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All Journal TEKNIK INFORMATIKA Seminar Nasional Aplikasi Teknologi Informasi (SNATI) MATICS : Jurnal Ilmu Komputer dan Teknologi Informasi (Journal of Computer Science and Information Technology) Jurnas Nasional Teknologi dan Sistem Informasi CESS (Journal of Computer Engineering, System and Science) 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 Eksakta : Berkala Ilmiah Bidang MIPA Building of Informatics, Technology and Science Zonasi: Jurnal Sistem Informasi Jurnal Informatika Ekonomi Bisnis Journal of Computer System and Informatics (JoSYC) Jurnal Sistem Komputer dan Informatika (JSON) Jurnal Computer Science and Information Technology (CoSciTech) Bulletin of Computer Science Research KLIK: Kajian Ilmiah Informatika dan Komputer Bulletin of Information Technology (BIT) Malcom: Indonesian Journal of Machine Learning and Computer Science Jurnal Informatika Ekonomi Bisnis Science, Technology, and Communication Journal
Surya Agustian
UNIVERSITAS ISLAM NEGERI SULTAN SYARIF KASIM RIAU
Aerospace Engineering Agriculture, Biological Sciences & Forestry 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 Economics, Econometrics & Finance Electrical & Electronics Engineering Energy Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Materials Science & Nanotechnology Mathematics Mechanical Engineering Neuroscience Transportation

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Perbandingan Klasifikasi Citra CT-Scan Kanker Paru-Paru Menggunakan Image Enhancement CLAHE Pada EfficientNet-B0 Dzaky Abdillah Salafy; Febi Yanto; Surya Agustian; Fitri Insani
KLIK: Kajian Ilmiah Informatika dan Komputer Vol. 4 No. 3 (2023): Desember 2023
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/klik.v4i3.1514

Abstract

In recent years, there has been a significant increase in the global cancer related mortality rate. Among various cancer types, lung cancer has emerged as one of the highest incidence cases. Lung cancer predominantly affects males and is attributed to several factors, including exposure to cigarette smoke, long-term air pollution, and exposure to carcinogenic compounds such as radon, asbestos, arsenic, coal tar, and diesel fuel emissions. The growth of cancerous cells in the lungs can be detected using various imaging techniques, with CT-Scan being one of them. This research focuses on the classification of normal lung organs and those affected by cancerous cells. The classification process employs two types of data: original data and data processed with Contrast Limited Adaptive Histogram Equalization (CLAHE). The data is initially divided with 90:10 ratios before being trained using a Convolutional Neural Network (CNN). The CNN architecture used is EfficientNet-B0, with the assistance of different optimizers and learning rates. After testing, the model's performance is evaluated using a confusion matrix to compare the results between the use of original data and CLAHE-processed data. The use of CLAHE processed data yields higher evaluation metrics compared to the original data, achieving a precision of 87.9%, recall of 85.6%, F1-score of 85.11%, and accuracy of 85.29% in the 90:10 data split, with the Adam optimizer and a learning rate of 10-1. The research results reveal that the utilization of image enhancement, specifically Contrast Limited Adaptive Histogram Equalization (CLAHE), with an appropriate combination of clip limit and tile grid, can impact the model's performance in classifying image data.
Klasifikasi Sentimen Masyarakat di Twitter Terhadap Ganjar Pranowo dengan Metode Support Vector Machine Syaiful Azhar; Yusra; Muhammad Fikry; Surya Agustian; Iis Afrianty
KLIK: Kajian Ilmiah Informatika dan Komputer Vol. 4 No. 3 (2023): Desember 2023
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/klik.v4i3.1537

Abstract

The classification of public sentiment towards Ganjar Pranowo on Twitter can provide insights into his popularity, support, or criticism. This research aims to classify public sentiment towards Ganjar Pranowo on Twitter using the Support Vector Machine (SVM) method. The research data consists of 4000 tweets collected from Twitter. After undergoing preprocessing, these tweets are classified using SVM into positive or negative classes. The classification method is optimized to produce the most optimal model by testing the influence of feature selection stages and SVM parameter tuning. The data is divided into 80% training (TRAIN_SET) and 20% testing (TEST_SET). The optimal model is validated using 10% of the randomly selected TRAIN_SET for validation data. Sixteen experiments are conducted to explore the optimal model, with the highest validation results (top rank 4 models) tested on the TEST_SET, yielding F1-scores of 84.13%, 84.13%, 84.13%, and 84.13% for experiment IDs 1, 7, 14, and 16, respectively. In this research, SVM proves to be sufficiently effective in classifying sentiment-related tweets about Ganjar Pranowo on Twitter
Klasifikasi Sentimen Masyarakat Terhadap Pemerintah Terkait Penerapan Kebijakan New Normal Menggunakan Metode K-Nearest Neighbor Fuji Astuti; Reski Mai Candra; Surya Agustian; Siti Ramadhani
Jurnal Nasional Komputasi dan Teknologi Informasi (JNKTI) Vol 5, No 3 (2022): Juni 2022
Publisher : Program Studi Teknik Komputer, Fakultas Teknik. Universitas Serambi Mekkah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32672/jnkti.v5i3.4455

Abstract

Abstrak - Instagram memiliki berbagai macam pengguna tanpa terkecuali pemerintah, akun instagram pemerintah sendiri memiliki fungsi sebagai salah satu media informasi bagi masyarakat untuk mengetahui berita terbaru. Pemerintah telah melakukan berbagai upaya untuk mengurangi dan memutus rantai penyebaran virus covid-19, salah satu langkah yang diambil pemerintah dalam menghadapi covid-19 dengan menerapkan kebijakan New Normal. Tujuan penulis dari penelitian ini adalah untuk menghitung akurasi metode K-Nearest Neighbor pada klasifikasikan sentimen dari opini masyarakat terkait penerapan kebijakan new normal. Penelitian ini dilakukan melalui beberapa tahapan yaitu pengumpulan data dari akun yang dikelola pemerintah. Setelah itudata tersebut dilakukan proses preprocessing. Lalu membuat model dengan menggunakan word embedding FastText. Selanjutnya menggunakan feature engineering dalam melakukan improve untuk menghasilkan fitur terbaik. Dataset dalam penelitian ini berjumlah 4717 data yang terbagi dari 1456 data kelas pro, 1662 data kelas netral,  1599 data kelas kontra. Pengujian yang dilakukan menggunakan confusion matrix dengan cara menguji data latih dan data uji sebanyak 3 kali dengan perbandingan data 90:10 yaitu nilai akurasi sebesar 60,169% dan pada k-40 dengan perbandingan 80:20 yaitu nilai akurasi 60,16%, sedangkan akurasi terendah terdapat pada perbandingan data 70:30 yaitu 58,12%.Kata kunci: New Normal, Instagram, Klasifikasi, Sentimen, K-Nearest Neighbor Abstract - Instagram has various kinds of users without the exception of the government, the government's own Instagram account has a function as a medium of information for the public to find out the latest news. The government has made various efforts to reduce and break the chain of the spread of the COVID-19 virus, one of the steps taken by the government in dealing with COVID-19 by implementing the New Normal policy. The author's aim of this study is to calculate the accuracy of the K-Nearest Neighbor method in classifying sentiments from public opinion regarding the implementation of the new normal policy. This research was conducted through several stages, namely collecting data from accounts managed by the government. After that, the data is preprocessed. Then create a model using word embedding FastText. Then use feature engineering to improve to produce the best features. The dataset in this study amounted to 4717 data which was divided into 1456 pro class data, 1662 neutral class data, 1599 contra class data. The test was carried out using a confusion matrix by testing training data and test data 3 times with a data comparison of 90:10, namely the accuracy value of 60.169% and at k-40 with a ratio of 80:20, namely the accuracy value of 60.16%, while the lowest accuracy there is a comparison of 70:30 data that is 58.12%.Keywords: New Normal, Instagram, Classification, Sentiment, K-Nearest Neighbor 
Algoritme Logistic Regression untuk Mendeteksi Ujaran Kebencian dan Bahasa Kasar Multilabel pada Twitter Berbahasa Indonesia Ayu Fransiska; Surya Agustian; Fitri Insani; Muhammad Fikry; Pizaini Pizaini
Jurnal Nasional Komputasi dan Teknologi Informasi (JNKTI) Vol 5, No 4 (2022): Agustus 2022
Publisher : Program Studi Teknik Komputer, Fakultas Teknik. Universitas Serambi Mekkah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32672/jnkti.v5i4.4524

Abstract

Abstrak - Ujaran kebencian semakin meningkat bersamaan dengan banyaknya pengguna media sosial. Twitter merupakan salah satu media sosial yang membantu penyeberan ujaran ujaran melalui fitur twit-nya yang dilakukan berulang-ulang. Penelitian ini dilakukan untuk mengklasifikasi apakah sebuah twit mengandung ujaran kebencian atau bahasa kasar, dan jika terdeteksi mengandung ujaran kebencian maka akan diukur tingkatannya. Dataset yang digunakan diambil dari twitter sebanyak 13.126 twit asli. Klasifikasi menggunakan Algoritma logistic Regression dan fitur teks word embedding. Dilakukan beberapa kali percobaan untuk mendapatkan model terbaik agar pengujian didapatkan secara optimal. Rata-rata akurasi yang dari ketiga kelas sebesar 75,59%, untuk kelas hate speech 75,86%,kelas abusive 80,05%, kelas level 70,86% dengan komposisi 90:10.Kata kunci: Klasifikasi, Logistic Regression, Ujaran Kebencian, Twitter. Abstract - Hate speech is increasing along with the number of social media users. Twitter is one of the social media that helps spread utterances through its repeated tweet features. This study was conducted to classify whether a tweet contains hate speech or abusive language, and if it is detected to contain hate speech, the level will be measured. The dataset used was taken from twitter as many as 13,126 original tweets. Classification using Logistic Regression Algorithm and word embedding text feature. Several experiments were carried out to get the best model so that the test was obtained optimally. The average accuracy of the three classes is 75.59%, for the hate speech class is 75.86%, the abusive class is 80.05%, the level class is 70.86% with a composition of 90:10.Keyword : Classification, Logistic Regression, Hate Speech, Twitter.
Penerapan Deep Learning Menggunakan Gated Recurrent Unit Untuk Memprediksi Harga Minyak Mentah Dunia Saputra, Nugroho Wahyu; Insani, Fitri; Agustian, Surya; Sanjaya, Suwanto
Building of Informatics, Technology and Science (BITS) Vol 5 No 1 (2023): June 2023
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v5i1.3552

Abstract

Crude oil is a much-needed energy for the whole world. Each country is inseparable from the use of crude oil for use in various sectors, such as transportation, so that the price of world crude oil is the most important variable for the world. Fluctuations in oil prices will cause various problems, such as inflation, changes in market prices, and others. Therefore, the prediction of world crude oil prices is very important as a consideration for decision making. This study implements deep learning using the Gated Recurrent unit model. The data used is the price of Brent crude oil with a total of 5834 data, starting from January 4, 2000 to December 19, 2022. The parameters used are the number of GRU units, batch size, and lookback. The best model produced in this study is the GRU model with hyperparameters consisting of 30 lookbacks, 50 GRU units, and 256 batch sizes with the lowest MAPE value among the other models, which is 2.25%. The MAPE value states that predictions using the GRU model are said to be very good at predicting world crude oil prices
Klasifikasi Sentimen Terhadap Pengangkatan Kaesang Sebagai Ketua Umum Partai PSI Menggunakan Metode Support Vector Machine .Safrizal, Safrizal; Agustian, Surya; Nazir, Alwis; Yusra, Yusra
Building of Informatics, Technology and Science (BITS) Vol 6 No 1 (2024): June 2024
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v6i1.5340

Abstract

The appointment of Kaesang Pangarep as the Chairman of the Indonesian Solidarity Party (PSI) has sparked various responses on social media, particularly on Twitter. This research aims to classify public sentiment regarding the appointment using the Support Vector Machine (SVM) algorithm with FastText feature representation. The data used for classification involves a small training dataset. The text preprocessing process includes cleaning, case folding, tokenizing, normalization, stopword removal, and stemming. FastText word embedding is used to convert words into vectors, and an SVM model with Grid Search is used for parameter tuning to obtain the optimal model. The use of external datasets to expand the initially limited training dataset enhances data representation and improves the model's performance in sentiment classification. The Covid dataset was expanded by adding 100, 200, and 300 tweets for each negative, positive, and neutral label. From the experiments conducted, the best accuracy on the test data was found in experiment ID C2 with an F1-Score of 53.59% and an accuracy of 62.73%. In experiment ID C3 with the same dataset, the F1-Score was 50.46% and the accuracy was 60.46%. Finally, in experiment ID C7 with the same dataset, the F1-Score was 47.19% and the accuracy was 53.09%.
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.
SVM Method with FastText Representation Feature for Classification of Twitter Sentiments Regarding the Covid-19 Vaccination Program Mukti M Kusairi; Agustian, Surya
Digital Zone: Jurnal Teknologi Informasi dan Komunikasi Vol. 13 No. 2 (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.v13i2.11531

Abstract

Covid-19 is a virus that has a high level of spread, making the government implement a mass vaccination program throughout Indonesia. This program received a lot of responses from the public, with positive and negative opinions or comments. Currently, the public's response through social media is also an input and consideration for the government to implement a program. Therefore, this study was conducted to produce a method approach to assessing the Covid-19 vaccination program by calculating the percentage of each sentiment class. The method used is the Support Vector Machine (SVM) and the fasttext language model feature as a representation of words in the Covid-19 vaccination sentiment dataset collected from Twitter. The data used has been dataset balancing, feature selection and parameter tuning, the optimal SVM model is obtained with a composition of 2536 training data, 778 development data and testing of 400 testing data, resulting in the best value of fi-1 score of 59% with an accuracy rate of 68%. The system is quite successful in detecting sentiment in tweets compared to before. Keywords: sentiment classification, FastText, SVM, Covid-19 vaccine.
Peringkas teks otomatis pada artikel berbahasa indonesia menggunakan metode maximum marginal relevance Idhafi, Zaky; Agustian, Surya; Yanto, Febi; Safaat H, Nazruddin
Computer Science and Information Technology Vol 4 No 3 (2023): Jurnal Computer Science and Information Technology (CoSciTech)
Publisher : Universitas Muhammadiyah Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37859/coscitech.v4i3.6311

Abstract

Automated text summarization is a method for retrieving the essence of one or more text documents. Automatic Text Summarizer is needed for a faster and more efficient process of reading, searching, and understanding information. This study proposes the Maximum Marginal Relevance method to carry out the text summarization process automatically. The method was developed and tested on each of the 150 Indonesian article documents. The summary is generated from the similarity score between sentences calculated using cosine similarity. MMR's performance in producing summaries was evaluated using ROUGE (Recall-Oriented Understudy for Gisting Evaluation), which compares them to gold-generated summaries. Test results for a compression rate of 50% gave F1 scores on ROUGE-1, ROUGE-2, and ROUGE-L at 71.86%, 64.18%, and 71.56%, respectively. In comparison, the test results with a compression rate of 30% produced F1-scores for ROUGE-1, ROUGE-2, and ROUGE-L, respectively 62.95%, 53.61%, and 62.47%. Compared to previous studies, this study produced better scores.
Question Answering System pada Chatbot Telegram Menggunakan Large Language Models (LLM) dan Langchain (Studi Kasus UU Kesehatan): Question Answering System on Telegram Chatbot Using Large Language Models (LLM) and Langchain (Case Study: Health Law) Lubis, Anggun Tri Utami BR.; Harahap, Nazruddin Safaat; Agustian, Surya; Irsyad, Muhammad; Afrianty, Iis
MALCOM: Indonesian Journal of Machine Learning and Computer Science Vol. 4 No. 3 (2024): MALCOM July 2024
Publisher : Institut Riset dan Publikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57152/malcom.v4i3.1378

Abstract

Di bidang kesehatan, peraturan yang diterapkan dikenal sebagai hukum kesehatan, yang bertujuan untuk melindungi kepentingan pasien dan meningkatkan standar praktik medis. Pada tahun 2023, Indonesia menerapkan UU No 17 Tahun 2023 tentang Kesehatan, mencakup hak pasien, standar layanan, dan partisipasi masyarakat. Omnibus Law ini diharapkan menyelesaikan masalah kesehatan dan melindungi penyedia layanan. Penelitian ini bertujuan untuk mengembangkan Question Answering System (QAS) berbasis chatbot yang terintegrasi dengan Telegram. Metode yang digunakan adalah Langchain dan Large Language Models (LLM). Langchain digunakan untuk memfasilitasi pembangunan chatbot, sementara LLM adalah jenis model AI yang menggunakan pendekatan pembelajaran mesin untuk menghasilkan teks yang serupa dengan bahasa manusia. Sumber data yang digunakan sebagai basis pengetahuan adalah UU No 17 tahun 2023 tentang kesehatan. Chatbot yang dibangun telah berhasil memberikan jawaban kepada pengguna dengan hasil pengujian menggunakan BERTScore mendapatkan rata-rata nilai precision, recall, f1-score masing-masing sebesar 76%, 80%, 78%. Sedangkan untuk ROUGE-1 sebesar 60%, 45%, 50%, untuk ROUGE-2 sebesar 34%, 25%, 28%,  dan untuk ROUGE-L sebesar 45%,34%,38%.
Co-Authors .Safrizal, Safrizal Afdhal Zikri Afriyanti, Liza Aftari, Dhea Putri AGUNG SUCIPTO Ahmad, Rizmah Zakiah Nur Alfitra Salam Arasy, Abdurrahman Ash Shiddicky Aulia Ramadhani Ayu Fransiska Delifah, Nur Dermawan, Jozu Dzaky Abdillah Salafy Eka Pandu Cynthia, Eka Pandu El Saputra, Yoga Elin Haerani Elvia Budianita Fahrezy, Irgi Faizah Husniah Fauzan Ray T Fauzi Ihsan Febi Yanto Febrian Rizki Adi Sutiyo Fitri Insani Fitri Insani Fitri Wulandari Fitri, Dina Deswara Fuji Astuti Habib Hakim Sinaga Hadi, Mukhlis Halimah Hasibuan, Ilham Habibi Heru Wibowo Idhafi, Zaky Iffa, Marwika Rifattul Ihsan, Miftahul Iis Afrianty Iis Afrianty Iis Afrianty Iis Afrianty Illahi, Ridho Iman Fauzi Aditya Sayogo Indri Pangestuti Iwan Iskandar Jasril Jasril Jasril Jasril Jasril Jasril Lestari Handayani Lubis, Anggun Tri Utami BR. Miftah Farid Muhammad Fikry Muhammad Fikry Muhammad Iqbal Maulana Muhammad Irsyad Muhammad Irsyad Muhammad Ravil Muktar Sahbuddin Mukti M Kusairi Mulyadi, Syahrul Nadila Handayani Putri naldi, Afri Nazir, Alwis Nazruddin Safaat Nazruddin Safaat H Nazruddin Safaat H Negara, Benny Sukma Novriyanto Novriyanto Novriyanto Nurul Fatiara 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, M Ridho Saputra, Nugroho Wahyu Sinaga, Habib Hakim Siska Kurnia Gusti Siti Ramadhani Siti Ramadhani Siti Ramadhani Sri Puji Utami A. Subhi, Yazid Abdullah Suci Rahayu Sulistia Ningsih, Sulistia Suwanto Sanjaya Syaiful Azhar Trya Ayu Pratiwi Utari, Roid Fitrah Yusra Yusra Yusra, Yusra