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Identifying Degree-of-Concern on COVID-19 topics with text classification of Twitters Hasanah, Novrindah Alvi; Suciati, Nanik; Purwitasari, Diana
Register: Jurnal Ilmiah Teknologi Sistem Informasi Vol 7, No 1 (2021): January
Publisher : Information Systems - Universitas Pesantren Tinggi Darul Ulum

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26594/register.v7i1.2234

The COVID-19 pandemic has various impacts on changing peopleâ€™s behavior socially and individually. This study identifies the Degree-of-Concern topic of COVID-19 through citizen conversations on Twitter. It aims to help related parties make policies for developing appropriate emergency response strategies in dealing with changes in peopleâ€™s behavior due to the pandemic. The object of research is 12,000 data from verified Twitter accounts in Surabaya. The varied nature of Twitter needs to be classified to address specific COVID-19 topics. The first stage of classification is to separate Twitter data into COVID-19 and non-COVID-19. The second stage is to classify the COVID-19 data into seven classes: warnings and suggestions, notification of information, donations, emotional support, seeking help, criticism, and hoaxes. Classification is carried out using a combination of word embedding (Word2Vec and fastText) and deep learning methods (CNN, RNN, and LSTM). The trial was carried out with three scenarios with different numbers of train data for each scenario. The classification results show the highest accuracy is 97.3% and 99.4% for the first and second stage classification obtained from the combination of fastText and LSTM. The results show that the classification of the COVID-19 topic can be used to identify Degree-of-Concern properly. The results of the Degree-of-Concern identification based on the classification can be used as a basis for related parties in making policies to formulate appropriate emergency response strategies in dealing with changes in public behavior due to a pandemic.

Pemantauan Perhatian Publik terhadap Pandemi COVID-19 melalui Klasifikasi Teks dengan Deep Learning Novrindah Alvi Hasanah; Nanik Suciati; Diana Purwitasari
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 5 No 1 (2021): Februari 2021
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Monitoring public concern in the surrounding environment to certain events is done to address changes in public behavior individually and socially. The results of monitoring public attention can be used as a benchmark for related parties in making the right policies and strategies to deal with changes in public behavior as a result of the COVID-19 pandemic. Monitoring public attention can be done using Twitter social media data because the users of the media are quite high, so that they can represent the aspirations of the general public. However, Twitter data contains varied topics, so a classification process is required to obtain data related to COVID-19. Classification is done by using word embedding variations (Word2Vec and fastText) and deep learning variations (CNN, RNN, and LSTM) to get the classification results with the best accuracy. The percentage of COVID-19 data based on the best accuracy is calculated to determine how high the public's attention is to the COVID-19 pandemic. Experiments were carried out with three scenarios, which were differentiated by the number of data trains. The classification results with the best accuracy are obtained by the combination of fasText and LSTM which shows the highest accuracy of 97.86% and the lowest of 93.63%. The results of monitoring public attention to the time vulnerability between June and October show that the highest public attention to COVID-19 is in June.

Pemodelan Topik dengan LDA untuk Temu Kembali Informasi dalam Rekomendasi Tugas Akhir Diana Purwitasari; Aida Muflichah; Novrindah Alvi Hasanah; Agus Zainal Arifin
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 5 No 3 (2021): Juni 2021
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Undergraduate thesis as the final project, or in Indonesian called as Tugas Akhir, for each undergraduate student is a pre-requisite before student graduation and the successfulness in finishing the project becomes as one of learning outcomes among others. Determining the topic of the final project according to the ability of students is an important thing. One strategy to decide the topic is reading some literatures but it takes up more time. There is a need for a recommendation system to help students in determining the topic according to their abilities or subject understanding which is based on their academic transcripts. This study focused on a system for final project topic recommendations based on evaluating competencies in previous academic transcripts of graduated students. Collected data of previous final projects, namely titles and abstracts weighted by term occurences of TF-IDF (term frequency–inverse document frequency) and grouped by using K-Means Clustering. From each cluster result, we prepared candidates for recommended topics using Latent Dirichlet Allocation (LDA) with Gibbs Sampling that focusing on the word distribution of each topic in the cluster. Some evaluations were performed to evaluate the optimal cluster number, topic number and then made more thorough exploration on the recommendation results. Our experiments showed that the proposed system could recommend final project topic ideas based on student competence represented in their academic transcripts.

Topic Modelling Using VSM-LDA For Document Summarization Luthfi Atikah; Novrindah Alvi Hasanah; Agus Zainal Arifin
ULTIMATICS Vol 14 No 2 (2022): Ultimatics : Jurnal Teknik Informatika
Publisher : Faculty of Engineering and Informatics, Universitas Multimedia Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31937/ti.v14i2.2854

Summarization is a process to simplify the contents of a document by eliminating elements that are considered unimportant but do not reduce the core meaning the document wants to convey. However, as is known, a document will contain more than one topic. So it is necessary to identify the topic so that the summarization process is more effective. Latent Dirichlet Allocation (LDA) is a commonly used method of identifying topics. However, when running a program on a different dataset, LDA experiences "order effects", that is, the resulting topic will be different if the train data sequence is changed. In the same document input, LDA will provide inconsistent topics resulting in low coherence values. Therefore, this paper proposes a topic modelling method using a combination of LDA and VSM (Vector Space Model) for automatic summarization. The proposed method can overcome order effects and identify document topics that are calculated based on the TF-IDF weight on VSM generated by LDA. The results of the proposed topic modeling method on the 1300 Twitter data resulted in the highest coherence value reaching 0.72. The summary results obtained Rouge 1 is 0.78, Rouge 2 is 0.67 dan Rouge L is 0.80.

Fuzzy TOPSIS Implementation for the Determination of Priority Scale in Improving Service Quality Hasanah, Novrindah Alvi; Faisal, Muhammad; Angreani, Linda Salma
Ultimatics : Jurnal Teknik Informatika Vol 16 No 2 (2024): Ultimatics : Jurnal Teknik Informatika
Publisher : Faculty of Engineering and Informatics, Universitas Multimedia Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31937/ti.v16i2.3564

Service quality plays a crucial role in economic development, particularly in the service industry, such as hotel services. Despite this, many hotels lack a systematic approach to help management identify areas that require improvement based on customer feedback. This research aims to develop a system that supports efforts to enhance service quality, utilizing the Fuzzy TOPSIS method. The study incorporates 150 data points obtained from questionnaires distributed to hotel service customers. The research involves two trials: service improvement priority and service eligibility. The results indicate an 84.45% accuracy level for service improvement priority testing, based on 120 out of 150 data points. Additionally, the accuracy level for service eligibility testing is 85.34%, derived from 131 data points out of the total 150. The research findings highlight the cafeteria as a significant area requiring improvement in service quality, aligning with the insights of hospitality experts. These results can serve as a foundation for management to enhance service quality based on selected criteria and alternatives.

Penerapan Metode Pose to Pose dalam Film Animasi 3D Edukasi Nura Sang Cahaya Harapan Mubarok, Muhamad Husni; Nugroho, Fresy; Hasanah, Novrindah Alvi
JOURNAL OF APPLIED MULTIMEDIA AND NETWORKING Vol 9 No 1 (2025): Journal of Applied Multimedia and Networking
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jamn.v9i1.9887

This research aims to develop an educational 3D animation film titled "Nura the Light of Hope" by applying the Pose to Pose method as the main technique in character animation. The story focuses on the main character, Nura, a humble old lamp, which teaches moral values such as gratitude, humility, and the importance of appreciating differences. The animation production process is divided into three main stages, namely pre-production (script writing, character design, and storyboard), production (modeling, texturing, rigging, animating, rendering), and post-production (final rendering, dubbing, and editing). The results obtained show that the use of the Pose to Pose method is able to create expressive character movements and in accordance with the emotions of the story, thus supporting the delivery of educational messages effectively to children aged 7-12 years. This movie is expected to be an alternative learning media that is interesting and meaningful in the context of children's education in the digital era.

Strategi Pengolahan Sampah Organik Berkelanjutan melalui Pembuatan Eco-Enzyme di Kalangan Siswa SMA Bahrul Maghfiroh Malang Dwi Hanggara, Fuad; Aziza, Miladina Rizka; Hasanah, Novrindah Alvi; Zafirah, Yasmin; Junikhah, Allin; Zul Fahmi, Fariz Rifqi
Jurnal SOLMA Vol. 13 No. 2 (2024)
Publisher : Universitas Muhammadiyah Prof. DR. Hamka (UHAMKA Press)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22236/solma.v13i2.14981

Background: Setiap tahunnya, Indonesia telah mengirimkan 13,8 m3 sampah ke tempat pembuangan sampah, tetapi hanya sekitar 2,6% dari total sampah yang didaur ulang di sumbernya. Adanya eco-enzyme telah memberikan pendekatan baru yang bertujuan untuk mengurangi sampah TPA. Metode: Penelitian ini bertujuan untuk melatih siswa SMA Islam Bahrul Maghfiroh Malang tentang pengelolaan sampah, mulai bulan Mei 2023 hingga Juli 2023. Kegiatan ini merupakan upaya bersama antara tim pengabdian masyarakat Universitas Islam Negeri Maulana Malik Ibrahim Malang dan siswa SMA Bahrul Maghfiroh. Kegiatan yang dilakukan mengikuti model ABCD (Asset Based Community Development), meliputi sosialisasi, demonstrasi, praktik, dan monitoring. Hasil: Setelah dilakukan intervensi melalui kegiatan sosialisasi program pengabdian masyarakat, terjadi peningkatan pada pengetahuan, perilaku, emosi, dan kesadaran ekologi. Sehingga, warga sekolah mulai memilah sampah organik dan mengolahnya menjadi eco-enzyme. Kesimpulan: Kegiatan sosialisasi dan pelatihan pengolahan sampah organik menjadi eco-enzyme dapat disimpulkan bahwa terdapat perubahan pengetahuan, perilaku, emosi dan kesadaran ekologis setelah dilakukan intervensi melalui kegiatan sosialisasi.

Fuzzy TOPSIS Implementation for the Determination of Priority Scale in Improving Service Quality Hasanah, Novrindah Alvi; Faisal, Muhammad; Angreani, Linda Salma
ULTIMATICS Vol 16 No 2 (2024): Ultimatics : Jurnal Teknik Informatika
Publisher : Faculty of Engineering and Informatics, Universitas Multimedia Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31937/ti.v16i2.3564

Service quality plays a crucial role in economic development, particularly in the service industry, such as hotel services. Despite this, many hotels lack a systematic approach to help management identify areas that require improvement based on customer feedback. This research aims to develop a system that supports efforts to enhance service quality, utilizing the Fuzzy TOPSIS method. The study incorporates 150 data points obtained from questionnaires distributed to hotel service customers. The research involves two trials: service improvement priority and service eligibility. The results indicate an 84.45% accuracy level for service improvement priority testing, based on 120 out of 150 data points. Additionally, the accuracy level for service eligibility testing is 85.34%, derived from 131 data points out of the total 150. The research findings highlight the cafeteria as a significant area requiring improvement in service quality, aligning with the insights of hospitality experts. These results can serve as a foundation for management to enhance service quality based on selected criteria and alternatives.

Pengendalian Gerak Robot Beroda Menggunakan Sarung Tangan Pintar dengan Neural Network Backpropagation Arif, Yunifa Miftachul; Mustofa, Ahmad Habibil; Holle, Khadijah Fahmi Hayati; Wibowo, Muhammad Ismail Arjun; Aziza, Miladina Rizka; Junikhah, Allin; Hasanah, Novrindah Alvi
SinarFe7 Vol. 7 No. 1 (2025): SinarFe7-7 2025
Publisher : FORTEI Regional VII Jawa Timur

Show Abstract | Download Original | Original Source | Check in Google Scholar

Pengendalian robot berbasis remote control konvensional kerap memerlukan adaptasi dan pembelajaran baru bagi pengguna, khususnya bagi mereka yang belum terbiasa dengan tata letak tombol yang kompleks. Penelitian ini menawarkan solusi yang lebih intuitif melalui pendekatan Hand Gesture Recognition berbasis sarung tangan pintar (smart glove) yang dilengkapi sensor MEMS berupa akselerometer dan giroskop. Data pergerakan tangan yang diperoleh diolah menggunakan metode Neural Network Backpropagation untuk mengenali lima jenis gerakan, yaitu diam, maju, mundur, belok kiri, dan belok kanan. Sistem dikembangkan pada mikrokontroler STM32F10C dengan modul nirkabel NRF24L01 sebagai media transmisi data ke robot beroda. Pengujian dilakukan oleh satu orang pengguna dengan sepuluh kali percobaan untuk setiap gerakan. Hasil klasifikasi menunjukkan tingkat akurasi rata-rata sebesar 82,8%, dengan respon yang cepat dan stabil terhadap perintah yang diberikan. Temuan ini membuktikan bahwa pengendalian robot dapat dilakukan secara lebih natural, efisien, dan responsif hanya dengan gerakan tangan, sehingga berpotensi dikembangkan untuk aplikasi yang lebih luas di masa depan.

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