Garuda - Garba Rujukan Digital

Comparison of Salp Swarm Algorithm and Particle Swarm Optimization as Feature Selection Techniques for Recession Sentiment Analysis in Indonesia Kristiyanti, Dinar Ajeng; Sanjaya, Samuel Ady; Irmawati, Irmawati; Ekachandra, Kristian; Suhali, Jason; Hairul Umam, Akhmad
JOIV : International Journal on Informatics Visualization Vol 9, No 5 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.5.3102

Amidst global economic uncertainty, this study focuses on Twitter sentiment during the global recession issue on social media, especially in Indonesia. By utilizing sentiment analysis, this study uses machine learning algorithms such as Naïve Bayes (NB), Support Vector Machine (SVM), K-Nearest Neighbor (KNN) which are still less than optimal on high-dimensional Twitter data. The purpose of this study is to improve the accuracy of conventional machine learning using basic metaheuristic algorithms, namely the Salp Swarm Algorithm (SSA) and Particle Swarm Optimization (PSO) as feature selection. From January to May 2023, this study captures the evolving sentiment in response to economic conditions. Data preprocessing, including labeling through the TextBlob and VADER libraries, sets the stage for the analysis. Performance is compared based on labeling techniques, feature selection, and classification algorithms. Specifically, when applied to VADER labeled data without feature selection, the SVM model achieves an outstanding accuracy of 83% and an F1 score of 67%—notably, the application of SSA and PSO results in a reduction in model accuracy by 1%. However, the application of SSA and PSO slightly reduced the model accuracy performance by 1%. On the TextBlob labeled data, SVM showed an outstanding performance (80% accuracy, 77% F1 score). Interestingly, PSO on TextBlob data with SVM significantly decreased the model's performance. These findings contribute significantly to understanding the intricacies of sentiment dynamics during economic uncertainty on social media platforms, with SVM emerging as a strong choice for practical sentiment analysis.

Digitalization of village based on information technology through developing BUMDes MSMEs website and logo Kristiyanti, Dinar Ajeng; Alexandra, Yoanita; Situmorang, Ringkar; Athira, Reva Fakhrana; William, Juanito Arvin
Jurnal Inovasi Hasil Pengabdian Masyarakat (JIPEMAS) Vol 7 No 1 (2024)
Publisher : University of Islam Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33474/jipemas.v7i1.20803

The development of technology has transformed the global economic landscape, including in the realm of Micro, Small, and Medium Enterprises (MSMEs). However, MSME actors in various villages and regions often still have limitations in understanding the importance of digitalization, especially in utilizing technology to promote their products. Therefore, this Community Engagement activity is initiated with the main goal of creating and disseminating understanding about the importance of implementing a profile website, financial management application, and logo in efforts to increase visibility and sales of products for MSME actors in the Serdang Tirta Kencana Village-Owned Enterprises (BUMDes) in Tangerang, Indonesia. The implementation method is based on Community-Based Participatory Research Program (CBPR) with the following stages are location survey, website and logo creation, socialization, and evaluation. Through close collaboration with local stakeholders from BUMDes Serdang Tirta Kencana, this activity has successfully empowered MSME actors with a strong visual identity and significant digital presence. The result is a 95% increase in the skills of MSME actors in BUMDes Serdang Tirta Kencana. It is hoped that through this activity, MSME actors can become competitive and have a positive impact on local economic growth and community empowerment

A Comparative Analysis of Building Hidden Layer, Activation Function, and Optimizer on Neural Network Sentiment Analysis Sanjaya, Samuel Ady; Kristiyanti, Dinar Ajeng; Irmawati, Irmawati; Hadinata, Faustine Ilone; Karaeng, Cristin Natalia
JOIV : International Journal on Informatics Visualization Vol 9, No 3 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.3.2906

The increasing diversity of opinions on social media offers a rich source for sentiment analysis, especially on controversial issues like the potential recession in Indonesia. This study aims to examine social media sentiment by utilizing three Deep Learning methods: Long Short-Term Memory (LSTM), Convolutional Neural Network (CNN), and Recurrent Neural Network (RNN). The main objective is to configure key hyperparameters, including the number of hidden layers, activation functions, and optimizers, to optimize performance. A dataset of 38,000 cleaned Twitter posts was used for this study. The preprocessing steps involve various techniques to prepare analysis, including case folding to standardize text, removal of punctuation to eliminate noise, stemming to reduce words to their root forms, and sentiment labeling using advanced tools like VADER and BERT to ensure accurate classification. Each deep learning model is trained using a diverse range of configurations for activation functions, such as Sigmoid and Swish, as well as optimizers like Adam and others to fine-tune performance. Among the models, the CNN, configured with 15 hidden layers, a Sigmoid activation function, and the Adam optimizer, outperformed the others, achieving the highest accuracy of 0.870 and a low loss of 0.316. The results highlight that while the number of hidden layers influences model performance, the choice of activation function and optimizer has a more significant impact on accuracy. Furthermore, the findings offer implications for future research, suggesting that activation functions and optimizers should be prioritized over hidden layers when aiming for improved sentiment analysis performance in various contexts.

DEBUT YOUROWNHERO: KAMPANYE PENYADARTAHUAN PENCEGAHAN KEKERASAN SEKSUAL UNTUK SATUAN PENDIDIKAN Primadini, Intan; Hidayat, Wanda Gema Prasadio Akbar; Kurniawan, Paulus Heru Wibowo; Kristiyanti, Dinar Ajeng; Demi, Sita Winiawati; Willona, Yemima
Community Development Journal : Jurnal Pengabdian Masyarakat Vol. 5 No. 1 (2024): Volume 5 No 1 Tahun 2024
Publisher : Universitas Pahlawan Tuanku Tambusai

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/cdj.v5i1.24707

Penelitian ini bertujuan untuk membangun pemahaman bersama di antara seluruh pihak di satuan pendidikan mengenai bentuk-bentuk dasar kekerasan seksual yang sering terabaikan. Para peneliti membagi rangkaian penelitian ini menjadi tiga tahap penting. Tahap pertama yaitu tahap persiapan. Tahap kedua adalah implementasi yang meliputi kegiatan offline dan tantangan. Tahap terakhir adalah pengujian dan evaluasi hasil. Berdasarkan hasil evaluasi menunjukkan adanya peningkatan pemahaman yang signifikan di kalangan siswa perwakilan kelas 9 SMP Negeri Cisauk yang telah mengikuti program edukasi terkait isu kekerasan seksual melalui Debut # YourOwnHero. Program ini berdampak positif terhadap peningkatan kesadaran, pemahaman, dan komitmen mereka dalam menangani dan mencegah keterbukaan informasi dan kekerasan seksual. Hal ini terlihat dari hasil rata-rata nilai evaluasi kegiatan Debut secara offline yang menunjukkan bahwa program ini berhasil memberikan pengetahuan yang relevan, memperkuat komitmen penyelesaian kekerasan dan kekerasan seksual, serta membantu mereka memahami pentingnya tindakan preventif dan responsif. dalam situasi yang berkaitan dengan pemahaman dan kekerasan seksual. Selain itu, dalam kegiatan berani Debut #YourOwnHero yaitu edukasi melalui platform media sosial Instagram, mencapai target yang obyektif. Oleh karena itu, dapat disimpulkan bahwa program Debut #YourOwnHero yang berani dan menarik berhasil mencapai targetnya.

DEBUT #YOUROWNHERO: KAMPANYE PENYADARTAHUAN PENCEGAHAN KEKERASAN SEKSUAL UNTUK SATUAN PENDIDIKAN Primadini, Intan; Hidayat, Wanda Gema Prasadio Akbar; Kurniawan, Paulus Heru Wibowo; Kristiyanti, Dinar Ajeng; Demi, Sita Winiawati; Willona, Yemima
Community Development Journal : Jurnal Pengabdian Masyarakat Vol. 5 No. 1 (2024): Volume 5 No 1 Tahun 2024
Publisher : Universitas Pahlawan Tuanku Tambusai

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/cdj.v5i1.25169

Penelitian ini bertujuan untuk membangun pemahaman bersama di antara seluruh pihak di satuan pendidikan mengenai bentuk-bentuk dasar kekerasan seksual yang sering terabaikan. Para peneliti membagi rangkaian penelitian ini menjadi tiga tahap penting. Tahap pertama yaitu tahap persiapan. Tahap kedua adalah implementasi yang meliputi kegiatan offline dan tantangan. Tahap terakhir adalah pengujian dan evaluasi hasil. Berdasarkan hasil evaluasi menunjukkan adanya peningkatan pemahaman yang signifikan di kalangan siswa perwakilan kelas 9 SMP Negeri Cisauk yang telah mengikuti program edukasi terkait isu kekerasan seksual melalui Debut # YourOwnHero. Program ini berdampak positif terhadap peningkatan kesadaran, pemahaman, dan komitmen mereka dalam menangani dan mencegah keterbukaan informasi dan kekerasan seksual. Hal ini terlihat dari hasil rata-rata nilai evaluasi kegiatan Debut secara offline yang menunjukkan bahwa program ini berhasil memberikan pengetahuan yang relevan, memperkuat komitmen penyelesaian kekerasan dan kekerasan seksual, serta membantu mereka memahami pentingnya tindakan preventif dan responsif. dalam situasi yang berkaitan dengan pemahaman dan kekerasan seksual. Selain itu, dalam kegiatan berani Debut #YourOwnHero yaitu edukasi melalui platform media sosial Instagram, mencapai target yang obyektif. Oleh karena itu, dapat disimpulkan bahwa program Debut #YourOwnHero yang berani dan menarik berhasil mencapai targetnya.

Perancangan dan Implementasi E-Commerce Corrugated Carton Box Menggunakan Metode Rapid Application Development Takasili, Tevin; Kristiyanti, Dinar Ajeng
Jurnal Teknik Informatika dan Sistem Informasi Vol 11 No 1 (2025): JuTISI
Publisher : Maranatha University Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28932/jutisi.v11i1.9415

PT Hora Cipta Jaya is a company specializing in the sale of corrugated cardboard boxes. PT Hora Cipta Jaya typically uses corrugated cardboard boxes to package goods or products for shipping. Due to their strength, durability, and recyclability, many industrial companies use them to package their products. PT Hora Cipta Jaya still conducts sales processes manually, starting from transactions, marketing, to recording and reporting. Employees still need to manually record all company data during sales using Excel. Errors often occur during the purchase and sale of products, such as mistakes in recording and pricing calculations, as well as errors in order data entry and shipping. The aim of this research is to assist the company in managing data and designing a system that features online ordering, payment, shipping, reporting, and product promotion. We developed this system using the RAD method, utilizing PHP programming language and MySQL database. The system development process is divided into four stages: requirements analysis, system design, system implementation, and application feasibility analysis. The research resulted in an e-commerce system that helps the company manage data, facilitates online product sales transactions and broader product marketing, and automatically records invoices and reports. All features of this system are now operational after successfully completing testing phases for both admin and customer parts using the black box testing method. The system scored 78 for the admin section and 79.5 for the customer section from each respondent using the System Usability Scale, achieving a class B rating (good category).

WORD2VEC OPTIMALIZATION USING TRANSFER LEARNING IN INDONESIAN LANGUAGE FOR HIGHER EDUCATION Hadianti, Sri; Riana, Dwiza; Tohir, Herdian; Jarwadi, Jarwadi; Rosdiana, Tjaturningsih; Sopandi, Evi; Kristiyanti, Dinar Ajeng
JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) Vol. 11 No. 2 (2025): JITK Issue November 2025
Publisher : LPPM Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/jitk.v11i2.6051

Natural language processing (NLP) in Indonesian faces challenges due to limited linguistic resources, particularly in developing optimal word embedding models. This study optimizes the Word2Vec model for Indonesian in higher education contexts by leveraging transfer learning and lexicon expansion. Using a dataset of 4,463 higher education related tweets consisting of positive and negative sentiment categories, the proposed NewWord2Vec model combined with a Support Vector Machine (SVM) classifier achieved a 4% improvement in word detection accuracy compared to the standard Word2Vec. This enhancement demonstrates better performance in capturing linguistic nuances and sentiment orientation in Indonesian text. However, the model’s applicability remains limited to higher education terminology, and potential biases from transfer learning must be addressed. Future research should expand the dataset to diverse domains and refine the transfer learning process to better capture contextual variations in Indonesian. These findings contribute to advancing NLP applications in Indonesian, particularly for automated assessment systems, recommendation tools, and academic decision-making processes

Classifying classical music’s therapeutic effects using deep learning: a review Angelin, Angelin; Sanjaya, Samuel Ady; Kristiyanti, Dinar Ajeng
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 6: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i6.pp4933-4942

Mental health issues are the leading cause of global disability, increasing the need for treatment options. While there is much research on the emotional recognition of music in general, there is a gap in studies that directly connect musical features with their therapeutic effects using deep learning. This systematic literature review explores the use of deep learning in classifying the therapeutic effects of classical music for mental health. Following the preferred reporting items for systematic reviews and meta-analyses (PRISMA) framework, a total of 15 papers were reviewed. This review synthesized studies on the role of musical elements that affect mental states. Different feature extraction methods, including mel-frequency cepstral coefficients (MFCCs), spectral contrast, and chroma features, are discussed for their roles in classifying these therapeutic effects. This review also looks at deep learning algorithms like convolutional neural network (CNN), deep neural network (DNN), long short-term memory (LSTM) network, and combined models to assess their effectiveness. Common evaluation methods are also assessed to measure the performance of these models in audio classification. This review highlights the potential of combining deep learning with classical music for mental health support, and to future possibilities for applying these methods in the real world.

Title

Found 18 Documents
Search

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Title Search

Found 18 Documents Search

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Title

Found 18 Documents
Search