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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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K-Means Clustering with KNN and Mean Imputation on CPU Benchmark Compilation Data Syauqi, Rofiq Muhammad; Sabrina, Puspita Nurul; Santikarama, Irma
Journal of Applied Informatics and Computing Vol. 7 No. 2 (2023): December 2023
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v7i2.6491

Abstract

In the rapidly evolving digital age, data is becoming a valuable source for decision-making and analysis. Clustering, as an important technique in data analysis, has a key role in organizing and understanding complex datasets. One of the effective clustering algorithms is k-means. However, this algorithm is prone to the problem of missing values, which can significantly affect the quality of the resulting clusters. To overcome this challenge, imputation methods are used, including mean imputation and K-Nearest Neighbor (KNN) imputation. This study aims to analyze the impact of imputation methods on CPU Benchmark Compilation clustering results. Evaluation of the clustering results using the silhouette coefficient showed that clustering with mean imputation achieved a score of 0.782, while with KNN imputation it achieved a score of 0.777. In addition, the cluster interpretation results show that the KNN method produces more information that is easier for users to understand. This research provides valuable insights into the effectiveness of imputation methods in improving the quality of data clustering results in assisting CPU selection decisions on CPU Benchmark Compilation data.
Optimization of ACS712 Sensor Current Measurement in Solar Power System through Regression Modeling Rumpa, Lantana Dioren; Ambabunga, Yusri AM; Pineng, Martina
Journal of Applied Informatics and Computing Vol. 7 No. 2 (2023): December 2023
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v7i2.6511

Abstract

This study aims to improve the accuracy of current measurements in solar power systems using the ACS712 sensor and linear regression modeling. While the ACS712 sensor is commonly used for current measurement in solar systems, it often faces accuracy issues. In this research, we measured current using the ACS712 sensor alongside a validated reference device and applied a linear regression model to correct any inaccuracies. The results show that our linear regression model significantly boosts the accuracy of ACS712 sensor current measurements. We also conducted performance tests with the model on the Arduino Uno platform, which revealed increased measurement accuracy in various testing scenarios. Before implementing the model, the average difference between ACS712 sensor measurements and reference device readings was 0.364. After implementing the model, this difference dropped substantially to just 0.044.
Sentiment Analysis of the Top 5 E-commerce Platforms in Indonesia using Text Mining and Natural Language Processing (NLP) Sapanji, R. A. E. Virgana Targa; Hamdani, Dani; Harahap, Parlindungan
Journal of Applied Informatics and Computing Vol. 7 No. 2 (2023): December 2023
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v7i2.6517

Abstract

This research attempts to depict a sentiment comparison of the top 5 E-commerce platforms in Indonesia by gathering the emotional tone behind sentence contents related to customer sentiments, customer experiences, and the brand reputation of E-commerce. Data were collected using Python 3.11.4 with the google-play-scraper library, extracted from user reviews/comments on each play store page of the top 5 E-commerce platforms in Indonesia. A sampling of 10,000 records was taken to form a long document term matrix (DTM) of 59,981,785 due to the limitation of CPU capacity for data matrix size. R Programming version 4.3.1 was employed for sentiment analysis in this study. It can be concluded that user comments or reviews on the top five (5) E-commerce platforms in Indonesia show positive sentences indicating user satisfaction (3664 sentences), neutral sentences indicating average user appreciation (2282 sentences), and negative sentences indicating user dissatisfaction (4054 sentences). At least with more positive and neutral sentences, it is indicated that 59.64% of E-commerce users in Indonesia express a positive opinion on the performance of the top 5 E-commerce platforms in the country.
Improving Helpdesk Chatbot Performance with Term Frequency-Inverse Document Frequency (TF-IDF) and Cosine Similarity Models Setiawan, Gede Herdian; Adnyana, I Made Budi
Journal of Applied Informatics and Computing Vol. 7 No. 2 (2023): December 2023
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v7i2.6527

Abstract

Helpdesk chatbots are growing in popularity due to their ability to provide help and answers to user questions quickly and effectively. Chatbot development poses several challenges, including enhancing accuracy in understanding user queries and providing relevant responses while improving problem-solving efficiency. In this research, we aim to enhance the accuracy and efficiency of the Helpdesk Chatbot by implementing the Term Frequency-Inverse Document Frequency (TF-IDF) model and the Cosine Similarity algorithm. The TF-IDF model is a method used to measure the frequency of words in a document and their occurrence in the entire document collection, while the Cosine Similarity algorithm is used to measure the similarity between two documents. After implementing and testing TF-IDF and Cosine Similarity models in the Helpdesk Chatbot, we achieved a 75% question recognition rate. To increase accuracy and precision, it is necessary to increase the knowledge dataset and improve pre-processing, especially in recognition and correct inaccurate spelling
Emotion Classification of Indonesian Tweets using BERT Embedding Algifari, Muhammad Habib; Nugroho, Eko Dwi
Journal of Applied Informatics and Computing Vol. 7 No. 2 (2023): December 2023
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v7i2.6528

Abstract

Twitter is one of the social media that has the largest users in the world. Indonesia is one of the countries that has the 5th largest number of Twitter users in the world which causes a high possibility of conflict between Indonesian Twitter users due to emotional tension in tweets. In this paper, we will compare the BERT embedding method with CNN and LSTM. The results of this experiment are BERT-CNN has the best performance results which has an accuracy of 61% compared to BERT-LSTM. In the experiment several stages of data preprocessing, data cleaning, data spiting and data training were carried out and the results were evaluated using confusion metrics.
Performance Analysis of Family Welfare Empowerment Application: A Kanban Method Approach Zaidir, Zaidir; Sujarweni, Veronika Wiratna; Listiawan, Indra
Journal of Applied Informatics and Computing Vol. 7 No. 2 (2023): December 2023
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v7i2.6534

Abstract

This research examines the application of the Kanban method in testing a family welfare empowerment application. The Kanban method, initially developed by Toyota in manufacturing, has been effectively applied in software development. This study involves a series of tests involving various features within the application, such as user registration, village data collection, processing of the family welfare empowerment data at the Village/District level, and more. The test results show that most tests were successful, highlighting the application's success in executing essential functions such as user registration and event scheduling. However, some tests failed, primarily in inputting village, hamlet, and community unit data. These results indicate that using the Kanban method in testing a family welfare empowerment application can potentially enhance development and testing efficiency. Metrics such as testing time, test success, and time efficiency have provided valuable insights into the application's performance. In conclusion, this testing provides a foundation for further application development, focusing on improving the areas that experienced testing failures. This research also opens up opportunities for further studies on using the Kanban method in software testing in various other application development contexts.
Rental Price Prediction of Boarding Houses in Batam City Using Linear Regression and Random Forest Algorithms Jerry, Jerry; Christian, Yefta; Herman, Herman
Journal of Applied Informatics and Computing Vol. 7 No. 2 (2023): December 2023
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v7i2.6732

Abstract

Boarding houses, commonly known as "kost," are residential places typically rented by individuals, serving a function similar to hotels, but with more affordable pricing. With the proliferation of boarding house businesses, residents and newcomers in Batam city face challenges in selecting suitable accommodation based on both price and amenities. Leveraging machine learning, a branch of artificial intelligence (AI), and incorporating various algorithms, a system can be developed to predict the rental prices of boarding houses. This helps individuals make informed decisions regarding the suitability of a boarding house based on their preferences and budget. The algorithms utilized in this study are Linear Regression and Random Forest. The modeling process resulted in R2 Scores, with Linear Regression achieving a score of 64%, while Random Forest outperformed with an impressive 99% R2 Score. Due to the higher R2 Score of Random Forest, this model was selected for the development of a website using the Scrum framework. The outcome of this research is a predictive pricing website for boarding houses, offering a valuable tool for residents and visitors in Batam when seeking to rent or lease a boarding house.
Hyperparameter Tuning on Graph Neural Network for the Classification of SARS-CoV-2 Inhibitors Himawan, Salamet Nur; Sohiburoyyan, Robieth; Iryanto, Iryanto
Journal of Applied Informatics and Computing Vol. 7 No. 2 (2023): December 2023
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v7i2.6735

Abstract

COVID-19 is caused by the SARS-CoV-2 virus, which results in a range of symptoms, from mild to severe, and can lead to fatalities. As of October 2023, WHO has recorded 771 cases of COVID-19 globally. Various efforts have been made to control the spread of the virus, including vaccination, isolation measures, and intensive medical care. The emergence of new SARS-CoV-2 variants has led to the ongoing evolution of virus transmission. Continued research is essential to understand this virus and develop strategies to address the pandemic. Inhibitors of SARS-CoV-2 play a crucial role in the vaccine development process. Inhibitors can impede the virus's development, helping reduce disease severity and control the pandemic. The classification of inhibitors is expected to serve as a foundation for selecting compounds that can be developed into vaccines. This research develops a Graph Neural Network model for inhibitor classification and uses the random search method for hyperparameter tuning. Graph Neural Networks are chosen due to their excellent performance in modelling graph data. This study demonstrates the success of hyperparameter tuning in improving the performance of the Graph Neural Network for accurate classification of SARS-CoV-2 inhibitors.
Implementation of Finite State Machine Algorithm for Interactive Physics Learning in a 3D Game Nugraha, Nur Budi; Santosa, Yaqutina Marjani; Mulyani, Esti
Journal of Applied Informatics and Computing Vol. 7 No. 2 (2023): December 2023
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v7i2.6738

Abstract

Physics is a subject taught in high schools as per the established curriculum. Teachers often employ traditional teaching methods where students study independently without active participation, leading to boredom and reduced enthusiasm for learning. Physics is frequently perceived as difficult and perplexing by most students, and the utilization of 3D games as a learning tool can help overcome these challenges. This research aims to integrate the Finite State Machine (FSM) algorithm into a 3D game to create a more effective and engaging learning experience for students. The study employs the waterfall method in application development, encompassing stages such as needs analysis, application design, FSM implementation in games, and game testing and evaluation. 3D physics games have been successfully developed and tested for their feasibility. This game serves as an effective means of entertainment and learning, aiding students in enhancing their understanding of physics subjects. According to the results of a questionnaire with 50 respondents, it is evident that this 3D game is quite user-friendly (90%) and possesses a very good user interface (89%). Approximately 78% of respondents stated that their experience in using the game was very good. Moreover, 82% of respondents found that this educational physics game was highly beneficial for learning physics material.
Catfish Fry Detection and Counting Using YOLO Algorithm Takyudin, Takyudin; Fitri, Iskandar; Yuhandri, Yuhandri
Journal of Applied Informatics and Computing Vol. 7 No. 2 (2023): December 2023
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v7i2.6746

Abstract

The development of computer vision technology is growing very fast and penetrating all sectors, including fisheries. This research focuses on detecting and counting catfish fry. This research aims to apply deep learning in detecting catfish fry objects and counting accurately so as to help farmers and buyers reduce the risk of loss. The detection system in this research uses digital image processing techniques as a way to obtain information from the detection object. The research method uses YOLO Object Detection which has a very fast ability to identify objects. The object detected is a catfish puppy object that is given a bounding box and the detection label displays the class name and precision value. The dataset amounted to 321 images of catfish puppies from internet and photography sources that were trained to produce a new digital image model. The number of split training, validation and testing datasets is worth 831 annotation images, 83 validation images and 83 images for the testing process. The value of the training model mAP 50.39 %, Precision 61.17 % and Recall 58 % Detection test results based on the YOLO method obtained an accuracy rate of 65.7%. The avg loss value in the final model built with YOLO is 4.6%. Based on the results of tests carried out with the number of objects 50 to 500 tail size 2-8 cm using video, objects in the image are successfully recognized with an accuracy of 63% to 70%. Calculations using the YOLO algorithm show quite good results.

Page 17 of 70 | Total Record : 695

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