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All Journal Seminar Nasional Aplikasi Teknologi Informasi (SNATI) Digital Zone: Jurnal Teknologi Informasi dan Komunikasi Eksakta : Berkala Ilmiah Bidang MIPA Zonasi: Jurnal Sistem Informasi Jurnal Computer Science and Information Technology (CoSciTech) Bulletin of Computer Science Research KLIK: Kajian Ilmiah Informatika dan Komputer
Surya Agustian
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Agriculture, Biological Sciences & Forestry Humanities Astronomy Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Decision Sciences, Operations Research & Management Energy Engineering Environmental Science Languange, Linguistic, Communication & Media Materials Science & Nanotechnology Mathematics Mechanical Engineering

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Perancangan Online Network Monitoring Berbasis PHP dan SNMP Sri Puji Utami A.; Surya Agustian; Iman Fauzi Aditya Sayogo
Seminar Nasional Aplikasi Teknologi Informasi (SNATI) 2006
Publisher : Jurusan Teknik Informatika, Fakultas Teknologi Industri, Universitas Islam Indonesia

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Abstract

Pada paper ini penulis membangun manajemen jaringan dengan membuat suatu program tentang manajemen jaringan berbasis PHP dan SNMP menggunakan sistem operasi Linux Mandriva Limited Edition 2005. Hasil dari program tersebut adalah dalam bentuk tampilan web. Pemantauan dengan tampilan web tersebut dapat memperlihatkan informasi yang dibutuhkan dari suatu link dan device yang terdapat pada Campus Network.Kata kunci: SNMP, agen, manajer, MIB, PHP.
LSTM (Long Short Term Memory) for Sentiment COVID-19 Vaccine Classification on Twitter Ihsan, Miftahul; Benny Sukma Negara; Surya Agustian
Digital Zone: Jurnal Teknologi Informasi dan Komunikasi Vol. 13 No. 1 (2022): Digital Zone: Jurnal Teknologi Informasi dan Komunikasi
Publisher : Publisher: Fakultas Ilmu Komputer, Institution: Universitas Lancang Kuning

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31849/digitalzone.v13i1.9950

Abstract

The implementation of the Covid-19 vaccination carried out by Indonesian government was ignited pros and contras among the public. Certainly, there will be pros and cons about the vaccination from the community. This attituded of pros and cons, which is also called sentiment, can influence people to accept or refuse to be vaccinated. Todays, people express their sentiment in social media in comments, post, or status. One of the methods used to detect sentiment on social media, whether positive or negative, is through a categorisation of text approach. This research provides a deep learning technique for sentiment classification on Twitter that uses Long Short Term Memory (LSTM), for positive, neutral and negative classes. The word2vec word embeddings was used as input, using the pretrained Bahasa Indonesia model from Wikipedia corpus. On the other hand, the topic-based word2vec model was also trained from the Covid-19 vaccination sentiment dataset which collected from Twitter. The data used after balanced is 2564 training data, 778 data validation data, and 400 test data with 1802 neutral data, 1066 negative data, and 566 positive data. The best results from various parameter processes give an F1-Score value of 54% on the test data, with an accuracy of 66%. The result of this research is a model that can classify sentiments with new sentences.
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

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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.
Klasifikasi Sentimen Pada Dataset yang Terbatas Menggunakan Algoritma Convolutional Neural Network Saputra, M Ridho; Surya Agustian; Jasril; Novriyanto
Bulletin of Computer Science Research Vol. 5 No. 4 (2025): June 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v5i4.613

Abstract

This study aims to analyze public responses to the appointment of Kaesang Pangarep as the Chairman of the Indonesian Solidarity Party (PSI) using a sentiment classification approach based on the Convolutional Neural Network (CNN) algorithm. The primary dataset consists of 300 Indonesian-language tweets categorized into three sentiment classes: positive, negative, and neutral. The limited size of the training data presents a major challenge, as it can hinder the model's ability to generalize. To address this issue, data augmentation was carried out by incorporating external datasets with Covid-19 and Open Topic themes. The preprocessing stages include text cleaning, normalization, and tokenization. The developed CNN model utilizes a layered architecture and applies regularization techniques such as L2 and dropout to reduce the risk of overfitting. Accuracy, F1-score, precision, and recall were used as performance evaluation metrics. Experimental results show that the best performance was achieved when the Kaesang and Covid-19 datasets were combined, yielding an F1-score of 0.62 on the validation set and 0.51 on the test set. These findings indicate that adding external data can improve classification accuracy, even under limited data conditions. This study contributes to the development of deep learning-based sentiment classification methods for Indonesian-language texts.
Analisis Sentimen Ulasan Aplikasi Indodax Pada Google Play Store Dengan Algoritma Random Forest Muhammad Iqbal Maulana; Yusra; Muhammad Fikry; Surya Agustian; Siti Ramadhani
Bulletin of Computer Science Research Vol. 5 No. 4 (2025): June 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v5i4.626

Abstract

Crypto assets have become a global phenomenon with a significant increase in the number of investors in Indonesia. Indodax, as the largest crypto asset trading platform in Indonesia, has contributed to the growth of this ecosystem and received many user reviews through the Google Play Store. With more than 5 million downloads and 100 thousand reviews, sentiment analysis is an important tool to understand user perceptions of Indodax services. The results of manual labeling show that the majority of reviews are positive (3989 reviews), while neutral and negative sentiments are 477 and 534 reviews respectively. From the research and testing that has been carried out using the Random Forest method and optimizing with Hyperparameter Tuning GridSearchCV on 4 test scenarios. The best results were obtained in Scenario 4 (3 Preprocessing Stages (Cleaning, Case Folding, and Tokenization) + Random Forest & Hyperparameter Tuning) producing the best value, with Precision 81%, Recall 64%, F1-Score 70% and Accuracy 89%. With the best parameter values ??{'criterion': 'entropy', 'max_depth': None, 'max_features': 'sqrt', 'min_samples_leaf': 1, 'min_samples_split': 2, 'n_estimators': 100}. This study shows that every experimental model that is optimized produces a higher value than experimental model that is not optimized.
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%
KOMPARASI METODE K-NEAREST NEIGHBORS DAN LONG SHORT TERM MEMORY PADA KLASIFIKASI TERJEMAHAN AL-QUR’AN Nurul Fatiara; Nazruddin Safaat H; Surya Agustian; Yusra; Iis Afrianty
ZONAsi: Jurnal Sistem Informasi Vol. 6 No. 2 (2024): Publikasi Artikel ZONAsi: Periode Mei 2024
Publisher : Universitas Lancang Kuning

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31849/zn.v6i2.19863

Abstract

Al-Qur’an merupakan kitab suci yang diturunkan untuk umat islam. Secara harfiah, Al-Qur'an berasal dari kata qara’a yang artinya membaca atau mengumpulkan. Namun untuk memahami terjemahan dari Al-Qur’an tidaklah mudah. Salah satu cara yang dapat dilakukan untuk memahami dan mempelajarinya adalah melakukan klasifikasi terhadap terjemahan ayat Al-Qur’an. Penelitian ini mengklasifikasikan terjemahan Al-Qur'an bahasa Indonesia ke enam kelas yang berbeda. Metode yang digunakan adalah K-Nearest Neighbor (KNN) dan Long Short Term Memory (LSTM) dan membandingkan kedua metode untuk mendapatkan hasil performa klasifikasi yang tertinggi. Hasil klasifikasi menunjukkan model LSTM menghasilkan performa klasifikasi yang lebih tinggi yaitu berupa rata-rata F1-Score sebesar 65% dan rata-rata accuracy 96% dibandingkan model KNN dengan nilai rata-rata F1-Score sebesar 55% dan rata-rata accuracy 93%.
Classification of Covid-19 Vaccine Sentiment Using K-Nearest Neighbor and Fasttext on Twitter Safrizal, Afri Naldi; Surya Agustian
EKSAKTA: Berkala Ilmiah Bidang MIPA Vol. 25 No. 03 (2024): Eksakta : Berkala Ilmiah Bidang MIPA (E-ISSN : 2549-7464)
Publisher : Faculty of Mathematics and Natural Sciences (FMIPA), Universitas Negeri Padang, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24036/eksakta/vol25-iss03/384

Abstract

 In late 2019 came a flu-like illness that infected the lungs in the city of Wuhan. It is suspected that the disease is suspected to have originated in bats. WHO named this disease Covid-19 and the virus spread throughout the world, causing a pandemic. The government took a vaccination drive to overcome this virus, but received a response of pros and cons from the public. There are many studies that discuss people's sentiments towards vaccination, one of which is the classification of sentiments. This study discusses the classification of sentiment towards covid-19 vaccines using the K-Nearest Neighbor and Fasttext algorithms on twitter. Data is obtained by crawling using the pyton programming language and Twitter API.  Data labeling is carried out by crowdsourcing and majority voting techniques.  The data used after the balancing process are 6000 training data, 778 development data and 400 test data.  The test results after various experiments and feature engineering got the best results with an accuracy value of 69% and an f1-score of 60%. This result is the best result compared to previous studies with the same dataset.
Co-Authors Benny Sukma Negara Fauzan Ray T Febi Yanto Febrian Rizki Adi Sutiyo Ihsan, Miftahul Iis Afrianty Iman Fauzi Aditya Sayogo Jasril Jasril Muhammad Fikry Muhammad Iqbal Maulana Nazruddin Safaat H Nazruddin Safaat H Novriyanto Nurul Fatiara Pizaini Reski Mai Candra Safrizal, Afri Naldi Saputra, M Ridho Siti Ramadhani Sri Puji Utami A. Yusra