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Jurnal Informatika
Published by Universitas Bina Sarana Informatika
ISSN : 23556579     EISSN : 25282247     DOI : https://doi.org/10.31294/ji.v4i2
Core Subject : Science,
Computer Science & IT
Jurnal Informatika respects all researchers Technology and Information field as a part spirit of disseminating science resulting and community service that provides download journal articles for free, both nationally and internationally. The editorial welcomes innovative manuscripts from Technology and Information field. The scopes of this journal are: Expert System Decision Support System Data Mining Artificial Intelligence System Machine Learning Genetic Algorithms Business Intelligence and Knowledge Management Big Data the manuscripts have primary citations and have never been published online or in print. Every manuscript will be checked the plagiarism using Turnitin software. If the manuscript indicated major plagiarism, the manuscript is rejected.
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 8 Documents
 1 
Search results for , issue "Vol 11, No 2 (2024): October" : 8 Documents clear
Sentiment Analysis of Sirekap Application Review Using Logistic Regression Algorithm Hagi, Audi; Rarasati, Dionisia Bhisetya
Jurnal Informatika Vol 11, No 2 (2024): October
Publisher : Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/inf.v11i2.22066

Abstract

General Elections (Pemilu) is one of the crucial moments in democracy to elect representatives of the people. The General Elections Commission (KPU) launched the Sirekap application as an aid in the election process. This application allows polling station officers (KPPS) to record the vote count electronically. However, there have been some complaints and feedback from the public regarding the Sirekap application. To understand public sentiment towards the Sirekap application, this study was conducted by analyzing user reviews on the Google Play Store. The Logistic Regression algorithm is used to classify review sentiment into positive and negative. The analysis process involves data preprocessing, z-score normalization, dividing the data set into 80% training data and 20% test data, weighting words using the TF-IDF method, training the model using the Logistic Regression algorithm, and testing the model with a confusion matrix. The results of the analysis show that the Logistic Regression algorithm is effective in classifying the sentiment of the Sirekap application reviews with an accuracy of 91%. The precision score for the positive and negative classes are 90% and 92%, respectively. The recall score for the positive and negative classes are 94% and 87%, respectively. The f1-score for the positive and negative classes are 92% and 90%, respectively. The results of this sentiment analysis can also be used by the KPU to understand the level of user satisfaction and improve the quality of the Sirekap application for the 2024 Regional Head Elections (Pilkada).
Implementation of You Only Look Once Version 8 Algorithm to Detect Multi-Face Drivers and Vehicle Plates Saputra S, Kana; Taufik, Insan; Ramadhani, Irham; Siregar, Angginy Akhirunnisa; Pinem, Josua; Lubis, Afiq Alghazali; Pane, Yeremia Yosefan; Putri, Rezkya Nadilla
Jurnal Informatika Vol 11, No 2 (2024): October
Publisher : Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/inf.v11i2.22026

Abstract

Checking the identity of motorcycle owners when leaving the college area is a mandatory activity for security officers to ensure that vehicles entering and exiting the college are the same driver. The conventional checking process often causes the impact of vehicle queues when the volume of vehicles increases. Therefore, an intelligent system is needed to detect multi-plate vehicles automatically. One approach in the world of image detection of an object is the use of the YOLO (You Only Look Once) algorithm. This algorithm predicts bounding boxes and possible classes in a single frame. This research divides objects into 3 classes, namely vehicles, driver's faces, and vehicle plates. The dataset used was 74 varied images consisting of 50 training data, 12 validation data and 12 testing data. The image was trained using 300 epochs and a batch size of 8 and resulted in an F1 score calculation for detecting objects reaching 92%.
The Implementation of Convolutional Neural Network in Recognize Lontara Text Arthanugraha, Wahyu; Zainuddin, Zahir; Arda, Abdul Latief
Jurnal Informatika Vol 11, No 2 (2024): October
Publisher : Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/inf.v11i2.20328

Abstract

The introduction of the Lontara text that is applied is still conventional in that it comes from books or teaching materials and there is no application for a translator of the Lontara text, making it difficult for students, especially those from outside the Bugis tribe. This research aims to measure the ability and apply the CNN method in the context of introducing the Lontara text. The input that use in this research is words taken from the original text or writing manuscripts of the ancient literature of the Bugis tribe "Sureq Maqkelluqna Nabittaq", a book that was used as a tool to spread Islam in 1611. The model used is the VGG-16 architecture. This research uses the R&D (Research and Development) method which is used to create products and test their level of effectiveness. As for the research results obtained, the system accuracy obtained in classifying the Lontara text using the VGG16 architecture was 97,65%. In addition, the system is also able to display the translation of the Lontara text according to the database input.
Analysis of FastText with Support Vector Machine for Hate Speech Classification on Twitter Social Media Nuraini, Nabila; Latipah, Asslia Johar; Verdikha, Naufal Azmi
Jurnal Informatika Vol 11, No 2 (2024): October
Publisher : Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/inf.v11i2.21107

Abstract

Hate speech refers to sentences or words that aim to demean or insult individuals, groups, or communities based on factors such as ethnicity, religion, race, or social class. In this study, Natural Language Processing (NLP) techniques were employed using FastText feature extraction and SVM algorithm for text classification. The evaluation was conducted using F1 Score as the performance metric. The data was divided using the Cross-Validation method with 10 folds, and the experiment was performed with four SVM kernels: RBF, Linear, Polynomial, and Sigmoid. The results of this research, based on the effectiveness of the FastTextSVM method combination, demonstrate a strong performance in hate speech classification. By adopting FastText parameters from previous studies and involving four SVM kernels, this research achieved a satisfactory average F1 Score. The results obtained for the Polynomial kernel showed the best performance with an F1 Score of 0.813, followed by the Linear kernel with 0.809, the RBF kernel with 0.808, and the Sigmoid kernel with 0.805. This indicates that the F1 Score results do not show significant differences in outcomes.
Stock Price Prediction on IDX30 Index using Long Short-Term Memory Algorithm William, Ken; Rarasati, Dionisia Bhisetya
Jurnal Informatika Vol 11, No 2 (2024): October
Publisher : Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/inf.v11i2.22156

Abstract

The capital market plays a significant role in a country's economy, facilitating corporate financing and providing investment opportunities for the public. One popular investment instrument is stocks, yet many investors struggle to make profitable investment decisions due to a lack of understanding of stock investments. Therefore, predicting stock prices can be a way to determine the future value of a stock. This research aims to address this issue by applying the Long Short-Term Memory (LSTM) algorithm to predict stock prices on the IDX30 index. LSTM is capable of processing sequential data, such as stock price data, complexly because it can store information over long periods. The testing is conducted using various parameters in layers, epochs, and time steps to obtain the best prediction model. The LSTM architecture used consists of four layers: the LSTM layer with 128 neurons, dropout and dense layers with 64 neurons, and an additional dense layer that converts the output from the previous layer into prediction results. This study demonstrates that the LSTM algorithm can accurately predict stock prices based on evaluation metrics such as Mean Absolute Percentage Error (MAPE) and Root Mean Square Error (RMSE). The best results for PT Bank Central Asia Tbk show a MAPE of 1.14% and RMSE of 137.71, PT Bank Rakyat Indonesia Tbk shows a MAPE of 1.58% and RMSE of 87.4, and PT Bank Mandiri Tbk shows a MAPE of 1.64% and RMSE of 88.26.
Comparison of Social Media BOT Functions Using the K-Nearest Neighbor Method Against User Satisfaction Frastika, Nayny; Yunita, Yunita
Jurnal Informatika Vol 11, No 2 (2024): October
Publisher : Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/inf.v11i2.22056

Abstract

Social media is currently an alternative medium in conveying messages in the form of news and can be used as a tool to exchange news from different places. Many people use social media to express opinions, express feelings, as well as experiences and things that can be of concern. In this study, the data processing used was the K-Nearest Neighbor Algorithm with the classification method as a media for comparing the functions of the two bots, namely the WhatsApp and Telegram bots. Using SPSS (Statistical Product and Serive Solutions) and Rapidminer as a place to perform calculations and analysis. Based on the results of testing data mining with Rapid Miner, the calculation results are obtained which will be used as information to support user satisfaction in using Social Media Bots. User satisfaction is found in WhatsApp Bot users 72.22% and Telegram Bot users 28.57%. Calculations are carried out with a data mining process obtained from the K-Nearest Neighbor algorithm to make it easier to find Bot user satisfaction on both WhatsApp and Telegram social media. The WhatsApp bot is the best choice and has several useful functions as a digital communication media tool on the Internet of Things.   
Development of a Solar System Learning Application Using Markerless Augmented Reality Based on Android Aditya, Bintang; Al Ikhsan, Safaruddin Hidayat; Wulandari, Berlina
Jurnal Informatika Vol 11, No 2 (2024): October
Publisher : Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/inf.v11i2.19084

Abstract

The use of technology in learning has opened up new opportunities to create more interesting and effective learning applications. In learning solar system material, especially at elementary school level, the teaching method still uses books, 2D pictures and teaching aids. However, the limitations of teaching aids which can only be used in class and do not allow them to be taken home can create obstacles in the learning process. To overcome these obstacles, innovation is needed in the development of learning media. One solution that can be used is to apply augmented reality technology. In this research, a solar system object learning application was created that applies markerless augmented reality technology. This application can be used as an alternative to using teaching aids in studying solar system objects. The methodology used in this research is the Multimedia Development Life Cycle (MDLC). The development of this augmented reality application was developed using tools Android Studio by implementing ARCore SDK and Sceneform in implementing markerless augmented reality. The results of this research are in the form of an Android-based learning application that applies markerless augmented reality technology and based on field testing, the effectiveness of the application in delivering solar system materials through the quiz feature is 75%, while 85% of users feel satisfied with the visual and ease of use of the application. 
The Effect of Knowledge Sharing and Enrichment on Lecturer Innovation Performance in General Achmad Yani University Pudjiantoro, Tacbir Hendro; Anggoro, Sigit; Destiani, Dea
Jurnal Informatika Vol 11, No 2 (2024): October
Publisher : Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/inf.v11i2.20503

Abstract

The annual evaluation carried out by LLDIKTI looked at the performance of lecturers in their regions, and it was revealed that some lecturers, especially lecturers at Jenderal Ahmad Yani University (UNJANI), still had difficulty fulfilling the tridharma duties of teaching, research and community service, which are important aspects of higher education. To consistently fulfill these obligations, lecturers are required to innovate. A study aimed at exploring how knowledge exchange and enrichment impact the innovative performance of UNJANI lecturers, collecting data through an online survey distributed to them. This research aims to understand the influence of knowledge sharing and enrichment on lecturer innovation, and its success is assessed through questionnaire analysis. These findings underscore the important positive correlation between knowledge sharing, enrichment, and increased innovative performance among UNJANI lecturers, indicating that encouraging these practices can increase the fulfillment of tri dharma obligations.

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