Article Per Year (5 Year)
p-Index From 2021 - 2026
10.216
P-Index
This Author published in this journals
All Journal TEKNIK INFORMATIKA Seminar Nasional Aplikasi Teknologi Informasi (SNATI) MATICS : Jurnal Ilmu Komputer dan Teknologi Informasi (Journal of Computer Science and Information Technology) EXPERT: Jurnal Manajemen Sistem Informasi dan Teknologi Jurnas Nasional Teknologi dan Sistem Informasi CESS (Journal of Computer Engineering, System and Science) Sistemasi: Jurnal Sistem Informasi Jurnal Teknologi dan Sistem Komputer JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING JURNAL MEDIA INFORMATIKA BUDIDARMA Seminar Nasional Teknologi Informasi Komunikasi dan Industri Prosiding Seminar Nasional Teknoka Jurnal Nasional Komputasi dan Teknologi Informasi Digital Zone: Jurnal Teknologi Informasi dan Komunikasi Jurnal Riset Informatika Eksakta : Berkala Ilmiah Bidang MIPA Building of Informatics, Technology and Science bit-Tech Zonasi: Jurnal Sistem Informasi Jurnal Informatika Ekonomi Bisnis Journal of Computer System and Informatics (JoSYC) Jurnal Sistem Komputer dan Informatika (JSON) TIN: TERAPAN INFORMATIKA NUSANTARA Jurnal Computer Science and Information Technology (CoSciTech) Bulletin of Computer Science Research KLIK: Kajian Ilmiah Informatika dan Komputer Malcom: Indonesian Journal of Machine Learning and Computer Science Jurnal Informatika Ekonomi Bisnis
Surya Agustian
UNIVERSITAS ISLAM NEGERI SULTAN SYARIF KASIM RIAU
Aerospace Engineering Agriculture, Biological Sciences & Forestry Arts Humanities Astronomy Automotive Engineering 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 Dentistry Earth & Planetary Sciences Economics, Econometrics & Finance Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Immunology & microbiology Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Materials Science & Nanotechnology Mathematics Mechanical Engineering Medicine & Pharmacology Neuroscience Nursing Physics Social Sciences Transportation Veterinary Other

Published : 78 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 78 Documents
Search

 3   4   5   6   7 
Klasifikasi Sentimen pada Dataset Terbatas Menggunakan Random Forest dan Word2Vec Fitri, Dina Deswara; Agustian, Surya; Pizaini, Pizaini; Sanjaya, Suwanto
Journal of Computer System and Informatics (JoSYC) Vol 6 No 1 (2024): November 2024
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josyc.v6i1.6246

Abstract

Sentiment measurement of public opinion on social media is essential for understanding societal views on various issues, including public figures and political events. This research explores the effectiveness of the Random Forest algorithm with Word2Vec-based word representation for sentiment classification on a limited dataset. The case study involves tweets regarding Kaesang Pangarep as the Chairman of PSI, supplemented by external data related to Covid-19 and general topics. The dataset was processed using cleaning techniques, case folding, stopword removal, stemming, and tokenization. Words in the dataset were represented using the Word2Vec model with a Continuous Bag of Words (CBOW) architecture and a vector dimension of 500. Random Forest was employed to classify sentiment into positive, negative, or neutral categories. In the initial phase, the model was trained using 300 samples per label; however, the results showed unsatisfactory performance with an F1-Score of 49.00% and an accuracy of 50.00%. To improve performance, the dataset was expanded by adding 900 samples from Kaesang and 1,080 samples from external topics. The final results indicated an improvement with an F1-Score of 49.89%, an accuracy of 58.29%, precision of 49.16%, and recall of 56.47%. This research confirms that the use of Random Forest with word representation from Word2Vec can enhance sentiment classification performance, even with a limited dataset, and contributes to the development of sentiment analysis techniques in the field of machine learning.
Pengaruh Agregasi Data pada Klasifikasi Sentimen untuk Dataset Terbatas Menggunakan SGD Classifier Fauzan Ray T; Surya Agustian; Febi Yanto; Pizaini
Computer Science and Information Technology Vol 5 No 3 (2024): Jurnal Computer Science and Information Technology (CoSciTech)
Publisher : Universitas Muhammadiyah Riau

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

Abstract

Social media, especially Twitter or X, is a rich source of data for sentiment analysis. However, dataset limitation is a major challenge in utilizing machine learning, especially to produce fast and accurate sentiment analysis. This research applies data aggregation techniques to expand the training dataset and tests various preprocessing steps, such as cleaning, case folding, normalization, stemming, and lexicon-based methods. The classification method used is Stochastic Gradient Descent Classifier with text representation using Fast Text language model to generate word embedding. Lexicon-based preprocessing, particularly for emoji and emoticon handling, shows significant impact when data is added, as it is able to capture additional emotion and context that is often overlooked in conventional text analysis. Experimental results show that data addition and preprocessing optimization improved F1 Score from a baseline of 40% to 52.13%, surpassing the organizer which reached 51.28%. These findings emphasize the importance of data aggregation, preprocessing optimization, and parameter tuning using grid search in improving model performance on text sentiment classification with limited datasets.
Gated Recurrent Unit (GRU) for Sentiment Classification on Imbalanced Data: The COVID-19 Vaccine Program in Twitter Hadi, Mukhlis; Agustian, Surya
MATICS: Jurnal Ilmu Komputer dan Teknologi Informasi (Journal of Computer Science and Information Technology) Vol 17, No 1 (2025): MATICS
Publisher : Department of Informatics Engineering

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18860/mat.v17i1.27995

Abstract

Abstract— The initial implementation of the COVID-19 vaccination by the Indonesian government sparked mixed reactions from the public, ranging from strong support to fierce opposition. These differing opinions influenced individuals' decisions to either accept or refuse the vaccination program for themselves or their families. Public sentiment, expressed through posts, comments, or status updates, provides valuable insights into vaccine acceptance or rejection. This study conducts sentiment analysis using deep learning techniques, specifically employing the Gated Recurrent Unit (GRU) method on Twitter data. The dataset consists of three sentiment classes: positive, negative, and neutral. The Word2Vec word embedding model was used as input and trained on a COVID-19 vaccination sentiment dataset collected from Twitter. Since the classes in the existing data tweets are imbalanced, some other steps are required to improve the classification. The best-performing model achieved an F1-score of 66% and an accuracy of 69%. This classification model effectively addresses the class imbalance problem, delivering competitive results compared to other methods.
Pengembangan Aplikasi Pendeteksi Daging Sapi dan Babi Menggunakan Deep Learning Arsitektur EfficientNet-B6 Berbasis Android Pangestu, Yoga; Sanjaya, Suwanto; Jasril; Agustian, Surya; Safaat, Nazruddin
Jurnal Informatika Ekonomi Bisnis Vol. 7, No. 2 (June 2025)
Publisher : SAFE-Network

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37034/infeb.v7i2.1195

Abstract

The advancement of digital technology has generated a demand for applications that assist the public in ensuring the halal status of food products, particularly in distinguishing between beef and pork. This study aims to develop an Android-based application for detecting beef and pork using Deep Learning methods with the EfficientNet-B6 architecture, employing the eXtreme Programming software development approach. The image classification model utilizes a Convolutional Neural Network architecture integrated into a Python-based server, while the user interface is developed with Java in Android Studio. System testing was conducted using black-box methods on several Android devices, with varying room conditions and meat types. The results show that the application can classify meat with an accuracy of 66.7%, considering room conditions such as light and dark environments, and meat types including fatty and non-fatty. This application provides fast response times and a user-friendly interface. This application is expected to enable users to independently and efficiently verify the halal status of meat, thereby supporting the needs of Muslim consumers in the digital era.
Pengembangan Aplikasi Pendeteksi Daging Sapi dan Babi Menggunakan Deep Learning Arsitektur EfficientNet-B6 Berbasis Android Pangestu, Yoga; Sanjaya, Suwanto; Jasril; Agustian, Surya; Safaat, Nazruddin
Jurnal Informatika Ekonomi Bisnis Vol. 7, No. 2 (June 2025)
Publisher : SAFE-Network

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37034/infeb.v7i2.1195

Abstract

The advancement of digital technology has generated a demand for applications that assist the public in ensuring the halal status of food products, particularly in distinguishing between beef and pork. This study aims to develop an Android-based application for detecting beef and pork using Deep Learning methods with the EfficientNet-B6 architecture, employing the eXtreme Programming software development approach. The image classification model utilizes a Convolutional Neural Network architecture integrated into a Python-based server, while the user interface is developed with Java in Android Studio. System testing was conducted using black-box methods on several Android devices, with varying room conditions and meat types. The results show that the application can classify meat with an accuracy of 66.7%, considering room conditions such as light and dark environments, and meat types including fatty and non-fatty. This application provides fast response times and a user-friendly interface. This application is expected to enable users to independently and efficiently verify the halal status of meat, thereby supporting the needs of Muslim consumers in the digital era.
Perbandingan Performa Random Forest dan Long Short-Term Memory dalam Klasifikasi Teks Multilabel Terjemahan Hadits Bukhari: Comparison of Random Forest and Long Short-Term Memory Performance in Multilabel Text Classification of Bukhari Hadith Translation Ahmad, Rizmah Zakiah Nur; Harahap, Nazruddin Safaat; Agustian, Surya; Iskandar, Iwan; Sanjaya, Suwanto
MALCOM: Indonesian Journal of Machine Learning and Computer Science Vol. 5 No. 3 (2025): MALCOM July 2025
Publisher : Institut Riset dan Publikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57152/malcom.v5i3.2046

Abstract

Hadits merupakan fondasi utama kedua dalam Islam, yang memandu umat Islam dalam menafsirkan nilai-nilai Islam dan mengimplementasikannya secara nyata dalam berbagai aspek kehidupan. Salah satu perawi hadits yang paling dihormati adalah Imam Bukhari, yang dikenal dengan ketelitian dan ketegasannya dalam memilih hadits-hadits yang otentik. Penelitian ini menggunakan data dari terjemahan hadis dari Sahih Bukhari ke dalam bahasa Indonesia yang telah diklasifikasikan ke dalam tiga kategori utama, yaitu anjuran, larangan, dan informasi. Untuk mengidentifikasi karakteristik masing-masing kategori, klasifikasi teks dilakukan dengan menggunakan dua metode populer, yaitu Random Forest (RF) dan Long Short-Term Memory (LSTM), yang dikenal efektif dalam memproses data teks berskala besar dan kompleks. Tujuan dari penelitian ini adalah untuk menguji perbedaan kinerja antara kedua metode tersebut dalam mengelompokkan hadis yang datanya telah lengkap. Hasil evaluasi menunjukkan bahwa metode RF mencapai akurasi tertinggi sebesar 89,48%, sedikit lebih unggul dari LSTM yang memperoleh 88,52%. Kedua metode mencatat nilai Hamming Loss yang sama, yaitu 0,1048 (89,52%). Temuan ini menunjukkan bahwa kelengkapan dan kualitas data hadis Bukhari berkontribusi dalam meningkatkan akurasi klasifikasi dengan memberikan konteks dan variasi yang lebih baik untuk model.
Perbandingan Performa Metode Klasifikasi Teks Multilabel Hadis Terjemahan Bukhari Menggunakan Support Vector Machine dan Long Short Term Memory: Performance Comparison of Multilabel Text Classification Methods on Translated Hadiths of Bukhari Using Support Vector Machine and Long Short Term Memory Ramadhani, Aulia; Safaat, Nazruddin; Agustian, Surya; Iskandar, Iwan; Sanjaya, Suwanto
MALCOM: Indonesian Journal of Machine Learning and Computer Science Vol. 5 No. 3 (2025): MALCOM July 2025
Publisher : Institut Riset dan Publikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57152/malcom.v5i3.2051

Abstract

Hadis merupakan sumber hukum kedua dalam Islam, dan salah satu kitab hadis yang paling dikenal adalah Shahih al-Bukhari. Untuk mendukung pemahaman dan pengamalan yang tepat, hadis perlu diklasifikasikan secara akurat. Mengingat satu hadis dapat mengandung lebih dari satu informasi, pendekatan klasifikasi multilabel menjadi sangat relevan. Penelitian ini bertujuan untuk memberikan kontribusi dalam bidang klasifikasi teks dengan mengeksplorasi kombinasi metode dan parameter yang optimal untuk klasifikasi multilabel hadis. Hasil penelitian menunjukkan bahwa Support Vector Machine (SVM) memberikan performa terbaik pada label Larangan dengan Macro F1-score sebesar 82,57%, melalui kombinasi SVM + TF-IDF menggunakan kernel = linear, parameter C (regularization parameter) = 1 tanpa stopword removal dan tanpa balancing. Sementara itu, Long Short Term Memory (LSTM) juga unggul pada label Larangan dengan Macro F1-score 82,66% pada kombinasi parameter Epoch = 20, Dropout = 0.5, Dense = 128 dan Batch Size = 64 tanpa stopword removal dan tanpa balancing kombinasi ini juga menghasilkan nilai Hamming Loss terendah sebesar 10,452%, yang lebih baik dibandingkan dengan penelitian sebelumnya serta menunjukkan bahwa LSTM terbukti lebih efektif secara keseluruhan dengan penyetelan parameter yang tepat. Penelitian ini juga berkontribusi dalam peningkatan kualitas data dengan melengkapi matan hadis yang digunakan, sehingga menghasilkan performa klasifikasi yang lebih baik.
Klasifikasi Sentimen Menggunakan Metode Multilayer Perceptron dengan Fitur TF-IDF: Sentiment Classification Using Multilayer Perceptron Algorithm with TF-IDF Features Arasy, Abdurrahman; Agustian, Surya; Handayani, Lestari; Iskandar, Iwan
MALCOM: Indonesian Journal of Machine Learning and Computer Science Vol. 5 No. 3 (2025): MALCOM July 2025
Publisher : Institut Riset dan Publikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57152/malcom.v5i3.2052

Abstract

Media sosial, khususnya Twitter (X), telah menjadi platform utama dalam diskusi politik dan kebijakan pemerintah. Istilah dalam pengiriman pesan pada Twitter dikenal sebagai Tweet yang terdiri dari pesan dengan maksimal 280 karakter. Meskipun Tweet seringkali hanya berupateks, juga dapat menyertakan hyperlink, video, dan jenis media lainnya yang dapat digunakan untuk mengukur opini publik. penelitian ini bertujuan mengklasifikasikan sentimen masyarakat terkait pengangkatan Kaesang Pangarep sebagai Ketua Umum Partai Solidaritas Indonesia (PSI) dengan metode Multi-Layer Perceptron (MLP) Classifier dengan pendekatan Term Frequency-Inverse Document Frequency (TF-IDF) menggunakan bahasa pemograman python. Data yang digunakan terdiri dari 300 tweet, dengan 100 tweet perkelas atau opsi untuk hasil yang optimal. Tiga kategori tersebut adalah positif, netral, dan negatif. Berdasarkan penelitian yang telah dilakukan metode terbaik mencapai F1-score sebesar 0,6767 dan akurasi 0,6667. Hasil ini menunjukkan bahwa kombinasi MLP Classifier dan TF-IDF dapat mengatasi keterbatasan dataset hingga tingkat tertentu dibandingkan metode baseline. Penelitian ini juga memberikan wawasan tentang optimasi klasifikasi sentimen dalam kondisi data terbatas, yang dapat diterapkan pada topik lain dengan permasalahan serupa
Pebandingan Metode Decision Tree dan XGBoost untuk Klasifikasi Sentimen Vaksin Covid-19 di Twitter Sinaga, Habib Hakim; Agustian, Surya
Jurnal Nasional Teknologi dan Sistem Informasi Vol 8 No 3 (2022): Desember 2022
Publisher : Departemen Sistem Informasi, Fakultas Teknologi Informasi, Universitas Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25077/TEKNOSI.v8i3.2022.107-114

Abstract

Pemerintah Indonesia melaksanakan vaksinasi dalam upaya pencegahan virus COVID-19. Namun upaya tersebut memicu pro dan kontra dalam masyarakat. Pro dan kontra tersebut dapat dikatakan sebagai sentimen. Sentimen dapat diungkapkan di berbagai media, salah satunya adalah media sosial. Teknik yang digunakan untuk mendeteksi sentimen pada media sosial salah satunya adalah klasifikasi teks dengan machine learning. Penelitian ini akan membandingkan Decision tree dan XGBoost untuk mengklasifikasikan sentimen di twitter. Data diperoleh dengan cara crawling menggunakan pemograman pyton dan Twitter API. Data diberi label dengan teknik crowdsourcing dan majority voting. Data yang digunakan setelah diseimbangkan adalah 6000 data latih, 778 data validasi dan 400 data uji. Hasil pengujian Decision tree dan XGBoost mendapatkan hasil terbaik pada model XGBoost dengan nilai akurasi sebesar 66% dan f1-score sebesar 57%. Hasil ini juga merupakan yang terbaik dibanding metode yang digunakan pada penelitian sebelumnya dengan dataset yang sama.
Implementasi Question Answering Berbasis Chatbot Telegram Pada Tafsir Al-Jalalain Menggunakan Langchain dan LLM Febrian Rizki Adi Sutiyo; Harahap, Nazruddin Safaat; Surya Agustian; Reski Mai Candra
KLIK: Kajian Ilmiah Informatika dan Komputer Vol. 4 No. 5 (2024): April 2024
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/klik.v4i5.1784

Abstract

Technological developments are very important for efficient, accurate and fast information retrieval. Tafsir Al-Jalalain is one of the famous Tafsir Al-Qur’an, and is used as a source of life guidance for muslims. To get information about tafsir, you can go through information media such as the internet or from experts in Tafsir Al-Qur’an. However, to get information it takes a lot of time to filter the information efficiently, accurately and quickly. This problem requires a system that is able to answer human questions accurately, effectively and quickly. In this research, it is hoped that the implementation of telegram Chatbot-based Question Answering using Langchain and LLM will be a solution for providing information on Tafsir Al-Jalalain that is accurate, effective and fast. The Question Answering system will carry out learning on the Tafsir Al-Jalalain data using a language model, namely the Large Language Model, so that it is expected to be able to provide accurate, effective and fast information. The evaluation results of the research by distributing questionnaires to students majoring in Al-Qur'an and Tafsir Science at UIN SUSKA Riau, as many as seven respondents, obtained a percentage of 84.29%
Co-Authors .Safrizal, Safrizal Abdillah, Rahmad Achmad Yamin Harahap Afdhal Zikri Afriyanti, Liza Aftari, Dhea Putri AGUNG SUCIPTO Ahmad Kurniawan Ahmad, Rizmah Zakiah Nur Alfitra Salam Arasy, Abdurrahman Arif Kurniawan Ash Shiddicky Aulia Ramadhani Ayu Fransiska Baehaqi Dermawan, Jozu Dzaky Abdillah Salafy Eka Pandu Cynthia El Saputra, Yoga Elin Haerani Elvia Budianita Fadhilah Syafria Faizah Husniah Fauzan Ray T Fauzi Ihsan Febi Yanto Febrian Rizki Adi Sutiyo Fitra Kurnia Fitri Insani Fitri Insani Fitri, Dina Deswara Fuji Astuti Gusti, Siska Kurnia Habib Hakim Sinaga Hadi, Mukhlis Halimah Heru Wibowo Idhafi, Zaky Iffa, Marwika Rifattul Ihsan, Miftahul Iis Afrianty Iis Afrianty Ikhwan Habibi Ilham Habibi Hasibuan Illahi, Ridho Iman Fauzi Aditya Sayogo Indri Pangestuti Iwan Iskandar Jasril Jasril Jasril Jasril Jasril Jasril Jauhari, Najwa Lestari Handayani Lubis, Anggun Tri Utami BR. M Ridho Saputra Marsha Cahyani Dwisyakilla Melyana Hasibuan Miftah Farid Muhammad Affandes Muhammad Affandes Muhammad Elfarizi Muhammad Fikry Muhammad Fikry Muhammad Iqbal Maulana Muhammad Irsyad Muhammad Irsyad Muhammad Ravil Muhammad Tirta Syakban Muktar Sahbuddin Mukti M Kusairi Mulyadi, Syahrul Nadila Handayani Putri naldi, Afri Nazir, Alwis Nazruddin Safaat Nazruddin Safaat H Nazruddin Safaat H Nazruddin Safaat H Negara, Benny Sukma Novi Yanti Novriyanto Novriyanto Novriyanto Novriyanto Nurika Dwi Wahyuni Nurul Fatiara Okfalisa Okfalisa Oktavia, Lola Pangestu, Yoga Pizaini Pizaini Pranata, Joni Prima Yohana Putri Zahwa Putri, Adilah Atikah Putri, Atika Rahmad Abdillah Rahmad Kurniawan Ramadhani, Siti Reski Mai Candra Reski Mai Candra Rizqa Raaiqa Bintana Safrizal, Afri Naldi Salam Kurniawan Saputra, Ikhsan Dwi Saputra, Nugroho Wahyu Satira, Husna Sinaga, Habib Hakim Siti Ramadhani Siti Ramadhani Sri Puji Utami A. Subhi, Yazid Abdullah Suci Rahayu Sulistia Ningsih, Sulistia Suwanto Sanjaya Syaiful Azhar Tarmizi, Veci Cahyono Teddie D Trya Ayu Pratiwi Utari, Roid Fitrah Wahyu Reinaldy Wan Sobri Amin Yusra Yusra Yusra Yusra Yusra, Yusra