Garuda - Garba Rujukan Digital

How to support teenagers in social media utilization: based on mother's experience Ira Kusumawaty; Fadly; Tri Basuki Kurniawan; Yunike
International Journal of Advanced Health Science and Technology Vol. 2 No. 3 (2022): June
Publisher : Forum Ilmiah Teknologi dan Ilmu Kesehatan (FORITIKES)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/ijahst.v2i3.14

The complexity and vulnerability of physical, psychological and social conditions result in the possibility of adolescents slumping in the circumstance of social media addiction in the existing era of digitalization. The problem of mother’s support to adolescents is very complex and undeniably becomes very necessary in directing teenagers throughout the utilization of social media. However, research related to the assistance of a mother towards adolescents in accompanying social media is still highly restricted. This study intends to explore forms of mother assistance to adolescents in utilizing social media based on their experiences. This qualitative study applied a phenomenological approach, involving twelve mothers with adolescent children and determined based on purposive sampling techniques. Data collection is done through in-depth interviews and compiled into transcripts, further determining the code, categories, and themes. The analysis process uses Colaizzi method, so that after the theme is formulated, the validation of the theme by participants is carried out. Trustworthiness, peer de briefing is conducted to ensure the validity of research results. The three themes produced related to the mother's experiences in accompanying adolescents are educating themselves, openness in communicating and formulating a mutual commitment. It can be concluded that the mother is a strong figure in keeping teenagers from falling into the heartbreaking condition of social media addiction. It is recommended that social media addiction prevention programs be developed through the coordination of families, teachers and peers based on the policies of the education department.

Analisa Tingkat Pelayanan IT Service Management pada Penerapan Sistem Ujian Nasional berbasis Komputer dengan menggunakan Kerangka Kerja ITIL v3 Evi Novilia; Widya Cholil; Tri Basuki Kurniawan
Sains, Aplikasi, Komputasi dan Teknologi Informasi Vol 2, No 1 (2020): Sains, Aplikasi, Komputasi dan Teknologi Informasi
Publisher : Universitas Mulawarman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30872/jsakti.v2i1.2570

Ujian Nasional berbasis Komputer (UNBK) sudah menjadi bagian dari evaluasi pendidikan yang bertujuan untuk memetakan bagaimana kondisi pendidikan di Indonesia. UNBK pertama kali dilaksanakan pada tahun 2014 secara dari dan dilakukan secara terbatas. Namun sejak tahun 2017 proses evaluasi berbasis komputer sudah diwajibkan di seluruh sekolah atas, termasuk MAN 1 Palembang, MAN 2 Palembang dan MAN Musi Rawas. Dalam mendukung proses evaluasi berbasis komputer diperlukan sistem Ujian Nasional yang terintegrasi. Kendala yang terjadi adalah hanya beberapa sekolah atas yang lolos uji verifikasi, umumnya disebabkan oleh jaringan, proses data, proses sistem yang berjalan dan layanan dari segi TI. Penelitian dilakukan dengan meninjau bagaimana kualitas, kontinuitas dan kinerja layanan TI dalam sistem dengan pendekatan service design Framework ITIL v3, dengan menggunakan 7 parameter utama yakni Service Catalog Management, Service Level Management, Service Design Process, Service Design Technology Related Activities, Organizing for Service Design, Service Design Technology Consideration, Service Design Process Implementation Consideration. Dari hasil asesmen, dihasilkan nilai asesmen tertinggi untuk Service Catalog Management dan nilai asesmen terendah Service Design Technology Related Activities

KLASIFIKASI OPINI MASYARAKAT TENTANG HARGA KARET MENGGUNAKAN MACHINE LEARNING Yustida Bellini; M Izman Herdiansyah; Tri Basuki Kurniawan; Yesi Novaria Kunang
Jusikom : Jurnal Sistem Komputer Musirawas Vol 8 No 1 (2023): Jusikom : Jurnal Sistem Komputer Musirawas JUNI
Publisher : LPPM UNIVERSITAS BINA INSAN

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32767/jusikom.v8i1.2058

Indonesia is the world’s largest rubber producer after Thailand with an average income of 3.107.544 tons/year, with these data many Indonesian people become rubber farmers to make ends meet. The price of rubber has a large impact on the lives of the Indonesian people so that when the price of tubber decreases or increases, many people speculate and have opinions about the price of rubber, therefore this study aims to classify public opinion regarding the price of rubber. People’s opinions and expressions regarding the price of rubber are often expressed by them on the social networking site Twitter, Twitter is the most common public means of expressing the emotions of people all over the world today. In this study, the classification of public opinion regarding rubber prices was carried out based on public opinion on the social networking site Twitter. The purpose of this study is to find out the sentiments circulating on Twitter regarding the price of rubber using Naïve Bayes algorithm. Based on the result of the research that has been done, negative sentiment is obtained by 92% and positive sentiment is obtained by 62%, while the accuracy value is obtained by 84%. The conclusion of this study is that the community has more negative than positive opinions about the price of rubber.

KLASIFIKASI OPINI MASYARAKAT TENTANG HARGA KARET MENGGUNAKAN MACHINE LEARNING Yustida Bellini; M Izman Herdiansyah; Tri Basuki Kurniawan; Yesi Novaria Kunang
Jusikom : Jurnal Sistem Komputer Musirawas Vol 8 No 1 (2023): Jusikom : Jurnal Sistem Komputer Musirawas JUNI
Publisher : LPPM UNIVERSITAS BINA INSAN

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32767/jusikom.v8i1.2058

Indonesia is the world’s largest rubber producer after Thailand with an average income of 3.107.544 tons/year, with these data many Indonesian people become rubber farmers to make ends meet. The price of rubber has a large impact on the lives of the Indonesian people so that when the price of tubber decreases or increases, many people speculate and have opinions about the price of rubber, therefore this study aims to classify public opinion regarding the price of rubber. People’s opinions and expressions regarding the price of rubber are often expressed by them on the social networking site Twitter, Twitter is the most common public means of expressing the emotions of people all over the world today. In this study, the classification of public opinion regarding rubber prices was carried out based on public opinion on the social networking site Twitter. The purpose of this study is to find out the sentiments circulating on Twitter regarding the price of rubber using Naïve Bayes algorithm. Based on the result of the research that has been done, negative sentiment is obtained by 92% and positive sentiment is obtained by 62%, while the accuracy value is obtained by 84%. The conclusion of this study is that the community has more negative than positive opinions about the price of rubber.

Identifikasi Komentar Spam Pada Sosial Media Zena Lusi; Ayu Eka Saputri; Tri Basuki Kurniawan
Neptunus: Jurnal Ilmu Komputer Dan Teknologi Informasi Vol. 2 No. 2 (2024): Mei: Neptunus: Jurnal Ilmu Komputer Dan Teknologi Informasi
Publisher : Asosiasi Riset Teknik Elektro dan Informatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61132/neptunus.v2i2.100

The use of social media is already powerful and difficult to avoid. Social media users are not only limited to the general public, but also public figures and even economic actors who use social media as a means of marketing. In every post from the account owner, there will always be followers who can give likes and comments. Unfortunately, not all comments are related to the uploaded post. One of the most annoying comments is spam comments. Spam comments are comments that are not clear and contain about business (promos / selling), links or various other things that are promoting something. Using the Naive Bayes algorithm, this study wants to identify spam comments, especially on Instagram social media. Where the data is retrieved using the tools provided by Google. Which is then processed with the Rapidminer application to get the Naive Bayes calculation results.

A Proposed Model for Detecting Learning Styles Based on the Felder-Silverman Model Using KNN and LR with Electroencephalography (EEG) Muhammad Said Hasibuan; R Rizal Isnanto; Deshinta Arrova Dewi; Tri Basuki Kurniawan; Ming-Lang Yeh; Adi Wijaya
Journal of Applied Data Sciences Vol 6, No 2: MAY 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v6i2.659

The identification of learning styles plays a crucial role in enhancing personalized education and optimizing learning outcomes. This research proposes a model for detecting learning styles based on the Felder-Silverman model using two machine learning algorithms: K-Nearest Neighbors (KNN) and Linear Regression (LR). Electroencephalography (EEG) data, known for its ability to capture cognitive and neural activity, serves as the primary dataset for this study. The proposed model was tested on a dataset comprising EEG signals collected during various learning tasks. Feature extraction and preprocessing techniques were employed to ensure high-quality input for the learning algorithms. The experimental results revealed that the LR-based model achieved an accuracy of 96.4%, significantly outperforming the KNN-based model, which obtained an accuracy of 89.9%. These findings highlight the potential of EEG-based models for accurately identifying learning styles, offering valuable insights for educators and researchers aiming to implement adaptive learning systems. This study demonstrates the feasibility and effectiveness of combining EEG data with machine learning techniques for learning style detection, paving the way for more personalized and efficient educational approaches. Future research will explore the integration of additional physiological data and advanced machine learning methods to further improve model accuracy and applicability.

Deep Learning Based Face Mask Detection System Using MobileNetV2 for Enhanced Health Protocol Compliance Fadly Fadly; Tri Basuki Kurniawan; Deshinta Arrova Dewi; Mohd Zaki Zakaria; Putri Aisha Athira binti Hisham
Journal of Applied Data Sciences Vol 5, No 4: DECEMBER 2024
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v5i4.476

Personal protective equipment (PPE) is crucial in mitigating the spread of infections within the pharmacy industry, manufacturing sectors, and healthcare facilities. Airborne particles and contaminants can be released during the handling of pharmaceuticals, the operation of machinery, or patient care activities. These particles can be transmitted through close contact with an infected individual or by touching contaminated surfaces and then touching one's face (mouth, nose, or eyes). PPE, including face masks, plays a vital role in minimizing the risk of transmission of infectious diseases. Although mandates for wearing face masks might relax as situations improve and vaccination rates increase, staying prepared for potential future outbreaks and the resurgence of infectious diseases remains important. Therefore, an automated system for face mask detection is important for future use. This research proposes real-time face mask detection by identifying who is (i) not wearing a mask and (ii) wearing a mask. This research presents a deep-learning approach using a pre-trained model, MobileNet-V2. The model is trained on a 10,000 dataset of images of individuals with and without masks. The result shows that the pre-trained MobileNet-V2 model obtained a high accuracy of 98.69% on the testing dataset.

Scalable Machine Learning Approaches for Real-Time Anomaly and Outlier Detection in Streaming Environments Deshinta Arrova Dewi; Harprith Kaur Rajinder Singh; Jeyarani Periasamy; Tri Basuki Kurniawan; Henderi Henderi; M. Said Hasibuan
Journal of Applied Data Sciences Vol 5, No 4: DECEMBER 2024
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v5i4.444

The prevalence of streaming data across various sectors poses significant challenges for real-time anomaly detection due to its volume, velocity, and variability. Traditional data processing methods often need to be improved for such dynamic environments, necessitating robust, scalable, and efficient real-time analysis systems. This study compares two advanced machine learning approaches—LSTM autoencoders and Matrix Profile algorithms—to identify the most effective method for anomaly detection in streaming environments using the NYC taxi dataset. Existing literature on anomaly detection in streaming data highlights various methodologies, including statistical tests, window-based techniques, and machine learning models. Traditional methods like the Generalized ESD test have been adapted for streaming data but often require a full historical dataset to function effectively. In contrast, machine learning approaches, particularly those using LSTM networks, are noted for their ability to learn complex patterns and dependencies, offering promising results in real-time applications. In a comparative analysis, LSTM autoencoders significantly outperformed other methods, achieving an F1-score of 0.22 for anomaly detection, notably higher than other techniques. This model demonstrated superior capability in capturing temporal dependencies and complex data patterns, making it highly effective for the dynamic and varied data in the NYC taxi dataset. The LSTM autoencoder's advanced pattern recognition and anomaly detection capabilities confirm its suitability for complex, high-velocity streaming data environments. Future research should explore the integration of LSTM autoencoders with other machine-learning techniques to enhance further the accuracy, scalability, and efficiency of anomaly detection systems. This study advances our understanding of scalable machine-learning approaches and underscores the critical importance of selecting appropriate models based on the specific characteristics and challenges of the data involved.

Fake vs Real Image Detection Using Deep Learning Algorithm Fatoni Fatoni; Tri Basuki Kurniawan; Deshinta Arrova Dewi; Mohd Zaki Zakaria; Abdul Muniif Mohd Muhayeddin
Journal of Applied Data Sciences Vol 6, No 1: JANUARY 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v6i1.490

The purpose of this research project is to address the growing issues presented by modified visual information by developing a deep learning model for identifying between real and fake images. To enhance accuracy, this project evaluates the effectiveness of deep learning algorithms such as Residual Neural Network (ResNet), Visual Geometry Group 16 (VGG16), and Convolutional Neural Network (CNN) together with Error Level Analysis (ELA) as preprocessing the dataset. The CASIA dataset contains 7,492 real images and 5,124 fake images. The images included are from a wide range of random subjects, including buildings, fruits, animals, and more, providing a comprehensive dataset for model training and validation. This research examined models' effectiveness through experiments, measuring their training and validation accuracies. It comes out with the best accuracy of each model, which is for Convolutional Neural Network (CNN), 94% for training accuracy, and validation accuracy of 92%. For VGG16, with both training and validation accuracy reaching 94%. Lastly, Residual Neural Network (ResNet) demonstrated optimal performance with 95% training accuracy and 93% validation accuracy. This project also constructs a system prototype for practical applications, offering an interface for real-world testing. When integrating into the system prototype, only Residual Neural Network (ResNet) shows consistency and effectiveness when predicting both fake and real images, and this led to the decision to choose ResNet for integration into the system. Furthermore, the project identified several areas for improvement. Firstly, expanding the model comparison for discovering more successful algorithms. Next, improving the dataset preprocessing phase by incorporating filtering or denoising techniques. Lastly, refining the system prototype for greater appeal and user-friendliness has the potential to attract a larger audience.

Efficient Fruit Grading and Selection System Leveraging Computer Vision and Machine Learning Deshinta Arrova Dewi; Tri Basuki Kurniawan; Rajermani Thinakaran; Malathy Batumalay; Shabana Habib; Muhammad Islam
Journal of Applied Data Sciences Vol 5, No 4: DECEMBER 2024
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v5i4.443

Automated fruit grading is crucial to overcoming the time and accuracy challenges posed by manual methods, which are often limited by subjective human judgment. This study introduces an intelligent grading system leveraging computer vision and AI to improve speed and consistency in assessing fruit quality. Using high-resolution imaging and advanced feature extraction, including grayscale processing, binarization, and enhancement, the system achieves non-destructive, efficient sorting for fruits like apples, bananas, and oranges. Grayscale processing reduces image complexity while preserving essential details, binarization isolates the fruit from its background, and enhancement highlights critical features. Notably, the Edge Pixel method proved most effective, achieving 79.20% accuracy in grading, while the Grayscale Pixel method reached 93.94% accuracy for fruit types. Edge Pixel also achieved 80.32% in differentiating grading types, showcasing its ability to capture essential shapes and edges. Fruits are classified into four grades: Grade_01 (highest quality), Grade_02 (minor imperfections), Grade_03 (notable defects but consumable), and Grade_04 (unfit for consumption). A specialized dataset supports model training, ensuring practical real-world application. The study concludes that this automated system offers significant improvements over traditional grading, providing a scalable, objective, and reliable solution for the agricultural sector, ultimately enhancing productivity and quality assurance.

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