Article Per Year (5 Year)
p-Index From 2021 - 2026
4.299
P-Index
This Author published in this journals
All Journal Seminar Nasional Aplikasi Teknologi Informasi (SNATI) Journal of Information Systems Engineering and Business Intelligence Register: Jurnal Ilmiah Teknologi Sistem Informasi Indonesia Symposium on Computing Indonesian Journal on Computing (Indo-JC) IJoICT (International Journal on Information and Communication Technology) JOIN (Jurnal Online Informatika) Sinkron : Jurnal dan Penelitian Teknik Informatika JURNAL MEDIA INFORMATIKA BUDIDARMA Seminar Nasional Teknologi Informasi Komunikasi dan Industri Jurnal Nasional Pendidikan Teknik Informatika (JANAPATI) Journal of Computer Science and Informatics Engineering (J-Cosine) JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Building of Informatics, Technology and Science Journal of Computer System and Informatics (JoSYC) Journal of Applied Data Sciences Charity : Jurnal Pengabdian Masyarakat Indonesian Community Journal Jurnal Pengabdian Masyarakat Akademisi eProceedings of Engineering Community Service Seminar and Community Engagement (COSECANT) LOGIC: Jurnal Penelitian Informatika
Ade Romadhony
Universitas Telkom
Agriculture, Biological Sciences & Forestry Arts Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice Mathematics Public Health Social Sciences Other

Published : 45 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 45 Documents
Search

 1   2   3   4   5 
Lokakarya Mata Pelajaran Informatika dalam Meningkatkan Pemahaman Konseptual Algoritma Pemrograman para Guru Ciptasari, Rimba Whidiana; Selly Meliana; Ade Romadhony
Jurnal Pengabdian Masyarakat Akademisi Vol. 3 No. 3 (2024)
Publisher : Jurnal Pengabdian Masyarakat Akademisi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54099/jpma.v3i3.1063

Abstract

Salah satu tantangan utama dalam penerapan Kurikulum Merdeka adalah kesenjangan kompetensi antara kurikulum yang ditetapkan dan kesiapan guru dalam mengimplementasikannya. Banyak guru informatika di SMP belum sepenuhnya siap untuk mengadopsi pendekatan berpikir komputasional karena keterbatasan pelatihan dan sumber daya. Pada 2024, Biro Bebras Universitas Telkom mengadakan pelatihan pendampingan materi Algoritma dan Pemrograman (AP) kepada para guru SMP yang tergabung dalam Musyawarah Guru Mata Pelajaran (MGMP) Informatika Kabupaten Bandung. Pelatihan diberikan dalam tiga sesi yang mencakup konsep variabel, input/output, kondisional, event, pengenalan bahasa pemrograman visual/blok, membangun program sederhana, dan membangun program kreatif. Penilaian tingkat pemahaman para guru dilakukan melalui tes awal dan akhir. Analisis korelasi menunjukkan efektivitas penyampaian materi pelatihan ditandai dengan adanya peningkatan pemahaman konseptual materi AP.
Aspect-based Sentiment Analysis on Beauty Product Reviews using BERT and Long Short-Term Memory Al Aufar, Arya Prima; Romadhony, Ade
Jurnal Nasional Pendidikan Teknik Informatika: JANAPATI Vol. 14 No. 2 (2025)
Publisher : Prodi Pendidikan Teknik Informatika Universitas Pendidikan Ganesha

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23887/janapati.v14i2.94392

Abstract

In e-commerce, product reviews play a crucial role in influencing potential buyers by sharing user experiences and assessing product quality. This is especially important for beauty products, where poor quality can lead to physical harm. Reviews also help increase consumer interest in purchasing. Previous research has shown that product reviews differ in various aspects and content, making it challenging for consumers to quickly analyze them from multiple perspectives. This study applies aspect-based sentiment analysis to beauty product reviews on the Female Daily Network using a combination of BERT and LSTM. The goal is to provide more precise sentiment classification across different aspects, aiding consumers in selecting the best products. Several evaluation scenarios were conducted to assess different aspects of product reviews, including price, packaging, staying power, moisture, and aroma. The F-1 score revealed that the price aspect achieved the highest performance, reaching 100% in a 90%:10% test data scenario. However, the aroma aspect proved the most challenging to analyze, indicating that the model struggles to capture features related to scent effectively under the given evaluation setup.
Optimasi Optimasi Pengelolaan Learning Management System (LMS) untuk Perguruan Tinggi Aptikom Provinsi Jawa Barat Hasmawati, Hasmawati; Richasdy, Donni; Romadhony, Ade
Charity : Jurnal Pengabdian Masyarakat Vol. 7 No. 2 (2024): Charity-Jurnal Pengabdian Masyarakat
Publisher : PPM Universitas Telkom

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

Abstract

Besarnya manfaat penerapan teknologi dalam pembelajaran jarak jauh membuat Asosiasi Perguruan Tinggi Informatika dan Ilmu Komputer (APTIKOM) Provinsi Jawa Barat terus berupaya meningkatkan kualitas pembelajaran jarak jauh bagi perguruan tinggi khususnya anggota Aptikom provinsi Jawa Barat. Saat ini masih banyak institusi pendidikan anggota Aptikom provinsi jawa barat yang masih tertinggal dalam penggunaan teknologi dalam pembelajaran jarak jauh. Oleh karena itu, Aptikom provinsi Jabar berupaya untuk meningkatkan kualitas dan kuantitas sumber daya manusia di bidang teknologi informasi, terutama di perguruan tinggi yang berada di daerah-daerah yang masih tertinggal. Sebagai bagian dari upaya untuk meningkatkan kualitas pembelajaran jarak jauh, Aptikom Provinsi Jawa Barat bersama Telkom University mengusulkan kegiatan pengabdian kepada masyarakat dengan tema “Optimasi Pengelolaan Learning Management System (LMS) untuk Perguruan Tinggi Aptikom Provinsi Jawa Barat”. Kegiatan ini diharapkan dapat membantu anggota Aptikom Jabar memahami konsep dan praktek pembelajaran jarak jauh yang efektif, sehingga mereka dapat memperbaiki dan meningkatkan pengalaman pembelajaran jarak jauh di institusi pendidikan mereka. Dengan demikian, kegiatan ini berpotensi untuk meningkatkan kualitas pembelajaran jarak jauh di seluruh perguruan tinggi di Jawa Barat.
One Data Indonesia Policy Adoption for Telkom University Data Warehouse Framework Gozali, Alfian Akbar; Romadhony, Ade; A, Subaveerapandiyan
Register: Jurnal Ilmiah Teknologi Sistem Informasi Vol 9 No 2 (2023): July
Publisher : Information Systems - Universitas Pesantren Tinggi Darul Ulum

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26594/register.v9i2.3473

Abstract

The Indonesian government has implemented a data warehouse named One Data (Satu Data) Indonesia (ODI) to support its operations since 2019. However, the implementation of this concept in universities has been limited, with only a few universities adopting it. Telkom University is one of the few universities in Indonesia that has already taken steps to implement ODI at the university level. The adoption of ODI at Telkom University is known as the One Data Telkom University (ODTU) project. This project aims to create a platform for universities to share data and collaborate more effectively. This paper thoroughly examines the implementation of the ODI policy and data warehouse framework at Telkom University, focusing on the ODTU data warehouse design and architecture. This paper discusses the implementation of ODTU into several applications, including the One Data Portal, One Data Dashboard, and One Data Market. Moreover, it identifies the challenges encountered during the implementation process, such as data integration, data privacy and security, standardized data models, and the promotion of a shared vision among stakeholders with varying levels of data literacy. Our analysis results demonstrate the effectiveness of the ODTU framework in improving data management practices at Telkom University. The customer satisfaction index (CSI) shows that across key reliability, assurance, and responsiveness measures, Telkom University experienced average score improvements of 3-6% after implementing ODTU. This study contributes to the existing literature on ODI policy adoption in the context of higher education institutions, providing insights for institutions seeking to improve their data management practices.
Identifikasi Kesamaan Pertanyaan pada Soal Bahasa Indonesia Menggunakan Metode Recurrent Neural Network (RNN) Iqbal, Muhammad; Hasmawati; Romadhony, Ade
JOIN (Jurnal Online Informatika) Vol 8 No 2 (2023)
Publisher : Department of Informatics, UIN Sunan Gunung Djati Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15575/join.v8i2.1138

Abstract

In a question-and-answer forum, the identification of question similarity is used to determine how similar two questions are. This procedure makes sure that user-submitted questions are compared to the questions in a database for matches to improve system performance on the online Q&A platform. Currently, question similarity is mostly done in foreign languages. The purpose of this research is to identify question similarities and evaluate the effectiveness of the methods used in Indonesian language questions. The data used is a public dataset with labeled pairs of questions as 0 and 1 where label 0 for different pairs of questions and label 1 for the same pairs of questions. The method used is a Recurrent Neural Network (RNN) with the Manhattan Distance approach to calculate the similarity distance between two questions. The question pairs are taken as two inputs with a reference label to identify the similarity distance between the two question inputs. We evaluated the model using three different optimizers namely RMSprop, Adam, and Adagrad. The best results were obtained using the Adam optimizer with 80:20 ratio split-data and overall accuracy is 76%, precision is 74%, recall is 98.8%, and F1-score is 85.1%.
Buzzer Account Detection in Political Hate Tweets: Case Study of the Indonesian Presidential Election 2024 Herman, Fizio Ramadhan; Ade Romadhony
International Journal on Information and Communication Technology (IJoICT) Vol. 10 No. 2 (2024): Vol.10 No. 2 Dec 2024
Publisher : School of Computing, Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21108/ijoict.v10i2.1012

Abstract

The Indonesian Presidential Election of 2024 has seen a widespread use of social media such as Twitter for political campaigning and discussion. However, this has also enabled the spread of hate speech from buzzer accounts that are created to influence public opinions. This study implements a machine learning approach to classify buzzer accounts that are spreading hate speeches during the presidential election period. By utilizing IndoBERT for hate speech classification and a traditional machine learning model to classify buzzer accounts. This study analyzes 62,341 tweets for hate speech classification and 961 accounts for buzzer account classification. Our implementation of IndoBERT achieved a strong performance with 91.12% of precision and recall, and 91.19\% accuracy and F1-score in hate speech classification. While for buzzer account classification, we compared Decision Tree, Random Forest, and XGBoost, with Decision Tree achieving the highest performance of 64% precision, recall, accuracy, and F1-Score. Our results demonstrate the effectiveness of combining deep learning for hate speech classification with traditional machine learning for buzzer account classification, contributing to the development of more effective content filtering for election discourse on social media.
SAFE NUSANTARA: A semi-automatic framework for engineering and populating a Nusantara Food Ontology Wiharja, Kemas Rahmat Saleh; Barawi, Mohamad Hardyman; Romadhony, Ade; Atastina, Imelda; Dharayani, Ramanti; Othman, Mohd Kamal
International Journal on Information and Communication Technology (IJoICT) Vol. 10 No. 2 (2024): Vol.10 No. 2 Dec 2024
Publisher : School of Computing, Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21108/ijoict.v10i2.1042

Abstract

Constructing a comprehensive food ontology, particularly for culturally diverse cuisines like Southeast East Asian (Nusantara), is hindered by the variability of online recipes and the scarcity of structured data. This research introduces SAFE Nusantara, a novel semi-automated system designed to build and populate a Nusantara food ontology by extracting relevant terms from diverse online sources in Indonesian and Malaysian languages. By leveraging a combination of techniques, including topic modelling, natural language processing, and knowledge graph techniques, SAFE Nusantara addresses the challenges of data format diversity and language specificity. The system has demonstrated significant improvements in the accuracy of food classification and has the potential to enhance food recommendation systems and cultural heritage preservation efforts.
Sentiment Analysis on a Large Indonesian Product Review Dataset Romadhony, Ade; Al Faraby, Said; Rismala, Rita; Wisesty, Untari Novia; Arifianto, Anditya
Journal of Information Systems Engineering and Business Intelligence Vol. 10 No. 1 (2024): February
Publisher : Universitas Airlangga

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20473/jisebi.10.1.167-178

Abstract

Background: The publicly available large dataset plays an important role in the development of the natural language processing/computational linguistic research field. However, up to now, there are only a few large Indonesian language datasets accessible for research purposes, including sentiment analysis datasets, where sentiment analysis is considered the most popular task. Objective: The objective of this work is to present sentiment analysis on a large Indonesian product review dataset, employing various features and methods. Two tasks have been implemented: classifying reviews into three classes (positive, negative, neutral), and predicting ratings. Methods: Sentiment analysis was conducted on the FDReview dataset, comprising over 700,000 reviews. The analysis treated sentiment as a classification problem, employing the following methods: Multinomial Naí¯ve Bayes (MNB), Support Vector Machine (SVM), LSTM, and BiLSTM. Result: The experimental results indicate that in the comparison of performance using conventional methods, MNB outperformed SVM in rating prediction, whereas SVM exhibited better performance in the review classification task. Additionally, the results demonstrate that the BiLSTM method outperformed all other methods in both tasks. Furthermore, this study includes experiments conducted on balanced and unbalanced small-sized sample datasets. Conclusion: Analysis of the experimental results revealed that the deep learning-based method performed better only in the large dataset setting. Results from the small balanced dataset indicate that conventional machine learning methods exhibit competitive performance compared to deep learning approaches.   Keywords: Indonesian review dataset, Large dataset, Rating prediction, Sentiment analysis
Analisis Sentimen Pada Media Sosial Universitas Dengan Metode Berbasis Leksikon Nur, Farhan Ahmadi Javier; Romadhony, Ade; Hasmawat, Hasmawat
eProceedings of Engineering Vol. 10 No. 2 (2023): April 2023
Publisher : eProceedings of Engineering

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

Abstract

Abstrak- Media sosial seperti Facebook, Twitter, dan Instagram adalah platform yang umum digunakan untuk berbagi ide dan opini. Sebuah opini pada media sosial dapat mengandung sentimen positif, negatif, atau netral. Analisis sentimen atau sentiment analysis adalah sebuah studi untuk melakukan identifikasi sentimen secara otomatis, dan telah banyak diterapkan pada organisasi pemilik akun media sosial, termasuk universitas. Penelitian ini mengimplementasikan identifikasi sentimen pada komentar media sosial universitas dengan menggunakan metode berbasis leksikon. Cara kerja metode analisis sentimen berbasis leksikon adalah dengan menghitung orientasi semantic lexicon. Penelitian ini menggunakan kamus leksikon yang telah didefinisikan sebanyak 6599 kata negatif dan 3597 kata positif. Dari hasil eksperimen diperoleh precision sebesar 94,81%, recall sebesar 82,59%, dan F-1 score sebesar 88,28%. Berdasarkan perbandingan hasil prediksi sistem dengan label yang didefinisikan manual, dianalisis penyebab kesalahan identifikasi sentimen, yaitu terdapat beberapa kata yang tidak ditemukan pada kamus leksikon karena kata-kata tersebut merupakan bahasa asing, yaitu Bahasa Inggris dan Bahasa Arab.Kata kunci- media sosial, universitas, analisis sentimen, analisis sentimen berbasis leksikon
Ekstraksi Informasi Beasiswa dari Media Sosial menggunakan BiLSTM-CRF Setiawan, Muhammad Rizki Ramadhan; Romadhony, Ade; Hasmawati, Hasmawati
eProceedings of Engineering Vol. 10 No. 2 (2023): April 2023
Publisher : eProceedings of Engineering

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

Abstract

Abstrak-Sosial media merupakan tempat dimana orang-orang berkumpul dan saling bertukar informasi. Dari informasi tersebut dapat muncul berbagai macam peluang seperti beasiswa yang dikeluarkan oleh lembaga pendidikan. Peluang ini dapat banyak ditemukan pada sosial media seperti Twitter. Namun kebanyakan informasi yang dikeluarkan menggunakan format tersendiri sehingga menjadi tidak terstruktur dan menghambat upaya pengolahan informasi yang terkait. Melihat cepatnya informasi berlalu dan banyaknya kompetisi dalam meraih peluang tersebut, efisiensi menjadi faktor penting dalam mengumpulkan dan memproses informasi. Untuk mengatasi permasalahan tersebut, maka dilakukan ekstraksi informasi untuk mengubah informasi tidak terstruktur menjadi terstruktur menggunakan metode Bidirectional Long-Short Term Memory dengan Conditional Random Fields (BiLSTM-CRF). Metode ini digunakan karena dapat memberikan konteks informasi dari masa lalu dan masa depan pada teks sehingga sesuai untuk mengatasi tugas ekstraksi informasi. Tujuan penelitian ini adalah melakukan ekstraksi informasi dengan mengimplementasikan model BiLSTM-CRF untuk melakukan proses klasifikasi informasi yang diekstraksi sesuai dengan kategori pengelompokkan yang ditetapkan sehingga data yang terkumpul menjadi terstruktur dan mudah untuk dibaca. Hasil yang didapatkan dari implementasi model tersebut adalah nilai performansi dengan Precision 90%, Recall 51%, dan F1-Score sebesar 54%.Kata kunci - beasiswa, twitter, sequence labelling, BiLSTM-CRF, ekstraksi informasi
Co-Authors A, Subaveerapandiyan Aditia Rafif Khoerulloh Adiwijaya Affan Fattahila, Ananda Agung Toto Wibowo Al Aufar, Arya Prima Al Faraby, Said Alfian Akbar Gozali Ali Ridho Fauzi Rahman Ananda Wulandari Anditya Arifianto Anisa Herdiani Anisah Firli Ardiansyah, Yusfi Arya Prima Al Aufar Bambang Pudjoatmodjo Bambang Pudjotatmodjo Barawi, Mohamad Hardyman Bedy Purnama Bhudi Jati Prio Utomo Bimmo Satryo Wicaksono Brady Rikumahu Dadan Rahadian Dade Nurjanah Dana Kusumo Dana S Kusumo Dana S Kusumo Dodi Wisaksono Sudiharto Donni Richasdy Ema Rachmawati Ema Rachmawati Fat'hah Noor Prawira Fat’hah Noor Prawira Fat’hah Noor Prawira Fazainsyah Azka Wicaksono Fazmah Arif Yulianto Frima, Mariana Gheartha, I Gusti Bagus Yogiswara H Hasmawati Hamdy Nur Saidy Haryo Adi Nugroho Haryo Adi Nugroho Haryo Nugroho Hasmawat, Hasmawat Hasmawati Hasmawati Hasmawati Hasmawati Hasmawati Herman, Fizio Ramadhan Imelda Atastina Januarahman, Faishal Kemas Rahmat S.W Kemas Rahmat Saleh Wiharja Lintani Afina Hajar Raudhoti Luh Putri Ayu Ningsih Mahmud Dwi Sulistiyo Moch Arif Bijaksana Muhammad Arzaki Muhammad Aziz Pratama Muhammad Farrel Muhammad Iqbal Muhammad Iqbal Muhammad Taufik Wahdiat Muhammad Zaky Aonillah Nadine Azhalia Purbani Ningsih, Shabrina Retno Nugraha, Azhar Baihaqi Nur, Farhan Ahmadi Javier othman, mohd kamal Pramana, Rifki Adi Prawita, Fat’hah Noor Putu Harry Gunawan Ramanti Dharayani Rhesa Hermawan Ridea Valentini Peristiwari Siwabessy Rimba Whidiana Ciptasari Riska Junia Wulandari Rita Rismala Said Faraby Selly Meliana Setiawan, Muhammad Rizki Ramadhan Siti Saadah Tresna Ariesta, Bayu Untari Novia Wisesty Wijaya, Kurniadi Ahmad