cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
Ricky Firmansyah
Contact Email
ricky.rym@bsi.ac.id
Phone
+6281318340588
Journal Mail Official
jurnal.informatika@bsi.ac.id
Editorial Address
Jl. Kramat Raya No.98, Kwitang, Kec. Senen, Kota Jakarta Pusat, DKI Jakarta 10450
Location
Kota adm. jakarta barat,
Dki jakarta
INDONESIA
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 316 Documents
 28   29   30   31   32 
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.
Prediksi Kurs Mata Uang Rupiah Terhadap Ringgit Malaysia Menggunakan Algoritma Backpropagation Tirta, Muhamad Wahyu; Nursyarif, Muhammad Khumaidi; Hasmadi, Ipan; Akbar, Farhan; Yulianto, Fendy
Jurnal Informatika Vol 11, No 1 (2024): April 2024
Publisher : Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/inf.v11i1.20946

Abstract

Nilai tukar mata uang di era globalisasi memegang peran sentral dalam stabilitas ekonomi suatu negara. Diperlukan sebuah analisis pergerakan terhadap nilai tukar agar bisa mengantisipasi terjadinya lonjakan terhadap fluktuasi nilai tukar. Sehingga muncul tantangan baru dalam melakukan fluktuasi kurs mata uang Rupiah terhadap ringgit Malaysia. Dataset yang digunakan adalah Data Kurs mata uang Ringgit Malaysia ke Rupiah periode 1 Juli - 30 Oktober 2023 dengan total data sebanyak 109. Penelitian ini berfokus pada metode Backpropagation dalam meningkatkan akurasi prediksi. Hasil penelitian menggunakan Epoch 300, Neuron 3, dan Learning Rate 0,5 menghasilkan nilai RMSE pada pelatihan Data Training: 13,601 dan Data Testing: 10,721 hal ini menandakan bahwa model mampu memberikan prediksi yang akurat dan mampu menggeneralisasi dengan baik terhadap data yang belum pernah dilihat sebelumnya. Secara keseluruhan, pengembangan model prediksi menggunakan Algoritma Backpropagation ini dapat dianggap berhasil, dan model ini mempunyai potensi untuk menjadi alat yang bermanfaat dalam pengambilan keputusan terkait prediksi nilai tukar mata uang dalam konteks pasar keuangan. Currency exchange rates in the era of globalization play a central role in the economic stability of a country. An analysis of exchange rate movements is needed in order to anticipate spikes in exchange rate fluctuations. So new challenges arise in fluctuating the Rupiah exchange rate against the Malaysian ringgit. The dataset used is Malaysian Ringgit to Rupiah currency exchange data for the period 1 July - 30 October 2023 with a total of 109 data. This research focuses on the Backpropagation method in increasing prediction accuracy. The results of the research using Epoch 300, Neuron 3, and Learning Rate 0.5 produced an RMSE value for Training Data Training: 13.601 and Testing Data: 10.721. This indicates that the model is able to provide accurate predictions and is able to generalize well to data that has never been seen. previously. Overall, the development of a prediction model using the Backpropagation Algorithm can be considered successful, and this model has the potential to become a useful tool in making decisions regarding currency exchange rate predictions in the context of financial markets. 
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.
Pengelompokan Kasus Tuberculosis Dengan Algoritma K-Means Berdasarkan Kelurahan di Kota Bogor Putri, Isti Juliana; Riana, Freza; Wulandari, Berlina
Jurnal Informatika Vol 11, No 1 (2024): April 2024
Publisher : Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/inf.v11i1.20042

Abstract

Tuberculosis (TBC) merupakan penyakit menular yang masih menjadi masalah sampai saat ini, angka kesakitan dan kematian akibat bakteri ini pun sangat tinggi. Penyakit TBC masih menjadi masalah kesehatan baik di dunia maupun di Indonesia terkhususnya di Jawa Barat Kota Bogor. Dengan luas wilayah Kota Bogor sebesar 11.850 Ha, tidak bisa dipungkiri bahwa  terdapat penyebaran penyakit menular di Kota Bogor, penyakit menular di Kota Bogor masih cukup tinggi yang didominasi oleh penyakit TBC, DBD, dan HIV. Dengan banyaknya data kasus penyakit TBC di Kota Bogor diperlukan pengelompokkan penyakit TBC untuk mengetahui wilayah kelurahan mana saja yang kasus TBCnya itu tinggi atau rendah. Diperlukan metode yang cepat dan akurat untuk menentukan cluster di suatu daerah, karena dapat menjadi salah satu kunci pencegahan atau penyuluhan terkait penyakit TBC. Salah satu metode clustering adalah algoritma K-Means algoritma ini dapat membagi data menjadi satu atau lebih cluster dengan karakteristik yang mirip, metode ini mampu mencapai akurasi serta kecepatan prosesnya juga relatif tinggi. Penelitian ini diharapkan dapat digunakan bagi Dinas Kesehatan Kota Bogor sebagai data rujukan untuk menindak lanjuti penyebaran penyakit TBC di Kota Bogor. Berdasarkan hasil penelitian menggunakan algoritma K-Means diperoleh evaluasi dengan Silhouette Coefficient dipilih dua cluster karena memiliki nilai yang paling tinggi. Kelompok penyakit TBC tertinggi berada di cluster 2 dengan jumlah 22 kelurahan di Kota Bogor. Tuberculosis (TB) is an infectious disease that is still a problem today, the morbidity and mortality rate due to this bacteria is very high. TB is still a health problem both in the world and in Indonesia, especially in West Java, Bogor City. With an area of 11,850 hectares, it is undeniable that there is a spread of infectious diseases in Bogor City, infectious diseases in Bogor City are still quite high, dominated by tuberculosis, dengue fever, and HIV. With so much data on TB cases in Bogor City, it is necessary to group TB diseases to find out which urban villages have high or low TB cases. A fast and accurate method is needed to determine clusters in an area, because it can be one of the keys to prevention or counseling related to TB disease. One of the clustering methods is the K-Means algorithm, this algorithm can divide data into one or more clusters with similar characteristics, this method is able to achieve accuracy and the speed of the process is also relatively high. This research is expected to be used for the Bogor City Health Office as reference data to follow up on the spread of TB disease in Bogor City. Based on the results of research using the K-Means algorithm, evaluation with Silhouette Coefficient, two clusters were selected because they had the highest value. The highest TB disease group is in cluster 2 with a total of 22 villages in Kot.
Analisis Sentimen Publik pada Media Sosial Twitter Terhadap Tiket.com Menggunakan Algoritma Klasifikasi Budiman, Budiman; Silvana Anggraeni, Zulmeida; Habibi, Chairul; Alamsyah, Nur
Jurnal Informatika Vol 11, No 1 (2024): April 2024
Publisher : Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/inf.v11i1.17988

Abstract

Analisis sentimen merupakan proses identifikasi emosional seseorang terhadap suatu objek yang akan menghasilkan sentimen positif, negatif dan netral. Kemajuan teknologi ini tentu memberikan pengaruh terhadap berbagai pelaku bisnis untuk saling mengintegrasikan sistem bisnisnya satu sama lain, salah satunya Tiket.com. Hal tersebut tentu menghasilkan sentimen dari masyarakat Indonesia yang diunggah pada platform media sosial Twitter, sehingga membantu individu maupun organisasi dalam mengambil keputusan. Penelitian ini dilakukan untuk mengetahui klasifikasi sentimen masyarakat Indonesia terhadap Tiket.com menggunakan algoritma Naïve Bayes Classifier (NBC), K-Nearest Neighbor (KNN), Support Vector Machine (SVM) dan Random Forest (RF). Berdasarkan perhitungan data sentimen terhadap Tiket.com terdapat 90.3% sentimen positif dan 9.7% sentimen negatif. Persentase tersebut menunjukkan bahwa Tiket.com cukup berpengaruh positif terhadap penggunanya. Berdasarkan hasil pengujian algoritma klasifikasi, diketahui NBC memperoleh tingkat akurasi sebesar 88%, KNN dengan nilai k = 11 mendapatkan akurasi sebesar 91%, SVM menghasilkan tingkat akurasi sebesar 92%, dan tingkat akurasi RF mencapai 93% dengan n_estimators = 100. Kesimpulan pada penelitian ini, Random Forest merupakan algoritma yang memiliki tingkat akurasi paling tinggi dibanding dengan algoritma klasifikasi lain.
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. 
Implementasi Algoritma Random Forest untuk Klasifikasi Bidang MSIB di Prodi Pendidikan Informatika Aini, Nuru; Arif, Muchamad; Agustin, Irka Tri; Toyibah, Zulfah Binti
Jurnal Informatika Vol 11, No 1 (2024): April 2024
Publisher : Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/inf.v11i1.20637

Abstract

Magang dan studi independen bersertifikat (MSIB) merupakan salah satu program dari kurikulum MBKM yang mana pada program ini membuka kesempatan bagi mahasiswa untuk belajar langsung di tempat kerja/industri yang dapat menjadi bekal bagi mahasiswa untuk persiapan karier kedepannya. Dengan banyaknya pilihan bidang keahlian pada program MSIB, seringkali menjadi suatu tantangan bagi mahasiswa untuk dapat memilih bidang MSIB yang sesuai dengan keahlian mereka. Tujuan dari penelitian ini adalah mengimplementasikan salah satu algoritma klasifikasi machine learning, yakni algoritma Random Forest untuk membantu proses pemetaan bidang MSIB di Prodi Pendidikan Informatika. Dataset yang digunakan pada penelitian ini berjumlah 100 data, dimana label klasifikasi berjumlah 4 diantaranya MM (multimedia), RPL (rekayasa perangkat lunak), AI (kecerdasan buatan), dan TKJ (teknik komputer dan jaringan), atribut yang digunakan adalah nilai mata kuliah mahasiswa. Hasil yang didapatkan antara lain tingkat akurasi sebesar 80%, precision 80%, dan recall 82%. Maka dapat disimpulkan model klasifikasi bidang MSIB menggunakan algoritma Random Forest termasuk kategori baik. Certified internships and independent studies (MSIB) are one of the programs in the MBKM curriculum, where this program opens up opportunities for students to learn directly in the workplace/industry which can provide students with preparation for future careers. With so many areas of expertise to choose from in the MSIB program, it is often a challenge for students to be able to choose an MSIB area that suits their skills. The aim of this research is to implement one of the machine learning classification algorithms, namely the Random Forest algorithm, to assist the process of mapping the MSIB field in the Informatics Education Study Program. The dataset used in this research consists of 100 data, of which there are 4 classification labels, including MM (multimedia), RPL (software engineering), AI (artificial intelligence), and TKJ (computer and network engineering), the attribute used is the course grade. student. The results obtained include an accuracy rate of 80%, precision of 80%, and recall of 82%. So it can be concluded that the MSIB field classification model using the Random Forest algorithm is in the good category. 
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.
Predicting Stock Price Movements with Technical, Fundamental, and Sentiment Analysis Using the LSTM Model Saputra, Muhammad Ighfar; Nurmawati, Erna; Abyasa, Rayhan
Jurnal Informatika Vol 12, No 1 (2025): April
Publisher : Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/inf.v12i1.22299

Abstract

The challenge of minimizing risk and maximizing profit is what traders in the stock market have been endeavoring to solve for years. Stock prices typically exhibit the characteristic of volatility, influenced by various factors and necessitate a substantial amount of data to identify patterns in price movements. Considering the significant data requirements and the rapid advancement of big data and artificial intelligence, the LSTM (Long-Short Term Memory) model stands as a suitable approach for utilization in Deep Learning. The independent variables employed encompass technical indicator variables, currency exchange rates, interest rates, the Jakarta Composite Index (IHSG), and sentiment data extracted from Twitter tweets. The results indicate that sentiment analysis using the IndoBERT model achieved an accuracy of 0.69, while LSTM analysis produced the model with the smallest error for the fourth (4th) combination of variables, comprising closing price, technical indicators, IHSG, exchange rate, and Twitter sentiment, as well as the twelfth (12th) combination of variables, encompassing closing price, technical indicators, and IHSG. These combinations yielded average RMSE errors of 1.765E-04 and 1.978E-04, respectively. Following hyperparameter optimization, the best-identified model was the fourth (4th) combination of variables, yielding a minimal error of 7.580E-05 and an RMSE of 332.66 in the evaluation of test data. 

Page 30 of 32 | Total Record : 316

 28   29   30   31   32 

Filter by Year

2014 2025


Reset
Filter By Issues
All Issue Vol 12, No 2 (2025): October Vol 12, No 1 (2025): April Vol 11, No 1 (2024): April 2024 Vol 11, No 2 (2024): October Vol 10, No 2 (2023): October 2023 Vol 10, No 1 (2023): April 2023 Vol 9, No 2 (2022): Oktober 2022 Vol 9, No 1 (2022): April 2022 Vol 8, No 2 (2021): September 2021 Vol 8, No 1 (2021): April 2021 Vol 7, No 2 (2020): September 2020 Vol 7, No 1 (2020): April 2020 Vol 6, No 2 (2019): September 2019 Vol 6, No 1 (2019): April 2019 Vol 5, No 2 (2018): Jurnal INFORMATIKA Vol 5, No 1 (2018): Jurnal INFORMATIKA Vol 4, No 2 (2017): Jurnal INFORMATIKA Vol 4, No 1 (2017): Jurnal INFORMATIKA Vol 3, No 2 (2016): Jurnal INFORMATIKA Vol 3, No 1 (2016): Jurnal INFORMATIKA Vol 2, No 2 (2015): Jurnal INFORMATIKA Vol 2, No 1 (2015): Jurnal INFORMATIKA Vol 1, No 2 (2014): Jurnal INFORMATIKA Vol 1, No 1 (2014): Jurnal INFORMATIKA More Issue