Article Per Year (5 Year)
p-Index From 2020 - 2025
4.704
P-Index
This Author published in this journals
All Journal IAES International Journal of Artificial Intelligence (IJ-AI) ComEngApp : Computer Engineering and Applications Journal Seminar Nasional Aplikasi Teknologi Informasi (SNATI) Jurnal Media Infotama Bulletin of Electrical Engineering and Informatics Jurnal sistem informasi, Teknologi informasi dan komputer Jurnal Informatika dan Teknik Elektro Terapan ICON-CSE Khazanah Informatika: Jurnal Ilmu Komputer dan Informatika Sinkron : Jurnal dan Penelitian Teknik Informatika JURNAL MEDIA INFORMATIKA BUDIDARMA Dinamisia: Jurnal Pengabdian Kepada Masyarakat International Journal of New Media Technology INTECOMS: Journal of Information Technology and Computer Science MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer Jurnal Linguistik Komputasional Journal of Information Systems and Informatics Indonesian Journal of Electrical Engineering and Computer Science JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) Computer Science and Information Technologies Journal of Intelligent Computing and Health Informatics (JICHI) Sriwijaya Journal of Informatics and Applications Jurnal Generic Jurnal Pengabdian Kolaborasi dan Inovasi IPTEKS JuTISI (Jurnal Teknik Informatika dan Sistem Informasi)
Abdiansah, Abdiansah
Universitas Sriwijaya
Agriculture, Biological Sciences & Forestry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Library & Information Science Mathematics Medicine & Pharmacology Public Health Social Sciences Other

Published : 38 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 38 Documents
Search

 1   2   3   4 
Analisis Komparasi Klasifikasi Sentimen Pada Crime Indicated Opinion Cyberbullying Di Twitter Menggunakan Metode SVM Dan Naïve Bayes Soraya, Atika; Abdiansah, Abdiansah; Ermatita, Ermatita
INTECOMS: Journal of Information Technology and Computer Science Vol. 8 No. 2 (2025): INTECOMS: Journal of Information Technology and Computer Science
Publisher : Institut Penelitian Matematika, Komputer, Keperawatan, Pendidikan dan Ekonomi (IPM2KPE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31539/intecoms.v8i2.14111

Abstract

Cyberbullying merupakan salah satu tindakan yang melanggar UU ITE dimana kejahatan ini dilakukan di media sosial salah satunya aplikasi Twitter. Tindakan ini sulit terdeteksi jika tidak ada yang mereport tweet tersebut. Identifikasi tweet Cyberbullying bertujuan untuk mengklasifikasikan tweet yang mengandung konten bullying. Klasifikasi dilakukan dengan menggunakan metode Support Vector Machine dan Naïve Bayes dimana metode tersebut bertujuan untuk mencari perbandingan nilai akurasi dari setiap metode. Proses sistem dimulai dari text preprocessing dengan tahapan case folding, tokenisasi, stopword removal, stemming dan pembobotan. Proses selanjutnya melakukan klasifikasi berdasarkan pelabelan data bullying dan non bullying bertujuan mempermudah proses pencarian nilai akurasi klasifikasi dataset dengan metode Support Vector Machine dan naïve bayes. Hasil yang didapat dengan menggunakan metode Support Vector Machine sebesar 82.,29% lebih baik dari metode Naïve Bayes denga hasil sebesar 80,84%. Keywords: Naïve Bayes, Support Vector Machine, Cyberbullying, Bullying, Klasifikasi
Analisis Perbandingan Klasifikasi Intent Chatbot Menggunakan Deep Learning BERT, RoBERTa, dan IndoBERT Dwiyono, Aswin; Abdiansah, Abdiansah; Fachrurrozi, Muhammad
Journal of Information System Research (JOSH) Vol 6 No 1 (2024): Oktober 2024
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josh.v6i1.6051

Abstract

A chatbot is a software application to designed handle user inputs and generate appropriate replies based on those inputs, which are then communicated back to the user. In able to provide accurate responses, the chatbot must be able to understand the intent of the user accurately. An issue in the development of chatbots is how to accurate classify user intent. Incorrectly understanding user intent can result in irrelevant responses. In order to have a conversation with the user, the intent of the user needs to be classified correctly. This paper compares three state-of-the-art transformer-based models BERT (Bidirectional Encoder Representations from Transformers), RoBERTa (Robustly Optimized BERT Pretraining Approach), and IndoBERT (Indonesia Bidirectional Encoder Representations from Transformer) for the task of intent classification in chatbot systems. Various performance metrics, including accuracy, F1-score, precision, and recall, were analyzed to determine which model performs more effectively in the same parameter conditions. Performance metrics like accuracy and F1-score were compared to assess model BERT, RoBERTa and IndoBERT performs better in a University Chatbot Dataset in Indonesian language. The BERT model achieved an accuracy of 0.89, RoBERTa model achieved 0.84 and IndoBERT model achieved an accuracy of 0.94. The better performance of IndoBERT compared to BERT and RoBERTa is caused by more language-specific training, more relevant pretraining, and more effective adaptation to Indonesian context and structure.
Prediksi Gender Berdasarkan Nama Bahasa Indonesia Menggunakan Long Short Term Memory Arya Mulya Kusuma; Harisatul Aulia; Muhammad Alfaris Oktavian; Muhammad Rizky Akbar; Putri Patricia; Abdiansah Abdiansah
Jurnal Teknik Informatika dan Sistem Informasi Vol 9 No 2 (2023): JuTISI
Publisher : Maranatha University Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28932/jutisi.v9i2.6404

Abstract

Gender prediction is a form of prediction programme that outputs a gender type. In the research conducted in this case study, we used input in the form of text names of Indonesian people. Nowadays, there are many names of people that sometimes sound quite ambiguous and make us confused whether this person is MEN or WOMEN. From this case study we are looking for ways to use the Long short term memory method to predict and classify the names of Indonesian people to find out their gender based only on the name with the aim of improving science and looking for new innovations to support future research. The research limitations are the names used are Indonesian names because the dataset used is a dataset of Indonesian names and also the gender we classify is only 2 types of gender, namely MEN and WOMEN. The accuracy comparison of the training results of the baseline programme and the modified programme is the accuracy for the baseline programme of around 0.93, while the accuracy for the modified programme is around 0.90. The results showed an increase in accuracy after experimenting with testing data on the modified programme, which was 0.96.
Classification and similarity detection of Indonesian scientific journal articles Cahyani, Nyimas Sabilina; Stiawan, Deris; Abdiansah, Abdiansah; Afifah, Nurul; Permana, Dendi Renaldo
Computer Science and Information Technologies Vol 6, No 2: July 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/csit.v6i2.p147-158

Abstract

The development of technology is accelerating in finding references to scientific articles or journals related to research topics. One of the sources of national aggregator services to find references is Garba Rujukan Digital (GARUDA), developed by the Ministry of Education, Culture, Research, and Technology (Kemendikbudristek) of the Republic of Indonesia. The naïve Bayes method classifies articles into several categories based on titles and abstracts. The system achieves an F1-score of 98%, which indicates high classification accuracy, and the classification process takes less than 60 minutes. Article similarity detection is done using the cosine similarity method, and a similarity score of 0.071 reflects the degree of similarity between the title and the abstract that has been concatenated, while a score close to 1 indicates a higher similarity. Searching for similar scientific articles based on title and abstract, sort articles based on the results of the highest similarity score are the most similar articles, and generating article categories. The results of the research show that the proposed method significantly improves the classification and search processes in GARUDA, as well as accurate and efficient similarity detection.
ANALISIS KOMPARATIF BILSTM DAN BIGRU DENGAN WORD EMBEDDING GLOVE TERHADAP SENTIMEN PUBLIK TENTANG COVID-19 DI TWITTER Yudoyono, Vellanindhita Noorprameswari; Maulana, Jimmy; Alfath, Ahmad Riyo; Melati, Risma; Sihaloho, Mutiara Anastasya; Abdiansah, Abdiansah
Jurnal Informatika dan Teknik Elektro Terapan Vol. 13 No. 3 (2025)
Publisher : Universitas Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23960/jitet.v13i3.6588

Abstract

Pandemi COVID-19 telah memicu perdebatan publik di Twitter, yang dapat dijelaskan melalui analisis sentimen menggunakan pemrosesan bahasa alami. Karakter informal dan tak terstruktur cuitan Twitter menjadi tantangan tersendiri. Penelitian ini membandingkan kinerja arsitektur BiLSTM dan BiGRU dalam mengklasifikasikan sentimen publik terkait COVID-19. Model BiGRU dirancang dengan dua lapisan, disertai dengan implementasi GloVe embedding Twitter.27B.200d untuk representasi kata lebih baik dan dilengkapi dengan dilengkapi dropout, batch normalization, dan regularisasi L2, serta dioptimasi dengan AdamW, sedangkan BiLSTM menggunakan satu lapis standar. Hasil eksperimen menunjukkan BiGRU dua lapis mencapai validasi akurasi 86.78% dengan pelatihan yang lebih stabil dibandingkan BiLSTM 84.91% yang cenderung overfitting. Temuan ini mengindikasikan bahwa arsitektur double layer BiGRU lebih efektif memahami konteks dari cuitan yang tidak terstruktur Twitter, sehingga direkomendasikan untuk sistem analisis sentimen publik dan pengembangan pemrosesan bahasa alami di masa depan.
Improving the transfer learning for batik besurek textile motif classification Utami, Marissa; Ermatita, Ermatita; Abdiansah, Abdiansah
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 4: August 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i4.pp3172-3181

Abstract

This proposed research discussion is a new combination model for classifying batik besurek fabric from the implementation transfer learning with mixed contrast enhancement, activation function, and optimizer method. The size of the batik besurek fabric motif image as an input image is 250×250 with three channels consisting of red, green, and blue totaling five classes, namely kaligrafi, rafflesia, burung kuau, relung paku and rembulan. All images in the dataset will be divided into train data (1540 images), validate data (380 images), and test data (480 images) that are taken directly from the batik store in Bengkulu. The division method used is stratified random sampling to take all the data, shuffles it, and divides the data sets for each class. Based on the experiment results, ResNet50 obtained the best performance compared to MobileNetV2, InceptionV3, and VGG16, with a training accuracy of 99.60%, a validation accuracy of 97.44%, and a testing accuracy of 98.12%. In the improvement experiment phase, the ResNet50 model with Adam optimizer, rectified linear unit (ReLU) activation function and contrast limited adaptive histogram equalization (CLAHE) as the contrast enhancement method obtained the highest test accuracy (98.75%), showing that CLAHE was very effective in improving performance on batik besurek data.
A Image Classification of Poisonous Plants Using the MobileNetV2 Convolutional Neural Network Model Method Nazuli, Muhammad Furqan; Fachrurrozi, Muhammad; Rizqie, Muhammad Qurhanul; Abdiansah, Abdiansah; Ikhsan, Muhammad
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol. 24 No. 2 (2025)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/matrik.v24i2.4284

Abstract

Poisonous plants can be dangerous for many people, but some can be used as medicines or as pest killers. Some people, especially those in environments with a wide variety of plants, can take advantage of this poisonous plant. Lack of knowledge and information causes the use of this poisonous plant to be inappropriate. This research aims to develop software to classify images of poisonous plants using the Convolutional Neural Network method with the MobileNetV2 model and to compare the accuracy of classification results with various dataset configurations and varying parameters. The research method used is a Convolutional Neural Network, which has relatively high accuracy in classifying various digital images. The data used in this research consists of eight poisonous plants and several non-poisonous plants. The research results on 153 test data show that the accuracy value was 99.34%, precision was 99%, recall was 99%, and F1-Score was 99%. This research contributes to developing software that can quickly provide information and knowledge about poisonous plants, offering a high-accuracy solution for classifying poisonous plants using image data. Furthermore, implementing MobileNetV2 provides an efficient and lightweight model suitable for deployment on mobile devices, enhancing accessibility and usability in the field. The potential applications of this software extend beyond individual use, potentially benefiting agricultural, medical, and educational sectors. Future work will expand the dataset to include more plant species and refine the model to improve its robustness against diverse environmental conditions and image qualities.
Handwritten Kaganga script classification using deep learning and image fusion Dwika Putra, Erwin; Ermatita, Ermatita; Abdiansah, Abdiansah
Bulletin of Electrical Engineering and Informatics Vol 14, No 2: April 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i2.8747

Abstract

Classification of traditional handwriting script and to preserve many cultures have been developed in some parts of the world, including image classification of handwriting Kaganga script. This study aims to propose a new combination model by implementing top-hat transform (THT) and contrast-limited adaptive histogram equalization (CLAHE) with discrete wavelet transform (DWT) to support the performance of the convolutional neural network (CNN) in Kaganga script classification. The top-hat transform and contrast-limited adaptive histogram equalization with discrete wavelet transform Fusion L2 convolutional neural network (DWT-THCL L2 CNN) models get the best accuracy from the CNN with L1 regularization, CNN with dropout regularization, CNN with L2 regularization and CNN with L2 regularization and CLAHE models. Based on the experimental results, the DWT-THCL L2 CNN model successfully increased training accuracy by 7.76%, validation accuracy by 5.11%, and testing accuracy by 3.73% from the CNN L1 model. The DWT-THCL L2 CNN model received a training accuracy of 99.87%, validation accuracy of 82.61%, and testing accuracy of 82.61%, while the CNN model with L1 regularization (L1 CNN) only received a training accuracy of 92.11%, validation accuracy of 77.50%, and testing accuracy of 78.88%.
Co-Authors Abidullah, M. Dzawil Fadhol Adi Kurniawan Ahmad Fali Oklilas Ahmad Gustano Aidil Putrasyah Al Farissi Alfath, Ahmad Riyo Ali Ibrahim Alvi Syahrini Utami Amalia, Syavira Anny K. Sari Ari Firdaus Ari Wedhasmara Arrasyid, Muhammad Raihan Aruda, Syechky Al Qodrin Arya Mulya Kusuma Astero Nandito Azzahra, Firna Fatima Azzikra, Muhammad Adlan Cahyani, Nyimas Sabilina Dahlan, Bulan Fitri Deris Stiawan Dian Palupi Rini Dian Palupi Rini Dian Palupi Rini Dwiyono, Aswin Edi Winarko Elza Fitriana Saraswita Elza Fitriana Saraswita Ermatita - Erwin, Erwin Fathan, Fathir Fathoni - Febrian, Evan Frendredi Muliawan Hallatu, Nathania Calista Harisatul Aulia Hastie Audytra Hidayahni, Putri Husain, Sulaiman Al Illahi, Aripili Rahman Julian Supardi Kanda Januar Miraswan Kusuma, Arya Mulya Marcelio, Ch Angga Marcellino, Fernanditho Mastura Diana Marieska Maulana, Jimmy Megah Mulya Melati, Risma Mira Afrina Mufazzal, Dimas Putra Muhammad Afif Muhammad Alfaris Oktavian Muhammad Fachrurrozi Muhammad Ikhsan Muhammad Qurhanul Rizqie Muhammad Rizky Akbar Muwafa, Fadhil Zahran Nazuli, Muhammad Furqan Noprisson, Handrie Novi Yusliani Novran, Novran Permana, Dendi Renaldo Plakasa, Gerald Primanita, Anggina Putra, Erwin Dwika Putri Patricia Rabani, Diaz Dafa Ridho Putra Sufa Rizka Dhini Kurnia Saputra, Danny Mathew Saputra, Danny Matthew Satrio, Bagus Sihaloho, Mutiara Anastasya Siti Annisa, Siti Soraya, Atika Sri Hartati Yadi Utama Yudoyono, Vellanindhita Noorprameswari Zanzabili, Muhammad Reyhan