Article Per Year (5 Year)
p-Index From 2021 - 2026
7.427
P-Index
This Author published in this journals
All Journal Explore: Jurnal Sistem Informasi dan Telematika (Telekomunikasi, Multimedia dan Informatika) Scan : Jurnal Teknologi Informasi dan Komunikasi Jurnal Informatika dan Teknik Elektro Terapan Jurnal IPTEK Jurnal Sistem Informasi dan Bisnis Cerdas JURNAL INSTEK (Informatika Sains dan Teknologi) Prosiding Seminar Nasional Sains dan Teknologi Terapan JURNAL TEKNOLOGI DAN OPEN SOURCE Data Science: Journal of Computing and Applied Informatics Jurdimas (Jurnal Pengabdian Kepada Masyarakat) Royal IJEEIT : International Journal of Electrical Engineering and Information Technology Applied Technology and Computing Science Journal Journal of Information Systems and Informatics Abdimas Universal bit-Tech Jurnal Teknologi Dan Sistem Informasi Bisnis Aviation Electronics, Information Technology, Telecommunications, Electricals, Controls (AVITEC) JATI (Jurnal Mahasiswa Teknik Informatika) International Journal of Advances in Data and Information Systems Jifosi Nusantara Science and Technology Proceedings KLIK: Kajian Ilmiah Informatika dan Komputer Jurnal Penelitian Inovatif Konstelasi: Konvergensi Teknologi dan Sistem Informasi PELS (Procedia of Engineering and Life Science) Journal of Artificial Intelligence and Engineering Applications (JAIEA) NUSANTARA: Jurnal Pengabdian Kepada Masyarakat Jurnal Teknik Mesin, Industri, Elektro dan Informatika Prosiding Seminar Nasional Rekayasa Teknologi Industri dan Informasi ReTII Jurnal Ilmiah Teknik Informatika dan Komunikasi Madani: Multidisciplinary Scientific Journal Scientica: Jurnal Ilmiah Sains dan Teknologi Student Research Journal Jurnal Informatika Polinema (JIP) Merkurius: Jurnal Riset Sistem Informasi dan Teknik Informatika Neptunus: Jurnal Ilmu Komputer dan Teknologi Informasi Jurnal ilmiah teknologi informasi Asia Jurnal Pengabdian Masyarakat Multidisiplin
Amalia Anjani Arifiyanti
Universitas Pembangunan Nasional “Veteran” Jawa Timur
Religion Aerospace Engineering Agriculture, Biological Sciences & Forestry Arts Humanities 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 Energy Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Mechanical Engineering Medicine & Pharmacology Physics Public Health Social Sciences Transportation Other

Published : 69 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 3 Documents
Search
Journal : bit-tech

 1 
Convolutional Neural Network Approach for Aspect-Based Sentiment Analysis of Tourism Reviews Siti Oktavia Eka Putri; 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.
Comparison of Adam, RMSprop, and SGD on DenseNet121 for Tomato Leaf Disease Classification Heni Lusiana Dewi; 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.v8i1.2684

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.
Detection of ARP Poisoning on Wireless LAN Using Machine Learning: Random Forest and AdaBoost Ersamazaya, Rafi Dhafin; Arifiyanti, Amalia Anjani; Kartika, Dhian Satria Yudha
bit-Tech Vol. 8 No. 3 (2026): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i3.3364

Abstract

ARP poisoning is a prevalent security threat in Wireless Local Area Networks (WLANs), enabling attackers to manipulate ARP tables and perform man-in-the-middle attacks. This study develops a machine learning-based detection system to identify ARP poisoning incidents in real-time, using Random Forest, AdaBoost, and a hybrid Random Forest-AdaBoost ensemble model. Data was collected from a public Wi-Fi environment in Surabaya, consisting of 11,225 ARP traffic records, augmented with simulated ARP poisoning attacks. Data preprocessing included exploratory analysis, feature engineering, encoding, and dataset balancing to improve model performance. Experimental results demonstrate that the hybrid ensemble model achieved the highest accuracy (99.92% on validation and 99.94% on testing), but its inference time of 517.30 ms rendered it unsuitable for real-time deployment. In contrast, the AdaBoost model achieved similar accuracy with significantly faster inference latency (7.82–14.93 ms), making it the most efficient model for live monitoring. The optimized AdaBoost classifier was then deployed through a Telegram-based alert system integrated with Scapy for continuous packet inspection and immediate attack notifications. This study contributes to the advancement of real-time intrusion detection mechanisms for WLAN environments by demonstrating the effectiveness of ensemble learning in ARP poisoning detection. Furthermore, it emphasizes the importance of balancing detection accuracy with computational efficiency for practical deployment in dynamic network environments. The findings offer insights into developing scalable, low-latency security solutions and lay the groundwork for future research on adaptive, real-time detection frameworks.
Co-Authors Abdul Rezha Efrat Najaf Achmad Fauzi Aghni Qisthina Al Rahma Agung Brastama Putra Akira Permata Ramadhani Al Rahma, Aghni Qisthina alathoillah, abdul hanif Ana Wati3, Seftin Fitri Ananda Lakunti A Andhyni, Cyntia Prisya Anggy Oktaviana Syafira Anita Wulansari Anita Wulansari, S.Kom., M.Kom Annisa Lusyani Zahra Anwar Sodik, Anwar Aprilia, Eka Fahira AryaRafa, Daud Audrey Septya Rosanti Aufa, Taqiyuddin Ahmad Al Bagus Utomo Basma Eno Ketherin Brahmantio Widyo Trenggono Daniar, Ivan Faiz Devi, Ditha Lozera Dewi Safitri, Triyatul Dharmawan, Ega Dhian Satria Yudha Kartika Diana Aqidatun Nisa Ditha Lozera Devi Elfaretta, Syifa Saskia Ersamazaya, Rafi Dhafin Fachrurrozy Nurqoulby Fandi, Rico Satria Farhan Setiyo Darusman Farhan Setiyo Darusman Fariska, Rahmah Putri Ferdiansyah, Rizky Fernaldy, Fabiyan Atha Fidyah Salsabila Putri Sillehu Firsttama, Risav Arrahman Fitri, Anindo Saka Hardiartama, Rendi Heni Lusiana Dewi I Gusti Ayu Sri Deviyanti Indira Setia Amalia Indra Fajar Novian Jannatuzzahra, Khoirunisa Ketherin, Basma Eno Kusumantara, Prisa Marga Kusumantara, Prisa Marga M. Rizal Abdullah Rozi Mahanani, Anajeng Esri Edhi Marga Kusumantara, Prisa Marisca Amanda Hidayat Mashita Kustyani Maulana Arrasyid, Nizar Maulana Kharyska Abadi, Muhammad Mochamad Suhri Ainur Rifky Mochammad Fuad Pandji Mohamad Irwan Afandi Muhammad Burhanuddin F Narendra, Efriza Cahya Nilwanda, Leona Elsa Novian, Indra Fajar Nur Rachman Nidhi Suryono, Muhammad Nurisa Rahma Shantika Nurjanti Takarini Oktania Purwaningrum Oktania Purwaningrum Oktania Purwaningrum Pandu Rizki Maulidiah Permatasari, Reisa Pradana, Rhendy May Putra, Satrio Honggonagoro Pramono Putri, Youlan Indira Putu Anggi Suryantari Rafi Purwa Syahputra Raihana Sakhi Aswanda Rendi Panca Wijanarko Rhendy May Pradana Rivaldy, Adriano Femaz Rizka Hadiwiyanti Safitri, Eristya Maya Saka Fitri, Anindo Salma Nabila Seftin Fitri Ana Wati Sembilu, Nambi Sidhi Pamekas, Afu Siti Oktavia Eka Putri Solehudin Al Ayyubi Sudewantoro N M Sulistyowati Sulistyowati Sulistyowati Sulistyowati Tri Diana Rimadhani Tri Luhur Indayanti Sugata Tri Puspa Rinjeni Ubaidillah Fahmi, Rohmat Wahyuni, Eka Dyar Wati , Seftin Fitri Ana Wati, Seftin Fitri Ana Wibisono, Mahendra Priyo Wibowo, Nur Cahyo Wisnu Mukti Darwansah Yudha Yunanto Putra Yudha Yunanto Putra Zahra, Nabila Athifah