Article Per Year (5 Year)
p-Index From 2021 - 2026
7.876
P-Index
This Author published in this journals
All Journal JURNAL SISTEM INFORMASI BISNIS Jurnal Hubungan Internasional Jurnal Informatika dan Teknik Elektro Terapan INTENSIF: Jurnal Ilmiah Penelitian dan Penerapan Teknologi Sistem Informasi JURNAL ILMIAH INFORMATIKA JURNAL TEKNIK INFORMATIKA DAN SISTEM INFORMASI INTECOMS: Journal of Information Technology and Computer Science JURNAL TEKNOLOGI DAN OPEN SOURCE Simtek : Jurnal Sistem Informasi dan Teknik Komputer Journal of Information Systems and Informatics bit-Tech JATI (Jurnal Mahasiswa Teknik Informatika) Nusantara Science and Technology Proceedings Jurnal FASILKOM (teknologi inFormASi dan ILmu KOMputer) Sawala : Jurnal pengabdian Masyarakat Pembangunan Sosial, Desa dan Masyarakat Jurnal Pendidikan dan Teknologi Indonesia International Journal of Engineering, Science and Information Technology Djtechno: Jurnal Teknologi Informasi Jurnal Mahasiswa Sistem Informasi (JMSI) KLIK: Kajian Ilmiah Informatika dan Komputer International Journal of Data Science, Engineering, and Analytics (IJDASEA) JITSI : Jurnal Ilmiah Teknologi Sistem Informasi COMSERVA: Jurnal Penelitian dan Pengabdian Masyarakat Abdimas Altruis: Jurnal Pengabdian Kepada Masyarakat Jurnal Informatika Teknologi dan Sains (Jinteks) Jurnal Ilmiah Sistem Informasi dan Ilmu Komputer JUSIFOR : Jurnal Sistem Informasi dan Informatika Journal of Artificial Intelligence and Digital Business ILTEK : Jurnal Teknologi Scientica: Jurnal Ilmiah Sains dan Teknologi ITIJ Jupiter: Publikasi Ilmu Keteknikan Industri, Teknik Elektro dan Informatika Router : Jurnal Teknik Informatika dan Terapan Repeater: Publikasi Teknik Informatika dan Jaringan Neptunus: Jurnal Ilmu Komputer dan Teknologi Informasi Router : Jurnal Teknik Informatika dan Terapan
Abdul Rezha Efrat Najaf
Department Of Information System, Faculty Of Computer Science, Universitas Pembangunan Nasional “Veteran” Jawa Timur, Surabaya 60294, Indonesia
Religion Aerospace Engineering Agriculture, Biological Sciences & Forestry Arts Humanities Astronomy Automotive Engineering Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry 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 Energy Engineering Environmental Science Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Medicine & Pharmacology Physics Social Sciences Transportation Other

Published : 65 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 4 Documents
Search
Journal : bit-Tech

 1 
Convolutional Neural Network Approach for Aspect-Based Sentiment Analysis of Tourism Reviews Eka Putri, Siti Oktavia; Amalia Anjani Arifiyanti; Abdul Rezha Efrat Najaf
bit-Tech Vol. 8 No. 1 (2025): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i1.2582

Abstract

The tourism industry is a key economic sector in Indonesia, with East Java ranking highest in tourist visits. This study aims to enhance tourism development by applying aspect-based sentiment analysis (ABSA) using convolutional neural networks (CNN) to analyze online reviews. CNN was selected for this study due to its proven efficiency in capturing local n-gram features and its relatively lower computational cost compared to other deep learning model. Reviews from TripAdvisor and Google Maps were collected focusing on four aspects: attraction, amenities, access, and price. Five different models were developed in this research: one multilabel aspect classifier designed to identify multiple aspects mentioned within each review, and four sentiment classifiers focused on evaluating the sentiment polarity for each specific aspect. These models were trained and evaluated using various combinations of word embeddings, including static embeddings like Word2Vec, and contextualized embeddings such as BERT and IndoBERT. Additionally, the impact of preprocessing through stemming was investigated to understand how simplifying word forms affects model performance. Results indicate that IndoBERT-CNN attains the best overall sentiment classification, reaching F1-scores up to 0.71 for attraction and 0.93 for amenities, while Word2Vec-CNN with stemming leads multilabel classification. Meanwhile stemming improves performance for static embeddings like Word2Vec by simplifying word forms, it reduces effectiveness in transformer-based models like BERT and IndoBERT that rely on natural language context. These findings highlight the benefit of choosing appropriate embeddings and preprocessing for different tasks, thus providing practical insights for improving tourism services through better tourist reviews analysis.
Information System Development for Web-Based Creative Services E-Commerce Using Rapid Application Development Method Adiyono, Bagus Dwi Putra; Najaf, Abdul Rezha Efrat; Permatasari, Reisa
bit-Tech Vol. 8 No. 1 (2025): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i1.2615

Abstract

This study aims to design and develop a web-based e-commerce information system for creative services at Cahaya Kreativ using the Rapid Application Development (RAD) method, addressing the company's lack of an integrated digital platform for managing service orders, portfolios, consultations, and online payments. The RAD method was chosen due to its emphasis on speed, prototyping, and close user collaboration—making it more suitable than traditional methods like Waterfall for projects requiring rapid development and ongoing user input in a dynamic service environment. The system was built using React.js for the frontend, Express.js for the backend, PostgreSQL as the database, Tailwind CSS for UI design, and Midtrans integration as a payment gateway. The development process included two iterations covering requirement analysis, system design (use case diagram, sequence diagram, class diagram, ERD), implementation, and testing through Blackbox Testing and User Acceptance Testing (UAT). Results indicate that the system operates according to specifications and has received positive user feedback. It is expected to enhance Cahaya Kreativ’s operational efficiency, expand market reach, and improve the digital experience for customers ordering creative services online, while also supporting streamlined business processes, data consistency, and increased user engagement through responsive and interactive features. Stakeholder involvement throughout the development ensured the system closely matched real business requirements, resulting in a comprehensive digital solution that enhances customer satisfaction and operational performance through technology-driven innovation.
BISINDO Sign Language Interpreter System Using YOLOv8 and CNN Farhana, Farizah; Abdul Rezha Efrat Najaf; Reisa Permatasari
bit-Tech Vol. 8 No. 1 (2025): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i1.2638

Abstract

Many deaf people in Indonesia use sign language as their communication medium, one of which is BISINDO (Bahasa Isyarat Indonesia). However, limited understanding of BISINDO among the general public often becomes a significant barrier to effective communication. To address this, the study aims to develop a BISINDO hand gesture detection system based on computer vision using the YOLOv8 algorithm. The method used is a Convolutional Neural Network (CNN) with the YOLOv8 architecture, trained on a labeled dataset obtained from the Roboflow platform consisting of BISINDO alphabet gestures (A–Z). The system was developed using Python and Streamlit, providing two types of user input: real-time camera feed and manual image upload. Detected gestures are translated into text and converted into speech using the Google Text-to-Speech (gTTS) API. The model was trained over 50 epochs and evaluated using metrics including accuracy, precision, recall, and F1-score. The evaluation results show an accuracy of 89.74%, precision of 89.28%, recall of 96.15%, and F1-score of 92.16%, indicating strong model performance and generalization. Some misclassifications occurred, such as 'R' detected as 'L', and background images mistakenly classified as valid letters. Nonetheless, the system is able to detect and translate BISINDO sign language gestures in semi-realtime with high reliability. This study contributes both practically and theoretically to the field of assistive technologies by providing an accessible, web-based platform for BISINDO recognition, promoting more inclusive communication for the Indonesian deaf community.
Comparison of Adam, RMSprop, and SGD on DenseNet121 for Tomato Leaf Disease Classification Dewi, Heni Lusiana; Arifiyanti, Amalia Anjani; Najaf, Abdul Rezha Efrat
bit-Tech Vol. 8 No. 1 (2025): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i1.2684

Abstract

Diseases affecting tomato leaves can severely impact agricultural productivity, as they can reduce crop yields and quality significantly. A swift and dependable identification of these diseases is vital for ensuring prompt interventions and the successful implementation of disease control strategies. This study focus on evaluating and comparing the efficiency of three separate optimizers, such as Adam, RMSProp, and SGD on the pretrained Convolutional Neural Network (CNN) architecture DenseNet121. There has been no previous research that directly compares the performance of Adam, RMSProp, and SGD optimizers on the DenseNet121 model for classifying tomato leaf diseases using the Plant Village dataset. These optimizers are crucial in the training process by influencing the model’s ability to converge and generalize well on new, unseen data. Experimental procedures were performed using a labeled dataset of tomato leaf images, which included healthy leaves and various disease classes. Out of the three optimization techniques tested, the DenseNet121 model trained with the Adam optimizer consistently outperformed the others. It achieved the highest evaluation metrics, with an accuracy of 0.9800, precision of 0.9807, recall of 0.9800, and F1-score of 0.9800 on the test set. These outcomes suggest that the model has a strong and balanced classification performance, capable of correctly identifying disease conditions with minimal errors. Based on these findings, the DenseNet121 architecture combined with the Adam optimizer is considered the optimal model used to recognize various tomato leaf diseases in this study.
Co-Authors Adiyono, Bagus Dwi Putra Afandi, Mohamad Irwan Agussalim Agussalim Agussalim, Agussalim Agussalim, Agussalim Aisha Ramadhana Indira Santoso AlHafizh, Hadyan Alla, Muhammad Anbiya Fath Amalia Anjani Arifiyanti Amalia Anjani Arifiyanti Ana Wati, Seftin Fitri Anastasia Lidya Maukar Andreas Nugroho Sihananto Anggreani, Nilam Kumallah Anik Vega Vitianingsih Anindo Saka Fitri Apriyanti, Narti Arief Yahya Prasetio Arinta Ardiyono, Taufiq Arochma, Novita Maulana Aryo Sulistiono, Wisnu Asif Faroqi Aulia Putri Fajar Aulia Rahma, Faradhiya Aulia, Ervina Rosa Aviolla Terza Damaliana Badriyyah, Shofiyyatul Bilqis, Thufailah Nafiisah Bonda Sisephaputra Cahyo Wibowo, Nur Candra, Devilia Dwi Chilmi, Farid Daniar, Ivan Faiz Dewangga Nanda Arjuna Dewi, Heni Lusiana Dhian Satria Yudha Kartika Egga Naufal Daffa Tanadi Eka Putri, Siti Oktavia Fais Irwanda Fajar Kurnia Farhana, Farizah Ferdiansyah, Rizky Fitri Ana Wati, Seftin Fitri, Anindo Saka Hamdan, Arva Rizqullah Haq, Ahmad Nashirul Indayanti Sugata, Tri Luhur Iqbal Ramadhani Mukhlis Ivan Faiz Daniar Jannah Arum Kemangi, Anisya Khanza Afiatul Jeremy David Alexander Jojok Dwiridotjahjono Kadek Dwi Natasya Pradnyani Khoirul Tarmidzi M Aldan Adiar Firdaus Manti, Rival Septian Jeflin Maulana, M. Kandias Happy Mawardi, Alfiandi Imam Mohamad Irwan Afandi Muhammad Muharrom Al Haromainy Mustika, Yesi Rahma Nabila, Achmad Wildan Nisrina, Nasywa Nugraha, Rizky Nur Cahyo Wibowo Nurdin, Andi Nuryananda, Praja Firdaus Pamungkas, Dimas Fajri Permatasari, Reisa Purnaningsih, Elwis Ghaitza Putra, Agung Brastama Raden Mohamad Herdian Bhakti Radhyana Gayatri Faradilla Rafli Fahreza Reisa Permatasari Rizka Hadiwiyanti Robawa, Rizki Setyo Putro Rudiany, Novita Putri Saka Fitri, Anindo Sakti, Ciptagusti Sila Salsabilla, Kharisma Agustya Zahra Seftin Fitri Ana Wati Setiawan, Moch Rezeki Shafira, Putri Dian Shofiyyatul Badriyyah Sila Sakti, Ciptagusti Sriyanti, Zilvi Azus Sulastri, Eka Nanda Suryo Widodo Tita Ayu Rospricilia Wahyuni, Eka Dyar Wardani, Lina Wati, Seftin Fitri Ana Wibowo, Nur Cahyo Wulansari, Anita Yudha, Dhian Satria Zein, Isynariyah