Garuda - Garba Rujukan Digital

APPLYING USER-CENTERED DESIGN FOR MOBILE APPLICATIONS INTERFACE DESIGN Fidya Farasalsabila; Verra Budhi Lestari; Jangkung Tri Nugroho; Arvi Pramudyantoro; Ema Utami
JURTEKSI (Jurnal Teknologi dan Sistem Informasi) Vol 10, No 1 (2023): Desember 2023
Publisher : STMIK Royal

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33330/jurteksi.v10i1.2619

Abstract: The Healthy Food Supplier App, which introduces groundbreaking innovations to increase public access to and delivery of healthy food, has emerged as a new phenomenon in the food sector. This research intends to examine and describe how the use of healthy food suppliers contributes to improving public health by making healthy food accessible to obtain and deliver it quickly. The development of mobile applications with the aim of providing healthy food and ingredients for the community is the subject of this research. The aim of this app is to provide original responses to issues related to raising awareness of quality of life and the value of nutritious food in everyday life. The User Centered Design (UCD) method needs to be applied to categorize various user information needs and package them into a mobile application design model. The results of the application design showed that the interface design for the pioneer food and health food raw material application was successfully created using the implementation of the UCD method with a number of revisions that have been updated since the evaluation as one of the feature developments in the application. The food recording feature and personalized recommendations implemented in this mobile application provide users with awareness and guidance in adopting healthier eating patterns. Keywords: Design Interface; Mobile Applications; Requirements Engineering; User-Centered Design Abstrak: Aplikasi Pemasok Makanan Sehat yang memperkenalkan inovasi terobosan untuk meningkatkan akses publik ke dan pengiriman makanan sehat, telah muncul sebagai fenomena baru di sektor makanan. Penelitian ini bermaksud untuk mengkaji dan mendeskripsikan bagaimana penggunaan pemasok makanan sehat berkontribusi dalam peningkatan kesehatan masyarakat dengan membuat makanan sehat dapat diakses untuk mendapatkan dan mengantarkannya dengan cepat. Pengembangan aplikasi mobile dengan tujuan menyediakan makanan dan bahan makanan sehat bagi masyarakat menjadi pokok bahasan penelitian ini. Tujuan dari aplikasi ini adalah untuk memberikan tanggapan orisinal terhadap masalah yang terkait dengan peningkatan kesadaran kualitas hidup dan nilai makanan bergizi dalam kehidupan sehari-hari. Metode User Centered Design (UCD) perlu diterapkan untuk mengkategorikan berbagai kebutuhan informasi pengguna dan mengemasnya ke dalam model desain aplikasi mobile. Hasil perancangan aplikasi didapatkan bahwa desain antarmuka aplikasi pelopor makanan dan bahan baku makanan kesehatan berhasil dibuat dengan menggunakan implementasi metode UCD dengan sejumlah revisi yang sudah diperbarui sejak evaluasi sebagai salah satu pengembangan fitur pada aplikasi. Fitur pencatatan makanan dan rekomendasi personal yang diterapkan pada aplikasi mobile ini memberikan pengguna kesadaran dan panduan dalam mengadopsi pola makan yang lebih sehat. Kata Kunci: Design Interface; Mobile Applications; Requirements Engineering; User-Centered Design

ANALYSIS OF PUBLIC OPINION ON INDONESIAN TELEVISION SHOWS USING SUPPORT VECTOR MACHINE Fidya Farasalsabila; Ema Utami; Muhammad Hanafi
JURTEKSI (Jurnal Teknologi dan Sistem Informasi) Vol 10, No 2 (2024): Maret 2024
Publisher : STMIK Royal

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33330/jurteksi.v10i2.2935

Abstract: There are a great number of academics that are now conducting research on sentiment analysis by employing supervised and machine learning techniques. The research can be carried out with the assistance of a variety of sources, including reviews of movies, reviews of Twitter, reviews of online products, blogs, discussion forums, and other social networks. With the progress of technology, individuals may now effortlessly utilize social media platforms to access and share information, as well as express their viewpoints to the general public, without any constraints of distance or time. Twitter is a social media network that serves as a repository for opinions. Diverse techniques are employed to provide optimal and realistically precise pressure detection. The analysis and discussion affirm that the Support Vector Machine (SVM) was effectively employed in this study, utilizing public opinion data on television program reviews in Indonesia. An SVM classifier is employed to examine the Twitter data set by utilizing various parameters. The study successfully completed the preprocessing process by collecting a total of 400 data points, consisting of 320 reviews from 4 television shows for training data and 80 reviews for testing. The data was filtered and classified using SVM, with 200 positive and 200 negative data points for comparison. The experiment utilized the SVM method using TF-IDF to achieve the most accurate test results. The test accuracy was 80%, while the training data accuracy reached 100%. Keywords: Sentiment Analysis; Support Vector Machine; Television Shows Review, TF-IDF, Abstrak: Saat ini, banyak akademisi sedang menyelidiki analisis sentimen melalui pemanfaatan teknik yang diawasi dan pembelajaran mesin. Kajian dapat dilakukan dengan menggunakan beberapa sumber seperti review film, review Twitter, review produk online, blog, forum diskusi, atau jejaring sosial lainnya. Dengan kemajuan teknologi, masyarakat kini dapat dengan mudah memanfaatkan platform media sosial untuk mengakses dan berbagi informasi, serta menyampaikan pandangan mereka kepada masyarakat umum, tanpa batasan jarak dan waktu. Twitter adalah jaringan media sosial yang berfungsi sebagai gudang opini. Beragam teknik digunakan untuk menghasilkan deteksi tekanan yang optimal dan presisi secara realistis. Analisis dan pembahasan menegaskan bahwa Support Vector Machine (SVM) efektif digunakan dalam penelitian ini, memanfaatkan data opini publik tentang review program televisi di Indonesia. Pengklasifikasi SVM digunakan untuk memeriksa kumpulan data Twitter dengan memanfaatkan berbagai parameter. Penelitian berhasil menyelesaikan proses preprocessing dengan mengumpulkan total 400 titik data yang terdiri dari 320 review dari 4 acara televisi untuk data pelatihan dan 80 review untuk pengujian. Data disaring dan diklasifikasikan menggunakan SVM, dengan 200 titik data positif dan 200 titik data negatif sebagai perbandingan. Percobaan ini menggunakan metode SVM dengan menggunakan TF-IDF untuk mencapai hasil pengujian yang paling akurat. Akurasi pengujiannya mencapai 80%, sedangkan akurasi data pelatihan mencapai 100%. Kata kunci: Analisis Sentimen, Review Tayangan Televisi, TF-IDF, Support Vector Machine

Deteksi Cyberbullying Menggunakan BERT dan Bi-LSTM Farasalsabila, Fidya; Utami, Ema; Hanafi, Hanafi
Jurnal Teknologi Vol 17 No 1 (2024): Jurnal Teknologi
Publisher : Jurnal Teknologi, Fakultas Teknik, Universitas AKPRIND Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34151/jurtek.v17i1.4636

Cyberbullying is a digital problem that is not a new phenomenon. This existed before the advent of social networks, and cyberbullying has a wide impact, including a person's mental and physiological conditions such as sadness, anxiety and depression. The main objective of this research is to develop an effective cyberbullying detection system using natural language processing techniques. The method used in this research includes the application of the BERT (Bi-Directional Encoder Representations from Transformers) and Bi-LSTM (Bi-Directional Long Short-Term Memory) models as a deep learning approach to analyze text and detect cyberbullying behavior. This approach allows the system to understand complex language contexts and capture patterns that traditional methods may find difficult to identify. Testing was carried out using a dataset that included various types of Indonesian language texts containing cyber bullying acts. The research results show that the combination of BERT and Bi-LSTM is able to provide superior detection performance with a high accuracy rate of 90% and the ability to identify variations of cyber bullying. This research makes a significant contribution to efforts to protect individuals from the negative impacts of cyber bullying through the development of a sophisticated and adaptive detection system.

Deteksi Cyberbullying Menggunakan BERT dan Bi-LSTM Farasalsabila, Fidya; Utami, Ema; Hanafi, Hanafi
Jurnal Teknologi Vol 17 No 1 (2024): Jurnal Teknologi
Publisher : Jurnal Teknologi, Fakultas Teknik, Universitas AKPRIND Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34151/jurtek.v17i1.4636

Cyberbullying is a digital problem that is not a new phenomenon. This existed before the advent of social networks, and cyberbullying has a wide impact, including a person's mental and physiological conditions such as sadness, anxiety and depression. The main objective of this research is to develop an effective cyberbullying detection system using natural language processing techniques. The method used in this research includes the application of the BERT (Bi-Directional Encoder Representations from Transformers) and Bi-LSTM (Bi-Directional Long Short-Term Memory) models as a deep learning approach to analyze text and detect cyberbullying behavior. This approach allows the system to understand complex language contexts and capture patterns that traditional methods may find difficult to identify. Testing was carried out using a dataset that included various types of Indonesian language texts containing cyber bullying acts. The research results show that the combination of BERT and Bi-LSTM is able to provide superior detection performance with a high accuracy rate of 90% and the ability to identify variations of cyber bullying. This research makes a significant contribution to efforts to protect individuals from the negative impacts of cyber bullying through the development of a sophisticated and adaptive detection system.

Boosting Methods for Multi-label Data Cyberbullying Farasalsabila, Fidya; Aritonang, Mhd Adi Setiawan; Jabnabillah, Faradiba; Moniva, Anip; Lestari, Verra Budhi; Handayani, Rizky
JURIKOM (Jurnal Riset Komputer) Vol 12, No 3 (2025): Juni 2025
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v12i3.8721

Easy accessibility to the internet and social media allows individuals to communicate anonymously, providing opportunities for abusive and harmful behavior. The psychological impact of cyberbullying can be very detrimental, triggering stress, depression, and even causing more serious consequences such as suicide. This paper describes cyberbullying sentiment analysis with a focus on the use of four different boosting methods, namely Gradient Booster, Gradient Booster, XGBoost, AdaBoost, dan LightGBM on a multi-label public dataset covering 6 categories. The aim of this research is to compare and analyze the relative performance of these boosting methods in overcoming the challenges of multi-label sentiment analysis in the context of cyberbullying. Results reveal that XGBoost and LightGBM have a tendency to more effectively overcome the challenges of detecting cyberbullying in more complex categories, making a positive contribution to the development of superior detection systems in the context of multi-label sentiment analysis. This research contributes to the field by providing a comparative analysis of state-of-the-art boosting algorithms, highlighting their strengths in multi-label classification tasks, and offering practical insights for developing more accurate and reliable cyberbullying detection systems. The findings from this study are expected to serve as a reference for future development of machine learning-based tools that can help mitigate the psychological harm caused by online abuse, particularly in detecting subtle and complex forms of cyberbullying behavior.

Pelatihan dasar pemrograman untuk membangkitkan minat siswa pada dunia pemrograman di SMA Kartini Batam Nasution, Nadia Widari; Candra, Joni Eka; Farasalsabila, Fidya; Reza, Widya; Kurnia, Okki; Simatupang, Devid Trinaldo
SELAPARANG: Jurnal Pengabdian Masyarakat Berkemajuan Vol 9, No 5 (2025): September
Publisher : Universitas Muhammadiyah Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31764/jpmb.v9i5.33988

AbstrakTuntutan global menuntut dunia pendidikan untuk selalu dan senantiasa menyesuaikan perkembangan teknologi terhadap usaha dalam peningkatan mutu pendidikan. SMA Kartini Batam adalah sekolah menengah atas swasta di Batam, Kepulauan Riau. Dengan visi dan misi yang jelas, SMA Kartini di Yayasan Keluarga Batam bertujuan untuk menciptakan lingkungan pembelajaran yang dinamis, iklusif, dan mendukung pengembangan karakter positif. Pemrograman Dasar adalah salah satu mata pelajaran dasar dalam bidang Teknologi, Informasi, dan Komunikasi (TIK). Pada pembelajaran sekarang ini sangat penting memiliki pengetahuan TIK. Namun, SMA Kartini Batam belum terbiasa menggunakan bahasa pemrograman Python untuk pemrograman. Oleh karena itu, dasar pemrograman penting dipelajari khususnya siswa/i jurusan teknik informatika dan komputer. Menerapkan dasar pemrograman di sekolah dapat membantu meningkatkan minat siswa dalam bidang teknologi seperti pembuatan aplikasi, game dan website. Dengan adanya pelatihan dasar pemrograman bagi siswa/i SMA Kartini Batam sebagai landasan yang penting untuk mempelajari bahasa pemrograman dan mengembangkan keterampilan pemrograman. Python merupakan bahasa pemrograman yang populer dan mudah dipelajari. Python banyak digunakan dalam pengembangan perangkat lunak, data science, dan machine learning. Menerapkan dasar pemrograman di sekolah juga dapat membantu meningkatkan minat siswa dalam bidang teknologi seperti pembuatan aplikasi, game dan website. Kata kunci: teknologi; dasar pemrograman; python; minat siswa; mutu pendidikan. AbstractGlobal demands require the world of education to always and continuously adapt to technological developments in efforts to improve the quality of education. SMA Kartini Batam is a private high school in Batam, Riau Islands. With a clear vision and mission, SMA Kartini at the Batam Family Foundation aims to create a dynamic, inclusive learning environment and support positive character development. Basic Programming is one of the basic subjects in the field of Technology, Information, and Communication (ICT). In today's learning, it is very important to have knowledge of ICT. However, SMA Kartini Batam is not yet accustomed to using the Python programming language for programming. Therefore, basic programming is important to learn, especially for students majoring in informatics and computer engineering. Implementing basic programming in schools can help increase student interest in technology fields such as application, game and website development. With basic programming training for SMA Kartini Batam students as an important foundation for learning programming languages and developing programming skills. Python is a popular and easy-to-learn programming language. Python is widely used in software development, data science, and machine learning. Implementing basic programming in schools can also help increase student interest in technology fields such as application, game and website development. Keywords: technology; basic progamming; python; student interests; quality of education.

Boosting Methods for Multi-label Data Cyberbullying Farasalsabila, Fidya; Aritonang, Mhd Adi Setiawan; Jabnabillah, Faradiba; Moniva, Anip; Lestari, Verra Budhi; Handayani, Rizky
JURNAL RISET KOMPUTER (JURIKOM) Vol. 12 No. 3 (2025): Juni 2025
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v12i3.8721

Easy accessibility to the internet and social media allows individuals to communicate anonymously, providing opportunities for abusive and harmful behavior. The psychological impact of cyberbullying can be very detrimental, triggering stress, depression, and even causing more serious consequences such as suicide. This paper describes cyberbullying sentiment analysis with a focus on the use of four different boosting methods, namely Gradient Booster, Gradient Booster, XGBoost, AdaBoost, dan LightGBM on a multi-label public dataset covering 6 categories. The aim of this research is to compare and analyze the relative performance of these boosting methods in overcoming the challenges of multi-label sentiment analysis in the context of cyberbullying. Results reveal that XGBoost and LightGBM have a tendency to more effectively overcome the challenges of detecting cyberbullying in more complex categories, making a positive contribution to the development of superior detection systems in the context of multi-label sentiment analysis. This research contributes to the field by providing a comparative analysis of state-of-the-art boosting algorithms, highlighting their strengths in multi-label classification tasks, and offering practical insights for developing more accurate and reliable cyberbullying detection systems. The findings from this study are expected to serve as a reference for future development of machine learning-based tools that can help mitigate the psychological harm caused by online abuse, particularly in detecting subtle and complex forms of cyberbullying behavior.

APPLYING USER-CENTERED DESIGN FOR MOBILE APPLICATIONS INTERFACE DESIGN Farasalsabila, Fidya; Lestari, Verra Budhi; Nugroho, Jangkung Tri; Pramudyantoro, Arvi; Utami, Ema
JURTEKSI (jurnal Teknologi dan Sistem Informasi) Vol. 10 No. 1 (2023): Desember 2023
Publisher : Lembaga Penelitian dan Pengabdian Kepada Masyarakat (LPPM) STMIK Royal Kisaran

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33330/jurteksi.v10i1.2619

Abstract: The Healthy Food Supplier App, which introduces groundbreaking innovations to increase public access to and delivery of healthy food, has emerged as a new phenomenon in the food sector. This research intends to examine and describe how the use of healthy food suppliers contributes to improving public health by making healthy food accessible to obtain and deliver it quickly. The development of mobile applications with the aim of providing healthy food and ingredients for the community is the subject of this research. The aim of this app is to provide original responses to issues related to raising awareness of quality of life and the value of nutritious food in everyday life. The User Centered Design (UCD) method needs to be applied to categorize various user information needs and package them into a mobile application design model. The results of the application design showed that the interface design for the pioneer food and health food raw material application was successfully created using the implementation of the UCD method with a number of revisions that have been updated since the evaluation as one of the feature developments in the application. The food recording feature and personalized recommendations implemented in this mobile application provide users with awareness and guidance in adopting healthier eating patterns. Keywords: Design Interface; Mobile Applications; Requirements Engineering; User-Centered Design Abstrak: Aplikasi Pemasok Makanan Sehat yang memperkenalkan inovasi terobosan untuk meningkatkan akses publik ke dan pengiriman makanan sehat, telah muncul sebagai fenomena baru di sektor makanan. Penelitian ini bermaksud untuk mengkaji dan mendeskripsikan bagaimana penggunaan pemasok makanan sehat berkontribusi dalam peningkatan kesehatan masyarakat dengan membuat makanan sehat dapat diakses untuk mendapatkan dan mengantarkannya dengan cepat. Pengembangan aplikasi mobile dengan tujuan menyediakan makanan dan bahan makanan sehat bagi masyarakat menjadi pokok bahasan penelitian ini. Tujuan dari aplikasi ini adalah untuk memberikan tanggapan orisinal terhadap masalah yang terkait dengan peningkatan kesadaran kualitas hidup dan nilai makanan bergizi dalam kehidupan sehari-hari. Metode User Centered Design (UCD) perlu diterapkan untuk mengkategorikan berbagai kebutuhan informasi pengguna dan mengemasnya ke dalam model desain aplikasi mobile. Hasil perancangan aplikasi didapatkan bahwa desain antarmuka aplikasi pelopor makanan dan bahan baku makanan kesehatan berhasil dibuat dengan menggunakan implementasi metode UCD dengan sejumlah revisi yang sudah diperbarui sejak evaluasi sebagai salah satu pengembangan fitur pada aplikasi. Fitur pencatatan makanan dan rekomendasi personal yang diterapkan pada aplikasi mobile ini memberikan pengguna kesadaran dan panduan dalam mengadopsi pola makan yang lebih sehat. Kata Kunci: Design Interface; Mobile Applications; Requirements Engineering; User-Centered Design

ANALYSIS OF PUBLIC OPINION ON INDONESIAN TELEVISION SHOWS USING SUPPORT VECTOR MACHINE Farasalsabila, Fidya; Utami, Ema; Hanafi, Muhammad
JURTEKSI (jurnal Teknologi dan Sistem Informasi) Vol. 10 No. 2 (2024): Maret 2024
Publisher : Lembaga Penelitian dan Pengabdian Kepada Masyarakat (LPPM) STMIK Royal Kisaran

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33330/jurteksi.v10i2.2935

Abstract: There are a great number of academics that are now conducting research on sentiment analysis by employing supervised and machine learning techniques. The research can be carried out with the assistance of a variety of sources, including reviews of movies, reviews of Twitter, reviews of online products, blogs, discussion forums, and other social networks. With the progress of technology, individuals may now effortlessly utilize social media platforms to access and share information, as well as express their viewpoints to the general public, without any constraints of distance or time. Twitter is a social media network that serves as a repository for opinions. Diverse techniques are employed to provide optimal and realistically precise pressure detection. The analysis and discussion affirm that the Support Vector Machine (SVM) was effectively employed in this study, utilizing public opinion data on television program reviews in Indonesia. An SVM classifier is employed to examine the Twitter data set by utilizing various parameters. The study successfully completed the preprocessing process by collecting a total of 400 data points, consisting of 320 reviews from 4 television shows for training data and 80 reviews for testing. The data was filtered and classified using SVM, with 200 positive and 200 negative data points for comparison. The experiment utilized the SVM method using TF-IDF to achieve the most accurate test results. The test accuracy was 80%, while the training data accuracy reached 100%. Keywords: Sentiment Analysis; Support Vector Machine; Television Shows Review, TF-IDF, Abstrak: Saat ini, banyak akademisi sedang menyelidiki analisis sentimen melalui pemanfaatan teknik yang diawasi dan pembelajaran mesin. Kajian dapat dilakukan dengan menggunakan beberapa sumber seperti review film, review Twitter, review produk online, blog, forum diskusi, atau jejaring sosial lainnya. Dengan kemajuan teknologi, masyarakat kini dapat dengan mudah memanfaatkan platform media sosial untuk mengakses dan berbagi informasi, serta menyampaikan pandangan mereka kepada masyarakat umum, tanpa batasan jarak dan waktu. Twitter adalah jaringan media sosial yang berfungsi sebagai gudang opini. Beragam teknik digunakan untuk menghasilkan deteksi tekanan yang optimal dan presisi secara realistis. Analisis dan pembahasan menegaskan bahwa Support Vector Machine (SVM) efektif digunakan dalam penelitian ini, memanfaatkan data opini publik tentang review program televisi di Indonesia. Pengklasifikasi SVM digunakan untuk memeriksa kumpulan data Twitter dengan memanfaatkan berbagai parameter. Penelitian berhasil menyelesaikan proses preprocessing dengan mengumpulkan total 400 titik data yang terdiri dari 320 review dari 4 acara televisi untuk data pelatihan dan 80 review untuk pengujian. Data disaring dan diklasifikasikan menggunakan SVM, dengan 200 titik data positif dan 200 titik data negatif sebagai perbandingan. Percobaan ini menggunakan metode SVM dengan menggunakan TF-IDF untuk mencapai hasil pengujian yang paling akurat. Akurasi pengujiannya mencapai 80%, sedangkan akurasi data pelatihan mencapai 100%. Kata kunci: Analisis Sentimen, Review Tayangan Televisi, TF-IDF, Support Vector Machine

Penerapan Algoritma Metaheuristik untuk Optimasi Nilai K Pada K-NN dalam Pengelompokan Faktor Penyebab Stunting Aritonang, Mhd Adi Setiawan; Ardilla Siregar, Maya; Ani Arnomo, Sasa; Farasalsabila, Fidya
Jurnal Pustaka AI (Pusat Akses Kajian Teknologi Artificial Intelligence) Vol 5 No 3 (2025): Pustaka AI (Pusat Akses Kajian Teknologi Artificial Intelligence)
Publisher : Pustaka Galeri Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55382/jurnalpustakaai.v5i3.1367

Stunting is a health issue that has a negative impact on people's quality of life and the development of a bangsa. Finding the cause of stunting accurately is a crucial step in developing effective mitigation strategies. K-Nearest Neighbor (K-NN) is one machine learning algorithm that is frequently used for classification and data analysis, but its performance is greatly impacted by the choice of an ideal K parameter. This study highlights the use of metaheuristic algorithms, such as genetic algorithms (GA) and particle swarm optimization (PSO), to optimize K in K-NN in order to identify the stunting factor. This method uses the kekuatan eksplorasi and eksploitasi algoritma metaheuristik to determine parameter K that yields optimal accuracy. Based on the results of the metaheuristic algorithm, it is concluded that without optimization, K-NN only achieves an accuracy of roughly 63%, highlighting the importance of choosing a suitable K value. When GA is used in K-NN optimization, the accuracy increases significantly, reaching 73%, indicating its ability to effectively explore the solution space. On the other hand, PSO also increases accuracy to 74%. It is hoped that the results of this study will provide significant contributions to the development of a more reliable model for analyzing the factors that contribute to stunting, thereby enhancing the use of data-based decision-making in attempts to address the stunting problem holistically.

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