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All Journal Sainteks Jurnal Ilmu Komputer dan Informasi JURNAL SISTEM INFORMASI BISNIS Coding: Jurnal Komputer dan Aplikasi Jurnal Edukasi dan Penelitian Informatika (JEPIN) JUITA : Jurnal Informatika Infotech Journal CYBERNETICS BULETIN AL-RIBAATH JIKA (Jurnal Informatika) Jurnal Digital Teknologi Informasi Jurnal Filsafat Indonesia
Yulrio Brianorman
Informatics Department, Faculty Of Engineering, Universitas Muhammadiyah Pontianak, Indonesia
Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Law, Crime, Criminology & Criminal Justice Library & Information Science Mechanical Engineering Medicine & Pharmacology

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Pelatihan Pembuatan Sabun Dari Limbah Minyak Jelantah Dalam Upaya Pengelolaan Limbah Rumah Tangga Pada Panti Asuhan Aisyiyah Nur Fauzi Pontianak Yulrio Brianorman; Syarifah Putri Agustini Alqadri
Jurnal Buletin Al-Ribaath Vol 18, No 1 (2021): Buletin Al-Ribaath
Publisher : Universitas Muhammadiyah Pontianak

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29406/br.v18i1.2533

Abstract

Minyak jelantah yang dibuang secara sembarangan dan tidak diuraikan terlebih dahulu akan menyebabkan minyak tersebut menjadi limbah. Dampak limbah yang dirasakan oleh masyarakat adalah penyumbatan saluran air karena terdapat pembekuan minyak pada pipa buangan rumah tangga. Minyak jelantah memiliki kandungan asam lemak dari minyak nabati yang tinggi sehingga dapat dimanfaatkan sebagai produk sabun ramah lingkungan. Minimnya pengetahuan masyarakat terhadap pengelolaan limbah minyak menjadi produk sabun bernilai ekonomis menyebabkan limbah ini menjadi terbuang percuma dan pada akhirnya mencemari ekosistem lingkungan. Kegiatan Pengabdian Kepada masyarakat yang dilakukan di Panti Asuhan Aisyiyah Nur Fauzi Pontianak bertujuan untuk mengedukasi masyarakat mengenai pengelolaan limbah dengan melatih masyarakat untuk membuat produk sabun batang dari limbah minyak jelantah. Pelatihan dilakukan secara virtual dengan memanfaatkan video tutorial dan diskusi tanya jawab melalui aplikasi pesan online.
Kebenaran dalam Perspektif Filsafat Ilmu Pengetahuan dan Implementasi dalam Data Science dan Machine Leaning Mohamad Idris; Riza Ibnu Adam; Yulrio Brianorman; Rinaldi Munir; Dimitri Mahayana
Jurnal Filsafat Indonesia Vol. 5 No. 2 (2022)
Publisher : Undiksha

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23887/jfi.v5i2.42207

Abstract

The development of data science or machine learning is experiencing swift growth. This development can become a pseudoscience due to the process of collecting, using algorithms, and processing data that are not per research standards. Machine learning is one of numerous artificial intelligence (AI) techniques that allow a machine to learn independently. Several machine learning implementations include consulting robots, process optimization, credit scoring, security, and services. Data science often uses supervised learning algorithms, which can be trapped intopseudoscience due to errors in the use and processing of data in research. The solution to avoid this is to apply the epistemological concept of Karl Popper, which is related to the falsification ofscience to solve the problem of demarcation. To enhance it, you can also use the principles of the Four Theory of Truth, namely coherence, correspondence, pragmatism, and consensus.
Perbandingan Pre-Trained CNN: Klasifikasi Pengenalan Bahasa Isyarat Huruf Hijaiyah Yulrio Brianorman; Rinaldi Munir
Jurnal Sistem Informasi Bisnis Vol 13, No 1 (2023): Volume 13 Nomor 1 Tahun 2023
Publisher : Diponegoro University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21456/vol13iss1pp52-59

Abstract

The number of documented deaf people continues to increase. To communicate with each other, the deaf use sign language. The problem arises when Muslims with hearing impairment or deafness need to recite the Al-Quran. Muslims recite Al-Quran using their voice, but for the deaf, there are no available means to do the reciting. Thus, learning hijaiyah letters using finger gestures is considered important to develop. In this study, we use the recognition of hijaiyah letters based on pictures as the learning model. The real-time-based recognition then uses the learning model. This study uses 4 CNN pre-trained models, namely MnetV2, VGG16, ResNet50, and Xception. The learning process shows that MnetV2, VGG16, and Xception reach the accuracy limit of 99.85% in 2, 3, and 11 s, respectively. Meanwhile, ResNet50 cannot reach the accuracy limit after processing 100 s. ResNet50 achieves 82.12% accuracy. The testing process shows that MnetV2, VGG16, and ResNet50 achieve 100% precision, recall, f1-score, and accuracy. ResNet50 shows figures 81.55%, 86.04%, 82.04%, and 82.58%. The implementing process of the learning outcomes from MnetV2 shows good performance for recognizing finger shapes in real-time.
Uji Coba Metode Multi-Criteria Decision Making Sebagai Pengolah Survei Indeks Kepuasan Masyarakat Liovan Aji Airlangga; Rachmat Wahid; Yulrio Brianorman
CYBERNETICS Vol 7, No 01 (2023): CYBERNETICS
Publisher : Universitas Muhammadiyah Pontianak

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29406/cbn.v7i01.5394

Abstract

Tujuan dari penelitian ini yakni mencoba mengkonversi data Indeks Kepuasan Masyarakat (IKM) yang didapat dari Survei Kepuasan Masyarakat (SKM) pada Dinas Pekerjaan Umum dan Penataan Ruang Provinsi Kalimantan Barat ke dalam metode Multi-Criteria Decision Making (MCDM) kemudian mengimplementasikan hasil konversi tersebut ke dalam suatu sistem Survei Indeks Kepuasan Masyarakat. Setelah data IKM tersebut berhasil dikonversi ke dalam metode MCDM, data hasil konversi melalui algoritma metode MCDM kemudian dibandingkan tingkat kesalahan/ketidaksama-annya terhadap perhitungan IKM yang sudah ada menggunakan metode Mean Absolute Percentage Error (MAPE). Terdapat 3 (tiga) metode MCDM yang dipilih untuk mencoba mengolah data IKM tersebut. Metode tersebut yakni metode Preference Selection Index (PSI), metode Weighted Sum Model (WSM), metode Weighted Product Model (WPM), dan metode Weighted Aggregated Sum Product Assessment (WASPAS). Metode-metode tersebut dipilih karena perhitungan algoritmanya dalam mengolah data hampir sama dengan kemampuan pengolahan data metode IKM. Hasil dari penelitian, telah didapatkan nilai persentase kesalahan/ketidaksama-an antara metode IKM dan keempat metode MCDM tersebut. Nilai tersebut yakni metode PSI memiliki tingkat kesalahan/ketidaksama-an sebesar 59,4432%, metode WSM memiliki tingkat kesalahan/ketidaksama-an sebesar 0%, dan metode WASPAS memiliki tingkat kesalahan/ketidaksama-an sebesar 4,16%. Jika disesuaikan pada range dari metode MAPE, maka metode WSM dan WASPAS tergolong dalam tingkat akurasi sangat baik karena persentase kesalahan/ketidaksama-annya berada dibawah 10%.
Sistem Antrian Generik Menggunakan Model Single Channel Single Phase Yulrio Brianorman; Sucipto Sucipto
Sainteks Vol 19, No 2 (2022): Oktober
Publisher : Lembaga Penelitian dan Pengabdian Masyarakat (LPPM)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30595/sainteks.v19i2.15143

Abstract

Kebutuhan terhadap sistem antrian pada saat ini menjadi hal yang dinilai penting. Studi literatur yang telah dilakukan menunjukkan bahwa sistem antrian yang dikembangkan bersifat spesifik untuk satu proses atau instansi tertentu saja. Oleh karena itu, tujuan dari penelitian ini adalah merancang dan mengembangkan sistem antrian yang dapat digunakan oleh siapapun dengan struktur antrian single-channel single-phase. Metodologi penelitian menggunakan design science research. Rancangan sistem antrian telah dibangun menjadi sebuah aplikasi berbasis website dengan nama tabarun.com. Kelebihan dari sistem ini yang juga menjadi kontribusi dalam penelitian adalah sistem antrian ini dapat digunakan oleh siapapun, antar pengguna sistem dapat menjadi follower/following dengan pengguna lainnya. Evaluasi pada sistem ini telah dilakukan black box yang memberikan hasil pengujian valid pada semua proses pengujian yang dilakukan. Selain itu juga dilakukan user acceptance test pada aspek aksesibilitas, aspek fungsional, dan aspek komunikasi visual. Secara keseluruhan, hasil pengujian menunjukkan bahwa sistem telah  diterima dengan baik oleh pengguna.
Comparative Analysis of CNN Architectures for SIBI Image Classification Yulrio Brianorman; Dewi Utami
JUITA: Jurnal Informatika JUITA Vol. 12 No. 1, May 2024
Publisher : Department of Informatics Engineering, Universitas Muhammadiyah Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30595/juita.v12i1.20608

Abstract

The classification of images from the Indonesian Sign Language System (SIBI) using VGG16, ResNet50, Inception, Xception, and MobileNetV2 Convolutional Neural Network (CNN) architectures is evaluated in this paper. With Google Colab Pro, a 224 × 224-pixel picture dataset was used for the study. A five-stage technique consisting of Dataset Collection, Dataset Preprocessing, Model Design, Model Training, and Model Testing was applied. Performance evaluation focused on accuracy, precision, recall, and F1-Score. The results identified VGG16 as the top-performing model with an accuracy of 99.60% and an equivalent F1-Score, followed closely by ResNet50 with nearly similar performance. Inception, XCeption, and MobileNetV2 demonstrated balanced performance but with lower accuracy. This study sheds light on the best CNN models to choose for SIBI image classification, and it makes recommendations for further research that include using sophisticated data augmentation methods, investigating novel CNN architectures, and putting the models to practical use.
Continuous Sign Language Recognition for Quranic Recitation by Deaf People Using Deep Learning Brianorman, Yulrio; Munir, Rinaldi; Maulidevi, Nur Ulfa
Jurnal Ilmu Komputer dan Informasi Vol. 19 No. 1 (2026): Jurnal Ilmu Komputer dan Informasi (Journal of Computer Science and Informatio
Publisher : Faculty of Computer Science - Universitas Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21609/jiki.v19i1.1600

Abstract

This study proposes a deep learning-based system for recognizing Quranic recitation in the sign language, aimed at enhancing accessibility for the Deaf Muslim community. A central contribution is the construction of a novel dataset comprising videos from three Deaf signers performing Surah Al-Fatihah and Surah Al-Ikhlas, guided by the 2022 official Quranic sign language standard introduced by Indonesia’s Ministry of Religious Affairs. The recognition task is framed as a continuous sign language recognition (CSLR) problem to handle unsegmented input sequences. Five pre-trained convolutional neural networks—EfficientNet, GoogleNet, MobileNetV2, ResNet18, and ShuffleNet—were evaluated as visual feature extractors. These were followed by a temporal encoder composed of 1D CNN and BiLSTM, with sequence alignment performed using the Connectionist Temporal Classification (CTC). The experimental results show that ResNet18 and MobileNetV2 achieved the best performance with Word Error Rates (WER) of 5.00% and 7.92% on the test set, respectively. A cross-participant evaluation was also conducted to assess model generalization, although the results revealed performance gaps likely due to signer variation and limited data. The study highlights the suitability of lightweight and residual architectures for CSLR tasks in religious contexts and provides a benchmark for future research on inclusive sign language technologies. In cross-participant evaluation, the model achieved a validation WER of 8.44% on seen signers and 50.46% on an unseen signer, reflecting generalization challenges commonly observed in low-resource CSLR settings. The proposed system lays the groundwork for AI-assisted Quranic education tools tailored to the Deaf Muslim population.
Co-Authors Adriantama, Tengku Barry Ceasar Octariadi Dewi Utami Dimitri Mahayana Liovan Aji Airlangga Mohamad Idris Nur Ulfa Maulidevi Permana, Andalas Rivaldi Rachmat Wahid Rinaldi Munir Rinaldi Munir Riza Ibnu Adam Sucipto Syarifah Putri Agustini Alkadri Waspodo, Waspodo