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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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Use of Ticketing System in Freelancing Platform to Maintaining Client Trust in Product Development Process Andri Sahata Sitanggang; Novrini Hasti; R Fenny Syafariani; Lusi Melian; Bondan Rachmat Santoso; Muhammad Daffa Shidiq
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.v17i1.32228

Abstract

Micro, small, and medium enterprises (MSMEs) are considered to be one of the important components in the economic development of a country, especially Indonesia. However, it has been found that MSMEs are lagging in digitalization, the adoption of information technology, and digital marketing. An information system where MSMEs can have easy access to IT and digital marketing professionals can be a solution to boost and encourage digitalization among local MSMEs. Developing such an information system requires the project to be able to quickly adapt and change based on the user’s needs and current trends. This study proposes an incremental solution to building an accessible information system catered for MSMEs by incorporating the ADDIE model into the development cycle. To understand the feasibility of the system, several group meetings are arranged to demonstrate and try out the system’s capability to the target users. The results indicate that the system is generally able to fit the needs of MSMEs and is quite effective at connecting the MSESs to IT and Digital marketing resources and experts.
Small Object Detection and Object Counting for Primary Roe Dataset Based on Yolo Wahyu Andi Saputra; Nicolaus Euclides Wahyu Nugroho; Dany Candra Febrianto; Andi Prademon Yunus; Muhammad Azrino Gustalika; Yit Hong Choo
JURNAL TEKNIK INFORMATIKA Vol 18, 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.v18i1.46063

Abstract

This research offers an initial exploration into the effectiveness of three variations of the YOLOv8 model original, trimmed, and YOLOv8n.pt in combination with two distinct datasets characterized by tight and loose distributions of roe, aimed at enhancing small object detection and counting accuracy. Utilizing a primary roe dataset across 776 images, the research systematically compares these model-dataset configurations to identify the most effective combination for precise object detection. The experimental results reveal that the YOLOv8n.pt model combined with the loosely distributed dataset achieves the highest detection performance, with a mean Average Precision (mAP) of 53.86%. This outcome underscores the critical impact of both model selection and data distribution on the detection accuracy in machine learning applications. The findings highlight the importance of tailored model and dataset synergies in optimizing detection tasks, particularly in complex scenarios involving small, densely clustered objects. This research contributes valuable insights into the strategic deployment of neural network architectures for refined object detection challenges.
Systematic Literature Review: Cybersecurity by Utilizing Cryptography Using the Data Encryption Standard (DES) Algorithm Annisa Desianty; Imelda Imelda
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.v17i1.37256

Abstract

The world of information technology is currently developing very rapidly. This opens up opportunities in the development of computer applications, but it also creates opportunities for threats to alter and steal data or what is often known as cyber-crime. This action is a violation that can cause direct or indirect losses. Therefore, cyber-security is very important to protect user information from cyber-crime. Based on this description, this research will conduct a Systematic Literature Review (SLR) on cyber-security by utilizing cryptography using the DES algorithm. By using the SLR method, literature searches were conducted on Google Scholar or Garuda with the keywords for national journals "Data Encryption Standard Algorithm (DES)" and keywords for international journals "Data Encryption Standard Algorithm (DES)" from both national and international journals, and limiting articles from 2019 to 2023, and obtained selection results as many as 10 articles used from national journals and 10 articles used from international journals. So that this research is expected to increase the understanding of literature that reviews cyber-security by utilizing cryptography using the DES algorithm.
Comparison of Hyperparameter Tuning Methods for Optimizing K-Nearest Neighbor Performance in Predicting Hypertension Risk Dimas Trianda; Dedy Hartama; Solikhun Solikhun
JURNAL TEKNIK INFORMATIKA Vol 18, 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.v18i1.42260

Abstract

Hypertension is a major cause of cardiovascular disease, making early risk prediction essential. According to WHO, hypertension cases are estimated to reach 1.28 billion by 2023. This study aims to optimize the K-Nearest Neighbor (KNN) algorithm for predicting hypertension risk through hyperparameter tuning. Three methods Grid SearchCV, Bayes SearchCV, and Random SearchCV are compared to determine the best parameter configuration. The dataset, obtained from Kaggle, consists of 520 balanced samples (260 positive and 260 negative) with 18 health-related features such as age, gender, blood pressure, cholesterol, glucose, and others. After preprocessing, the KNN model is tuned using each method by testing combinations of neighbors (k), weight types, and distance metrics. Results show Bayes SearchCV achieved the highest accuracy of 92%, outperforming the baseline KNN model, which had 85% accuracy. The ROC AUC score of 0.96191 also indicates excellent classification performance. In conclusion, Bayes SearchCV significantly improves KNN's predictive ability in hypertension risk classification.
Implementation of Design Thinking Method in UI/UX Redesign of Public Complaint Application (Case Study: Go Siaga App) Rafi Kurnia Pangestu; Muhamad Bahrul Ulum
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.v16i2.27416

Abstract

Go Siaga App is a mobile-based application by Tangerang Sub-district Police Office that provides special community services for the Tangerang sub-district community which provides features in the form of reports of disturbances in public security and reports of loss or damage. Since it is a new application released in March 2021 on Google Playstore, there are several things that need to be considered to maintain the usability of the application. This research aims to redesign the user interface and user experience (UI/UX) of the Go Siaga application using Design Thinking Method in the design process. Some of the supporting aspects for testing the user satisfaction such as effectiveness, efficiency, usefulness, satisfaction, and learnability are met in the usability testing. The results showed that the percentage of all the aspects in usability from the redesigned version were all higher than the current one with 80% of effectiveness, 80% of efficiency, 80% of usefulness, 86.67% of satisfaction, and 73.33% of learnability. Therefore, based on the research results, the redesign of Go Siaga is more effective, more efficient, more useful, more satisfying, and also easy to learn.
Sentiment Analysis of Pospay Application Reviews Using the Bert Deep Learning Method Mulyati, Erna; Muhammad Ibnu Choldun Rachmatullah; Adri Sapta Firmansyah
JURNAL TEKNIK INFORMATIKA Vol. 18 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.v18i2.41116

Abstract

E-money usage in Indonesia has grown significantly due to increasing internet penetration and smartphone adoption. Digital transactions are becoming more common, with platforms like GoPay, OVO, and Dana leading the market. The government and financial institutions actively support this shift through regulations and initiatives. This study analyzes user sentiment on the Pospay application using the BERT deep learning method, based on 16,760 Google Play Store reviews. To the best of our knowledge, this is the first study to apply BERT for sentiment analysis of Pospay user reviews in Indonesia. The goal is to understand user perceptions and satisfaction. BERT helps capture subtle nuances in reviews, including informal expressions and abbreviations like "gk" for negative sentiment. The model achieves high accuracy, with precision scores of 0.82 (negative) and 0.93 (positive), and recall scores of 0.92 (negative) and 0.93 (positive). Findings suggest PT Pos should enhance application stability, security, transaction processing, and customer service. Regular updates are recommended to improve performance and user satisfaction.
Sentiment Analysis of Twitter Discussions About Lampung Robusta Coffee: A Comparative Study of Machine Learning Algorithms with SVM as The Optimal Model Yuniarthe, Yodhi; Syarif, Admi; Shofi, Imam Marzuki; Fatimah Fahurian
JURNAL TEKNIK INFORMATIKA Vol. 18 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.v18i2.41316

Abstract

Lampung Robusta coffee is an important commodity in Indonesia, particularly in terms of local economic potential and global recognition. However, public perception of this product on social media, particularly Twitter, remains underexplored. This study addresses the need for a deeper understanding of consumer sentiment towards Lampung Robusta coffee, which could inform branding and marketing strategies. To approach this issue, we used five supervised machine learning algorithms-KNN, Naive Bayes, SVM, Decision Tree, and Logistic Regression-to perform sentiment classification on a dataset of tweets containing relevant keywords. The dataset was pre-processed using standard natural language processing techniques, including tokenization, stopword removal, and TF-IDF feature extraction. The SVM achieved the best performance on the unbalanced dataset for all metrics, with high and consistent accuracy and F1 scores. Logistic regression followed closely with similarly strong and stable results. Therefore, SVM is recommended as the final model. These results suggest that machine learning approaches can effectively classify sentiment in social media discussions about regional agricultural products and that random forest may provide the most robust performance in this context  
Evaluating BiLSTM  Performance with BERT, RoBERTa, and DistilBERT in Online Bullying News Detection Zamroni, Moh. Rosidi; Sholihin, Miftahus; Hayati, Erna; Rahayu A Hamid; Nurul Aswa Omar
JURNAL TEKNIK INFORMATIKA Vol. 18 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.v18i2.42459

Abstract

This study examines the performance of BiLSTM combined with three transformer-based word embeddings—BERT, RoBERTa, and DistilBERT—in classifying bullying news in online media. BiLSTM was chosen for its significant advantages in processing text sequences compared to traditional RNN and LSTM models. The study used a dataset of 2,800 articles from three major Indonesian news portals, with 2,000 articles for training and 800 for testing, labeled using the lexicon method. The testing results showed that the combination of BiLSTM and RoBERTa achieved the best performance, with an accuracy of 94% and a near-perfect precision of 99%. Statistical significance tests confirmed that BiLSTM with RoBERTa performs significantly better than with BERT or DistilBERT. These findings suggest that the BiLSTM and RoBERTa combination is the most effective for classifying bullying news, especially for new or unseen data. This research contributes to the development of automatic bullying content detection systems to enhance content moderation on news platforms.
Evaluating User Satisfaction in The Halodoc Application Using a Hybrid CNN-BiLTSM Model for Sentiment Analysis Kurniasari, Dian; Su'admaji, Arif; Lumbanraja, Favorisen Rosyking; Warsono
JURNAL TEKNIK INFORMATIKA Vol. 18 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.v18i2.42762

Abstract

The growing demand for digital healthcare services in Indonesia has driven the adoption of Online Healthcare Applications (OHApps) such as Halodoc. Despite over 65 million users, maintaining user satisfaction remains a challenge. This study employs sentiment analysis using a hybrid Convolutional Neural Network (CNN) and Bidirectional Long Short-Term Memory (BiLSTM) model to classify user review ratings. A dataset of 10,000 Google Play Store reviews was divided into COVID-19 and post-pandemic segments. The methodology includes data collection, pre-processing, and dataset segmentation for training, validation, and testing. Results indicate that the CNN-BiLSTM model surpasses traditional machine learning by combining CNN’s feature extraction with BiLSTM’s long-term dependency capture, achieving 98.71% accuracy on COVID-19 data and 98.16% post-pandemic. Additionally, the model demonstrates strong performance across other key evaluation metrics, with precision, recall, and F1-score. Misclassification analysis highlights minor errors, particularly in ratings 4 and 5. These findings help healthcare providers enhance digital services by identifying user concerns, improving platform features, and optimizing customer engagement. Beyond healthcare, this approach has real-world applications in e-commerce and financial services, where sentiment analysis informs user experience improvements.
Voice Spoofing Classification Using Residual Bidirectional Long Short Term Memory Kasyidi, Fatan; Sukma, Rifaz Muhammad; Sopian, Annisa Mufidah; Anbiya, Dhika Rizki
JURNAL TEKNIK INFORMATIKA Vol. 18 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.v18i2.43281

Abstract

Voice spoofing attacks are a major security concern for speech-based biometric systems. Detection and classification of spoofed voice are essential steps for preventing unauthorized accesses. This study proposes a novel approach to voice spoofing classification using a Residual Bidirectional Long Short Term Memory (R-BLSTM) network. The goal is to enhance the accuracy and robustness of voice spoofing detection using the power of deep learning and residual connections. The current proposed approach based on bidirectional LSTM with residual connections is designed to capture long-range dependencies and latent characteristics of speech signals. Experimental evidence that the R-BLSTM model is superior to classic ML techniques is also demonstrated by observing an accuracy of 95.6% on the ASVspoof 2019 collection. The designed system can be further utilized for enriching the security of speech-based biometrics modalities and making anti-voice spoofing attacks ineffective.

Page 21 of 27 | Total Record : 262

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