Article Per Year (5 Year)
p-Index From 2021 - 2026
3.215
P-Index
This Author published in this journals
All Journal Seminar Nasional Aplikasi Teknologi Informasi (SNATI) JUTI: Jurnal Ilmiah Teknologi Informasi Transformasi: Jurnal Pengabdian Masyarakat Seminar Nasional Informatika (SEMNASIF) Register: Jurnal Ilmiah Teknologi Sistem Informasi Indonesia Symposium on Computing Indonesian Journal on Computing (Indo-JC) SISFOTENIKA Paradigma JURNAL MEDIA INFORMATIKA BUDIDARMA Masyarakat Telematika Dan Informasi : Jurnal Penelitian Teknologi Informasi dan Komunikasi Jurnal Sisfokom (Sistem Informasi dan Komputer) INTEK: Jurnal Penelitian Jurnal Linguistik Komputasional EDUMATIC: Jurnal Pendidikan Informatika Building of Informatics, Technology and Science Jurnal Teknologi Informasi dan Terapan (J-TIT) JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) Journal of Computer System and Informatics (JoSYC) Charity : Jurnal Pengabdian Masyarakat KLIK: Kajian Ilmiah Informatika dan Komputer eProceedings of Engineering Jurnal INFOTEL
Moch Arif Bijaksana
Universitas Telkom, Bandung, Indonesia
Religion Arts Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Education Electrical & Electronics Engineering Engineering Environmental Science Industrial & Manufacturing Engineering Library & Information Science Mathematics Social Sciences

Published : 107 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 107 Documents
Search

 7   8   9   10   11 
Analyzing Public Sentiments on Disaster Relief Efforts Through Social Media Data Fakhruddin, Muhammad Rafi; Wijaya, Rifki; Bijaksana, Moch Arif
INTEK: Jurnal Penelitian Vol 11 No 1 (2024): April 2024
Publisher : Politeknik Negeri Ujung Pandang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31963/intek.v11i1.4773

Abstract

Social media has become a source of quick but not necessarily accurate information. Especially in social media X, which is often used to share information. This research aims to conduct sentiment analysis on posts related to natural disasters that aim to maximize assistance to victims of natural disasters. This research takes datasets from tweets on social media X, the data will be labeled into positive and negative. And then the preprocessing process will be carried out, in this study, categorization will be carried out on each tweet related to the category, then the data will be divided into training and testing. Then the Term Frequency-Inverse Document Frequency (TF-IDF) feature is used to assist in reducing the weight of words that often appear in the dataset, The next step involves designing a system with a focus on applying the Support Vector Machine (SVM) Polynomial Kernel algorithm which becomes a classifier which will later be used to find the best hyperline or decision boundary that divides each review into two classes, namely positive tweets and negative tweets. Then obtained with a value of Precision of 86.49%, Recall 99.21%, F1-Score 92.42%, and Accuracy of 87.01%. This research is expected to provide involvement in making a fast and effective decision for victims of natural disasters.
Perbandingan Analisis Sentimen pada Ulasan Aplikasi Sirekap Menggunakan Support Vector Machines dan Naive Bayes Khalid, khalid; Wijaya, Rifki; Bijaksana, Moch Arif
INTEK: Jurnal Penelitian Vol 12 No 1 (2025): April 2025
Publisher : Politeknik Negeri Ujung Pandang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31963/intek.v12i1.5196

Abstract

This research analyzes the sentiment reviews of the SIREKAP application on the Google Play Store using two machine learning algorithms, namely Naïve Bayes and Support Vector Machine (SVM). The dataset used consists of 19,925 reviews that have gone through preprocessing stages, including text cleaning, stopword removal, stemming, and tokenization. To overcome data imbalance, oversampling and undersampling techniques were applied. Furthermore, TF-IDF is used for feature extraction, converting text into numerical representation. The dataset is divided into 80% training data (15,940 data) and 20% test data (3,985 data). The results show that oversampling provides better performance than undersampling. In the oversampling method, the SVM algorithm achieved the highest accuracy of 95%, with consistent precision, recall, and F1-score values across all sentiment classes. The Naïve Bayes algorithm also performed quite well, with an accuracy of 77% on the oversampled data. In contrast, in the undersampling method, both algorithms have the same accuracy of 61%. This study confirms that the combination of oversampling technique and SVM algorithm is the best approach to handle imbalanced data and provides important insights into user perception of the SIREKAP application.
Development Grouping of Synonym Set Thesaurus Vocabulary The Qur’an in English Using Hierarchical Clustering Algorithm Fauziah, Salma; Bijaksana, Moch Arif
JURNAL INFOTEL Vol 12 No 3 (2020): August 2020
Publisher : LPPM INSTITUT TEKNOLOGI TELKOM PURWOKERTO

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20895/infotel.v12i3.477

Abstract

Research in the field of text mining to process entries or words from the Qur'an is very beneficial for Muslims. This study aims to establish a set of synonyms for the thesaurus in the words of the Qur'an. This research is used because the source of knowledge about the science of the Qur'an is still lacking. The dataset in this study uses the Corpus Qur'an and English Translation. This research is a research development of an article that has been published, namely "The Development of Al-Qur'an Vocabulary Set Synonyms with WordNet Approach" by Laras Gupitasari. Input from this research system uses nouns from the translation of English words in the Quran. The output of the system produces several groups that have the same level of closeness of meaning displayed, the first group means the word in the group has a close meaning. To produce output, this study uses word grouping with a hierarchical grouping method and calculates distances using common paths, then groups results according to the closeness of meaning from word entries. The evaluation in this study produced an F-Measure value of 76%, F-Measure Value is an evaluation to measure the accuracy of predictions issued by the system.
Pencarian Dan Perbandingan Isim Ma’rifat Dengan Remove Diacritic Pada Al-Quran Dan Hadis Kitab Ibnu Majah Patra , Gifaro Andyano; Darwiyanto , Eko; Bijaksana, Moch Arif
eProceedings of Engineering Vol. 11 No. 4 (2024): Agustus 2024
Publisher : eProceedings of Engineering

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Abstrak — Penelitian ini bertujuan untuk mengatasiketerbatasan dalam ketersediaan situs web yang secarakomprehensif menyajikan daftar rinci mengenai Isim Ma'rifatdalam Al-Quran dan Sunan Ibnu Majah. Kekurangan sumberdaya telah menghambat studi dan perbandingan Isim Ma'rifatdalam kedua teks ini. Untuk mengatasi kendala ini, kamimengembangkan pendekatan berbasis pemrosesan bahasaalami dengan program tokenizer Java yang terintegrasi denganbasis data MySQL yang memuat Sunan Ibnu Majah dan teksteks Al-Quran.Program ini mengidentifikasi kehadiran awalan"alif lam" dan menghapus tanda diakritik untukmempermudah perbandingan antara ayat-ayat dalam keduateks. Fokus utama penelitian adalah mengidentifikasi IsimMa'rifat yang dimulai dengan "alif lam" yang hanya terdapatdalam Al-Quran (1183 kata), yang hanya terdapat dalam SunanIbnu Majah (2.894 kata), serta kesamaan antara keduanya (415kata). Hasil analisis ini memberikan pemahaman komprehensifmengenai perbedaan dan persamaan Isim Ma'rifat yangdimulai dengan "alif lam" antara Al-Quran dan Sunan IbnuMajah.Hasil temuan ini memberikan kontribusi berharga bagiproyek Quranpedia, yang bertujuan mengembangkan sumberdaya yang mudah diakses tentang Al-Quran dan Kutubus Sittahuntuk studi Islam. Penelitian ini diharapkan dapatmeningkatkan pemahaman tentang Isim Ma'rifat dalamkonteks agama dan bahasa Arab, serta mendukungperkembangan pemrosesan bahasa alami dalam bahasa Arab. Kata kunci— quranpedia, al-quran, kitab hadis ibnu majah, isim ma’rifat, penghapusan diakritik
Klasifikasi Sentimen pada Dataset Ulasan Film menggunakan Machine Learning dan OpenAI Text Embedding Abdurrahman, Azzam; Bijaksana, Moch. Arif; Lhaksmana, Kemas Muslim
eProceedings of Engineering Vol. 12 No. 4 (2025): Agustus 2025
Publisher : eProceedings of Engineering

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Analisis sentimen pada ulasan film menjadi semakin penting seiring dengan meningkatnya volume data tekstual. Performa model machine learning untuk tugas ini sangat bergantung pada kualitas representasi teks yang digunakan. Penelitian ini bertujuan untuk mengevaluasi efektivitas model embedding teks kontekstual dari OpenAI, Text-embedding-3-large, untuk klasifikasi sentimen pada dataset Movie Reviews. Metodologi penelitian mencakup dua pendekatan klasifikasi: supervised learning menggunakan Support Vector Machine dan Logistic Regression, serta klasifikasi zero-shot. Performa Text-embedding-3-large dibandingkan secara langsung dengan model embedding statis Word2Vec pada dataset yang telah dibersihkan dan dataset asli. Hasil penelitian menunjukkan bahwa Text-embedding-3-large secara signifikan mengungguli Word2Vec, dengan peningkatan F1-score dari 78.01% menjadi 93.20%. Konfigurasi terbaik dicapai oleh kombinasi Support Vector Machine dengan hyperparameter default pada dataset yang tidak dibersihkan, yang mengindikasikan kemampuan model memanfaatkan informasi kontekstual dari tanda baca. Selain itu, pendekatan zero-shot menunjukkan kinerja yang cukup baik dengan F1-score 86.29%, yang membuktikan kapabilitas generalisasi model tanpa memerlukan data latih berlabel. Kata kunci : klasifikasi sentimen, ulasan film, machine learning, openai, embedding teks, zero-shoT
Implementation of Dependency Parser Using Artificial Neural Network Methods Izzah, Nurul; Bijaksana, Moch Arif; Huda, Arief Fatchul
Indonesian Journal on Computing (Indo-JC) Vol. 5 No. 3 (2020): December, 2020
Publisher : School of Computing, Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34818/INDOJC.2020.5.3.504

Abstract

In recent years, parsing has become very popular within the scope of NLP (Natural Language Processing) with the presence of Dependency Parser. However, almost all existing Dependency Parser do classifications based on millions of sparse indicator features. This feature is not only bad in drawing conclusions, but also significantly limits the speed of parsing so that the resulting parsing is not optimal. To overcome these problems, changing the use of sparse features becomes dense features to reduce sparsity between words. The Artificial Neural Network classification method is used to produce fast and concise parsing in the Transition-Based Dependency Parser by using 2 hyperparameters. The dataset used in this study is Arabic, Chinese, English, and Indonesian. Based on the evaluation that has been done, it shows a higher result using the second hyperparameter. In testing with English test data, the accuracy value of LAS (Labeled Attachment Score) is 80.4% and UAS (Unlabelled Attachment Score) is 83%, Then with dev data obtained an accuracy value of LAS 81.1% and UAS 83.7%, and parsing speed of 98 sentences per second (sent/s).Keywords: Parsing, dependency parser, transition-based dependency parsing.
People Entity Recognition in Indonesian Alquran Translation using Roberta Mutia, Aufa; Bijaksana, Moch Arif
Journal of Information System Research (JOSH) Vol 5 No 2 (2024): Januari 2024
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josh.v5i2.4838

Abstract

The Quran was revealed in Arabic, which has a complex linguistic structure, a unique writing system, and intricate grammar, making it challenging to understand. Therefore, understanding and interpreting the Quran is a primary goal for Muslims. To comprehend the teachings contained in the Quran, Muslims need an understanding of the human entities mentioned in it. However, manually labeling human entities in the Quran can be a complex task prone to errors. The aim of this research is to facilitate the process of labeling human entities in Quranic texts by building a model with good performance. RoBERTa is a Named Entity Recognition (NER) model that is an extension of BERT, trained with enhanced training methodologies. This study focuses on the use of the RoBERTa model to identify human entities in the translated text of the Quran in Bahasa Indonesia. The input to this system consists of translated Quranic sentences, which are then processed by the model to generate output in the form of predicted labels for those sentence entities. The model is constructed by utilizing a dataset from the Tanzil Quran corpus, covering chapters 1 to 6. Data preprocessing involves punctuation removal, tokenization, and case folding. The dataset is divided into training data (80%) and testing data (20%). The RoBERTa model is trained with hyperparameters such as epochs, learning rate, and batch size. Evaluation is performed using metrics such as Precision, Recall, and F-Score on the testing data. The evaluation results of the constructed RoBERTa model show an F-Score value of 52%. This score is not better compared to the BERT model, indicating that the RoBERTa model tends to have inferior performance in identifying human entities in the translated text of the Quran.
Co-Authors Abdul Raffi Malikul Mulki Abdurrahman, Azzam Ade Romadhony Adelya Astari Aditya Hanif Utama Ageng Prasetio Agni Octavia Agung Wardhana Z. Nasution Akip Maulana Al Faraby, Said Alfiya El Hafsa Alfredo Primadita Ali Ridho Fauzi Rahman Angelina Sagita Sastrawan Annisa Dian Muktiari annisa Imadi Puti Anugerah, Sri Mulyani Aqila, Neca Ardhi Akmaludin Jadhira Arie Ardiayanti Suryani Arie Ardiyanti Arie Ardiyanti Suryani Arief Fatchul Huda Arief Fatchul Huda Arief Fatchul Huda Arini Rohmawati Arlinda Dwi Ardiyani aulia khemas Heikhmakhtiar Bagus Ardisaputra Bambang Ari Wahyudi Bening Suryani Pratiwi Bhudi Jati Prio Utomo Darwiyanto , Eko Dea Delvia Arifin Dhafin Putra Aldi dina juni restina Djusnimar Zultilisna Donni Richasdy Dwi Marlina Sari Dzaky Ikram Dzidny, Dimitri Irfan Eki Rifaldi Eko Darwiyanto Fairuz Ahmad Hirzani Fakhruddin, Muhammad Rafi Falia Amalia Fauzan Ramadhan Fauziah, Salma Fernandy Marbun Floribertus Yericho Pramudya Galih Rizky Prabowo Gde Surya Pramartha Grace Duma Tambunan Hafsa, Alfiya El Huda, Arief Fatchul Huda, Arief Fatchul I Gusti Ayu Chandra Devi I Komang Resnawan Tri Putra I Made Darma Yoga I Nyoman Cahyadi Wiratama I Putu Prima Ananda Ibnu Asror Idzhari Syaeful Ma'mun - Ina Rofi’atun Nasihati Indra Lukmana Sardi Intan Khairunnisa Fitriani IZZAH, NURUL Jihan Ratnasari1 KD Krisna Dwipayana Kemas M Lhaksmana Kemas Muslim Lhaksmana Khalid kurnia sari lingga Kurniawan Adina Kusuma Luh Putri Ayu Ningsih Lukman Abdurrahman Meiditia Mustika Rani Miftahul Adnan Rasyid Mochamad Agung Permana Mohamad Syahrul Mubarok Mubaroq Iqbal Muhamad Jibril Muhammad Adib Imtiyazi Muhammad Althoof Nabalah Muhammad Aris Maulana Muhammad Budi Hartanto Muhammad Fakhri Ar-Razi Muhammad Faris Abdussalam Muhammad Haerunnur Syahnur Muhammad Rizki Chairulloh Muhammad Zidny Naf'an Munirsyah Munirsyah Muthia Virliani Mutia, Aufa Naufal Rasyad Neca Aqila Nisaa' 'Ainulfithri Nur Indrawati, Nur Nurul Izzah Patra , Gifaro Andyano Pramudita Oktaviani Prasetio, Ageng Puruhita Ananda Arsaningtyas Purwita, Naila Iffah Putri Cendikia Rahmad Geri Kurniawan Ramadhyni Rifani Ramanti Dwi Indrapurasih Rendy Andrian Saputra Retno Diah Ayu Ningtias Rifki Wijaya Riska Junia Wulandari Rizky Caesar Irjayana Ryan Fahreza Maliki Said Al Farab Sakinah Rahmi Sang Made Naufal Caesarya Mahardhika Saputro3,, Widyanto Adi Sarah Suryaningsih Sarja Asra Winata Sendika Panji Anom Shaufiah . Shervano Naodias Siagian Siti Sa'adah Siti Sa’adah Suryaningsih, Sarah Tegar Graha Adiwiguna Teuku Muhammad Ikhsan Totok Suhardijanto Triawati, Candra Valentino Rossi Fierdaus Wahyu Kurniawan Wahyu Purbaningrum Warih Maharani Widi Astuti Winda Eka Samodra Wiwin Aminah Yusuf Anugrah Putra Aditama ZK Abdurahman Baizal