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All Journal Jurnal Teknologi Fountain of Informatics Journal Jurnal Teknologi dan Sistem Komputer JOIV : International Journal on Informatics Visualization Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) METHOMIKA: Jurnal Manajemen Informatika & Komputerisasi Akuntansi Jurnal Informatika Universitas Pamulang Jurnal Teknoinfo Jurnal Sisfokom (Sistem Informasi dan Komputer) Jurnal Kreativitas PKM INFOMATEK: Jurnal Informatika, Manajemen dan Teknologi Jurnal Inti Talafa : Jurnal Teknik Informatika JASIEK (Jurnal Aplikasi Sains, Informasi, Elektronika dan Komputer) International Journal of Applied Sciences and Smart Technologies Jurnal Riset dan Aplikasi Mahasiswa Informatika (JRAMI) JUSTIN (Jurnal Sistem dan Teknologi Informasi) Ilmu Komputer untuk Masyarakat Data Sciences Indonesia (DSI) "JAMASTIKA" Jurnal Mahasiswa Teknik Informatika Jurnal Ilmiah Teknik Informatika & Elektro (JITEK) Scientific Journal of Informatics JuTISI (Jurnal Teknik Informatika dan Sistem Informasi)
Purwayoga, Vega
Universitas Siliwangi
Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Mathematics Mechanical Engineering Medicine & Pharmacology Nursing Physics Public Health Other

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Cluster Change Analysis to Assess the Effectiveness of Speaking Skill Techniques using Machine Learning Wulandari, Nunik Herani; Purwayoga, Vega
International Journal of Applied Sciences and Smart Technologies Volume 07, Issue 1, June 2025
Publisher : Universitas Sanata Dharma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24071/ijasst.v7i1.9667

Abstract

This study aims to compare effective teaching methods for speaking using machine learning. The classes used in this study consisted of three classes: conventional, vlog project, and picture series. The data used were students' pre-test and post-test scores. The machine learning algorithm used is K-Means. K-Means clusters the pre-test and post-test data. The results of K-Means clustering on the pre-test and post-test identified the differences in student groups between the pre-test and post-test. Students who experienced the most cluster movement were those in the vlog project class, the conventional class, and the picture series class.
Perbandingan Metode Pengukuran Jarak pada Analisis Potensi Banjir Menggunakan Spatial Skyline Query Purwayoga, Vega; Gusnadi, Zakwan
INFOMATEK Vol 27 No 1 (2025): Juni 2025
Publisher : Fakultas Teknik, Universitas Pasundan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23969/infomatek.v27i1.24149

Abstract

Bencana banjir merupakan bencana yang sering terjadi pada beberapa wilayah di Indonesia. Bencana banjir dapat terjadi karena beberapa faktor diantaranya curah hujan yang tinggi, perubahan tutupan lahan, daya tampung sungai dan lain-lain. Kesiapsiagaan bencana merupakan salah cara untuk mengatasi banjir, dengan mencoba memetakan daerah yang berpotensi terdampak banjir. Pada proses rekomendasi daerah yang berpotensi banjir perlu dilakukan tahapan awal yaitu praproses data untuk mengukur kedekatan suatu daerah dengan daerah lainnya. Sehingga perlu mengidentifikasi metode terbaik dalam pengukuran jarak. Area studi penelitian ini yaitu Kabupaten Garut dimana Kabupaten Garut merupakaan salah satu kabupaten dengan tingkat bencana yang tinggi di Indonesia. Data yang digunakan dalam penelitian ini yaitu data iklim, batas administrasi daerah, data digital elevation model (DEM). Penelitian ini menerapkan algoritme skyline query dengan menambahkan aspek spasial pada pengujian dominasi. Hasil pengujian dominasi merupakan proses rekomendasi daerah yang berpotensi terdampak banjir dan daerah yang menyebabkan daerah lain terkena banjir. Dimana sebelum proses pengujian dominasi dilakukan perbandingan metode pengukuran jarak yang terbaik. Metode terbaik adalah metode yang selisihnya paling kecil dengan fungsi measure pada ArcMap. Pada penelitian haversine distance lebih baik kinerjanya dibandingkan dengan euclidean distance, dimana haversine distance memiliki selisih 0.28 km, sedangkan euclidean distance sebesar 2.41 km. Hasil pengukuran metode terbaik adalah yang digunakan untuk pengujian dominasi. Hasil pengujian dominasi menerangkan bahwa ada daerah yang direkomendasikan menjadi daerah yang berpotensi terdampak banjir dan daerah yang terindikasi penyebab banjir berdasarkan kriteria ketinggian, jarak antar daerah dan curah hujan.
SOCA-YOLO: Smart Optic with Coordinate Attention Model for Vision System-Based Eye Disease Detection Rianto, Rianto; Purwayoga, Vega; Aradea; Mikail, Ali Astra; Yumna, Irsalina
Scientific Journal of Informatics Vol. 12 No. 3: August 2025
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v12i3.29293

Abstract

Purpose: The purpose of this research is to identify eye diseases using a modified YOLOv9. In particular, we modified YOLOv9 with the addition of Coordinate Attention (CA) for better eye disease detection performance, the use of Programmable Gradient Information (PGI), and Generalized Efficient Layer Aggregation Network (GELAN) for higher computational efficiency and accuracy. Methods: This study consists of several stages, including the acquisition of eye disease data obtained from the Roboflow website, data annotation, image augmentation, modeling using a modified YOLOv9, and model evaluation. Result: SOCA-YOLO model achieved an F1 score of 87,2% and mAP50 of 92,9%, outperforming YOLOv9-e by 1,7%. It also surpassed YOLOv6-L6 by 11,1%, YOLOv10-X by 0,8% in mAP50, and YOLOv8-X by 1,1% in recall, showcasing its superior detection accuracy and recall performance. Novelty: This research contributes by introducing the SOCA-YOLO model in improving the performance of the YOLOv9 by modifying the addition of Coordinate Attention (CA) for better eye disease detection performance, alongside Programmable Gradient Information (PGI) and Generalized Efficient Layer Aggregation Network (GELAN) for better computational efficiency and accuracy.
PERANCANGAN SISTEM INFORMASI PERPUSTAKAAN DIGITAL BERBASIS WEBSITE MENGGUNAKAN METODE WATERFALL DI JURUSAN INFORMATIKA UNIVERSITAS SILIWANGI Lukmana, Hen Hen; Alhusaini, Muhamad; Purwayoga, Vega
METHOMIKA: Jurnal Manajemen Informatika & Komputerisasi Akuntansi Vol. 7 No. 2 (2023): METHOMIKA: Jurnal Manajemen Informatika & Komputersisasi Akuntansi
Publisher : Universitas Methodist Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46880/jmika.Vol7No2.pp340-346

Abstract

The COVID-19 pandemic that occurred at the end of 2019 has accelerated the application and use of technology in universities. Various academic activities, such as learning, final project work, and UTS and UAS, were completed online. But unfortunately, supporting facilities such as digital libraries are needed as a source of reference and information that can store various material needs such as ebooks and modules that can be used for the learning process and can be accessed anywhere and anytime. This is the basis for designing the Digital Library Information System to make it easier for lecturers and students to find the books and modules they need. This library information system is web-based and made with HTML, CSS, Javascript, PHP, and MySQL programming languages. The method used in the development of this digital library information system is the waterfall method with the UML (Unified Modeling Language) software approach. With the Informatics Digital Library, it is hoped that it can help students and lecturers find various references and information for the learning process.
Clustering and Trend Analysis of Priority Commodities in the Archipelago Capital Region (IKN) using a Data Mining Approach Pangestu, Pandu; Maarip, Syamsul; Addinsyah, Yuldan Nur; Purwayoga, Vega
International Journal of Applied Sciences and Smart Technologies Volume 06, Issue 1, June 2024
Publisher : Universitas Sanata Dharma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24071/ijasst.v6i1.7798

Abstract

The policy of moving the capital from Jakarta to East Kalimantan planned by the President of the Republic of Indonesia Joko Widodo has caused a lot of polemic among the public. There are quite a few positive and negative comments on social media regarding the policy of moving the capital. The process of moving the capital requires careful preparation. One thing that needs to be considered is food security in IKN. This research provides recommendations for the main food commodities in IKN by applying data mining. We collect food productivity data available on the official website for East Kalimantan province. These data are processed and grouped into two groups, namely horticulture and livestock products using the K-Means method. After grouping, we predict the increase in productivity of each group using the ARIMA method. This research produces output in the form of grouping commodities into horticulture and livestock products. Productivity results for each type of commodity are displayed from 2016 to 2020 based on data on the official East Kalimantan Province website. Based on this data, predictions are made using the ARIMA method to predict productivity results from 2021 to 2025. Commodities with total productivity are grouped into high-priority commodities. Grouping the amount of productivity is carried out using the clustering method by comparing the amount of productivity for each commodity and producing commodities that are low priority, middle priority, priority and top priority based on the highest to lowest productivity numbers. The cluster quality for grouping horticultural commodities is 99.1%, while the cluster quality for grouping livestock commodities is 87.5%. Hasil prediksi terbaik yaitu ketika memprediksi produksi salak dan slaughter cattle dengan model ARIMA (0, 1, 0) dan ARIMA (2, 2, 2).
Aplikasi Cerdas Berbasis Website Prediksi Harga Emas dengan Implementasi Algoritma Smoothing Time Series Forecasting Al Husaini, Muhammad; Hermansyah, Aam; Purwayoga, Vega; Lukmana, Hen Hen; Ramadhan, Delvan
Data Sciences Indonesia (DSI) Vol. 2 No. 2 (2022): Article Research Volume 2 Issue 2, Desember 2022
Publisher : Yayasan Cita Cendikiawan Al Kharizmi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/dsi.v2i2.1888

Abstract

Investasi emas merupakan hal yang umum dilakukan oleh masyarakat pada saat ini. Harga emas adalah salah satu hal penting yang menjadi fokus utama dalam melakukan investasi emas yang perlu akurasi ketepatan prediksi baik dalam kurun waktu minggu, hari ataupun tahun sehingga mampu memudahkan untuk menggunakan prediksi tersebut dalam berinvestasi baik untuk membeli atau menjual emas tersebut. Aplikasi berbasis web dengan implementasi algoritma time series forecasting ini dibangun untuk memudahkan dalam prediksi harga emas dengan menggunakan metode pemulusan moving average simple exponential smoothing hingga holt’s exponential dan holt’s winter’s exponential smoothing. Metode penelitian yang digunakan pada rancang bangun aplikasi berbasis web ini menggunakan metode prototype dari pengumpulan atau analisa kebutuhan sistem, membangun prototyping, mengkodekan sistem, evaluasi sistem, pengujian sistem hingga penggunaan sistem. Implementasi menggunakan algoritma pemulusan time-series forecasting yaitu menggunakan dataset yang diambil dari application programming interface (API) https://metalpriceapi.com dengan jumlah data harga emas yang digunakan sejumlah 872 data yang dilakukan pengujian akurasi menggunakan mean absolute percentage error (MAPE) untuk menguji akurasi data aktual dan prediksi dari ketiga algoritma tersebut yaitu dengan menghasilkan 5,517 % untuk metode simple exponential smoothing, 4,93 % pada metode holt’s exponential smoothing, dan 2,78 % untuk holt’s winter’s exponential smoothing. Penggunaan algoritma holt’s-winter’s menghasilkan akurasi yang lebih baik dari kedua algoritma sebelumnya dengan persentase akurasi yang baik berdasarkan pengujian akurasi mean absolute percentage error dengan nilai pengujian kurang dari 5 %.
Sign Language Detection Models using Resnet-34 and Augmentation Techniques Hilal, Rizki Ramdhan; Aradea, Aradea; Purwayoga, Vega
International Journal of Applied Sciences and Smart Technologies Volume 07, Issue 2, December 2025
Publisher : Universitas Sanata Dharma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24071/ijasst.v7i2.12888

Abstract

For deaf or hard of hearing people, sign language is a primary means of communication, but low public understanding makes social engagement difficult. Researchers now use computer vision technology and Convolutional Neural Network (CNN) to detect sign language movements. Problems such as overfitting and missing gradients still exist. Using CNN and ResNet-34 architecture, as well as image augmentation to overcome this problem, this research builds a deep learning-based sign language detection model. The Indonesian Sign Language System (SIBI) dataset was used to test the model. The test results show that the model with image augmentation trained for more than 50 epochs obtained an accuracy of 99.4%, precision of 99.5%, recall of 99.5%, and an F1 score of 99.5%. The model without image augmentation produced an accuracy of 99.4%, recall of 99.3%, F1 score of 99.3%, and precision of 99.4%. ResNet-34 architecture overcomes the problem of missing gradients, while image augmentation avoids overfitting and improves model accuracy.
Pengukuran Skala Prioritas Data Logistik Bencana dengan K-Means Cluster dan Skyline Query Vega Purwayoga; Hen Hen Lukmana; Winda Ayu Anggraini
JASIEK (Jurnal Aplikasi Sains, Informasi, Elektronika dan Komputer) Vol. 6 No. 2 (2024): Desember 2024
Publisher : Universitas Merdeka Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26905/jasiek.v7i2.14067

Abstract

Analisis data logistik diperlukan untuk manajemen barang logistik secara efisin. Penelitian ini bertujuan untuk menerapakan algoritma clustering yaitu K-Means untuk mengelompokkan data logistik dengan memperhatikan aspek temporal. Penelitian ini tidak hanya mengelompokkan barang logistik, namun juga merekomendasikan waktu yang tempat untuk menyediakan barang. Data yang digunakan yaitu data barang logistik BPBD Kabupaten Purbalingga tahun 2020. Algoritma K-Means digunakan untuk mengelompokkan barang logistik pada setiap bulan yang berada terdapat pada tahun 2020. Rata-rata kualitas cluster yang dihasilkan K-Means setiap bulannya adalah 95.5 %. Tren setiap bulan hasil pengelompokkan K-Means dianalisis lebih lanjut untuk merekomendasikan waktu yang tepat untuk menambah stok barang logistik di BPBD.  Proses rekomendasi dibantu dengan algoritma skyline query dengan memanfaatkan suatu preferensi. Preferensi yang digunakan yaitu mencari bulan yang memiliki stok minimum, dan pengeluaran minimum. Bulan yang direkomendasikan untuk pengadaan barang yang termasuk ke dalam cluster C3 terdapat lima 5 bulan, sedangkan C2 sebanyak sepuluh bulan.
HyRoBERTa: Hybrid Robustly Optimized BERT Approach Model for Sentiment and Sarcasm Detection in Post-Flood Social Media Analysis Yuliyanti, Siti; Septi Asriani, Aveny; Purwayoga, Vega; Gusnadi, Zakwan
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 10 No 1 (2026): February 2026
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v10i1.6963

Abstract

The detection problem is a crucial step in sentiment classification because it strengthens the validity and reliability of the model's interpretation of ambiguous text, especially in complex social contexts such as post-disaster public communication. Without this detection, the model is prone to significant classification errors. This study presents a hybrid approach for sentiment analysis with sarcasm detection after a flood disaster by combining the RoBERTa model with sequential deep learning architectures such as GRU, LSTM, and BiLSTM. We used a dataset of 17,520 tweets that were pre-processed using cleaning, normalization, and tokenization. Then, the positive class is further detected to determine whether it is sarcasm. The model was trained using a transformer-based transfer learning method with a combination of hyperparameters: the number of epochs, batch size, dropout rate, and learning rate. The experimental results show that the RoBERTa-GRU model achieved the highest accuracy for sentiment classification at 97. 26%, whereas the RoBERTa-BiLSTM model excels in detecting sarcasm with an accuracy of 98. 74%. RoBERTa-BiLSTM excels in sarcasm detection because it provides a bidirectional sequential mechanism and better long-term memory, effectively leveraging RoBERTa's rich embedding to identify contextual contradictions that are characteristic of sarcasm. Meanwhile, RoBERTa-GRU succeeds in sentiment classification because its architecture is more concise yet effective enough to infer dominant sentiments that have been filtered from the robust representation provided by RoBERTa, making the model more efficient for less complex tasks.
Co-Authors Addinsyah, Yuldan Nur Al Husaini, Muhammad Alam Rahmatulloh Alfarez, Rifky Alhusaini, Muhamad Ali Astra Mikail Andi Nurkholis Aradea Aradea Ariyanto, Nabhan Thoriq Aziz, Nur Jannah Abdi Euis Nur Fitriani Dewi Gunawan, Rohmat Harry Gunawan Hen Hen Lukmana Hermansyah, Aam Hilal, Rizki Ramdhan Jasmin, Nur Shabrina Jatnika, Muhammad Dzulfaqori Kartini, Nuri Lisna Zahrotun Lukmana, Hen Hen Maarip, Syamsul Mikail, Ali Astra Muhammad Al Husaini Mustofa, Romy Faisal Nugraha, Cindera Syaiful Pangestu, Pandu Rachman, Andi Nur Ramadhan, Delvan Rianto Rianto, Rianto Rindiawan, Selvina Zalfa Rizal, Randi Salma Dewi Nur Faridah Septi Asriani, Aveny Setyawan, Resa Siti Yuliyanti, Siti Sokid, Sokid Susanto, Budi Tedy Setiadi Triono, Wahyu Virra Retnowati A’izzah Winda Ayu Anggraini Wulandari, Nunik Herani Yumna, Irsalina Zakwan Gusnadi