges faced by IISMA Awardees and students in general in Coventry University is choosing a comfortable place to live. Although various student accommodations are provided, differences in facilities and considerations from other parties such as parents and friends make the selection process complicated. This study develops a decision support system to help students choose student accommodation objectively without any intervention from others and provides a comparison of the use of different combinations of methods as additional guidance in the decision-making process. Two methods, Method Based on the Removal Effects of Criteria (MEREC) and Criteria Importance Through Intercriteria Correlation (CRITIC), are used to weight the criteria. The Multi-Objective Optimization (MOORA) method is used to determine the best alternative after the weight calculation is known. The results using a combination of the MEREC-MOORA method and a combination of the CRITIC-MOORA method place Alternative 5 (A5) in first place, while the remaining alternatives show a similar ranking order. In this study, scenario testing was also carried out by deleting and adding criteria and alternatives which then provided ranking results with a positive correlation even though different combinations of methods were used in determining the ranking.
Performance Analysis of Transfer Learning Models for Identifying AI-Generated and Real Images Arini Arini; Muhamad Azhari; Isnaieni Ijtima’ Amna Fitri; Feri Fahrianto
JURNAL TEKNIK INFORMATIKA Vol 17, No 2: JURNAL TEKNIK INFORMATIKA
Publisher : Department of Informatics, Universitas Islam Negeri Syarif Hidayatullah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15408/jti.v17i2.40453

Abstract

This study aims to analyze and compare the performance of three transfer learning methods, namely InceptionV3, VGG16, and DenseNet121, in detecting AI-generated and real images. The background of this research is the unknown performance of transfer learning methods for detecting AI-generated and real images. This study introduces innovation by conducting 54 experiments involving three types of transfer learning, three dataset split ratios (60:40, 70:30, and 80:20), three optimizers (Adam, SGD, and RMSprop), two numbers of epochs (20 and 50), and the addition of dense and flatten layers during fine tuning. Performance evaluation was conducted using binary cross entropy loss and confusion matrix. This research provides significant benefits in determining the most effective transfer learning model for detecting AI-generated and real images and offers practical guidance for further development. The results show that the InceptionV3 model with the Adam optimizer, an 80:20 split ratio, and 20 epochs achieved the highest accuracy of 84.26%, with a loss of 39.54%, precision of 81.33%, recall of 82.43%, and an F1-Score of 81.88%.

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