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All Journal Jurnal Informatika Upgris Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Jurnal Abdimas BSI: Jurnal Pengabdian Kepada Masyarakat Jurnal Informatika JURNAL MEDIA INFORMATIKA BUDIDARMA Indonesian Journal of Artificial Intelligence and Data Mining JITK (Jurnal Ilmu Pengetahuan dan Komputer) Dinamisia: Jurnal Pengabdian Kepada Masyarakat SELAPARANG: Jurnal Pengabdian Masyarakat Berkemajuan Jurnal ICT : Information Communication & Technology Vygotsky: Jurnal Pendidikan Matematika dan Matematika Infotekmesin Society : Jurnal Pengabdian dan Pemberdayaan Masyarakat Jurnal Ilmiah Teknik Mesin, Elektro dan Komputer (JURITEK) Jurnal Komunikasi Global Jurnal Informatika: Jurnal Pengembangan IT Prosiding Seminar Nasional Pemberdayaan Masyarakat (SENDAMAS) Jurnal Informatika
Primandani Arsi
Universitas Amikom Purwokerto
Agriculture, Biological Sciences & Forestry Arts Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Health Professions Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mathematics Mechanical Engineering Medicine & Pharmacology Nursing Public Health Social Sciences Other

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SENTIMENT ANALYSIS ON RENEWABLE ENERGY ELECTRIC USING SUPPORT VECTOR MACHINE (SVM) BASED OPTIMIZATION Pungkas Subarkah; Bagus Adhi Kusuma; Primandani Arsi
JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) Vol. 10 No. 2 (2024): JITK Issue November 2024
Publisher : LPPM Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/jitk.v10i2.5575

Abstract

Government policy regarding the discourse on the use of renewable energy in electricity, this discourse is widely discussed in the community, especially on social media twitter. The public's response to the implementation of the use of renewable energy varies, there are positive, negative and neutral responses to this government policy. Sentiment analysis is part of Machine Learning which aims to identify responses in the form of text. The data used in this study amounted to 1,367 tweets. The purpose of this study is to determine the sentiment analysis of government discourse related to the use of renewable energy using an optimisation-based Support Vector Machine (SVM) algorithm approach. This research involves several stages including data collection, data pre-processing, experiments and modelling and evaluation. The data is divided into 3 classes, 120 positive, 1221 neutral and 26 negative. In this research, there are five optimisation models used namely Forward Selection, Backward Elimination, Optimised Selection, Bagging and AdaBoost. The results obtained are the use of Optimised Selection (OS) optimisation with the Support Vector Machine (SVM) algorithm obtained an increase in accuracy from 93% to 96%. The increase in the use of SVM using selection optimization obtained the highest increase, because other optimization techniques only reached 1% and 2% of the original results using the SVM algorithm, namely the accuracy value of 93% to 96% (high accuracy). From the research that has been done, it is certainly important to understand public sentiment towards renewable energy policies, especially renewable energy electricity, the hope is that this research will become a reference for the government.
Opinion Mining on Spotify Music App Reviews Using Bidirectional LSTM and BERT Primandani Arsi; Reza Arief Firmanda; Iphang Prayoga; Pungkas Subarkah
Jurnal Informatika Vol. 12 No. 2 (2025): October
Publisher : Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/

Abstract

The increasing number of user reviews on digital music platforms such as Spotify highlights the importance of sentiment analysis to better understand user perceptions. This study aims to develop a sentiment classification model for Spotify user reviews using a Bidirectional Long Short-Term Memory (BiLSTM) approach combined with BERT embeddings. The dataset consists of multilingual user reviews collected from the Google Play Store. Preprocessing steps include text cleaning, tokenization, and padding. BERT is utilized to generate contextual word embeddings, which are then processed by the BiLSTM model to classify sentiments as either positive or negative. The model’s performance is evaluated using a confusion matrix with accuracy, precision, recall, and F1-score metrics. The results show that the BiLSTM-BERT model achieves an F1-score of 0.8852, a recall of 0.9396, a precision of 0.8375, and an accuracy of 0.8374. These findings demonstrate the model’s effectiveness in handling multilingual sentiment analysis tasks, offering valuable insights for developers in enhancing user experience through data-driven decision-making.
Co-Authors Akhmad Mustolih Aldi Setia Pambudi Ali Nur Ikhsan Andi Dwi Riyanto Arbangi Puput Sabaniyah Azizan Nurhakim Azizan Nurhakim Bagus Adhi Kusuma Berlilana Berlilana Desta Nur Efika Ardini Desty Rakhmawati Dewi Fortuna Dwi Krisbiantoro Faridatun Nida Iphang Prayoga Iphang Prayoga Irma Darmayanti Jali Suhaman Joko Prayogi Khoirul Abbi Rokhman Laili Nur Hidayati Mohammad imron Muhammad Hasyim Asyari Nurhopipah, Ade Oman Somantri Prasetyo, Agung Pungkas Subarkah Raditya Danar Dana Retno Waluyo Reza Arief Firmanda Rizki Wahyudi Rosana Fadilla Sari Saeful Karim Septi Fajarwati Septi Oktaviani Nur Hidayah Sulis waningsih Tri Astuti Trian Damai Wawa Maisa Yuni Novita Yusyida Munsa Idah