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NLP-Based Sentiment Analysis of Alfagift and Klik Indomaret Application Reviews: A Comparative Study Fuji Lestari, Nur Laili Indah; Naraya, Tri Vani Diah; Anggraini, Handari Niken; Fahmi, Faisal
Journal of Information System and Informatics Vol 7 No 3 (2025): September
Publisher : Universitas Bina Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51519/journalisi.v7i3.1178

Abstract

Amid competition for online shopping applications, Alfagift and Klik Indomaret compete for the same market share. This study aims to analyze and compare user reviews of both applications using sentiment analysis based on Natural Language Processing (NLP) with the E-Servqual approach, focusing on Efficiency and System Availability indicators, to determine the advantages and disadvantages of each application and provide a basis for service improvement, strategic decision making, and reference for users in choosing online shopping applications that suit their needs. Methods include data collection, data grouping, data processing, selecting analyzed samples with consensus, and data analysis to describe user perceptions of the quality of service of each application. The results showed that on the positive side, both apps experienced an increase in efficiency although not significant, with gradual improvements in user experience. Alfagift showed improvements in technical responsiveness and ease of use, while Klik Indomaret was relatively stable with a simple user experience. On the negative side, efficiency issues still arise consistently and impact user perception. Alfagift often faces access and login issues, while Klik Indomaret tends to be slow when accessing various features. These findings reflect that despite year-on-year improvements, both apps still face technical challenges that need to be resolved to improve the overall quality of digital services.
Analisis Sentimen terhadap Ulasan Pengguna Aplikasi Gojek dengan Menggunakan Pemrosesan Bahasa Alami Yuliani, Silvia Putri; Muharani, Ari Ati Putri; Fatmawati, Riyana Qori; Fahmi, Faisal
Journal of System and Computer Engineering Vol 6 No 4 (2025): JSCE: October 2025
Publisher : Universitas Pancasakti

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61628/jsce.v6i4.2062

Abstract

Gojek, a leading proponent of on-demand services in Indonesia, has garnered a total of 142 million downloads. However, it has received the fewest reviews compared to other on-demand applications. The objective of this research is to identify sentiment in Gojek application user reviews on Google Playstore using Natural Language Processing (NLP) approaches and machine learning algorithms through the Orange platform. The reviews utilized in this study were collected in June 2025 and encompass a total of 3,615 data points, including 2,892 training data and 723 testing data. Sentiments are classified into two categories based on their ratings: positive (rating 4-5) and negative (rating 1-2). The research process is comprised of four primary stages: data collection and labeling, text pre-processing, feature transformation using TF-IDF, and testing five classification algorithms: neural network, naïve Bayes, random forest, decision tree, and k-nearest neighbors. The evaluation results indicate that the Neural Network model demonstrates optimal performance, exhibiting 93.20% accuracy, 93.00% F1-score, and 75.80% MCC. These findings suggest that the NLP approach can be utilized effectively to comprehend user perceptions of applications. It is anticipated that this research will assist Gojek developers in the monitoring and enhancement of service quality, with this enhancement being informed by user feedback.
Analisis Pengaruh Makro Ekonomi Terhadap Perkembangan Indeks Saham di BSI Pada Tahun 2021-2024 Fahmi, Faisal; Maidalena, Maidalena; Tambunan, Khairina
Journal of Economics and Management Scienties Volume 8 No. 1, December 2025
Publisher : SAFE-Network

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37034/jems.v8i1.271

Abstract

Penelitian ini bertujuan untuk menganalisis pengaruh indikator makroekonomi yang terdiri dari inflasi, kurs, dan Produk Domestik Bruto (PDB) terhadap indeks saham PT Bank Syariah Indonesia Tbk (BSI) pada periode Juli 2021–Desember 2024. Penelitian ini menerapkan pendekatan kuantitatif dengan analisis regresi linier berganda. Sumber data yang digunakan berupa data sekunder yang diperoleh dari publikasi resmi Bank Indonesia, Badan Pusat Statistik, serta Bursa Efek Indonesia. Hasil penelitian menunjukkan bahwa secara parsial inflasi berpengaruh negatif signifikan terhadap indeks saham BSI, yang berarti kenaikan inflasi cenderung menurunkan harga saham. Kurs juga berpengaruh negatif signifikan, sehingga pelemahan nilai tukar rupiah berdampak pada penurunan indeks saham BSI. Sementara itu, PDB berpengaruh positif signifikan, menandakan bahwa pertumbuhan ekonomi nasional mendorong peningkatan kinerja saham BSI. Hasil uji simultan (F-test) memperlihatkan bahwa ketiga variabel bebas secara bersama-sama berpengaruh signifikan terhadap indeks saham BSI dengan nilai F-hitung 23,954 lebih besar dari F-tabel 2,76 dan signifikansi 0,000 < 0,05. Nilai Adjusted R² sebesar 0,671 menunjukkan bahwa 67,1% variasi pergerakan saham BSI dapat dijelaskan oleh variabel inflasi, kurs, dan PDB, sementara 32,9% sisanya dipengaruhi oleh faktor lain di luar model penelitian ini.
AI-driven creativity in software development using services information Fahmi, Faisal; Wang, Feng-Jian; Subramaniam, Hema
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 6: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i6.pp4474-4483

Abstract

In competitive markets, organizations that do not innovate risk becoming outdated. At it is core, innovation depends on creativity, with creative outcomes typically characterized by novelty, usefulness, and surprisingness. In software development, creative solutions are often generated through brainstorming sessions. However, brainstorming is constrained by the knowledge of the participant and facilitator. In this paper, we present an artificial intelligence (AI)-based method to generate creative solutions in software by leveraging service information. The presented method includes two phases, where the first phase involves constructing creativity resources through text clustering (TF-IDF, K-medoid) and capability extraction (dependency parsing), and the second phase employs semantic similarity along with structured creativity techniques (exploration, transformation, and combination) to generate creative solutions in software. Besides, experimental results showed that the AI-based method achieved comparable creativity scores to traditional brainstorming with more limited time, demonstrating fast and strong performance in generating novel and useful solutions, although participants perceived some results as less surprising due to overlap with brainstorming outcomes.
Between Engagement and Real Impacts: A Literature Review of Library Social Media Promotion in Indonesian Libraries Using the AIDA Model Rahmandani, Charissa; Zaskia Julia Putri Rahmadani; Faisal Fahmi
Tibanndaru : Jurnal Ilmu Perpustakaan dan Informasi Vol. 10 No. 1 (2026): Tibanndaru: Jurnal Ilmu Perpustakaan dan Informasi
Publisher : Universitas Wijaya Kusuma Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30742/tb.v10i1.5140

Abstract

Purpose Research. This study critically examine the effectiveness of social media promotion strategies implemented by libraries in using AIDA model (Attention, Interest, Desire, Action) as a conceptual framework. Research Methods. This study employs a literature review method using secondary data from journal articles, theses, and institutional reports published between 2019 and 2025. Analysis Data. The data were systematically analyzed using six thematic dimensions: platform selection, content strategy, engagement quality, success metrics, structural barriers, and strategic alignment, with a qualitative-descriptive and critical approach. Results. Instagram dominates as the primary promotional platform. However, most library only achive the Attantion stage, with minimal progression to Interest, Desire, and Action. Engagement metrics remain surface-level, disconnected from measurable behavioral outcomes such as library visits or service utilization. Social media has not been strategically as a promotion tool in Indonesia libraries. This study contributes a critical AIDA-based evaluative framework that bridges the gap between engagement metrics and real impact, offering practical implications for integrated content planning and impact-based evaluation in library management. Keywords: Social Media Promotion; Libraries; AIDA Model; Indonesian Libraries
Pengembangan Minat dan Kegemaran Membaca Berbasis Media Sosial di Masyarakat Kecamatan Siman Kabupaten Ponorogo Fahmi , Faisal; Sugihartati, Rahma; Prasyesti Kurniasari, Meinia
Smart Dedication: Jurnal Pengabdian Masyarakat Vol. 3 No. 1 (2026): Smart Dedication: Jurnal Pengabdian Masyarakat
Publisher : SMART SCIENTI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70427/smartdedication.v3i1.259

Abstract

Kegiatan pengabdian masyarakat ini bertujuan meningkatkan minat dan kegemaran membaca di Kecamatan Siman, Kabupaten Ponorogo, yang masih rendah berdasarkan data Tingkat Kegemaran Membaca (TGM) tahun 2022. Perpustakaan Desa Manuk memiliki koleksi cukup banyak, namun jumlah pengunjung dan peminjam masih minim. Strategi promosi sebelumnya dianggap kurang efektif, sehingga diusulkan pengembangan minat baca berbasis media sosial. Metode yang digunakan mencakup sosialisasi dan pelatihan bagi 20 pengelola perpustakaan dalam lima tahap. Sosialisasi dilakukan melalui ceramah dan diskusi, sedangkan pelatihan menggunakan metode demonstrasi dan praktik dengan smartphone. Hasil sementara menunjukkan peningkatan pengetahuan mitra berdasarkan pretest dan post-test. Empat dari lima tahap telah diselesaikan, sementara evaluasi dan finalisasi masih berlangsung. Evaluasi selanjutnya akan menilai penerapan pengetahuan dalam praktik. Diharapkan strategi berbasis media sosial ini dapat meningkatkan literasi masyarakat secara lebih efektif. Kegiatan pengabdian masyarakat ini bertujuan meningkatkan minat dan kegemaran membaca di Kecamatan Siman, Kabupaten Ponorogo, yang masih rendah berdasarkan data Tingkat Kegemaran Membaca (TGM) tahun 2022. Perpustakaan Desa Manuk memiliki koleksi cukup banyak, namun jumlah pengunjung dan peminjam masih minim. Strategi promosi sebelumnya dianggap kurang efektif, sehingga diusulkan pengembangan minat baca berbasis media sosial. Metode yang digunakan mencakup sosialisasi dan pelatihan bagi 20 pengelola perpustakaan dalam lima tahap. Sosialisasi dilakukan melalui ceramah dan diskusi, sedangkan pelatihan menggunakan metode demonstrasi dan praktik dengan smartphone. Hasil sementara menunjukkan peningkatan pengetahuan mitra berdasarkan pretest dan post-test. Empat dari lima tahap telah diselesaikan, sementara evaluasi dan finalisasi masih berlangsung. Evaluasi selanjutnya akan menilai penerapan pengetahuan dalam praktik. Diharapkan strategi berbasis media sosial ini dapat meningkatkan literasi masyarakat secara lebih efektif.
Sentiment Classification of Instagram Poster Comments on the Documentary Film "Pesta Babi": A Comparative Study of SVM, Random Forest, and Naive Bayes with a Keyword-Based Exploration of Sociocultural Themes Barirotut Taqiyyah; Julianti Damiar Rakhmawati damiar; Aurallia Titania Agnes Syafa’i; Faisal Fahmi
The Indonesian Journal of Computer Science Vol. 15 No. 4 (2026): The Indonesian Journal of Computer Science
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v15i4.5213

Abstract

This study analyzes public sentiment in 704 Instagram comments responding to the poster of the Indonesian documentary "Pesta Babi: Kolonialisme di Zaman Kita," which triggered national debate in 2026. Comments were collected through total sampling and sentiment-labeled with AI assistance (partially via an Excel-integrated AI tool, partially via an AI-generated Python script executed in PyCharm), yielding an imbalanced distribution of 514 Positive and 190 Negative comments. After text preprocessing (regex-based tokenization and lowercasing) and binary Bag-of-Words feature extraction with L1 regularization, three algorithms Support Vector Machine, Random Forest, and Naive Bayes were compared using 10-fold cross-validation on the Orange platform. Random Forest achieved the highest overall accuracy (0.786) and AUC (0.858), while Naive Bayes showed the strongest balanced performance on the minority Negative class (MCC = 0.398). A supplementary keyword-based analysis found only 19.7% of comments referenced religious, artistic, or ethical-normative themes, indicating public discourse was dominated by other issues, notably political and funding-related accusations.