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Development of a Village Information System for Acceleration of Village Services in Desa Tegal Kecamatan Kemang Bogor Ardiansyah, Deden; Harsani, Prihastuti; Tosida, Eneng Tita; Saputra, Abimanyu Oki; Bhayangkari, Andhika
JISA(Jurnal Informatika dan Sains) Vol 5, No 1 (2022): JISA(Jurnal Informatika dan Sains)
Publisher : Universitas Trilogi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31326/jisa.v5i1.1113

Abstract

The Village Information System (SID) is an information system that changes raw data into ready-to-use information. In addition, SID will provide convenience to village officials in providing services to the community. The development of this SID is expected to be able to provide acceleration and improve the performance of village officials in terms of service quality to the community, productivity, responsiveness, responsibility and productivity. The development of a village information system in service activities in Tegal village is a transformation from manual to computerized, so systematic efforts are needed in the preparation involving subjects, objects and methods related to the transformation process. The development of the village information system uses the software development life cycle (SDLC). Efforts to control the quality of the Tegal Village Information System use four characteristics of ISO 9126, to know that the parts in the application system have correctly displayed error messages if an error occurs in inputting data.The result of this service activity is that every Village Apparatus can understand the material that has been submitted and can practice the results of the village administration work in a computerized manner based on the Village Information System.
Electric Vehicles Sentiment Analysis of Electric Vehicles on Social Media Using Bidirectional Encoder Representations from Transformers (BERT) and Long Short-Term Memory (LSTM): BERT, LSTM, Sentiment Analysis, Electric Vehicles , Social Media Muhammad Fadhillah Harahap; Yusma Yanti; Prihastuti Harsani
Komputasi: Jurnal Ilmiah Ilmu Komputer dan Matematika Vol. 23 No. 1 (2026): Komputasi: Jurnal Ilmiah Ilmu Komputer dan Matematika.
Publisher : Program Studi Ilmu Komputer, Universitas Pakuan

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Abstract

Electric vehicles (EVs) are widely recognized as an environmentally sustainable alternative capable of reducinggreenhouse gas emissions; however, their adoption in Indonesia remains limited. Data from the IndonesianMinistry of Transportation, as recorded in the Type Approval Registration System (SRUT), indicate thatapproximately 195,084 Battery Electric Vehicles (BEVs) were registered nationwide by early 2024. This studyinvestigates public sentiment toward electric vehicles using social media data from X, Instagram, and TikTok,while also comparing the effectiveness of two text classification approaches: Bidirectional EncoderRepresentations from Transformers (BERT) and Long Short-Term Memory (LSTM). A total of 5,172Indonesian-language comments were collected through crawling and scraping techniques using electricvehicle-related keywords over the period January 2021 to January 2025. The comments were categorized intofive sentiment classes: very positive, positive, neutral, negative, and very negative. The analytical processfollowed the Knowledge Discovery in Databases (KDD) framework, including data preprocessing,transformation, classification, and evaluation using a confusion matrix. The results indicate that IndoBERTsubstantially outperformed LSTM, achieving an accuracy of 91% compared to 36% for LSTM. Sentimentanalysis reveals a dominance of negative and very negative opinions, primarily reflecting public concernsregarding cost, performance, and maintenance of electric vehicles. These findings offer important insights forpolicymakers and the automotive industry in designing targeted promotion strategies, improving publicawareness, and strengthening supporting infrastructure. Future research is encouraged to explore dataaugmentation techniques to improve model performance, particularly for deep learning models such as LSTM,in order to better support evidence-based electric vehicle adoption policies.