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All Journal Jurnal Algoritma JMM (Jurnal Masyarakat Mandiri) Journal of Community Development Jurnal PkM MIFTEK Jurnal Algoritma Journal of Digital Literacy and Volunteering Indonesian Journal of Computer Science and Engineering Journal of Digital Literacy and Volunteering
Rinda Cahyana
Sekolah Tinggi Teknologi Garut
Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Electrical & Electronics Engineering Engineering Languange, Linguistic, Communication & Media Nursing Public Health Social Sciences Other

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Tinjauan Analisis Sentimen Terkait COVID-19 Al Sidqi, Muhammad Affan; Afrizal, Nabil Nur; Rodiansyah, Novan; Cahyana, Rinda
Journal of Digital Literacy and Volunteering Vol. 3 No. 1 (2025): January
Publisher : Puslitbang Akademi Relawan TIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57119/litdig.v3i1.118

Abstract

The Covid-19 pandemic control policy has created public opinion on social media. The government controls sentiment so that people remain compliant with the policy. Several previous studies have analyzed sentiment with various approaches. This study aims to describe how to analyze sentiment related to the pandemic of earlier studies with a traditional literature review approach through the literature survey stage. The results of the review found various approaches to data collection and sentiment analysis that have been applied by previous studies and their challenges, as well as opportunities for further research.
Rancang Bangun Sistem Pemetaan Kesenian Garut Berbasis Web Nuraisah, Siti; Cahyana, Rinda
Jurnal Algoritma Vol 22 No 2 (2025): Jurnal Algoritma
Publisher : Institut Teknologi Garut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33364/algoritma/v.22-2.1821

Abstract

Distinctive arts and local community culture as cultural heritage inherited from generation to generation, traditional art is present as a window to the local wisdom of ancestors, this art has various forms such as dance, music, theater, fine arts, to literature. The Garut Regency Culture and Tourism Office stated that traditional art information facilities still use book media, the renewal of this information system aims to map the arts in Garut Regency which is still considered ineffective for this digital era. Furthermore, the purpose of this research is to produce a web-based Garut Traditional Arts mapping system. In analyzing and designing this mapping system, the Rational Unified Process (RUP) method is used with several stages, namely Inception, Elaboration, Construction, Transition and for modeling using Unified Modelling Langguange (UML). The system was tested using the blackbox testing method. The Garut art location mapping system by applying Leaflet to display traditional art locations. This research results in a Garut art mapping system that can provide information on locations, facilities and activities as promotional media.
Aplikasi Pengingat Minum Obat Dengan Monitoring Tenaga Kesehatan Berbasis Mobile Menggunakan Metode Prototype Firdaus Al Anwari, M Riadi; Nuraeni, Fitri; Cahyana, Rinda; Fitriani, Leni; Setiawan, Ridwan; Septiana, Yosep
Jurnal Algoritma Vol 22 No 2 (2025): Jurnal Algoritma
Publisher : Institut Teknologi Garut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33364/algoritma/v.22-2.2580

Abstract

The process of administering medication to patients requires timeliness and consistency to ensure optimal therapeutic outcomes. In practice, many patients struggle to remember their medication schedules, particularly when treatment extends over a long period. Addressing this issue, the present study aims to develop an Android-based medication reminder application that assists patients in adhering to their treatment schedules while enabling healthcare providers to digitally monitor patient activity. The application was designed using a prototyping method, which emphasizes iterative system development based on user feedback. The development process was conducted in two phases. The first phase involved initial design and testing of core features, such as reminder notifications and patient medication intake reporting forms. The second phase focused on improvements based on user feedback, particularly the addition of a disease information feature that provides educational content about patient diagnoses following checkups, such as hypertension and tuberculosis. Testing was carried out using a black-box testing approach to ensure proper functionality, alongside feedback collection through interviews. The results showed that the application performed effectively; its features were usable by both patients and healthcare providers as intended, and the information displayed was considered helpful in enhancing patients’ understanding of their health conditions. Furthermore, the system contributed to improving patient adherence to medication regimens and facilitated continuous monitoring by healthcare providers.
Analisis Sentimen Ulasan Wisata Budaya Menggunakan Metode Support Vector Machine dan Long Short-Term Memory Ramdani, Rizki; Cahyana, Rinda
Jurnal Algoritma Vol 22 No 2 (2025): Jurnal Algoritma
Publisher : Institut Teknologi Garut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33364/algoritma/v.22-2.2585

Abstract

In the era of digital transformation, tourist behavior in expressing perceptions of travel destinations has increasingly shifted toward online platforms such as Google Maps and Twitter. These digital reviews not only represent individual experiences but also reflect collective opinions that can serve as a foundation for formulating data-driven tourism development policies. This study aims to conduct sentiment analysis on public opinion regarding Kampung Naga by comparing the performance of two classification algorithms: Support Vector Machine (SVM) and Long Short-Term Memory (LSTM). The methodological approach employed is SEMMA (Sample, Explore, Modify, Model, Assess). The dataset comprises 2,469 reviews obtained through web scraping techniques from Google Maps and Twitter. All data underwent preprocessing stages including cleaning, tokenization, stopword removal, and automatic sentiment labeling using the ChatGPT language model, with three classification labels: positive, neutral, and negative. Modeling was performed using SVM with TF-IDF representation and LSTM with an embedding layer. Model evaluation utilized precision, recall, and F1-score metrics. The results indicate that SVM achieved an accuracy of 83% and performed best on neutral sentiment, while LSTM recorded an accuracy of 81% with stable performance on positive and neutral sentiments. This research contributes to the development of text-based public opinion analysis systems to support the promotion and management of cultural tourism destinations.
Analisis Sentimen Publik Pada Media Sosial Multi-Platform Terhadap Kinerja Presiden Prabowo Subianto Menggunakan Algoritma Naive Bayes Fauzi, Sandi Muhtar; Cahyana, Rinda
Jurnal Algoritma Vol 22 No 2 (2025): Jurnal Algoritma
Publisher : Institut Teknologi Garut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33364/algoritma/v.22-2.2586

Abstract

In the rapidly evolving landscape of digital politics, a deep understanding of public sentiment on social media has become crucial due to its significant influence on public opinion. This study aims to analyze public sentiment toward the performance of President Prabowo Subianto by utilizing data from three popular social media platforms: Twitter, TikTok, and Instagram.The classification method employed is the Naïve Bayes algorithm, implemented within the SEMMA framework, which consists of five stages: Sample, Explore, Modify, Model, and Assess. Data from each platform was collected and processed through text cleaning, TF-IDF transformation, and class balancing using the SMOTE technique. Evaluation was conducted using Stratified K-Fold Cross Validation and the F1-score metric to assess model performance.The results indicate that classification performance varies across platforms. The model achieved the highest F1-score on Twitter data (0.82), followed by Instagram (0.72), and TikTok (0.68). Overall, the model reached an average accuracy of 75.41%. These findings suggest that the linguistic characteristics and text structures of each platform significantly affect sentiment classification effectiveness.This research provides practical implications for the application of AI-based sentiment analysis in the realm of digital politics. It offers actionable insights for policymakers to monitor public opinion in real time and for system developers to design sentiment analysis algorithms that are more adaptive to the unique characteristics of each platform.
Pemodelan Analisis Tren Topik Penelitian Sistem Informasi Menggunakan Latent Dirichlet Allocation Nursaadah, Siti; Cahyana, Rinda
Jurnal Algoritma Vol 22 No 2 (2025): Jurnal Algoritma
Publisher : Institut Teknologi Garut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33364/algoritma/v.22-2.2596

Abstract

Topic modeling is one of the text mining techniques that can be used to explore research themes in a collection of scientific documents. This study aims to identify and compare topic trends in SINTA-indexed national journal publications with student articles published in the ITG Algorithm Journal in the field of informatics and computers. The research data consisted of article abstracts that were analyzed through text preprocessing and text representation using bag-of-words, then modeled using Latent Dirichlet Allocation (LDA). The optimal number of topics was determined based on the coherence score, visualized using pyLDAvis, and labeled with the help of ChatGPT to clarify the interpretation. The results show that national journals emphasize application and information system development, while the ITG Algorithm Journal tends to address cutting-edge issues such as machine learning and data science. These findings contribute to mapping the development of information system research and can serve as a reference for formulating research policy directions at the local and national levels.
Tinjauan Persepsi dan Analisis Sentimen Mahasiswa Dalam Implementasi Program Merdeka Belajar Kampus Merdeka Cahyana, Rinda; Hakim, Annas Nurul; Al-Husein, Fajar; Munawar, Deni Wildan
Journal of Digital Literacy and Volunteering Vol. 4 No. 1 (2026): January
Publisher : Puslitbang Akademi Relawan TIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57119/litdig.v4i1.193

Abstract

The Independent Learning Campus (MBKM) program is a higher education initiative that provides students with learning freedom and practical experience. However, its implementation still faces challenges, particularly related to low student participation rates, despite positive sentiment toward the program. This study aims to systematically review the literature discussing student perceptions and sentiments regarding their decision to participate in the MBKM program. The method used was a literature review of various scientific publications that examine survey approaches and sentiment analysis using machine learning algorithms. The review results indicate that the main obstacles influencing student decisions include a lack of socialization, limited infrastructure, and a lack of technical understanding related to the program. This study concludes that the integration of survey approaches and digital data analysis has the potential to strengthen understanding of the factors influencing student participation and provide a basis for more responsive and data-driven MBKM policy-making.
Co-Authors ., Bunyamin Afrizal, Nabil Nur Al Sidqi, Muhammad Affan Al-Husein, Fajar Alawiyah, Dini Angga Kusumah, Angga Asep Nurzaman Asri Mulyani Asyah, Cha Cha Nisya Bajuri, Ahmad Dede Kurniadi Dewi Tresnawati Dianto, Fajar Eri Dimitri Mahayana Dini Destiani Siti Fatimah Eri Satria Fathonah, Trisma Nur Fauzi, Sandi Muhtar Firdaus Al Anwari, M Riadi Fitri Nuraeni Hakim, Annas Nurul Hidayat, Achmad Lutfi Hilmi Aulawi Indri Tri Julianto Latifah, Ayu Leni Fitriani, Leni Luthfi Nurwandi Muhammad Rikza Nashrulloh Muhammad Syauqi Mubarok, Muhammad Syauqi Munawar, Deni Wildan Nasrulloh, Muhammad Rikza Nurhidayanti, Shopi Ramadhan, Taopik Ramdani, Rizki Ridwan Setiawan Rodiansyah, Novan Rostilawati, Detila Santoso, Bambang Tri Siti Nuraisah, Siti Siti Nursaadah, Siti SRI RAHAYU Taupik Hidayat, Taupik Yosep Septiana Yudi Setiawan