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INDONESIA
JOURNAL OF APPLIED INFORMATICS AND COMPUTING
Published by Politeknik Negeri Batam
ISSN : -     EISSN : 25486861     DOI : 10.3087
Core Subject : Science,
Computer Science & IT
Journal of Applied Informatics and Computing (JAIC) Volume 2, Nomor 1, Juli 2018. Berisi tulisan yang diangkat dari hasil penelitian di bidang Teknologi Informatika dan Komputer Terapan dengan e-ISSN: 2548-9828. Terdapat 3 artikel yang telah ditelaah secara substansial oleh tim editorial dan reviewer.
Arjuna Subject : -
Articles 695 Documents
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Persepsi Wisatawan Melalui Analisis Sentimen Untuk Mendukung Pengembangan Pariwisata di Provinsi Maluku Tuhuteru, Hennie; Refialy, Leonardo Petra; Laturake, Marlisa; Pattirane, Shyrel Gildion
Journal of Applied Informatics and Computing Vol. 8 No. 1 (2024): July 2024
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v8i1.6989

Abstract

Tourist perceptions obtained by sentiment analysis can provide an overview of tourism development in Maluku Province. This study aims to determine the perception of tourists towards destinations in Maluku based on the results of sentiment analysis. This research uses a quantitative approach by analyzing scrapping and snipping data from Facebook, Instagram, TikTok, Google Maps Review, and Trip Advisor. Sentiment analysis is done by comparing the accuracy level of the Random Forest, Naive Bayes, and Support Vector Machine classification models. The results of the comparison of the three methods show that Random Forest has the best accuracy rate, which is 85%. The results of sentiment analysis both on the entire dataset and the results of analysis per district/city show that tourists' perceptions of tourist destinations in Maluku can be said to be good because they are dominated by negative sentiments. The existence of negative and neutral sentiments indicates that there is a need for improvement and improvement in the quality of tourist services in terms of human resources, transportation, accommodation, and infrastructure facilities.
Stock Market Index Prediction using Bi-directional Long Short-Term Memory Majid, Muhammad Althaf; Saputri, Prilyandari Dina; Soehardjoepri, Soehardjoepri
Journal of Applied Informatics and Computing Vol. 8 No. 1 (2024): July 2024
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v8i1.7195

Abstract

The IHSG (Indonesia Stock Exchange Composite Index) is a stock price index in the Indonesia Stock Exchange (BEI) that serves as an indicator reflecting the performance of company stocks through stock price movements. Therefore, IHSG becomes a reference for investors in making investment decisions. Advanced stock exchanges generally have a strong influence on other stock exchanges. Several studies have proven the influence of one global index on another. Global index is a term that refers to each country's index to represent the movement of its country's stock performance. Forecasting IHSG can be one of the analyses that help investors make wise decisions when investing. To obtain an IHSG forecasting model, an appropriate and suitable method is needed, especially for data that has a large amount. LSTM is a development of Recurrent Neural Network (RNN) which has the ability to remember information in a longer period of time, while Bi-LSTM is a development of LSTM which has the ability to remember information longer and can understand more complex patterns than LSTM. This research provide the IHSG forecasting based on global index factors. The results showed that the best Bi-LSTM model (6-9-1) had a better performance in predicting and forecasting JCI movements with a MAPE value of 0.572314% better than the best LSTM model (4-10-1) which had a MAPE of 0.74326%. With forecasting based on the Bi-LSTM model, it is expected to help investors in making decisions on the Indonesia Stock Exchange (IDX).
Enterprise Architecture Model of the New Student Admission System at Stella Maris University Sumba Ledi, Dian Fransiska; Dapadeda, Ardiyanto
Journal of Applied Informatics and Computing Vol. 8 No. 1 (2024): July 2024
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v8i1.7200

Abstract

This research aims to design an Enterprise Architecture (EA) model for the new student admission system at Stella Maris Sumba University. The background of this research is the need to improve efficiency, transparency, and integration in the new student admission process, which currently still faces various administrative and technical challenges. The research method used is qualitative which includes literature studies and in-depth interviews with relevant parties. The data obtained was analyzed to identify needs and design the right EA model. The purpose of this research is to create a system capable of automating the admission workflow, ensuring data security, and providing real-time access for application status tracking. The results showed that the proposed Enterprise Architecture model can improve operational efficiency, user satisfaction, and support the strategic decisions of Stella Maris Sumba University based on accurate and integrated data.
Text Insertion and Encryption Using The Bit-Swapping Method in Digital Images Santoso, Kiswara Agung; Fakih, Muhammad Fahmil; Kamsyakawuni, Ahmad
Journal of Applied Informatics and Computing Vol. 8 No. 1 (2024): July 2024
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v8i1.7395

Abstract

Communication is an essential aspect of everyday life, involving the transmission of information through various media. Technological advances have made communication easier but have also increased privacy and data security risks. Several efforts are made to maintain the security of digital information, including coding information (cryptography) and hiding information (steganography). In this article, the author secures information through a combination of cryptography and steganography. To secure text data, we encrypt by exchanging bits between adjacent characters. Subsequently, the encrypted text is hidden within an image. The security analysis results show the successful reconstruction of the message from the stego image and the successful restoration of the message to its original form. The use of the bit swapping method in the text message encryption process has been proven to enhance the security level of the ciphertext, as indicated by the lower TPK calculation value of 0.33 compared to the TPK value in previous studies. Additionally, embedding the ciphertext into digital images has been demonstrated to further increase the security level of the message, evidenced by the NPCR calculation value of 0.0000109% and the UACI calculation value of 0.000000555%. These very small values indicate no significant changes.
Forecasting Air Quality Indeks Using Long Short Term Memory Ramadhani, Irfan Wahyu; Saputra, Filmada Ocky; Pramunendar, Ricardus Anggi; Saraswati, Galuh Wilujeng; Winarsih, Nurul Anisa Sri; Rohman, Muhammad Syaifur; Ratmana, Danny Oka; Shidik, Guruh Fajar
Journal of Applied Informatics and Computing Vol. 8 No. 1 (2024): July 2024
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v8i1.7402

Abstract

Exercise offers significant physical and mental health benefits. However, undetected air pollution can have a negative impact on individual health, especially lung health when doing physical activity in crowded sports venues. This study addresses the need for accurate air quality predictions in such environments. Using the Long Short-Term Memory (LSTM) method or what is known as high performance time series prediction, this research focuses on forecasting the Air Quality Index (AQI) around crowded sports venues and its supporting parameters such as ozone gas, carbon dioxide, etc. -others as internal factors, without involving external factors causing the increase in AQI. Preprocessing of the data involves removing zero values "‹"‹and calculating correlations with AQI and the final step performs calculations with the LSTM model. The LSTM model which adds tuning parameters, namely with epoch 100, learning rate with a value of 0.001, and batch size with a value of 64, consistently shows a reduction in losses. The best results from the AQI, PM2.5, and PM10 features based on performance are MSE with the smallest value of 6.045, RMSE with the smallest value of 4.283, and MAE with a value of 2.757.
Optimization Chatbot Services Based on DNN-Bert for Mental Health of University Students Dzaky, Azmi Abiyyu; Zeniarja, Junta; Supriyanto, Catur; Shidik, Guruh Fajar; Paramita, Cinantya; Subhiyakto, Egia Rosi; Rakasiwi, Sindhu
Journal of Applied Informatics and Computing Vol. 8 No. 1 (2024): July 2024
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v8i1.7403

Abstract

Attention to mental health is increasing in Indonesia, especially with the recent increase in the number of cases of stress and suicide among students. Therefore, this research aims to provide a solution to overcome mental health problems by introducing a chatbot system based on Deep Neural Networks (DNN) and BiDirectional Encoder Representation Transformers (BERT). The primary objective is to enhance accessibility and offer a more effective solution concerning the mental health of students. This chatbot utilizes Natural Language Processing (NLP) and Deep Learning to provide appropriate responses to mild mental health issues. The dataset, comprising objectives, tags, patterns, and responses, underwent processing using Indonesian language rules within NLP. Subsequently, the system was trained and tested using the DNN model for classification, integrated with the TokenSimilarity model to identify word similarities. Experimental results indicate that the DNN model yielded the best outcomes, with a training accuracy of 98.97%, validation accuracy of 71.74%, and testing accuracy of 71.73%. Integration with the TokenSimilarity model enhanced the responses provided by the chatbot. TokenSimilarity searches for input similarities from users within the knowledge generated from the training data. If the similarity is high, the input is then processed by the DNN model to provide the chatbot response. This integration of both models has proven to enhance the responsiveness of the chatbot in providing various responses even when the user inputs remain the same. The chatbot also demonstrates the capability to recognize various inputs more effectively with similar intentions or purposes. Additionally, the chatbot exhibits the ability to comprehend user inputs although there are many writing errors.
IndoBERT Model Analysis: Twitter Sentiments on Indonesia's 2024 Presidential Election Putri, Dwi Ismiyana; Alfian, Ari Nurul; Putra, Mardi Yudhi; Mulyo, Putro Dwi
Journal of Applied Informatics and Computing Vol. 8 No. 1 (2024): July 2024
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v8i1.7440

Abstract

Elections are one of the key moments in a country's democracy. Indonesian elections have a significant impact on regional and global politics. Twitter being one of the popular social media platforms becomes a powerful tool for political campaigns. This makes it an ideal source to analyze public opinion during the 2024 general election, particularly the upcoming Presidential Election (Pilpres). IndoBERT is the model chosen to analyse the sentiment from the dataset in this study using a zero-shot learning approach. Based on the evaluation results, the accuracy value of the 2024 presidential election classification is 0.60 (60%), tends to predict with a good value in the positive label of 0.74 (74%) for F1-Score. This model is considered quite good at predicting negative labels but the results are not too optimal with a value of 0.49 (49%). Confusion Matrix in this IndoBERT model is more likely to label tweets with positive things, by detecting negative labels quite well.
Analysis of pH and Turbidity Sensor Outputs in Shrimp Ponds for Vannamei Shrimp Commodities Musayyanah, Musayyanah; Bayunugraha, Erdasetya; Harianto, Harianto; Pratikno, Heri
Journal of Applied Informatics and Computing Vol. 8 No. 1 (2024): July 2024
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v8i1.7525

Abstract

Vannamei shrimp is a high-value, economically important brackish water aquaculture commodity that is easy to cultivate. Optimal growth of Vannamei shrimp can be achieved by monitoring water quality parameters such as pH and turbidity. The pH levels can be measured using a pH sensor, with a pH meter as a reference. Turbidity levels are measured with a turbidity sensor in NTU units, with a turbidity stick serving as a reference. Testing of these sensors was conducted from morning to noon over three days in the brackish water ponds of IBAP Banjar Kemuning, Sidoarjo, recording 100 data samples. The performance of both sensors fluctuated due to disturbances around the pond, prompting the use of the Moving Average (MA) filter method to improve accuracy. Applying MA with varying window sizes (wz) resulted in a performance increase of 0.24% in the morning and 0.1% at noon. Additionally, turbidity sensor testing indicated that the pond conditions were consistent with the turbidity measurements obtained using the turbidity stick.
Sentiment Analysis of Social Media X in the 2024 Indonesian Presidential Election Using the Naive Bayes Algorithm: Candidates' Backgrounds and Political Promises Prayudani, Santi; Situmorang, Dita Rouli Basa; Hidayah, Rizki; Ginting, Heri Sanjaya
Journal of Applied Informatics and Computing Vol. 8 No. 2 (2024): December 2024
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v8i2.7580

Abstract

In 2024, Indonesia holds a presidential election, and the candidates are making promises to each other to attract voters. Many people gave their opinions on X. This study uses the Naïve Bayes algorithm to analyze the sentiment of these tweets, with the aim of understanding the background of the candidates and their campaign promises. Data is collected from X by crawling technique, then data is pre-processed, trained using Naïve Bayes model, and evaluated for accuracy. Sentiments in tweets were classified as positive, negative, or neutral. The results showed that the Prabowo Subianto - Gibran Rakabuming Raka pair was the most talked about with 1005 tweets, followed by Anis Rasyid Baswedan - Muhaimin Iskandar with 707 tweets, and Ganjar Pranowo - Mohammad Mahfud M.D. with 572 tweets. The Prabowo Subianto - Gibran Rakabuming Raka pair received the most positive sentiment, which was 446 more than the other candidates.
Predictive Analytics for IMDb Top TV Ratings: A Linear Regression Approach to the Data of Top 250 IMDb TV Shows Husna, Meryatul; Purba, Lampson Pindahaman; Rinaldy, Muhammad Eri; Lubis, Arif Ridho
Journal of Applied Informatics and Computing Vol. 8 No. 1 (2024): July 2024
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v8i1.7600

Abstract

In the era of a growing entertainment industry, understanding audience preferences and predicting the financial performance of entertainment products such as films and television shows has become increasingly important. Previous research has demonstrated various approaches in understanding the factors that influence the financial performance of entertainment products. However, there is still a need for research to investigate other aspects of film and television show evaluation. This study aims to explore the contribution of linear regression in analysing the ratings and financial performance of IMDb's top TV shows. Through the incorporation of various data-informed and interpretative approaches, it is expected to gain a deeper understanding of the factors that influence the success of a television show. Using data from the Top 250 IMDb TV Shows, a predictive analysis was conducted to understand the relationship between the number of episodes and IMDb ratings. The results of the information showed a negative relationship between the number of episodes and IMDb rating, with the linear regression model predicting a decrease in IMDb rating as the number of episodes increases. Implications of this research include recommendations for content creators to consider both quality and quantity of content in the development of TV shows.

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