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All Journal Jurnal Teknologi Informasi dan Ilmu Komputer Jurnal Transformatika Jurnal Pengabdian UntukMu NegeRI SISFOTENIKA 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) 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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Journal : The Indonesian Journal of Computer Science

 1 
Opinion Mining menggunakan Algoritma Deep Learning untuk Menganalisis Penggunaan Aplikasi Jamsostek Mobile Azhari, Zahra; Efrizoni, Lusiana; Agustin, Wirta; Yanti, Rini
The Indonesian Journal of Computer Science Vol. 12 No. 2 (2023): 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.v12i2.3185

Abstract

BPJS Ketenagakerjaan berperan dalam menjaga kesejahteraan para pekerja dan buruh melalui program-program pendidikan dan pelatihan yang diberikan, pelayanan menjadi prioritas terhadap pelanggan untuk memberikan kenyamanan. Melalui aplikasi Jamsostek Mobile yang terdapat di google playstore akan diambil komentar-komentar untuk mendapatkan respon pelanggan terhadap aplikasi Jamsostek mobile untuk dilakukan opinion mining. Komentar yang diambil dari google playstore menggunakan bantuan googleplayscraper, sebanyak 3000 komentar berhasil diambil yang kemudian akan dilakukan tahap pembersihan data, pelabelan, pembobotan kata menggunakan word2vec 300 dimensi dan dilanjutkan menggunakan algoritma Long Short Term Memory. Hasil opinion mining menunjukkan dominasi sentimen negatif sebesar 80.58% dan 19.42% positif dengan tingkat akurasi terbaik yang dihasilkan oleh algoritma LSTM sebesar 87.36%. Hasil penelitian ini akan memberikan wawasan yang berguna bagi pengembang aplikasi untuk meningkatkan kualitas pelayanan dan pengalaman pengguna.
Heart Failure Disease Classification Using Random Forest Algorithm with Grid Search Cross Validation Technique Septia, Rapindra; Junadhi; Susi Erlinda; Wirta Agustin
The Indonesian Journal of Computer Science Vol. 14 No. 2 (2025): 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.v14i2.4765

Abstract

Heart failure is one of the leading causes of death worldwide and requires early detection to reduce the risk of serious complications. However, the imbalance in medical data poses a challenge in developing accurate prediction models. This study developed a heart failure classification model using the Random Forest algorithm, optimized with Grid Search Cross Validation to find the best combination of hyperparameters. The dataset consisted of 300 observations with 12 medical features and 1 target feature. Data preprocessing included outlier removal using the Interquartile Range (IQR) and Winsorize methods. The Synthetic Minority Oversampling Technique (SMOTE) was applied to address class imbalance, resulting in a more balanced training data distribution. The dataset was split into 80% training and 20% testing data using stratified sampling to maintain class proportions. The model was evaluated using accuracy, precision, recall, and F1-score metrics, with results showing 90% accuracy, 0.94 precision for class 0, 0.80 precision for class 1, 0.91 recall for class 0, and 0.86 recall for class 1. The model was implemented in a Streamlit-based application, allowing users to input health parameters and receive interactive predictions. This study demonstrates that optimizing the Random Forest algorithm with Grid Search Cross Validation can improve heart failure classification performance, providing a practical solution for supporting heart failure classification. Keywords: Heart Failure Classification, Random Forest, Hyperparameter Optimization, SMOTE, Model Evaluation.
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 Anam, M. Khairul Arita Fitri, Triyani Azhari, Zahra Bakaruddin Bakaruddin bambang suyono 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 Muhammad Bambang Firdaus Muhammad Ihza Mahendra Muhammad Oase Ansharullah Muhammad Oase Ansharullah Muharmi, Yulya Munawir Munawir Nora Lizarti Nurjayadi Nurjayadi Nurul fadillah, Nurul Pandu Pratama Putra 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