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All Journal Jurnal Ilmu Komputer dan Informasi Indonesian Journal on Computing (Indo-JC) JOIN (Jurnal Online Informatika) Jurnal Penelitian Pendidikan IPA (JPPIPA) Jurnal Nasional Pendidikan Teknik Informatika (JANAPATI) JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Journal of Computer System and Informatics (JoSYC)
Hasmawati Hasmawati
Telkom University
Agriculture, Biological Sciences & Forestry Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Computer Science & IT Decision Sciences, Operations Research & Management Education Engineering Industrial & Manufacturing Engineering Library & Information Science Materials Science & Nanotechnology Physics

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Increasing The Capacity of Headstega Based on Bitwise Operation Hasmawati Hasmawati; Ari Moesriami Barmawi
Jurnal Ilmu Komputer dan Informasi Vol 14, No 2 (2021): Jurnal Ilmu Komputer dan Informasi (Journal of Computer Science and Information
Publisher : Faculty of Computer Science - Universitas Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21609/jiki.v14i2.957

Abstract

Headstega (Head steganography) is a noiseless steganography that used email headers as a cover for concealing messages. However, it has less embedding capacity and it raises suspicion. For overcoming the problem, bitwise operation is proposed.  In the proposed method, the message was embedded into the cover by converting the message and the cover into binary representation based on a mapping table that was already known by the sender and the receiver. Furthermore, XOR bitwise operations were applied to the secret message and cover bits based on random numbers that were generated using a modular function. Moreover, the result was converted into characters that represent the secret message bits. After embedding the message into the cover, an email alias was generated to camouflage the secret message characters. Finally, the sender sends the embedded cover and the email alias to the recipient. Using the proposed method, the embedding capacity is 89% larger than using the original Headstega. For reducing the adversary’s suspicion, the existing email address was used instead of creating a new email address.
Modifikasi Headstega berdasarkan Penyisipan Karakter Hasmawati Hasmawati; Ari Moesriami Barmawi
Indonesia Journal on Computing (Indo-JC) Vol. 2 No. 1 (2017): Maret, 2017
Publisher : School of Computing, Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21108/INDOJC.2017.2.1.145

Abstract

AbstractHead steganography or Headstega is one of noiseless steganography paradigm, or Nostega.This method utilizes the email header as a media of  message concealment. There are several problems that can be enhanced in Headstega, i.e. low embedding capacity and high level of suspicion. Modified Headstega based on Character Hiding uses a combination of consonant vowel to embed the secret messages into email address. The messages embedding process using four consonant vowel combination that represented one character in Indonesian language.  From the experiments conducted, the results obtained that the Modified Headstega has a better performance than the Original Headstega in term of embedding capacity and also in suspicion level. Keyword : Steganography, Nostega, Headstega, Character Hiding
Question Entailment on Developing Indonesian Covid-19 Question Answering System Muhammad Zaky Aonillah; Hasmawati Hasmawati; Ade Romadhony
Journal of Computer System and Informatics (JoSYC) Vol 3 No 4 (2022): August 2022
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josyc.v3i4.2041

Abstract

Despite the severe impact of COVID-19 on humans has already decreased, people still need to be aware of the recent disease information. A continually updated Frequently Asked Questions (FAQ) system could help the public get valid and relevant information. To maintain a FAQ system manually needs much effort, hence an approach to develop the system automatically is needed. Question Answering System (QAS) is a system that can accept input in question sentences and produces an answer quickly, concisely, and relevantly, and could be used to provide COVID-19 information to the public. One method on developing a QAS is Recognizing Question Entailment (RQE). RQE is a form of relationship based on a cause-and-effect relationship between two pieces of text called text (T) and hypothesis (H). We present a study on developing Covid-19 QAS in Bahasa Indonesia using RQE. The datasets are collected from reputable sources and consist of 725 pairs of questions and answers. The experimental results show that the best performance results were obtained using the Logistic Regression model in training set 1, which contains 54.2% of positive question pairs and 45.8% of negative question pairs with an f-measure value of 83.65%. These results indicate that the RQE method can identify the entailment between new questions and questions in the dataset well.
IMPLEMENTATION OF QUESTION ENTAILMENT IN QUESTION ANSWERING SYSTEM FOR CHILDREN’S HEALTH TOPIC Arya Prima Al Aufar; Ade Romadhony; Hasmawati Hasmawati
JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Vol 7, No 3 (2022)
Publisher : STKIP PGRI Tulungagung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29100/jipi.v7i3.3101

Abstract

Kesehatan merupakan salah satu hal yang paling penting dalam kehidupan manusia khususnya pada anak-anak, yang memungkinkan anak-anak dapat tumbuh dan berkembang secara baik. Untuk menjaga kesehatan maka orang tua juga harus memiliki informasi yang tepat mengenai cara menjaga kesehatan, pola makan, pola hidup yang baik. Dalam mendapatkan informasi itu dibutuhkan sebuah platform untuk memudahkan pencarian informasi mengenai kesehatan terutama kesehatan anak. Pada penelitian ini diterapkan sebuah sistem tanya jawab atau question answering dengan menerapkan Question Entailment untuk memudahkan mencari pertanyaan mengenai kesehatan anak dan mendapatkan jawaban yang tepat. Dataset yang digunakan dibuat berdasarkan hasil pengumpulan daftar pertanyaan dan jawaban dari buku tanya jawab kesehatan anak dan FAQ di internet. Model Question Entailment dibangun berdasarkan training korpus yang dibuat dan diuji menggunakan algoritma Support Vector Machine(SVM), Logistic Regression, Naïve Bayes, dan J48 Decision Tree. Hasil pengujian menunjukkan bahwa algoritma SVM memberikan performa terbaik dalam mengidentifikasi pertanyaan serupa, dengan metrik precision, recall, dan f1(f-measure).
Sentiment Analysis of University Social Media Using Support Vector Machine and Logistic Regression Methods Fazainsyah Azka Wicaksono; Ade Romadhony; Hasmawati
Indonesia Journal on Computing (Indo-JC) Vol. 7 No. 2 (2022): August, 2022
Publisher : School of Computing, Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34818/INDOJC.2022.7.2.638

Abstract

Social media has become one of the most powerful platforms for information sharing. Colleges and universities now have official social media profiles to convey information about the campus and boost its branding and popularity. Instagram is a popular social networking website among college students. It is important for a university to comprehend its performance from the community's perspective, whether positive, negative, or indifferent toward the university. One solution is to examine the university's social media sentiment to establish the public's perception of the university. In this study, we will conduct a sentiment analysis on university social media based on public opinion or comments for each post on the university's Instagram to identify whether the comments are “Positive,” “Negative,” or “Neutral.” To classify posts on university Instagram, we use two methods: Support Vector Machine and Logistic Regression. The results suggest combining the Support Vector Machine approach with the TF-IDF feature yields the best F1-Score performance. In contrast, Logistic Regression with the FastText feature produces the worst performance of all models and feature extraction employed.
Recommendation System in the Form of an Ontology-Based Chatbot for Healthy Food Recommendations for Teenagers Nazar Azmi; Donni Richasdy; Hasmawati
Jurnal Penelitian Pendidikan IPA Vol 9 No 7 (2023): July
Publisher : Postgraduate, University of Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/jppipa.v9i7.4401

Abstract

Adolescents need adequate nutrition to support their growth and to avoid nutritional problems, such as malnutrition or obesity. Nutritional issues during adolescence can significantly influence health problems in adulthood. Although information about nutrition science is widely available on the internet, accurate interpretation requires specialized knowledge of nutrition science. Therefore, a system is needed to provide direct recommendations for healthy food to adolescents. In this study, a recommendation system in the form of a chatbot was developed to recommend healthy food that meets the nutritional needs of adolescents. The system was constructed using Ontology supplemented with Semantic Web Rule Language (SWRL), enabling the recommendation of food according to adolescents' health conditions. From the collected sample data, 150 food menus were recommended. Validation results by nutrition experts showed a precision value of 0.75, a recall of 1, and an F1-score of 0.857. These results indicate that the system is capable of providing appropriate food recommendations for adolescents.
Similar Questions Identification on Indonesian Language Subject Using Machine Learning Hasmawati; Ade Romadhony
Jurnal Nasional Pendidikan Teknik Informatika : JANAPATI Vol. 12 No. 2 (2023)
Publisher : Prodi Pendidikan Teknik Informatika Universitas Pendidikan Ganesha

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23887/janapati.v12i2.62582

Abstract

Question similarity is carried out to evaluate similarities between questions in a collection of questions in the question and answer forum and on other platforms. This is done to improve the performance of the question-and-answer forum so that new questions submitted by users can be identified as similar to existing questions in the database. Currently, research related to question similarity is still being carried out on foreign language datasets. The purpose of this research is to identify the similarity of questions in a collection of questions in Indonesian. The method used is Support Vector Machine and IndoBERT. For feature extraction, we evaluate the lexical features and syntax features of each question. For lexical feature extraction, we use the cosine similarity algorithm to calculate the distance between two objects which are represented as vectors. For syntax feature extraction we use the Indonesian part of speech tagger (POS Tag). The dataset used is a collection of questions on Indonesian subjects at the primary and secondary school levels. The results of this study show that the best performance of the Support Vector Machine is obtained from the use of the cosine similarity feature with an accuracy of 85%. While the use of the POS Tag feature or the combination of POS Tag and cosine similarity causes the model to be overfitted and the accuracy decreases to 77%. Meanwhile, for the IndoBERT model, an accuracy of 95% was obtained. 
Identifikasi Kesamaan Pertanyaan pada Soal Bahasa Indonesia Menggunakan Metode Recurrent Neural Network (RNN) Muhammad Iqbal; Hasmawati; Ade Romadhony
JOIN (Jurnal Online Informatika) Vol 8 No 2 (2023)
Publisher : Department of Informatics, UIN Sunan Gunung Djati Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15575/join.v8i2.1138

Abstract

In a question-and-answer forum, the identification of question similarity is used to determine how similar two questions are. This procedure makes sure that user-submitted questions are compared to the questions in a database for matches to improve system performance on the online Q&A platform. Currently, question similarity is mostly done in foreign languages. The purpose of this research is to identify question similarities and evaluate the effectiveness of the methods used in Indonesian language questions. The data used is a public dataset with labeled pairs of questions as 0 and 1 where label 0 for different pairs of questions and label 1 for the same pairs of questions. The method used is a Recurrent Neural Network (RNN) with the Manhattan Distance approach to calculate the similarity distance between two questions. The question pairs are taken as two inputs with a reference label to identify the similarity distance between the two question inputs. We evaluated the model using three different optimizers namely RMSprop, Adam, and Adagrad. The best results were obtained using the Adam optimizer with 80:20 ratio split-data and overall accuracy is 76%, precision is 74%, recall is 98.8%, and F1-score is 85.1%.
Implementation of IndoBERT for Sentiment Analysis of Indonesian Presidential Candidates Primanda Sayarizki; Hasmawati; Hani Nurrahmi
Indonesia Journal on Computing (Indo-JC) Vol. 9 No. 2 (2024): August, 2024
Publisher : School of Computing, Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34818/INDOJC.2024.9.2.934

Abstract

In this modern era, Indonesian society widely utilizes social media, particularly Twitter, as a means to express their opinions. Every day, various opinions of Indonesian citizens are disseminated on this platform, including their views on prospective presidential candidates for the year 2024. Analyzing public opinions regarding prospective presidential candidates in 2024 is crucial to understanding the sentiment of the people toward these candidates. Such sentiment analysis can be conducted using deep learning techniques such as IndoBERT to acquire knowledge regarding the classification of sentiments as positive, neutral, or negative. IndoBERT is employed to generate vector representations that encapsulate the meaning of tokens, words, phrases, or texts. These representation vectors can then be input into a classification model to perform sentiment analysis. The sentiment classification model undergoes testing with a diverse set of tweets in the test dataset, which represent a wide range of public opinions. The evaluation results indicate an overall accuracy rate of 80%, with precision rates of 62% for negative sentiment, 81% for neutral sentiment, and 85% for positive sentiment. Additionally, the recall rates for each sentiment are 64% for negative, 81% for neutral, and 84% for positive, with corresponding F1-scores of 63%, 81%, and 85%, respectively.
Co-Authors Ade Romadhony Ari Moesriami Barmawi Arya Prima Al Aufar Donni Richasdy Fazainsyah Azka Wicaksono Hani Nurrahmi Muhammad Farrel Muhammad Iqbal Muhammad Zaky Aonillah Nazar Azmi Primanda Sayarizki