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All Journal METHOMIKA: Jurnal Manajemen Informatika & Komputerisasi Akuntansi Jurnal Inotera Indonesian Journal of Electrical Engineering and Computer Science
Asri, Yessy
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Computer Science & IT Economics, Econometrics & Finance Engineering Industrial & Manufacturing Engineering Materials Science & Nanotechnology Mechanical Engineering

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Implementation of File Sharing and Remote Desktop Connection (RDC) by Utilizing Local Area Network (LAN) Sofyan; Asri, Yessy
Jurnal Inotera Vol. 9 No. 2 (2024): July - December 2024
Publisher : LPPM Politeknik Aceh Selatan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31572/inotera.Vol9.Iss2.2024.ID366

Abstract

The exchange of information between devices in a network is very important for efficiency and collaboration. One technology that supports the exchange of information is File Sharing and Remote Desktop Connection (RDC). This research aims to implement these two technologies in a Local Area Network (LAN) to facilitate file exchange and remote access to computer desktops. File sharing allows users to easily share data and resources between computers on a network. This study examines various methods and protocols used in file sharing, such as SMB (Server Message Block) and FTP (File Transfer Protocol), as well as RDC mechanisms such as RDP (Remote Desktop Protocol). This research also explores the security challenges faced in the implementation of these two technologies, including threats to data privacy and the potential for unauthorized access. Implementation begins with a stable and secure LAN network configuration. Then, using file sharing protocols such as SMB (Server Message Block) or NFS (Network File System), users can share and access files between devices on the network easily. Additionally, by utilizing the Remote Desktop Connection service, users can access a computer's desktop remotely from other devices on the network, allowing for efficient system administration as well as access to applications and files located on that desktop. The implementation methods used include configuring network settings, installing and configuring file sharing software and Remote Desktop Connection services, as well as testing to ensure optimal connection and security. Through case studies and experiments, this research proves that the use of LAN as network infrastructure can support high performance and stability in implementing file sharing and RDC. By utilizing a LAN, this research shows that implementing file sharing and RDC can improve operational efficiency and collaboration in the work environment. The results of this implementation are expected to increase productivity and collaboration in a LAN environment by enabling easy and secure access to files and computer desktops from various locations on the network and emphasize the importance of strong security settings and user training to maximize the benefits of this technology in local networks.
Sentiment analysis based on Indonesian language lexicon and IndoBERT on user reviews PLN mobile application Asri, Yessy; Kuswardani, Dwina; Suliyanti, Widya Nita; Manullang, Yosef Owen; Ansyari, Atikah Rifdah
Indonesian Journal of Electrical Engineering and Computer Science Vol 38, No 1: April 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v38.i1.pp677-688

Abstract

PLN mobile application as an integrated platform for self-service among mobile consumers, facilitating easier access to various services, including receiving information such as public complaints. The application can be downloaded through the Google Play Store and App Store, and users can express their opinions through reviews and ratings. In this era of advanced technology, aspects such as reviews, ratings, and evaluations have important value for business practitioners. However, there are often inconsistencies between ratings and reviews that do not fully represent the quality of the application. In response, a study was conducted to analyze the sentiment of user reviews from January to June 2022, by collecting 1,000 review samples from the Google Play Store. The data was collected using web scraping techniques and then processed into a dataset through text pre-processing methods. Sentiments were analyzed using an automatic labeling method in Indonesian based on a lexicon known as INSET (Indonesia sentiment), which resulted in 482 positive reviews, 144 negative reviews, and 374 neutral reviews. The next step is classification using Indonesian bidirectional encoder representations from transformers (IndoBERT). In this process, the data was divided into testing, training, and validation sets with a ratio of 80:10:10. The analysis managed to achieve an impressive accuracy rate of 81%.
Word embedding for contextual similarity using cosine similarity Asri, Yessy; Kuswardani, Dwina; Sari, Amanda Atika; Ansyari, Atikah Rifdah
Indonesian Journal of Electrical Engineering and Computer Science Vol 38, No 2: May 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v38.i2.pp1170-1180

Abstract

Perspectives on technology often have similarities in certain contexts, such as information systems and informatics engineering. The source of opinion data comes from the Quora application, with a retrieval limit of the last 5 years. This research aims to implement Indo-bidirectional encoder representations from transformers (BERT), a variant of the BERT model optimized for Indonesian language, in the context of information system (IS) and information technology (IT) topic classification with 414 original data, which, after being augmented using the synonym replacement method, The generated data becomes 828. Data augmentation aims to evaluate the performance of models by using synonyms and rearranging text while maintaining meaning and structure. The approach used is to label the opinion text based on the cosine similarity calculation of the embedding token from the IndoBERT model. Then, the IndoBERT model is applied to classify the reviews. The experimental results show that the approach of using IndoBERT to classify SI and IT topics based on contextual similarity achieves 90% accuracy based on the confusion matrix. These positive results show the great potential of using transformer-based language models, such as IndoBERT, to support the analysis of comments and related topics in Indonesian.
Meningkatkan Performa Ulasan Berbahasa Indonesia dengan Spelling Corrector Peter Norvig dan Pelabelan SentiStrength_id Asri, Yessy; Kuswardani, Dwina; Ferdinanda Purba TS, Josephine
METHOMIKA: Jurnal Manajemen Informatika & Komputerisasi Akuntansi Vol. 9 No. 1 (2025): METHOMIKA: Jurnal Manajemen Informatika & Komputersisasi Akuntansi
Publisher : Universitas Methodist Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46880/jmika.Vol9No1.pp92-98

Abstract

Digital transformation is driven by the increasing number of internet and mobile phone users in Indonesia, including public services such as the PLN Mobile application. The purpose of this study is to evaluate user sentiment towards PLN Mobile application reviews and find gaps between user ratings and reviews. Through web scraping on Google Play Store, with a total review data of 11,004 reviews between January 2022 and December 2023. During the preprocessing step, SentiStrength_id was used as the labeling approach, and Support Vector Machine was used for modeling. A spelling corrector using Peter Norvig was applied to correct spelling issues. The accuracy of sentiment analysis was much better with this procedure, reaching 82% at a data split ratio of 90:10. The percentage of sentiment obtained was 16.5% negative, 16.1% neutral, and 67.4% positive. The percentage of mismatched user ratings and reviews was 23.1% for negative reviews, 4.5% for neutral, and 72.49% for positive reviews.
Co-Authors Ansyari, Atikah Rifdah Dwina Kuswardani, Dwina Ferdinanda Purba TS, Josephine Manullang, Yosef Owen Sari, Amanda Atika Sofyan Suliyanti, Widya Nita