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ANALISIS SPEECH-TO-TEXT PADA VIDEO MENGANDUNG KATA KASAR DAN UJARAN KEBENCIAN DALAM CERAMAH AGAMA ISLAM MENGGUNAKAN INTERPRETASI AUDIENS DAN VISUALISASI WORD CLOUD Fahrudin, Tresna Maulana; Sari, Allan Ruhui Fatmah; Lisanthoni, Angela; Lestari, Amanda Ayu Dewi
SKANIKA: Sistem Komputer dan Teknik Informatika Vol 5 No 2 (2022): Jurnal SKANIKA Juli 2022
Publisher : Universitas Budi Luhur

Di era revolusi industri 4.0 saat ini, penggunaan media sosial sangat berkembang pesat dengan terjadinya interaksi dan komunikasi antarmanusia dalam dunia maya. Namun, terkadang ditemui adanya pengguna media sosial yang menyalahgunakan untuk kepentingan tertentu, salah satunya ceramah agama yang mengandung kata-kata kasar dan ujaran kebencian. Semakin banyak kekeliruan dalam memahami agama dikarenakan apa yang disampaikan oleh penceramah bukanlah tentang agama itu sendiri, tetapi justru menghasut, menghina dan memprovokasi para pendengarnya untuk tujuan tertentu. Oleh karena itu, penelitian ini mengusulkan analisis speech-to-text pada video yang mengandung kata-kata kasar dan ujaran kebencian dalam ceramah agama islam menggunakan interpretasi audiens dan visualisasi word cloud. Hasil penelitian menunjukkan bahwa sebanyak 3 penceramah agama dan total terdapat 9 video di mana masing-masing video berdurasi 3 menit mengandung kata-kata kasar dan ujaran kebencian.

Social Media Analysis and Topic Modeling: Case Study of Stunting in Indonesia Muhaimin, Amri; Fahrudin, Tresna Maulana; Alamiyah, Syifa Syarifah; Arviani, Heidy; Kusuma, Ade; Sari, Allan Ruhui Fatmah; Lisanthoni, Angela
Telematika Vol 20, No 3 (2023): Edisi Oktober 2023
Publisher : Jurusan Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31315/telematika.v20i3.10797

Purpose: Stunting is a problem that currently requires special attention in Indonesia. The stunting rate in 2022 will drop to 21.6%, and for the future, the government has set a target of up to 14% in 2024. Rapid technological developments and freedom of expression on the internet produce review text data that can be analyzed for evaluation. This study analyzes the text data of Twitter users' reviews on stunting. The method used is a text-mining approach and topic modeling based on Latent Dirichlet Allocation.Design/methodology/approach: The methodology used in this study is Latent Dirichlet Allocation. The data was collected from twitter with the keyword 'stunting'. After, the data was cleaned and then modeled using the Latent Dirichlet Allocation.Findings/results: The results show that negative sentiment dominates by 60.6%, positive sentiment by 31.5%, and neutral by 7.9%. In addition, this research shows that 'children', 'decrease', 'number', 'prevention', and 'nutrition' are among the words that often appear on stunting.Originality/value/state of the art: This study uses the keyword stunting and analyzes it. Social media analytics show that the people of Indonesia are primarily aware of stunting. Also, the Latent Dirichlet Analysis can be used to create the model.

Sentiment Analysis in Social Media: Case Study in Indonesia Muhaimin, Amri; Fahrudin, Tresna Maulana; Alamiyah, Syifa Syarifah; Arviani, Heidy; Kusuma, Ade; Sari, Allan Ruhui Fatmah; Lisanthoni, Angela
Nusantara Science and Technology Proceedings 8th International Seminar of Research Month 2023
Publisher : Future Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11594/nstp.2024.4106

Stunting is a problem that currently requires special attention in Indonesia. The stunting rate in 2022 will drop to 21.6% and for the future, the government has set a target of up to 14% in 2024. There have been many government efforts in implementing programs to reduce stunting rates. However, not everything runs optimally. Rapid technological developments and freedom of expression in the internet world produce review text data that can be analyzed for evaluation. This study aims to analyze the text data of Twitter users' reviews on stunting. The method used is a text-mining approach and topic modeling based on Latent Dirichlet Allocation (LDA). The results show that negative sentiment dominates by 60.6%, positive sentiment by 31.5%, and neutral by 7.9%. In addition, this research shows that 'anak', 'turun', 'angka', 'cegah' and 'gizi' are among the words that often appear on the topic of stunting.

XportID: Website for Clustering Indonesian Export Commodities by Destination Continent using Gaussian Mixture Model Lisanthoni, Angela; Trimono, Trimono; Prasetya, Dwi Arman
JTAM (Jurnal Teori dan Aplikasi Matematika) Vol 9, No 1 (2025): January
Publisher : Universitas Muhammadiyah Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31764/jtam.v9i1.27500

Exports play a crucial role in driving economic growth and increasing foreign exchange reserves. However, Indonesia's export performance has not yet reached its optimal potential, as evidenced by an 11% decline in export value in 2023. The decrease is partly attributed to the limited range of export destination markets. Therefore, this study aims to analyze export trade patterns to identify the most ideal and potential market locations. The research will employ a quantitative approach, using secondary data from the Central Bureau of Statistics and the 2022 BACI dataset, focusing on the top 5 HS2 commodity types by highest export quantity. Clustering analysis is applied to group markets based on similar characteristics, identifying countries with high, medium, and low export potential for Indonesia’s export strategy. The research develops a website-based clustering system called XportID, utilizing a Gaussian Mixture Model (GMM) with the Expectation-Maximization (EM) algorithm to determine optimal cluster parameters. GMM is preferred for its flexibility and probabilistic system, providing more accurate results compared to distance-based methods. There will be 3, 4, and 5 clusters formed and then the best cluster will be selected by comparing the silhouette score obtained. Results show that the Asian continent has 5 clusters with the best value of 0.7035, the American continent has 3 clusters with the best value of 0.8165, the African continent has 3 clusters with the best value of 0.8534, the Australian continent has 3 clusters with the best value of 0.8540, and the European continent has 4 clusters with the best value of 0.8654. Overall results, the clustering system is categorized as strong structure with average value of 0.8185. Countries with high export potential include Malaysia, Philippines, South Korea, Brazil, Mexico, New Zealand, and Spain. Specifically in Africa, commodities related to HS2-15 show potential for growth.

Optimizing Clustering Analysis to Identify High-Potential Markets for Indonesian Tuber Exports Prasetya, Dwi Arman; Sari, Anggraini Puspita; Idhom, Mohammad; Lisanthoni, Angela
Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol. 7 No. 1 (2025): February
Publisher : Jurusan Teknik Elektromedik, Politeknik Kesehatan Kemenkes Surabaya, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/skzqbd57

Agriculture is a key contributor to Indonesia's economic growth, with tubers representing the second most important food crop. Despite their significance, the export value of Indonesia’s tuber crops has not yet reached its full potential given the decline in the value of tuber exports since 2021. One of the contributing factors is the restricted range of export market options. This study aims to analyze export trade patterns to identify the most high-potential markets for Indonesian tuber commodities. Clustering analysis is used as a key method to identify market locations by grouping countries based on similar trade characteristics. Clustering was conducted using the Gaussian Mixture Model (GMM), which enhanced by Particle Swarm Optimization (PSO) and evaluated by silhouette score and DBI. The dataset is collected from Indonesia’s Central Bureau of Statistics from 2019 to 2023, focusing on 5 kinds of tuber exports with total of 455 entries and 8 columns. Using the AIC/BIC method, the optimal number of clusters obtained is 2 which are low market opportunities (cluster 0) and high market oppurtunities (cluster 1). Results showed that the GMM model without optimization has silhouette score of 0.7602 and DBI of 0.8398, while the GMM+PSO model achieved an improved silhouette score of 0.8884 and DBI of 0.5584. Both score are categorized as strong structure but, GMM+PSO has higher silhouette score and lower DBI score, demonstrating the effectiveness of PSO in enhancing the clustering model’s performance. The key potential markets for Indonesian tuber exports are primarily concentrated in Asia, including countries such as China, Malaysia, Thailand, Vietnam, Hong Kong, and United States.

Social Media Analysis and Topic Modeling: Case Study of Stunting in Indonesia Muhaimin, Amri; Fahrudin, Tresna Maulana; Alamiyah, Syifa Syarifah; Arviani, Heidy; Kusuma, Ade; Sari, Allan Ruhui Fatmah; Lisanthoni, Angela
Telematika Vol 20 No 3 (2023): Edisi Oktober 2023
Publisher : Jurusan Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31315/telematika.v20i3.10797

Purpose: Stunting is a problem that currently requires special attention in Indonesia. The stunting rate in 2022 will drop to 21.6%, and for the future, the government has set a target of up to 14% in 2024. Rapid technological developments and freedom of expression on the internet produce review text data that can be analyzed for evaluation. This study analyzes the text data of Twitter users' reviews on stunting. The method used is a text-mining approach and topic modeling based on Latent Dirichlet Allocation.Design/methodology/approach: The methodology used in this study is Latent Dirichlet Allocation. The data was collected from twitter with the keyword 'stunting'. After, the data was cleaned and then modeled using the Latent Dirichlet Allocation.Findings/results: The results show that negative sentiment dominates by 60.6%, positive sentiment by 31.5%, and neutral by 7.9%. In addition, this research shows that 'children', 'decrease', 'number', 'prevention', and 'nutrition' are among the words that often appear on stunting.Originality/value/state of the art: This study uses the keyword stunting and analyzes it. Social media analytics show that the people of Indonesia are primarily aware of stunting. Also, the Latent Dirichlet Analysis can be used to create the model.

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