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All Journal INTENSIF: Jurnal Ilmiah Penelitian dan Penerapan Teknologi Sistem Informasi JURNAL TEKNOLOGI DAN OPEN SOURCE Building of Informatics, Technology and Science Jurnal Tekinkom (Teknik Informasi dan Komputer) JISA (Jurnal Informatika dan Sains) Journal of Telenursing (JOTING) Innovation in Research of Informatics (INNOVATICS) Journal of Applied Data Sciences Jurnal Sistem Informasi dan Teknik Informasi DEVICE : JOURNAL OF INFORMATION SYSTEM, COMPUTER SCIENCE AND INFORMATION TECHNOLOGY
Junadhi, Junadhi
Unknown Affiliation
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering Health Professions Medicine & Pharmacology Nursing Other

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Journal : Building of Informatics, Technology and Science

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Penerapan Fitur Firebase Cloud Messaging Pada Sistem Kontrol Pembayaran Iuran BPJS Ketenagakerjaan Berbasis Mobile Meidina, Mita; Junadhi, Junadhi; Rio, Unang; Efendi, Yoyon
Building of Informatics, Technology and Science (BITS) Vol 4 No 1 (2022): June 2022
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (954.611 KB) | DOI: 10.47065/bits.v4i1.1603

Abstract

BPJS of Employment is the organizer of a social security program that functions to protect workers through four employment social security programs, namely Work Accident Insurance, Old Age Security, Death Security, and Pension Security. Information regarding the payment of BPJS Employment Contributions is an important factor in supporting social security programs for workers and employees. This is because there are still many participants who are in arrears in payment of the BPJS Employment Contribution and there is no immediate information regarding the payment of the BPJS Employment Contribution. In this study, an application was made so that an administrator could send notification messages regarding the payment of the BPJS Employment Monthly Contribution to participants and could receive reports on the payment of contributions that have been made by participants. The system design this time was assisted by Firebase Cloud Messaging (FCM) technology which functions as a web service. Messages that have been sent can be stored in a database that can be viewed on the admin web page. The results of this study are successful in building a BPJS employment payment control application by implementing the mobile-based Firebase Cloud Messaging (FCM) feature. This application can send notifications to participants regarding due information on dues payments in real time. Makes it easier for participants to get the latest information quickly. For the tests carried out using the functionality testing of the system by performing simulations on each function of the system made and concluded that the system functions can run well
Opinion Mining on TikTok Using Bidirectional Long Short-Term Memory for Enhanced Sentiment Analysis and Trend Prediction Muharnisa Haspin, Wafiq; Junadhi, Junadhi; Susanti, Susanti; Yenni, Helda
Building of Informatics, Technology and Science (BITS) Vol 7 No 2 (2025): September 2025
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v7i2.8019

Abstract

The widespread use of TikTok has generated a vast number of user reviews, offering a rich dataset for sentiment analysis. This study aims to classify TikTok reviews from the Google Play Store into positive, negative, and neutral categories, a complex task due to the informal and unstructured text. The research seeks to develop a reliable sentiment analysis model using deep learning to understand user perceptions, aiding platform improvements and marketing strategies. We collected 10,000 reviews via web scraping, preprocessed through text cleaning, normalization, tokenization, filtering, and stemming. Sentiment labels were assigned automatically using a lexicon-based approach, showing predominantly positive reviews. Word2Vec transformed text into numerical vectors for feature extraction. The Bidirectional Long Short-Term Memory (Bi-LSTM) model, with Embedding, Bidirectional LSTM, Dropout, and Dense layers, achieved 80% accuracy and an F1-score of 0.78 using a 90:10 train-test split. While effective for positive and negative sentiments, neutral expressions were less accurately detected due to lower recall. Compared to traditional methods like Naive Bayes, Support Vector Machine, and K-Nearest Neighbors, Bi-LSTM offered superior accuracy and better handling of linguistic variability, making it valuable for analyzing social media feedback.
Co-Authors Adyanata Lubis Agustin Agustin Anam, M. Khairul Andesa, Khusaeri Andhika, Rivaldi Dwi Asrofiq, Ahmad Desti Puswati Devita, Yeni Edwar Ali Efrizoni, Lusiana Erlinda, Susi Fadliellah, Mardiyat Fathur Rahman Fauzan, Bias Arbi Finanta Okmayura Fransiskus Zoromi, Fransiskus Habibie, Dedi Rahman Hadi Asnal, Hadi Handoko Handoko Helda Yenni, Helda Herwin Herwin Herwin, Herwin Husen, Ratna Andini Irfansyah Irfansyah Jamaris, M Kudadiri, Parlindungan Martina Sinta Kristanti Meidina, Mita Millenio, Benino Giordiola Muharnisa Haspin, Wafiq Muslim Muslim Negara, Ari Sukma Nita, Yureya Nu'man, Nu'man Perumal, Thinagaran Putra, Jean Riko Kurniawan Putri, Silvyana Dwi Rahmiati Rahmiati Retno Sari Sarjon Defit Surya, Heru Satria Susanti Susanti SUSI ERLINDA Triyani Arita Fitri Unang Rio Yoyon Efendi Yuda Irawan