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All Journal Teknika Jurnal Teknik Elektro Kontekstualita: Jurnal Penelitian Sosial Keagamaan Jurnal Al-Ijtimaiyyah TEKNOINFO Jurnal Ilmiah Peuradeun Tarbawi : Jurnal Ilmu Pendidikan Islamic Management: Jurnal Manajemen Pendidikan Islam PeTeKa Dinasti International Journal of Education Management and Social Science Matan: Journal of Islam and Muslim Society Abdimas Galuh: Jurnal Pengabdian Kepada Masyarakat Awwaliyah Al-Hijr: Journal of Adulearn World Journal of Da'wah Analisis Gender dan Agama AL QALAM JURNAL KEPENDIDIKAN Journal International Dakwah and Communication Qawwam Intelmatics Al-Mau'izhoh: Jurnal Pendidikan Agama Islam ALPHATEACH (JURNAL PROFESI KEPENDIDIKAN DAN KEGURUAN) Jurnal Inovasi Global Policy Brief Pertanian, Kelautan, dan Biosains Tropika Jurnal Faidatuna Journal of Research on Community Engagement Journal of Noesantara Islamic Studies Journal of Humanities Research Sustainability
Ahmad Zuhdi
Akademi Pengajian Islam Universiti Malaya
Religion Agriculture, Biological Sciences & Forestry Arts Humanities Astronomy Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Control & Systems Engineering Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mathematics Nursing Physics Public Health Social Sciences Other

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Journal : Intelmatics

 1 
Sentiment Analysis from Twitter Regarding E-SIM Services by Smartfren Using the Naïve Bayes Classifier Method Bintang Sarrang, Jhody; Zuhdi, Ahmad; B Ariwibowo, Anung
Intelmatics Vol. 4 No. 1 (2024): Januari-Juni
Publisher : Penerbitan Universitas Trisakti

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25105/itm.v4i1.17620

Abstract

Smartfren is a provider of internet, mobile phone, and Android-IOS smartphones that offers faster and more affordable services in Indonesia. E-SIM is a digital SIM that enables the activation of cellular packages from network operators without the need for a physical nano-SIM. Smartfren provides E-SIM services, making it the first provider to offer this service. Consequently, numerous tweets discussing this provider have emerged, resulting in a substantial amount of Twitter data related to Smartfren. This data can be leveraged for sentiment analysis. However, the tweets often contain ambiguous or unrevealing information, making them challenging to be properly classified, especially when using the Naïve Bayes Classifier as the classification method, as observed in previous studies. Nevertheless, employing appropriate methods can generate highly valuable insights for relevant stakeholders. This research aims to analyze public opinion regarding the services provided by Smartfren, using the Naïve Bayes Classifier method. The study is intended to provide comprehensive analytical results for the related provider, allowing for a thorough understanding of the sentiment analysis. Keywords : Sentiment Analysis, Smartfren, Naïve Bayes
Analisis Sentimen dan Pemodelan Topik Ulasan PengunjungObjek Wisata Pulau Bali pada Situs Tripadvisor MenggunakanMetode Lexicon-Based dan Latent Dirichlet Allocation (LDA) Aulia, Muhammad Azka; Solihah, Binti; Zuhdi, Ahmad
Intelmatics Vol. 5 No. 1 (2025): January-June
Publisher : Penerbitan Universitas Trisakti

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25105/v5i1.17619

Abstract

One sought-after type of information by internet users is related to tourist destinations. Hence, the need for information retrieval about a particular tourist spot they plan to visit. This study aims to analyze sentiments and identify the topics in the visitor reviews of Bali Island tourist attractions on TripAdvisor using Lexicon-based and Latent Dirichlet Allocation (LDA) methods. The data used for analysis consists of reviews from various tourist destinations on the island of Bali. For sentiment analysis, the author employs a Lexicon-based approach, focusing on both positive and negative sentiments. To identify the topics in the reviews, the author employs the LDA method to uncover the most frequently discussed topics. From 15,827 dataset, It is found that 87,6% of the responses are positive, 7.9% are negative, and the remaining 4.4% are neutral. As for the topic modeling results, the study identifies four main topics with the best coherence values based on the validation of topics with topic coherence. These four topics are: the first topic discusses experiences in Safari or Safari Park in Bali, the second topic talks about experiences in tourism in Kintamani, Bali, the third topic focuses on experiences in tourism in Nusa Penida, Bali, and the last topic discusses experiences in Scuba Diving activities
Analysis of CNN for Detecting Footsteps in Physical Traces App Nadin, Annisa; Zuhdi, Ahmad; Shofiati, Ratna
Intelmatics Vol. 5 No. 2 (2025): July-December
Publisher : Penerbitan Universitas Trisakti

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25105/v5i2.23892

Abstract

Step detection is an essential feature in promoting healthy living through mobile applications. This study evaluates the accuracy of a Convolutional Neural Network (CNN) model implemented in the Physical Traces application for detecting steps based on accelerometer and gyroscope sensor data. Data were collected through experimental activities where 60 participants walked 20 steps and ran 10 meters, repeated three times each. The results show average accuracy exceeding 100%, indicating a tendency for overcounting. Evaluation was performed using Absolute Error, Relative Error, Symmetric Accuracy, and SMAPE. Statistical analysis (Mann-Whitney, Kruskal-Wallis), reliability test (Cronbach’s Alpha = 0.9178), and validity test (positive correlations) revealed significant differences by gender and age group. These findings indicate that CNN-based step detection works effectively, but improvements are necessary to address individual variability and real-world conditions.
Co-Authors Admizal, Iril Ainul Yaqin Alfiandi Erman, Alfiandi Amanah, Yuni Ulfah Budi Ardinal, Eva Aulia, Muhammad Azka Azizah, Nahdia Nur B Ariwibowo, Anung Baity, Lailia Nur Bilhaq, M. Agus Muhtadi Bintang Sarrang, Jhody Binti Solihah Caesar, M. Yusuf Costa, Tiago Deswita, Melda Dina Destari Fachry Abda El Rahman Fahri Setiawan Fairus, Fairus Fakhri, Fakhri Farta Akhnuari, Dika Fatiatun Fatiatun, Fatiatun Fitri, Melta Asrina Fuad Fuad Gusfitriani, Beti H Nanda, Dendy Hadi Fitriansyah Hamzah, Muchotob Hariya Toni Helmin, Helmina Hidayatu Munawaroh, Hidayatu Hidayatullah, Yusuf Ikawati, Erna Indradewa, Rhian Irawan Irawan Jalwis Jarnawi Jarnawi Jasafat Khuluqi, Khusniatul Kumala, Ana Kunikmah, Arisma Labib, Hakki Akmal Linnaja, Ngathoillah Linnaja, Ngatoillah M. Yusuf Amin Nugroho Malintang, Jimmy Marhamah Marhamah Mariani, Mimi Marwan Marwan Mulyarti Murdani, T Muzaki, Wildan Afthon Nadin, Annisa Nugroho, Muhammad Yusuf Amin Nurhalis NURUL FAIZAH Paryadi Paryadi, Paryadi Putri, Diti Artanti Utami Putri, Lusiana Rahmadani Rafidah, Naila Rahman, Rifqi Aulia Rasyidah Rasyidah Ratna Shofiati Ronaldo, Ronaldo Rosyid, Muhammad Iqbal Safparudin, Safparudin Salamun, Ilham Salis Irvan Fuadi Sandy, Boy Dian Anugra Sembiring, Rinawati Siti Annijat Maimunah Syamsuddin AB, Syamsuddin Syamsudin, Fadly Syatria Adymas Pranajaya Ulfi Andrian Sari Utama Sp, Muhamad Septa Wibowo, Hariyadi Widiana, Divina Aufa Yusuf, Fakhri Zahra Novitasari, Nihaya Zuhroh, Ni’matuz