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All Journal Seminar Nasional Aplikasi Teknologi Informasi (SNATI) Prosiding Seminar Nasional Sains Dan Teknologi Fakultas Teknik Annual Research Seminar Proceeding of the Electrical Engineering Computer Science and Informatics JOIN (Jurnal Online Informatika) Jurnal Teknik Komputer AMIK BSI Jurnal Teknoinfo JITTER (Jurnal Ilmiah Teknologi Informasi Terapan) Jiko (Jurnal Informatika dan komputer) JUMANJI (Jurnal Masyarakat Informatika Unjani) MIND (Multimedia Artificial Intelligent Networking Database) Journal Jurnal ICT : Information Communication & Technology METIK JURNAL JATI (Jurnal Mahasiswa Teknik Informatika) JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) Tematik : Jurnal Teknologi Informasi Komunikasi Jurnal Teknik Informatika (JUTIF) Informatics and Digital Expert (INDEX) AJAD : Jurnal Pengabdian kepada Masyarakat Jurnal Locus Penelitian dan Pengabdian Jurnal Algoritma Industrial Research Workshop and National Seminar Jurnal Sosial dan Sains SisInfo : Jurnal Sistem Informasi dan Informatika J-Icon : Jurnal Komputer dan Informatika Jurnal Global Ilmiah Kesatria : Jurnal Penerapan Sistem Informasi (Komputer dan Manajemen) Khazanah Informatika : Jurnal Ilmu Komputer dan Informatika
Ridwan Ilyas
Program Studi Teknik Informatika, Universitas Jenderal Achmad Yani
Aerospace Engineering Humanities Astronomy Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice Library & Information Science Mathematics Mechanical Engineering Medicine & Pharmacology Physics Social Sciences Other

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Label Propagation Dalam Klasifikasi Kualitas Produk E-Commerce Menggunakan XGBOOST Dengan Random Search Dan BOHB Putra, Dion Revaldy; Umbara, Fajri Rakhmat; Ilyas, Ridwan
Jurnal Locus Penelitian dan Pengabdian Vol. 4 No. 9 (2025): : JURNAL LOCUS: Penelitian dan Pengabdian
Publisher : Riviera Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58344/locus.v4i9.4718

Abstract

E-commerce telah menjadi salah satu sektor yang berkembang pesat di era digital, didorong oleh perubahan pola konsumsi masyarakat dan inovasi teknologi. Namun, klasifikasi kualitas produk pada platform e-commerce tetap menjadi tantangan yang kompleks, terutama karena adanya volume data besar, ketidakseimbangan data, dan keragaman atribut produk. Penelitian ini bertujuan untuk mengoptimalkan klasifikasi kualitas produk e-commerce dengan menggunakan algoritma Extreme Gradient Boosting (XGBoost) yang telah terbukti andal dalam menangani data besar dan kompleks. Untuk meningkatkan akurasi model, penelitian ini mengintegrasikan optimasi hyperparameter menggunakan random search dan transformasi data dengan standardization. Pendekatan penelitian mencakup pengumpulan data dari platform e-commerce, pemrosesan data, pembentukan fitur, pembangunan model prediktif, serta evaluasi kinerja model. Dengan menerapkan metode ini, penelitian diharapkan mampu menghasilkan model klasifikasi dengan performa optimal, mengidentifikasi variabel-variabel penting yang memengaruhi kualitas produk, serta memberikan rekomendasi strategis bagi pelaku industri e-commerce.
Mosquito Species Classification Using Wingbeat Acoustic Signals Based on Bidirectional Long Short-Term Memory Dwifani, Bella Melati Wiranur; Kasyidi, Fatan; Ilyas, Ridwan
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 4 (2025): JUTIF Volume 6, Number 4, Agustus 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2025.6.4.4922

Abstract

The increasing prevalence of mosquito-borne diseases such as Dengue, chikungunya, and malaria underscores the urgent need for effective mosquito vector monitoring. This study proposes a non-invasive classification system of mosquito species based on wingbeat acoustic signals using a deep learning approach with Bidirectional Long Short-Term Memory (BiLSTM). The audio dataset was collected from the Wingbeats repository, consisting of six major mosquito species. Preprocessing was performed using Discrete Wavelet Transform (DWT) to reduce noise. Feature extraction combined Linear Predictive Coding (LPC) and Mel-Spectrogram to represent spectral and temporal signal characteristics. Each binary model was trained in a one-vs-rest scheme to recognize a target species against others, and a BaggingClassifier was used to fuse predictions from six BiLSTM models. Evaluation showed that the proposed system achieved a final accuracy of 96.85% and F1-score of 95.03%, with confusion matrices showing near-diagonal performance. The results indicate that the hybrid LPC-Mel features and ensemble BiLSTM architecture are effective for mosquito species classification using acoustic signals.
A BiLSTM-Based Approach For Speech Emotion Recognition In Conversational Indonesian Audio using SMOTE Nur Shabrina, Nariswari; Kasyidi, Fatan; Ilyas, Ridwan
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 5 (2025): JUTIF Volume 6, Number 5, Oktober 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2025.6.5.5183

Abstract

Speech Emotion Recognition (SER) identifies human emotions through voice signal analysis, focusing on pitch, intonation, and tempo. This study determines the optimal sampling rate of 48,000 Hz, following the Nyquist-Shannon theorem, ensuring accurate signal reconstruction. Audio features are extracted using Mel-Frequency Cepstral Coefficients (MFCC) to capture frequency and rhythm changes in temporal signals. To address data imbalance, Synthetic Minority Over-sampling Technique (SMOTE) generates synthetic data for the minority class, enabling more balanced model training. A One-vs-All (OvA) approach is applied in emotion classification, constructing separate models for each emotion to enhance detection. The model is trained using Bidirectional Long Short-Term Memory (BiLSTM), capturing contextual information from both directions, improving understanding of complex speech patterns. To optimize the model, Nadam (Nesterov-accelerated Adaptive Moment Estimation) is used to accelerate convergence and stabilize weight updates. Bagging (Bootstrap Aggregating) techniques are implemented to reduce overfitting and improve prediction accuracy. The results show that this combination of techniques achieves 78% accuracy in classifying voice emotions, contributing significantly to improving emotion detection systems, especially for under-resourced languages.
SAER : Comparison of Rule Prediction Algorithms on Constructing a Corpus for Taxation Related Tweet Aspect-Based Sentiment Analysis Sopian, Annisa Mufidah; Ilyas, Ridwan; Kasyidi, Fatan; Hadiana, Asep Id
JOIN (Jurnal Online Informatika) Vol 9 No 1 (2024)
Publisher : Department of Informatics, UIN Sunan Gunung Djati Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15575/join.v9i1.1275

Abstract

Twitter is a popular social media in Indonesia, and sentiment analysis on Twitter has an important role in measuring public trust, especially in taxation issues. Aspect extraction is an important task in sentiment analysis. In this research, we propose SAER, a Syntactic Aspect-opinion Extraction and Rule prediction, that used language rule-based approach using syntactic features for aspect and opinion extraction, and we compare several algorithm for rule prediction such as Random Forest Regression, Decision Tree Regression, K-Nearest Neighbor Regression (KNN), Linear Regression, Support Vector Regression (SVR), and Extreme Gradient Boosting Regression (XGBoost) that can generate rules with a tree-based approach. By employing syntactic features and rule prediction, it has been able to explore important features in a sentence. In rule prediction, comparison results show that Support Vector Regression (SVR) was identified as the most effective model for aspects rule prediction, providing the best results with a Mean Squared Error (MSE) of 0.022, Root Mean Squared Error (RMSE) of 0.150, and Mean Absolute Error (MAE) of 0.123. While XGBoost was identified as the most effective model for opinions rule prediction, with MSE of 0.013, RMSE of 0.117, and MAE of 0.075. Since we used syntactic feature-based approaches and rule prediction in this work, it is expected to be implemented for other cases, with other domain datasets.
Object Detection of BISINDO Sign Language Letters Using Residual Network Eriyadi, Maulidina Norick; Ilyas, Ridwan; Abdillah, Gunawan; Hadiana, Asep Id
Khazanah Informatika : Jurnal Ilmu Komputer dan Informatika Vol. 10 No. 1 (2024): April 2024
Publisher : Universitas Muhammadiyah Surakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23917/khif.v10i1.3670

Abstract

Indonesian Sign Language or BISINDO is an alternative language used by people who suffer from disabilities, especially those who have hearing impairments. This language grew and developed from the deaf community, so its use is based on the visual aspect. This research aims to apply Residual Networks to detect objects in the context of Bisindo Letter Sign Language, with the hope of increasing accuracy and efficiency in letter recognition. Object detection goes through 2 stages, namely feature extraction and model training. ResNet is a type of Convolutional Neural Network (CNN) architecture that utilizes models that have been previously trained, so it can save the time required in the model development process. In this research, Residual Network (ResNet) was used for feature extraction to recognize important aspects in the Bisindo letter sign image, such as hand position, finger shape characteristics, and direction of movement. The research results show that the new dataset used as training data and test data has a fairly good ability to detect with a division of 70% train set, 20% valid set and 10% test set with size 640x640 with 300 epochs for the training model.
Prediksi Capaian Bulanan Pajak Daerah Kabupaten Bandung Barat Menggunakan Metode Logistic Regression Ramdhani, Muhammad; Umbara, Fajri Rakhmat; Ilyas, Ridwan
Journal of Information System Research (JOSH) Vol 5 No 4 (2024): Juli 2024
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josh.v5i4.5330

Abstract

Tax is the main source of income for the state, so that tax revenue is the biggest contributor to government agencies. However, the Regional Revenue Agency (BAPENDA) sometimes has difficulties in predicting regional monthly income. The data owned by BAPENDA is very important for estimating tax increases every month, but often these estimates are wrong. Therefore, research on predicting monthly tax ACHIEVEMENT is very helpful. Researchers consider the data mining method approach is a technique that can help BAPENDA find predictive patterns that are important for making tax increase decisions. sebumnya has predicted the results of the Ann method where for neurons 20 it produces an rmse prediction of 0.12. In this study, the logistic regression algorithm approach was used to predict regional tax achievements in West Bandung Regency. In addition, experiments were carried out to evaluate which variables affect the probability value.
PRIMARY QUERY ANALYSIS ON SQL DATABASE RESTRUCTURING IN GEOGRAPHIC INFORMATION SYSTEMS Ilyas, Ridwan; Witanti, Wina; Syarafina, Fildzah
JIKO (Jurnal Informatika dan Komputer) Vol 7, No 2 (2024)
Publisher : Universitas Khairun

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33387/jiko.v7i2.8565

Abstract

Database restructuring is a crucial process aimed at enhancing data management and access efficiency by modifying the existing data structure. This research focuses on improving a Geographic Information System (GIS) for taxation by migrating and restructuring an inefficient and redundant database. The study conducts a comparative performance evaluation of the old and restructured databases using benchmarking tests with varying numbers of threads and ramp-ups. The results reveal a significant increase in average throughput (24.60%) following the restructuring, indicating a substantial improvement in the database's data processing capacity. However, there is also an average increase in response time (21.65%), suggesting a trade-off between enhanced throughput and slower response times. This increase in response time indicates that while the system can handle more data, it requires more time to process each query. Overall, the restructured database demonstrates enhanced performance and efficiency, though further optimization is necessary to achieve consistent throughput across different workloads and to mitigate the increased response times
Implementasi Yolo Untuk Menghitung Kepadatan Kendaraan Tempat Parkir Hidayat, Ferdian Afza; Umbara, Fajri Rakhmat; Ilyas, Ridwan
Jurnal Algoritma Vol 22 No 2 (2025): Jurnal Algoritma
Publisher : Institut Teknologi Garut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33364/algoritma/v.22-2.2919

Abstract

The significant increase in the number of vehicles entering the Universitas Jenderal Achmad Yani area—especially after the construction of the Faculty of Science and Informatics building—has caused congestion at several strategic points on campus, including the area in front of the campus mosque. This study aims to develop a real-time vehicle density monitoring system to support more efficient campus traffic management. The method used involves applying the YOLOv5 object detection algorithm to identify and count vehicles from video recordings in selected monitoring areas. The system is designed to deliver fast and accurate detection while providing real-time vehicle density information. Testing results show that the system achieved strong detection performance, with a maximum precision value of 1.00 at a confidence threshold of 0.983. The maximum recall value of 0.90 was obtained at a lower confidence threshold, reflecting the system’s ability to detect most objects present. These findings highlight the trade-off between model confidence in predictions and its ability to avoid missing relevant objects. The contribution of this study is the development of a prototype system capable of automatically and in real time monitoring vehicle density in campus areas. This system has the potential to become part of a smarter, data-driven campus traffic management solution to reduce congestion and improve the comfort and mobility of the academic community.
Co-Authors Achmad Aziz Adriana, Reyhan Agung Besti Agus Komarudin Akbar, Tzazkia Febriyana Aminuddin Ihsan, Aminuddin Ari Sri Windyaswari Ari Sri Windyaswari, Ari Sri Ariq Irawan, Muhamad Asendra, Irfan Asep Saepul Ridwan Ashaury, Herdy Aziz, Achmad Azmira Mifti Harjana Besti, Agung Chandani Nurul Hafizah Destri Wulansari Dhimas Ariya Wibiksana Djamal, Esmeralda Contesa Dwi Hendratmo Widyantoro Dwifani, Bella Melati Wiranur Eddie Khrisna Putra Eriyadi, Maulidina Norick Esmeralda C Djamal Esmeralda C Djamal Esmeralda C. Djamal Esmeralda C. Djamal Esmeralda Contessa Djamal Fadhilahsyah Ramadhan, Muhammad Diky Fahrauk Faramayuda, Fahrauk Fajri Rakhmat Umbara Fajri Umbara Fatimah Indrianti, Nisa Fitri Nur Suciani Gunawan Abdillah Gunawan Abdillah, Gunawan Hadiana, Asep Id Hidayat, Ferdian Afza Iqbal Prayoga Willyana Ismail, Nursafira Khairunnisa Iyan Taufik Hidayat Janjan Nurjaman Kania Ningsih, Ade Kasyidi, Fatan Luthfi Ahmad Fadhil Masayu Leylia Khodra Maulidina Norick Eriyadi Melina Melina Muhamad Ramdan, Muhamad Muhamad Rizal Firmansyah Muhammad Ramdhani, Muhammad Muhammad, Azri Naufal Akhfasy, Muhammad Neneng Nurhamidah NIDA MUTHI ANNISA Nur Shabrina, Nariswari Nurhamidah, Neneng Nursafira Khairunnisa Ismail Nurul S, Puspita Nurul Sabrina, Puspita Paramita, Veronika Santi Purnama Ginandjar, Ichas Putra, Dion Revaldy Putri, Dhiffa Namira Alifia Ramdani, Maullidan Alfa Rizki Fikri Ramdhan, Edvin Resa Abdilah Reyhan Adriana Deris Reza Dwi Putra Reza Indrawan Rezki Yuniarti Rezky Yuniarti ridwan fauzi Rifaz Muhammad Sukma Rizka Khoirunnisa Guntina Rizki Kurniawan, Moch. Sopian, Annisa Mufidah Susilowati, Merliana Tri Syarafina, Fildzah Tzazkia Febriyana Akbar Umbara, Fajri Rakhmat Wildan Pratama Wina Witanti Yamina Azmi Yoga Esa Mahendra Yulison Herry Chrisnanto Yustiana Fauziyah