Article Per Year (5 Year)
p-Index From 2019 - 2024
1.152
P-Index
This Author published in this journals
All Journal International Journal of Electrical and Computer Engineering Faktor Exacta STRING (Satuan Tulisan Riset dan Inovasi Teknologi) Journal of Applied Data Sciences International Journal of Engineering, Science and Information Technology International Journal Multidisciplinary Science
Achmad Benny Mutiara
Gunadarma University
Humanities Astronomy 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 Education Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Physics Social Sciences Transportation

Published : 7 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 7 Documents
Search

 1 
A Preference Model on Adaptive Affinity Propagation Rina Refianti; Achmad Benny Mutiara; Asep Juarna; Adang Suhendra
International Journal of Electrical and Computer Engineering (IJECE) Vol 8, No 3: June 2018
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1767.959 KB) | DOI: 10.11591/ijece.v8i3.pp1805-1813

Abstract

In recent years, two new data clustering algorithms have been proposed. One of them isAffinity Propagation (AP). AP is a new data clustering technique that use iterative message passing and consider all data points as potential exemplars. Two important inputs of AP are a similarity matrix (SM) of the data and the parameter ”preference” p. Although the original AP algorithm has shown much success in data clustering, it still suffer from one limitation: it is not easy to determine the value of the parameter ”preference” p which can result an optimal clustering solution. To resolve this limitation, we propose a new model of the parameter ”preference” p, i.e. it is modeled based on the similarity distribution. Having the SM and p, Modified Adaptive AP (MAAP) procedure is running. MAAP procedure means that we omit the adaptive p-scanning algorithm as in original Adaptive-AP (AAP) procedure. Experimental results on random non-partition and partition data sets show that (i) the proposed algorithm, MAAP-DDP, is slower than original AP for random non-partition dataset, (ii) for random 4-partition dataset and real datasets the proposed algorithm has succeeded to identify clusters according to the number of dataset’s true labels with the execution times that are comparable with those original AP. Beside that the MAAP-DDP algorithm demonstrates more feasible and effective than original AAP procedure.
Automated hierarchical classification of scanned documents using convolutional neural network and regular expression Rifiana Arief; Achmad Benny Mutiara; Tubagus Maulana Kusuma; Hustinawaty Hustinawaty
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 1: February 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i1.pp1018-1029

Abstract

This research proposed automated hierarchical classification of scanned documents with characteristics content that have unstructured text and special patterns (specific and short strings) using convolutional neural network (CNN) and regular expression method (REM). The research data using digital correspondence documents with format PDF images from pusat data teknologi dan informasi (technology and information data center). The document hierarchy covers type of letter, type of manuscript letter, origin of letter and subject of letter. The research method consists of preprocessing, classification, and storage to database. Preprocessing covers extraction using Tesseract optical character recognition (OCR) and formation of word document vector with Word2Vec. Hierarchical classification uses CNN to classify 5 types of letters and regular expression to classify 4 types of manuscript letter, 15 origins of letter and 25 subjects of letter. The classified documents are stored in the Hive database in Hadoop big data architecture. The amount of data used is 5200 documents, consisting of 4000 for training, 1000 for testing and 200 for classification prediction documents. The trial result of 200 new documents is 188 documents correctly classified and 12 documents incorrectly classified. The accuracy of automated hierarchical classification is 94%. Next, the search of classified scanned documents based on content can be developed.
Pengujian Algoritma Clustering Affinity Propagation dan Adaptive Affinity Propagation terhadap IPK dan Jarak Rumah Millati Izzatillah; Achmad Benny Mutiara
STRING (Satuan Tulisan Riset dan Inovasi Teknologi) Vol 4, No 3 (2020)
Publisher : Universitas Indraprasta PGRI Jakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (399.99 KB) | DOI: 10.30998/string.v4i3.6197

Abstract

Clustering which is a method to classify data easily is used for a purpose of looking at the correlation among data attributes. Clustering is also a data point grouping based on similarity value to determine the cluster center. Affinity Propagation (AP) and Adaptive Affinity Propagation (Adaptive AP) are clustering algorithms that produce number of cluster, cluster members and exemplar of each cluster. This research is conducted to find out a more effective algorithm when clustering data. Besides, to know the correction offered by Adaptive AP Algorithm which is the developed form of AP Algorithm, the researcher implemented and tested both algorithms by using Matlab R2013a 8.10 with 250 data taken from students’ GPA and the distance from their houses to campus. The analysis of test result application from both algorithms shows that the best algorithm is Adaptive AP because it produces optimal clustering. Another result is no correlation between GPA and home distance.
Comparison of Classification Algorithms for Predicting Indonesian Fake News using Balanced and Imbalanced Datasets Sayidati Karima; Achmad Benny Mutiara
Faktor Exacta Vol 16, No 1 (2023)
Publisher : LPPM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30998/faktorexacta.v16i1.16486

Abstract

Kemajuan teknologi informasi memberikan dampak yang besar, seperti penyebaran berita online. Namun, kabar yang tersebar belum tentu benar adanya. Dalam beberapa penelitian, pendeteksian berita hoax telah dilakukan. Namun, terdapat perbedaan hasil dari beberapa algoritma yang digunakan. Oleh karena itu, dalam penelitian ini dilakukan perbandingan antara algoritma Logistic Regression, Naïve Bayes, Random Forest dan Support Vector Machine untuk memprediksi berita hoax khusus Indonesia dengan dataset seimbang dan tidak seimbang. Tahapan perancangan sistem dimulai dari pengumpulan dataset, pelabelan data, pre-processing, pembobotan TF-IDF, klasifikasi model hingga pengujian. Hasil akurasi tertinggi baik dari jumlah dataset yang tidak seimbang maupun dataset yang seimbang didapatkan dari SVM dengan perbandingan 80:20. Dataset tidak seimbang memiliki akurasi 85,47% dan F1-score 90% dan dataset seimbang memiliki akurasi 84,36% dan F1-score 84,80%. Pada penelitian ini dataset tidak seimbang mendapatkan hasil akurasi yang lebih baik dengan menggunakan algoritma SVM dan jika jumlah dataset yang menjadi target kelas utama lebih banyak maka akan memberikan hasil yang lebih baik.
Sentiment Analysis of Honkai: Star Rail Indonesian Language Reviews on Google Play Store Using Bidirectional Encoder Representations from Transformers Method Zekri Fitra Ramadhan; Achmad Benny Mutiara
International Journal of Engineering, Science and Information Technology Vol 3, No 3 (2023)
Publisher : Department of Information Technology, Universitas Malikussaleh, Aceh Utara, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52088/ijesty.v3i3.462

Abstract

Online games are a type of entertainment that is done by humans to have fun and forget all the problems in everyday life. Honkai: Star Rail is a new online game application owned by miHoYo which is currently popular and widely downloaded on the Google Play Store. Reviews on the Honkai: Star Rail app are increasing over time so this makes it difficult for app developers to know past user reviews on their apps. Therefore, the author conducted a study to analyze sentiment towards Honkai: Star Rail application reviews in Indonesian on the Google Play Store using the Bidirectional Encoder Representations from Transformers (BERT) method to determine user sentiment towards the Honkai: Star Rail application and then processed further so that it becomes a record for developers, users, and prospective users of the Honkai: Star Rail application. This study uses Indonesian language review data from users of the Honkai: Star Rail application found on the Google Play Store website as many as 6000 reviews. The BERT method applied in this study consisted of data collection, dataset labeling, data preprocessing, dataset splitting, modeling, model training, and evaluation. Based on the evaluation results that have been carried out on the test data, 97 data are true positive with 27 data are false positive, 4 data are true neutral with 47 data are false neutral, and 381 data are true negative with 37 data are false negative. So, it can be concluded that the model still has difficulty predicting reviews with neutral sentiment but is good enough at predicting reviews with positive and negative sentiment. In addition, the accuracy of the model is 81% with a precision of 63% for positive sentiment reviews, 36% for neutral sentiment reviews, and 89% for negative sentiment reviews.
CONCEPTUAL REGIONAL ORIGIN RECOGNITION USING CNN CONVOUTION NEURAL NETWORK ON BANDUNG, BOGOR AND CIREBON REGIONAL ACCENTS Adam Huda Nugraha; Achmad Benny Mutiara; Dewi Agushinta Rahayu
International Journal Multidisciplinary Science Vol. 2 No. 2 (2023): June: International Journal Multidiciplinary
Publisher : Asosiasi Dosen Muda Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56127/ijml.v2i2.696

Abstract

Sound detection is a challenge in machine learning due to the noisy nature of signals, and the small amount of (labeled) data that is usually available. The need for sound detection in Indonesia is quite important because there are many community organizations that form groups according to the land of their origin. Especially in big cities, where people from various tribes gather and exchange cultures. However, it has a disadvantage that affects these tribes, namely the loss of the original culture of certain areas. The Sundanese are the object of this research, including Bandung, Bogor and Cirebon. Voice data is divided into 2 types, namely male and female, each region consists of 50 respondents with 25 male and female voices with a maximum voting time of 1 minute. The method used is CNN architecture based on supervised learning, preprocessing using MFCC (Mel Frequency Cepstral Coefficients) to obtain feature extraction from voice data. CNN architecture is carried out 3 times convolution with max pooling and dropout on each convolution.
A Lexicon-Based Long Short-Term Memory (LSTM) Model for Sentiment Analysis to Classify Halodoc Application Reviews on Google Playstore Rina Refianti; Achmad Benny Mutiara; Ryan Arya Putra
Journal of Applied Data Sciences Vol 5, No 1: JANUARY 2024
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v5i1.160

Abstract

The development of information and communication technology is developing very quickly, has made many new breakthroughs. One of these technological advances is in the health sector, the creation of telemedicine applications. During the Covid-19 pandemic, it is difficult for people to get access to health. Therefore, telemedicine applications are needed. Halodoc is one of the telemedicine applications that has successfully become the top health application on the Google PlayStore. The application has been used by more than ten million users throughout Indonesia and received a rating of 4.6. To be able to see ratings and satisfaction from the public, user reviews are needed. The very large number of reviews often contain errors, making them difficult to decipher. Based on this, this research aims to create a web application, which can classify user reviews of the Halodoc application, using a proposed lexicon-based Long Short-Term Memory (LSTM) Model. Application is built using the Flask framework and the Python programming language. Models are created and trained using the TensorFlow library. The results of the model evaluation get an accuracy of 85.3% with an average precision value of 85.3%, a recall value of 85.6% and an f1-score of 85.3%. The proposed LSTM model can be used to classify Halodoc review sentiment classes.
Co-Authors Adam Huda Nugraha Adang Suhendra Asep Juarna Dewi Agushinta R. Hustinawaty Hustinawaty, Hustinawaty Millati Izzatillah Rifiana Arief Rina Refianti Rina Refianti Ryan Arya Putra Sayidati Karima Tubagus Maulana Kusuma Zekri Fitra Ramadhan