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All Journal JURNAL MEDIA INFORMATIKA BUDIDARMA Building of Informatics, Technology and Science JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH)
Adiwijaya Adiwijaya
Telkom University, Bandung
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering

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A Multi-label Classification on Topic of Hadith Verses in Indonesian Translation using CART and Bagging Rendi Kustiawan; Adiwijaya Adiwijaya; Mahendra Dwifebri Purbolaksono
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 6, No 2 (2022): April 2022
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v6i2.3787

Abstract

Hadith is a source of law for Muslims after the al-qur'an, in which there are instructions in the form of words, actions, attitudes, and others. Hadith must be studied and practiced by Muslims, then used as a way of life after the al-qur'an. Classifying hadith is a way to make it easier for Muslims to learn hadith by looking at the text pattern in the translation of Bukhari hadith based on three classes or categories based on suggestions, prohibitions, and information. The classification carried out is a multi-label classification. The classification process uses N-gram and TF-IDF as feature extraction, CART and bagging as classification methods, and hamming loss as evaluation methods. Bagging is used to cover the shortcomings of CART, namely, the CART model is less stable, which, if there is a slight change in the training data, will have a significant effect on the resulting learning model. Several testing methods were carried out to obtain the best hammer loss value in this study. Based on several tests that have been carried out, the best hamming loss value is 0.1914 or 80.86%. These results indicate that the use of bagging can help increase accuracy by 5%.
Sentiment Analysis on Beauty Product Review Using Modified Balanced Random Forest Method and Chi-Square Antika Putri Permata Wardani; Adiwijaya Adiwijaya; Mahendra Dwifebri Purbolaksono
Journal of Information System Research (JOSH) Vol 4 No 1 (2022): October 2022
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (346.809 KB) | DOI: 10.47065/josh.v4i1.2047

Abstract

Internet users in Indonesia have used e-commerce services to buy various products. For example, one website that provides information services about women's beauty products is Female Daily. On the website, there are reviews of beauty products. The review feature is one feature that helps users in determining which beauty products to buy. Unfortunately, many reviews will take a long time to read, and it is almost impossible for users to read all the information. Therefore, research is needed to make it easier for users to consider products such as sentiment analysis. Sentiment analysis aims to classify opinions, namely, user reviews, into positive, neutral, and negative opinions. In this study, sentiment analysis uses the Modified Balanced Random Forest(MBRF) and Chi-square method as feature selection. The best model from this study produces an average accuracy and an average f1-score of 81.75% and 71.90%, respectively.
Smartphone Purchase Recommendation System Using the K-Nearest Neighbor (KNN) Algorithm Bayu Rahmat Setiaji; Dody Qori Utama; Adiwijaya Adiwijaya
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 6, No 4 (2022): Oktober 2022
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v6i4.4753

Abstract

Indonesia is in the fourth position of the countries with the most smartphone users worldwide. Smartphones are needed in today's modern times. Smartphones are also used not only for long-distance communication but also for carrying out daily work. Smartphones are currently used for study and work and also become entertainment to play. Therefore, smartphones are very much sought after for the suitability of users who carry out their daily activities. So this research is very helpful for users to find smartphones that support their daily activities such as studying, working, and playing. This research is based on a website that can make it easier for users to see their smartphone recommendations directly. The analysis uses the K-Nearest Neighbor (KKN) method to see the ratings reviewed by other users who have tried using their smartphones with different phone brands. The calculation method in the current study uses 3 KNN calculations and uses the concept of combining calculations to find the maximum recommendation results. The result of the recommendation system using the K-Nearest Neighbor method is in the form of a review stating whether the user agrees or disagrees. In the current study, there have been 100 reviews from users, and it has a percentage of 78% for success and 22% for failure.
Sentiment Analysis on Indonesian Movie Review Using KNN Method With the Implementation of Chi-Square Feature Selection Imam Prayoga; Mahendra Dwifebri Purbolaksono; Adiwijaya Adiwijaya
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 7, No 1 (2023): Januari 2023
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v7i1.5522

Abstract

The advancement and development of the internet is used by the people to support various sectors, one of which is the film industry. Nowadays, people can easily access various movies from available sites. This convenience had led to many reviews about a movie that can be obtained easily. This movie review is very influential on the variety of movies. Freedom of expression on the internet, makes the reviews of a movie vary. For this reason, it is necessary to analyze the sentiment of he movie reviews that are positive or negative. In this research, a sentiment analysis model is build using chi-square selection feature with the KNN algorithm. The final result of this research is able to provide the best classification model with the implementation of stemming. The value of k = 267 in selectkbest at the feature selection stage using chi-square, and using the value of K = 11 in the KNN parameter. This model produces f1 score value of 86.98%.
Method comparison of Naïve Bayes, Logistic Regression, and SVM for Analyzing Movie Reviews Muhammad Maulidan Aziz; Mahendra Dwifebri Purbalaksono; Adiwijaya Adiwijaya
Building of Informatics, Technology and Science (BITS) Vol 4 No 4 (2023): Maret 2023
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v4i4.2644

Abstract

A film can be categorized as a successful film based on the reviews given by the critics. The reviews can range from professional critics to public reviews from the general audience. Due to a large number of reviews and opinions on a film, this study aims to create a sentiment analysis model and compare the methods used to analyze datasets from a movie review. Sentiment Analysis is a method for studying and analyzing opinions, then classifying these opinions into several classes. This research will use the Naïve Bayes method, Logistic Regression, and Support Vector Machine (SVM) to analyze film review data. The film review dataset used is a collection of film reviews taken from the Rotten Tomatoes website and will be pre-processed before implementing the Naïve Bayes, Logistic Regression, and SVM methods. The SVM classifier with 80:20 data splitting has the best performance, with a result of 99.4% accuracy score and 93.5% F1 score.
Text Classification of Indonesian Translated Hadith Using XGBoost Model and Chi-Square Feature Selection Dita Julaika Putri; Mahendra Dwifebri; Adiwijaya Adiwijaya
Building of Informatics, Technology and Science (BITS) Vol 4 No 4 (2023): Maret 2023
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v4i4.2944

Abstract

Aside from the Holy Qur'an, Hadith is indeed a life guide that every Muslims in this world must follow. The technology for classifying texts and sentences, including categorizing hadiths, is evolving in tandem with the advancement of the times. The model used to perform classification has also been developed and optimized such as the use of the XGBoost algorithm which is more optimized than the previous tree algorithm. This can also make it easier for us as Muslims to study hadiths by categorizing them according to recommendations, prohibitions, and information. This study conducted text classification of Indonesian translations of hadith texts based on recommendations, prohibitions, and information using the XGBoost algorithm, TF-IDF for its feature extraction, and Chi-Square for its feature selection. In this study, experiments were carried out by changing the order of the preprocessing process for the stopword removal and stemming parts, performing the classification process with and without using chi-square as a feature selection, and adding parameter value during the modeling process with XGBoost and the highest final results obtained were 79% for accuracy, 79% for precision, 78% for recall and 78% for F1-score.
Co-Authors Antika Putri Permata Wardani Bayu Rahmat Setiaji Dita Julaika Putri Dody Qori Utama Imam Prayoga Mahendra Dwifebri Mahendra Dwifebri Purbalaksono Mahendra Dwifebri Purbolaksono Muhammad Maulidan Aziz Rendi Kustiawan