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All Journal Jurnal Teknologi Informasi dan Ilmu Komputer Jurnal Transformatika Jurnal Pengabdian UntukMu NegeRI SISFOTENIKA INTENSIF: Jurnal Ilmiah Penelitian dan Penerapan Teknologi Sistem Informasi IT JOURNAL RESEARCH AND DEVELOPMENT Indonesian Journal of Artificial Intelligence and Data Mining MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer Digital Zone: Jurnal Teknologi Informasi dan Komunikasi JOISIE (Journal Of Information Systems And Informatics Engineering) Zonasi: Jurnal Sistem Informasi Jurnal Tekinkom (Teknik Informasi dan Komputer) Journal of Applied Data Sciences Jurnal Computer Science and Information Technology (CoSciTech) Jurnal J-PEMAS Abdiformatika: Jurnal Pengabdian Masyarakat Informatika Prosiding Seminar Nasional Sisfotek (Sistem Informasi dan Teknologi Informasi) Jurnal Algoritma J-COSCIS : Journal of Computer Science Community Service JAIA - Journal of Artificial Intelligence and Applications SATIN - Sains dan Teknologi Informasi The Indonesian Journal of Computer Science INOVTEK Polbeng - Seri Informatika
Wirta Agustin
STMIK AMIK Riau
Religion Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Social Sciences

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Perbandingan Kinerja Xgboost Dan Lightgbm Dalam Klasifikasi Depresi Pada Mahasiswa Berdasarkan Faktor Demografi Dan Akademik Pratama, Farhan; Ali, Edwar; Rahmaddeni; Agustin, Wirta
Jurnal Algoritma Vol 22 No 2 (2025): Jurnal Algoritma
Publisher : Institut Teknologi Garut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33364/algoritma/v.22-2.2439

Abstract

Depresi merupakan salah satu gangguan mental yang umum dialami mahasiswa, sehingga berdampak signifikan terhadap kesejahteraan psikologis dan performa akademik mereka. faktor-faktor seperti jenis kelamin, usia, tekanan finansial, tekanan belajar, kepuasan studi, dan waktu belajar yang tidak proporsional diketahui berkontribusi dalam memengaruhi kondisi tersebut. penelitian ini bertujuan untuk membandingkan kinerja algoritma XGBoost dan LightGBM dalam mengklasifikasikan risiko depresi pada mahasiswa, serta mengembangkan model melalui teknik tuning parameter menggunakan RandomizedSearchCV untuk meningkatkan akurasi prediksi. dataset yang digunakan berasal dari platform Kaggle yang terdiri dari 502 baris data. evaluasi performa dilakukan menggunakan metrik akurasi, precision, recall, f1-score, dan AUC-ROC, pada skenario pembagian data 80:20 dan 70:30, baik dengan parameter default maupun setelah tuning. hasil penelitian menunjukkan bahwa model XGBoost dengan tuning pada pembagian data 80:20 memberikan performa terbaik dengan akurasi 82,18%, precision 85,11%, recall 78,43%, f1-score 81,63%, dan AUC-ROC sebesar 0,8973. terbaik kemudian diimplementasikan dalam bentuk aplikasi web menggunakan Streamlit, guna memberikan prediksi risiko depresi secara otomatis dan interaktif, sehingga memudahkan pengguna non-teknis dalam mendeteksi kondisi tersebut secara praktis.
Framework for Analyzing Netizen Opinions on BPJS Using Sentiment Analysis and Social Network Analysis (SNA) Anam, M Khairul; Mahendra, Muhammad Ihza; Agustin, Wirta; Rahmaddeni, Rahmaddeni; Nurjayadi, Nurjayadi
INTENSIF: Jurnal Ilmiah Penelitian dan Penerapan Teknologi Sistem Informasi Vol 6 No 1 (2022): February 2022
Publisher : Universitas Nusantara PGRI Kediri

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (514.221 KB) | DOI: 10.29407/intensif.v6i1.15870

Abstract

The Social Security Administrative Body is a legal entity established to administer social security programs. News about BPJS policies is often found online and social media that has received responses from netizens as a form of public opinion on the policy. One of them is the opinion of netizens on social media Twitter. Ideas can be positive, neutral, or negative. These opinions are processed using the Support Vector Machine (SVM) method, in some SVM studies still getting unsatisfactory results, with rates below 60%. For this reason, it is necessary to have feature selection or a combination with the other methods to obtain higher accuracy. To see the actors who influence the opinion of netizens on the topic of BPJS, the Social Network Analysis (SNA) method is used. Based on the SVM Method's test results, the best accuracy results are obtained in combining the SVM Method with Adaboost, with an accuracy rate of 92%. Compared to the pure SVM method by 91%, the Combination of SVM Particle Swarm Optimization (PSO) by 87% and SVM using Feature Selection Genetic Algorithm (GA) by 86%.
Hybrid Model for Sentiment Analysis of Bitcoin Prices using Deep Learning Algorithm Rizky Afrinanda; Lusiana Efrizoni; Wirta Agustin; Rahmiati Rahmiati
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol. 22 No. 2 (2023)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/matrik.v22i2.2640

Abstract

Bitcoin is a decentralized digital currency, which is not controlled by a single authority or government. Bitcoin uses blockchain technology to verify transactions and guarantee user security and privacy. The fluctuating value of bitcoin is influenced by opinions that develop because many people use these opinions as a basis for buying or selling bitcoins. Knowledge to find out the market conditions of bitcoin based on public opinion is very necessary. This study aims to develop a hybrid model for bitcoin sentiment analysis. The dataset used came from comments on the Indodax website chat room, as many as 2890 data were successfully collected, then do data preprocessing, translate to english, text labeling and used hybrid parallel CNN and LSTM using word embedding glove 100 dimensions. Results of the experiments conducted, at 90:10 data splitting and 100 epochs is the best model with 88% accuracy, 86% precision, 78% recall and 81% f1-score, while the classification of opinion text comments on indodax chat results in 64.22% neutral comments, 21.14% positive comments and 14.63% negative comments. Based on research results, use of a parallel hybrid model provides a high accuracy value in classifying text, from these results positive comments are more than negative so that investors are advised to buy bitcoins.
Analisis Pilkada Medan pada Sosial Media Menggunakan Analisis Sentimen dan Social Network Analyisis Anam, M. Khairul; Firdaus, Muhammad Bambang; Fitri, Triyani Arita; Lusiana; Agustin, Wirta; Agustin
The Indonesian Journal of Computer Science Vol. 11 No. 1 (2022): The Indonesian Journal of Computer Science
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v11i1.3027

Abstract

The simultaneous regional head elections were over, but during the campaign until it was decided to become regional head there were many comments, both pro and contra. The city of Medan is one of the regions that will hold the 2020 ELECTION during the pandemic. The Medan City Election has decided that the pair Bobby Nasution and Aulia Rachman have won. This victory certainly gets a variety of comments on social media, especially Twitter. This study conducts sentiment analysis to see the sentiment that occurs, namely seeing negative, positive, or neutral comments. This sentiment analysis uses two methods to see the resulting accuracy, namely Support Vector Machine (SVM) and Naïve Bayes Classifier (NBC). This study also looks at the interactions that occur using Social Network Analysis (SNA). In addition to sentiment analysis and SNA, this study also looks at the existence of BOT accounts used in the #PilkadaMedan. The results obtained from the sentiment analysis show that NBC has the highest accuracy, which is 81, 72% with a data proportion of 90:10. Then on SNA, the @YanHarahap account got the highest nodes, namely 911 nodes. Then from 10326 tweets, 11% were suspected of being BOT by the DroneEmprit Academic system.
Co-Authors -, Dwi Haryono -, T. Sy. Eiva Fadtha -, Tashid Adlan, Radhi Agustin Agustin Ahmad Zamsuri, Ahmad Anam, M Khairul Arita Fitri, Triyani Azhari, Zahra Bakaruddin Bakaruddin bambang suyono Edwar Ali Efrizoni, Lusiana Firdaus, Muhammad Bambang Fransiskus Zoromi Fransiskus Zoromi Hazira, Nadila Helda Yenni, Helda Hendrawan, Riki Irwanda Syahputra Junadhi Junadhi Karpen Kurniawan, Fadly Lathifah, Lathifah Lusiana Lusiana Efrizoni Lusiana Lusiana Lusiana Lusiana Mahendra, Muhammad Ihza Muhammad Bambang Firdaus Muhammad Oase Ansharullah Muhammad Oase Ansharullah Munawir Munawir Nora Lizarti Nurjayadi Nurjayadi Nurul fadillah, Nurul Pandu Pratama Putra Pratama, Farhan Putra, Ryanda Satria Radhi Adlan Rahmaddeni Rahmaddeni Rahmiati Rahmiati Rini Yanti Rini Yanti, Rini Rizky Afrinanda Rometdo Muzawi, Rometdo Sari, Atalya Kurnia Septia, Rapindra Suhada, Khairus SUSI ERLINDA Susi Erlinda Syahrul Imardi Syam, Salmaini Safitri Tashid, Tashid Torkis Nasution Tri Putri Lestari, Tri Putri Triyani Arita Fitri Unang Rio Unang Rio, Unang Rio Yaakub, Saleh Yanti, Rini Yoyon Efendi Yulya Muharmi Zahra Azhari