Garuda - Garba Rujukan Digital

AR Make-up Filter for Social Media using the HSV Color Extraction Maisevli Harika; Setiadi Rachmat; Nurul Dewi Aulia; Zulfa Audina Dwi; Vandha Pradwiyasma Widartha
JOIN (Jurnal Online Informatika) Vol 7 No 2 (2022)
Publisher : Department of Informatics, UIN Sunan Gunung Djati Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15575/join.v7i2.994

Choosing the appropriate cosmetics is an arduous task. Because cosmetics are tested directly on the skin to ensure each personâ€™s preferences are met. The consumer repeatedly tries a sample and then discards it until he discovers one that meets his tastes. The cosmetics business and consumers are affected by this move. Companies can utilize Augmented Reality (AR) technology as an alternative to mass-producing cosmetic samples. The difficulty of deploying augmented reality is the difficulty of putting cosmetics into camera video streams. Each individual bears the burden of skin color and its effect on light. HSV Color Extraction was the method employed for this study. The application of augmented reality intends to enable consumers to test cosmetics with their chosen color and assist businesses in competing in the industry by promoting items and engaging customers. This work makes it easier to choose cosmetics using augmented reality and social media. AR simulates the usage of the desired color cosmetics, whereas social media allows users to obtain feedback on their color preferences. The outcomes of this study indicate that augmented reality (AR) apps can display filters in bright, dim, and even wholly dark lighting conditions. This research contributes originality that cosmetic firms can utilize to market their products on social media.

Perbandingan Akurasi Algoritma K-nearest Neighbor Dan Logistic Regression Untuk Klasifikasi Penyakit Diabetes Raharjo Putra Kurniadi; Rd. Rohmat Saedudin; Vandha Pradwiyasma Widartha
eProceedings of Engineering Vol 8, No 5 (2021): Oktober 2021
Publisher : eProceedings of Engineering

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

Diabetes atau sering disebut sebagai penyakit kencing manis merupakan suatu penyakit akibat kelainan metabolik yang diakibatkan oleh tingginya kadar glukosa darah di tubuh dalam waktu yang lama. International Diabetes Federation (IDF) memperkirakan sedikitnya terdapat 463 juta jiwa di seluruh dunia menderita penyakit diabetes pada tahun 2019. Negara Indonesia berada di urutan ke-7 dari 10 negara dengan jumlah penderita diabetes terbanyak, yaitu sebesar 10,7 juta dan diprediksi akan berjumlah 16,6 juta jiwa pada tahun 2045. Banyak orang terdiagnosis penyakit diabetes setelah mengalami komplikasi. Pendeteksian penyakit dapat dilakukan dengan menggunakan data mining dalam menggali informasi dari kumpulan data penyakit diabetes. Dataset yang digunakan pada penelitian ini adalah dataset Pima Indians Diabetes Database. Dataset ini berisikan 768 pasien wanita dengan 8 atribut diagnosa kondisi medis yang berbeda dan 1 atribut tujuan atau atribut label. Penelitian ini membandingkan algoritma K-Nearest Neighbor dan Logistic Regression untuk klasifikasi data Pima Indians Diabetes Database. Pada penelitian ini, penulis melakukan penanganan missing value terhadap data dan menggunakan metode Grid Search untuk menemukan model dengan hasil akurasi yang optimal. Hasil akurasi dievaluasi dengan menggunakan confusion matrix dan menghitung nilai AUC. Diperoleh hasil algoritma K-Nearest Neighbor dengan nilai akurasi sebesar 85,06% dan algoritma Logistic Regression dengan akurasi sebesar 77,92%. Kata Kunci : diabetes, data mining, klasifikasi, k-nearest neighbor, logistic regression

Forensic Investigation of SEO Manipulation in Moodle LMS: Uncovering Illegal Content in Educational Platforms Rochmadi, Tri; Widartha, Vandha Pradwiyasma; Sarmento, Tito Apolinario; Harahap, Avrillaila Akbar; Ajis, Ibnu
Indonesian Journal of Information Systems Vol. 8 No. 1 (2025): August 2025
Publisher : Program Studi Sistem Informasi Universitas Atma Jaya Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24002/ijis.v8i1.12222

Learning Management Systems (LMS) like Moodle are frequently targeted by covert cyberattacks that exploit the credibility of academic domains for illicit purposes. This study uncovers an SEO-based attack method that infiltrates hidden links to gambling sites through Moodle's public directory. Digital forensic methodology was used to trace the perpetrators' footprints from server logs, HTML/JS files, and activity in Google Search Console. The results revealed a comprehensive exploit: fake admin accounts, redirect file injection, and Google indexing manipulation. This research not only highlights an under-researched threat but also offers a mitigation framework based on the ISO/IEC 27001 standard. Key contributions include identifying SEO-based attack techniques in LMSs, analyzing digital artifacts for perpetrator attribution, and strengthening cybersecurity governance in educational institutions.

CBTi-YOLOv5: Improved YOLOv5 with CBAM, Transformer, and BiFPN for Real-Time Safety Helmet Detection Dharmawan, Tio; Setiawan, Danang; Hidayat, Muhamad Arief; Widartha, Vandha Pradwiyasma
Journal of Information Systems Engineering and Business Intelligence Vol. 11 No. 3 (2025): October
Publisher : Universitas Airlangga

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20473/jisebi.11.3.353-363

Background: Some construction workers are often in a situation where injuries can occur from negligence in the use of safety helmets. To avoid this, supervision of the use of safety helmets should be conducted continuously during the work process through the application of computer vision technology. However, the complex background of the construction environment is a challenge to detecting small and densely packed safety helmets accurately. Objective: The construction environment is complex, and the wide workspace allows workers to be in an area far from supervision. The process makes it difficult for models to detect the use of safety helmets in complex, wide, and very high object density construction environments. Therefore, this study aims to overcome the problem by modifying YOLOv5s (You Only Look Once version 5) architecture. Methods: Real-time monitoring of the use of safety helmets could be performed using YOLOv5. This study proposed a modified YOLOv5s model called CBTi-YOLOv5s. The model incorporated Convolutional Block Attention Module (CBAM), Transformer, and Bi-directional Feature Pyramid Network (BiFPN) to improve feature extraction, multi-scale object representation, as well as detection accuracy, specifically on small and high-density objects in complex construction environments. Results: The results showed the modified YOLOv5s architecture had made an improvement of 3.7% in mean average precision (mAP) compared to the base YOLOv5s model. mAP of the base YOLOv5s model was 93.6%, while the modified CBTi-YOLOv5s model achieved 97.3%. The proposed modified YOLOv5s model also achieved an inference speed of 58 frames per second (FPS), and the base model achieved 104 FPS. Conclusion: CBTi-YOLOv5s improved the accuracy, mAP, and ability to detect objects of varying scales. However, this improvement had drawbacks, namely increased model size and decreased inferential speed due to increased model architectural complexity.. Keywords: Bi-FPN, CBAM, CBTi-YOLOv5s, Helmet Detection, Transformer, YOLOv5

AR Make-up Filter for Social Media using the HSV Color Extraction Harika, Maisevli; Rachmat, Setiadi; Aulia, Nurul Dewi; Dwi, Zulfa Audina; Widartha, Vandha Pradwiyasma
JOIN (Jurnal Online Informatika) Vol 7 No 2 (2022)
Publisher : Department of Informatics, UIN Sunan Gunung Djati Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15575/join.v7i2.994

Choosing the appropriate cosmetics is an arduous task. Because cosmetics are tested directly on the skin to ensure each personâ€™s preferences are met. The consumer repeatedly tries a sample and then discards it until he discovers one that meets his tastes. The cosmetics business and consumers are affected by this move. Companies can utilize Augmented Reality (AR) technology as an alternative to mass-producing cosmetic samples. The difficulty of deploying augmented reality is the difficulty of putting cosmetics into camera video streams. Each individual bears the burden of skin color and its effect on light. HSV Color Extraction was the method employed for this study. The application of augmented reality intends to enable consumers to test cosmetics with their chosen color and assist businesses in competing in the industry by promoting items and engaging customers. This work makes it easier to choose cosmetics using augmented reality and social media. AR simulates the usage of the desired color cosmetics, whereas social media allows users to obtain feedback on their color preferences. The outcomes of this study indicate that augmented reality (AR) apps can display filters in bright, dim, and even wholly dark lighting conditions. This research contributes originality that cosmetic firms can utilize to market their products on social media.

Clustering Penyebaran Covid-19 di Kota Bandung dengan Algoritma K-Means Zaenudin, Rifki; Akbar, Moh. Deni; Widartha, Vandha Pradwiyasma
eProceedings of Engineering Vol. 10 No. 3 (2023): Juni 2023
Publisher : eProceedings of Engineering

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

Abstrak-Covid-19 merupakan penyakit yang disebabkan oleh coronavirus. Covid-19 pertama kali masuk ke Indonesia pada awal Maret 2020. Pada saat ini penyebaran yang terjadi pada Provinsi Jawa Barat telah mencapai 706.800 jiwa. Penyebaran tertinggi pada Provinsi Jawa Barat berada pada Kota Bandung dengan penyebaran sebanyak 43.269 jiwa. Salah satu strategi untuk mengurangi dampak dari wabah ini, peneliti memanfaatkan machine learning yang mampu melakukan clustering untuk mengetahui skala prioritas. Metode clustering dapat menggunakan algoritma k-means. Salah satu kelebihan algoritma k-means yaitu memiliki hasil evaluasi cluster yang baik dan mudah untuk diimplementasikan. Hasil dari penelitian ini memiliki 9 cluster, yaitu C0 merupakan cluster dalam kategori sedang, C1 merupakan cluster dalam kategori sedang, C2 merupakan cluster dalam kategori rendah, C3 merupakan cluster dalam kategori rendah, C4 merupakan cluster dalam kategori tinggi, C5 merupakan cluster dalam kategori tinggi, C6 merupakan cluster dalam kategori tinggi, C7 merupakan cluster dalam kategori rendah, dan C8 merupakan cluster dalam kategori tinggi.Kata Kunci—Covid-19, clustering, algoritma K-Means.

Text Classification Using Genetic Programming with Implementation of Map Reduce and Scraping Wedashwara, Wirarama; Irmawati, Budi; Wijayanto, Heri; Arimbawa, I Wayan Agus; Widartha, Vandha Pradwiyasma
JOIV : International Journal on Informatics Visualization Vol 7, No 2 (2023)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.7.2.1813

Classification of text documents on online media is a big data problem and requires automation. Text classification accuracy can decrease if there are many ambiguous terms between classes. Hadoop Map Reduce is a parallel processing framework for big data that has been widely used for text processing on big data. The study presented text classification using genetic programming by pre-processing text using Hadoop map-reduce and collecting data using web scraping. Genetic programming is used to perform association rule mining (ARM) before text classification to analyze big data patterns. The data used are articles from science-direct with the three keywords. This study aims to perform text classification with ARM-based data pattern analysis and data collection system through web-scraping, pre-processing using map-reduce, and text classification using genetic programming. Through web scraping, data has been collected by reducing duplicates as much as 17718. Map-reduce has tokenized and stopped-word removal with 36639 terms with 5189 unique terms and 31450 common terms. Evaluation of ARM with different amounts of multi-tree data can produce more and longer rules and better support. The multi-tree also produces more specific rules and better ARM performance than a single tree. Text classification evaluation shows that a single tree produces better accuracy (0.7042) than a decision tree (0.6892), and the lowest is a multi-tree(0.6754). The evaluation also shows that the ARM results are not in line with the classification results, where a multi-tree shows the best result (0.3904) from the decision tree (0.3588), and the lowest is a single tree (0.356).

Students Demography Clustering Based on The ICFL Program Using K-Means Algorithm Andreswari, Rachmadita; Fauzi, Rokhman; Izzati, Berlian Maulidya; Widartha, Vandha Pradwiyasma; Pramesti, Dita
JOIV : International Journal on Informatics Visualization Vol 7, No 2 (2023)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.7.2.1916

Independent Campus, Freedom to Learn (ICFL) Program is one of the manifestations of student-centered learning. This program can help students reach their full potential by allowing them to pursue their passions and talents. This study aims to see how the segmentation of students participating in the ICFL program is based on demographic data. This research is based on survey responses from students participatingÂ in theÂ ICFLÂ program. The method used in this study is input data preparation, pre-processing, data cleansing, and data analysis. The information will be pre-processed before being utilized and evaluated. To help produce better outcomes in data clustering, the K-Means clustering approach is used, which is processed using the Python computer language. The data is clustered using the K-Means clustering approach based on gender characteristics, Grade Point Average (GPA), university entrance selection, ICFL category, and year or semester when participating in ICFL.Â This study resulted in three clusters with each of its criteria. The dominant gender is found in clusters 2 (100% female) and 3 (100% male). Software Development was the most popular ICFL category among students in cluster 1, accounting for 67%, while Design and Analysis Information Systems was the most popular in clusters 2 and 3. The most dominant ICFL program is found in three clusters. ICFL - Internship program in which at least 40% of participants come from each cluster. The research results are expected to assist stakeholders in evaluating the implementation of the ICFL program.Â Â

Title

Found 8 Documents
Search

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Title Search

Found 8 Documents Search

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Title

Found 8 Documents
Search