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All Journal Jurnal Infomedia JATI (Jurnal Mahasiswa Teknik Informatika) Jurnal Ilmiah Wahana Pendidikan International Journal of Global Accounting, Management, Education, and Entrepreneurship (IJGAME2) Jurnal Altifani Penelitian dan Pengabdian kepada Masyarakat J-Intech (Journal of Information and Technology) Jurnal Ilmiah Sistem Informasi dan Teknik Informatika (JISTI) Aurelia: Jurnal Penelitian dan Pengabdian Masyarakat Indonesia JOSAR (Journal of Students Academic Research) Jurnal ELTEK Jurnal Manajemen Informatika, Sistem Informasi dan Teknologi Komputer (JUMISTIK)
Mawaddah, Udkhiati
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Religion Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Nursing Public Health Social Sciences Other

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Journal : JOSAR (Journal of Students Academic Research)

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Sentiment Analysis On Evos Esports Team Instagram Social Media Using Convolutional Neural Network (CNN) Zen, Mohammad Amir Fatkhi; Yuana, Haris; Mawaddah, Udkhiati
JOSAR (Journal of Students Academic Research) Vol 10 No 2 (2025): September
Publisher : Universitas Islam Balitar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35457/4xw3mn98

Abstract

The rapid growth of the esports industry in Indonesia presents unique challenges for professional teams such as EVOS Esports, particularly in strengthening fan engagement and loyalty in the digital era. This study aims to analyze fan sentiment toward the official Instagram posts of EVOS Esports using a deep learning approach with a Convolutional Neural Network (CNN). The research process involved data collection through web scraping, followed by preprocessing stages such as cleaning, case transformation, normalization, tokenization, stopword removal, and stemming. The dataset was then labeled, split into training and testing sets (90:10), and used for CNN model training and evaluation through a confusion matrix. The results demonstrate that the CNN model successfully classified comments into three sentiment categories—positive, negative, and neutral—with an accuracy of 92%. The model also achieved a precision of 0.92, recall of 0.92, and an F1-score of 0.92, indicating very good classification performance. Sentiment distribution analysis of 11,305 comments showed that neutral sentiment dominated (47.24%), followed by positive (30.12%) and negative (22.64%). These findings provide valuable insights into fan perceptions of esports team performance on social media. For future research, expanding the sentiment lexicon with terms commonly used in online communities is recommended to further enhance classification accuracy.
Comparative Study Of Monthly Electricity Consumption Clusterization Using K-Means and DBSCAN Putri Rochfiani; Chulkamdi, Mukh Taofik; Mawaddah, Udkhiati
JOSAR (Journal of Students Academic Research) Vol 10 No 2 (2025): September
Publisher : Universitas Islam Balitar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35457/rqvgbf18

Abstract

Menganalisis pola konsumsi listrik sangat penting untuk meningkatkan efisiensi distribusi energi dan mengidentifikasi anomali seperti lonjakan yang tidak biasa atau kemungkinan pencurian listrik. Penelitian ini menyajikan analisis komparatif dua algoritma klaster—K-Means dan DBSCAN—dalam mengklasifikasikan penggunaan listrik bulanan pelanggan PT PLN (Persero) Rayon Ngunut, yang mencakup Kecamatan Rejotangan, Ngunut, Kalidawir, dan Pucanglaban. Dataset tersebut mencakup catatan konsumsi dari bulan November dan Desember 2024. Algoritma K-Means, yang menggunakan pendekatan klaster berbasis centroid, bekerja efektif pada dataset yang seragam, sementara DBSCAN, sebuah metode berbasis kepadatan, lebih mampu mengenali outlier dan pembentukan klaster yang tidak sferis. Kinerja kedua algoritma dievaluasi menggunakan Akurasi, Mean Squared Error (MSE), Presisi, Recall, dan F1-Score. Hasil eksperimen menunjukkan bahwa K-Means mencapai akurasi 96%, MSE 0,0400, presisi 0,71, recall 1,00, dan skor F1 0,83. Sebaliknya, DBSCAN mencapai akurasi 76%, MSE 0,2400, presisi 0,29, recall 1,00, dan skor F1 0,45. Hasil ini menunjukkan bahwa K-Means menghasilkan klaster yang lebih kompak dan konsisten, sementara DBSCAN lebih unggul dalam mengidentifikasi anomali, dengan total mendeteksi 17 outlier. Akibatnya, K-Means dianggap lebih cocok untuk pengelompokan konsumsi yang stabil, sedangkan DBSCAN direkomendasikan untuk tujuan deteksi anomali. Temuan ini diharapkan dapat membantu PT PLN (Persero) dalam mengembangkan strategi berbasis data dan adaptif untuk manajemen energi yang lebih efisien.
Co-Authors Abdi Pandu Kusuma Adi Purnama, Candra Alamsyah, Isnan Ridho Chulkamd, Mukh Taofik Chulkamdi, Mukh Taofik Chulkamdi, Mukh. Taofik Ferdianto, Hendra Ferdianto Hadi Irawan, Zidhan Hartami Santi, Indyah Lathoif, Miftakhul Lestanti, Sri Nugraha Putra, Muhammad Permana Putra, Ageng Eka Putri Rochfiani Raihan, Muhammad Iqbal Ratri Anggraheni, Dhiptya Saiful Nur Budiman Santi, Indyah Hartami Santiko, Mohandes Sutanto, Yufi Priyo Wahyuni, Ervika Dewi Wulansari, Zunita Yuana, Haris Zaenal Zaenal, Zaenal Zain Zakaria, Robby Zen, Mohammad Amir Fatkhi