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All Journal Jurnal Computech & Bisnis (e-Journal) International Journal of Quantitative Research and Modeling Jurnal Pengabdian Masyarakat Tapis Berseri Nuansa Informatika Jurnal Bhakti Karya dan Inovatif Jurnal Accounting Information System (AIMS) INTERNAL (Information System Journal) In Search (Informatic, Science, Entrepreneur, Applied Art, Research, Humanism)
Akbar, Imannudin
Sistem Informasi, Fakultas Teknologi Dan Informatika, Universitas Informatika Dan Bisnis Indonesia
Arts Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Engineering Environmental Science Library & Information Science Physics Social Sciences Other

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Sentiment Analysis Of Spotify App In Playstore Using Classification Method Akbar, Imannudin; Berly Bagoes Daniswara; Habibi, Chairul
Jurnal Computech & Bisnis (e-journal) Vol. 19 No. 1 (2025): Jurnal Computech & Bisnis (e-Journal)
Publisher : LPPM STMIK Mardira Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56447/jcb.v19i1.408

Abstract

Spotify is a globally renowned music streaming program.  The program receives a multitude of ratings, both favorable and unfavorable, on the Google Play Store from various users.  This study intends to evaluate the sentiment of user evaluations for the Spotify application employing various classification techniques, including Logistic Regression, Random Forest, Support Vector Machine (SVM), C4.5, and Extreme Gradient Boosting (XGBoost).  Review data was acquired via web scraping methodologies using the Google Play Scraper API.  After this, text preparation was conducted to sanitize the text, enabling the execution of the data.  Sentiment analysis was employed to ascertain whether a text expresses favorable or unfavorable opinions.  The Random Forest approach, which has been demonstrated to yield optimal outcomes, was employed in this investigation.  Testing was performed using training and test data ratios of 80:20%, 70:30%, and 60:40% across hundreds of review datasets.  The Random Forest approach, utilizing an 80%:20% data split ratio, produced a precision of 82%, recall of 81%, F1-Score of 81%, and accuracy of 81%, according to the test findings
Pelatihan Pengenalan Cara Kerja Search Engine Marketing untuk Platfom Digital Marketing kepada siswa SMAN 16 Bandung Akbar, Imannudin; Nursyanti, Reni; Ramadhani, Muhammad Wahyu; Setiawan, Dani
Jurnal Pengabdian Masyarakat Tapis Berseri (JPMTB) Vol. 4 No. 1 (2025): Jurnal Pengabdian Masyarakat Tapis Berseri (JPMTB) (Edisi April)
Publisher : Pusat Studi Teknologi Informasi Fakultas Ilmu Komputer Universitas Bandar Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36448/jpmtb.v4i1.137

Abstract

Over time, companies have begun transitioning their marketing systems from conventional methods to modern approaches by leveraging the internet. This shift is further supported by the ease of accessing social media, conducting transactions, and communicating online, all of which make internet-based marketing increasingly advantageous. The massive number of internet users and the high engagement with digital media have given rise to new business potentials and opportunities, widely known as digital marketing.Digital marketing strategies are estimated to influence up to 78% of a business unit’s competitive advantage in promoting its products. It enables adaptive digital relevance across a series of marketing activities, institutions, processes, and customers. This, in turn, drives a 20% annual growth in customers transitioning to digital platforms, with younger users becoming a dominant group of consumers.Search Engine Marketing (SEM) is an online marketing strategy designed to increase website visibility on search engine results pages. SEM serves as an effective promotional tool and is one of the fastest ways to direct potential consumers to a website, as effective SEM can position a site on the first page of search engine results.
Tantangan dan Peluang Bisnis Agribisnis Digital Sinaga, Arnold Ropen; Akbar, Imannudin; Lestari, Gita Ayu
Jurnal Pengabdian Masyarakat Tapis Berseri (JPMTB) Vol. 4 No. 1 (2025): Jurnal Pengabdian Masyarakat Tapis Berseri (JPMTB) (Edisi April)
Publisher : Pusat Studi Teknologi Informasi Fakultas Ilmu Komputer Universitas Bandar Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36448/jpmtb.v4i1.138

Abstract

The development of digital technology has driven significant transformation in the agribusiness sector, creating new opportunities while also presenting challenges. The background of this study stems from the urgent need to enhance the efficiency and competitiveness of the agricultural sector through the utilization of digital technology. The aim of this research is to identify the challenges and explore the opportunities in implementing digital agribusiness in Indonesia. A descriptive qualitative approach was employed, with data collected through literature review and interviews with agribusiness practitioners and technology experts. The findings indicate that digitalization opens up opportunities in terms of market access, increased productivity, and distribution efficiency. However, challenges such as limited infrastructure, low digital literacy among farmers, and unequal access to technology remain major obstacles. In conclusion, to optimize the potential of digital agribusiness, a collaborative strategy involving the government, private sector, and educational institutions is needed to create an inclusive and sustainable ecosystem.
A Bidirectional GRU Approach with Hyperparameter Optimization for Sentiment Classification in Game Reviews : Pendekatan GRU Dua Arah dengan Optimasi Hiperparameter untuk Klasifikasi Sentimen dalam Ulasan Game Alamsyah, Nur; Titan Parama Yoga; Budiman; Imannudin Akbar; Hendra, Acep; Januantara Prima, Alif
NUANSA INFORMATIKA Vol. 19 No. 2 (2025): Nuansa Informatika 19.2 Juli 2025
Publisher : FKOM UNIKU

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25134/ilkom.v19i2.399

Abstract

Sentiment analysis plays a vital role in understanding user perspectives, especially in domains such as game reviews where user feedback influences product perception and engagement. This study presents a comparative approach using Gated Recurrent Unit (GRU), hyperparameter-tuned GRU, and Bidirectional GRU models to classify sentiments in a dataset of game reviews. The experiment begins with standard preprocessing and tokenization steps, followed by vectorization and supervised training. Hyperparameter optimization is conducted using Keras Tuner to identify the most effective configuration of embedding dimensions, GRU units, dropout rates, and learning rates. The best model, a Bidirectional GRU with tuned parameters, achieves a validation accuracy of 85.37% and shows superior performance across key metrics such as precision, recall, and F1-score. Despite the relatively small and imbalanced dataset, the Bidirectional GRU model demonstrates robust generalization. This study also highlights future directions, including class balancing techniques and the integration of pretrained word embeddings to further improve model performance.
A Data-Driven Approach to Comparative Evaluation of Regression Models for Accurate House Price Prediction: Pendekatan Berbasis Data untuk Evaluasi Komparatif Model Regresi untuk Prediksi Harga Rumah yang Akurat Permata Hati, Tiara; Budiman, Budiman; Akbar, Imannudin; Alamsyah, Nur
NUANSA INFORMATIKA Vol. 19 No. 2 (2025): Nuansa Informatika 19.2 Juli 2025
Publisher : FKOM UNIKU

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25134/ilkom.v19i2.411

Abstract

This study aims to develop and evaluate a property price prediction model in Bandung by applying machine learning (ML) algorithms. The need for more accurate property price predictions is increasing due to fluctuations in the property market. This study analyzes property characteristics, including the number of bedrooms, bathrooms, land area, building area, and location, as well as their impact on house prices. The study evaluates four regression algorithms, including linear regression, K-Nearest Neighbors (KNN), Random Forest, and XGBoost. Finally, this study proposes price_per_m2 and building_land_ratio as new features recommended for improvement in accuracy. The bottleneck method is derived from the data collection area of the Rumah123.com website, encompassing data preprocessing and data exploration. The following metrics will be used to evaluate each model: Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and R-squared (R²). Based on our study, we conclude that both Random Forest Regression and XGBoost Regression achieve the highest accuracy, with R² values of 0.9941 and 0.9955, respectively, after adjustment. Conversely, Linear Regression and KNN Regression have the lowest accuracy, with KNN Regression being the least accurate. The primary contribution of this study is the development of a more accurate house price prediction model that can be applied in cities with similar market characteristics. These findings provide practical insights for property developers and buyers when making investment decisions.
Analisis Sentimen Aplikasi BYOND by BSI di Google Play Store Menggunakan Metode SVM Akbar, Imannudin; Sinaga, Arnold Ropen Sinaga; Yoga, Titan Parama; Hendra, Acep; Setiana, Elia Setiana
Jurnal Accounting Information System (AIMS) Vol. 8 No. 2 (2025)
Publisher : Ma'soem University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32627/aims.v8i2.1583

Abstract

The BYOND by BSI application has received various user reviews on the Google Play Store, reflecting user perceptions and satisfaction. Sentiment analysis is needed to understand these opinion patterns and support service quality improvement. This study aims to analyze the sentiment of BYOND by BSI user reviews by applying the Support Vector Machine (SVM) method. Review data were collected from the Google Play Store and processed through text preprocessing stages followed by SVM classification modeling. The results show a classification accuracy of 87%, with strong performance in the Positive class (F1-score 0.91) and Negative class (F1-score 0.88), but SVM failed to detect the Neutral class due to data imbalance, where the Neutral class accounted for only 5.85% of the total samples. In conclusion, these findings highlight the importance of handling class imbalance through approaches such as resampling, ensemble algorithms, or class-weight optimization in SVM to improve the accuracy of Neutral sentiment detection.
Co-Authors Acep Hendra Ari Rizki Fauzi Arnold Ropen Sinaga Berly Bagoes Daniswara Budiman Dani Setiawan Dena Susanti Fikri Muhamad Fahmi Habibi, Chairul Januantara Prima, Alif Lestari, Gita Ayu Marwondo Niqotaini, Zatin Nugraha Nugraha Nur Alamsyah Nursyanti, Reni Permata Hati, Tiara R. Yadi Rakhman Alamsyah Ramadhani, Muhammad Wahyu Reni Nursyanti Setiana, Elia Setiana, Elia Setiana Sinaga, Arnold Ropen Sinaga Syahira Putri Himmaniah Taufik Hidayah Titan Parama Yoga Venia Restreva Danestiara