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All Journal TEKNIK INFORMATIKA Jurnal Ilmiah Merpati (Menara Penelitian Akademika Teknologi Informasi) Sistemasi: Jurnal Sistem Informasi Seminar Nasional Teknologi Informasi Komunikasi dan Industri Krea-TIF: Jurnal Teknik Informatika Building of Informatics, Technology and Science Journal of Computer System and Informatics (JoSYC) Jurnal Sistem Komputer dan Informatika (JSON) TIN: TERAPAN INFORMATIKA NUSANTARA Jurnal Computer Science and Information Technology (CoSciTech) Bulletin of Computer Science Research JUTECH : Journal Education and Technology Journal of Comprehensive Science Knowbase : International Journal of Knowledge in Database Malcom: Indonesian Journal of Machine Learning and Computer Science Jurnal Teknik Indonesia Indonesian Journal of Innovation Multidisipliner Research Indonesian Journal of Innovation Multidisipliner Research INOVTEK Polbeng - Seri Informatika Jurnal Komtika (Komputasi dan Informatika)
Elin Haerani
UIN Suska Riau
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Analisis Algoritma Fuzzy C-Means Untuk Pengelompokan Data Keluarga Cahyadi, Wahyu; Haerani, Elin; Nazir, Alwis; Iskandar, Iwan
TIN: Terapan Informatika Nusantara Vol 6 No 7 (2025): December 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/tin.v6i7.8981

Abstract

Mapping the socio-economic conditions of the community plays a crucial role in supporting targeted development planning at the village level. This study aims to apply the Fuzzy C-Means (FCM) algorithm to cluster families in Bina Baru Village based on social, economic, and household environmental indicators. The variables used include family size, income sources, physical condition of the house, basic facilities, as well as monthly expenditure and income levels. This study uses population data from Bina Baru Village, consisting of 1,000 entries with 16 variables. The FCM algorithm was chosen for its ability to accommodate multiple degrees of membership (fuzzy membership), making it more adaptable in capturing the diversity and ambiguity of socio-economic characteristics. The results show that FCM produces two main clusters: Cluster 0, with 440 members, reflects families with middle to lower economic conditions, permanent housing, and adequate basic facilities; and Cluster 1, with 560 members, represents families with lower economic conditions, semi-permanent housing, and relatively smaller family sizes. Evaluation using the Xie–Beni index (35.4976), Fuzzy Partition Entropy (0.6843), and Fuzzy Cluster Index (0.4468) indicates that the two-cluster model has the best clustering quality compared to other numbers of clusters. Overall, the Fuzzy C-Means algorithm is effective in mapping variations in family welfare and can be used as a basis for formulating development policies and data-driven community empowerment programs in Bina Baru Village.
Klasifikasi Sentimen Bitcoin Terhadap Komentar Di Aplikasi X Menggunakan Metode Decision Tree C4.5 Indrizal, Habibi Putra; Syafria, Fadhilah; Haerani, Elin; Vitriani, Yelvi; Yusra, Yusra
Bulletin of Computer Science Research Vol. 6 No. 1 (2025): December 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v6i1.932

Abstract

Sentiment analysis is an important method for understanding user perceptions of cryptocurrency assets such as Bitcoin, whose price movements are strongly influenced by public opinion. This study aims to classify user sentiment from comments posted on the X platform into two classes, namely positive and negative, using the Decision Tree C4.5 algorithm. The dataset consists of 5,000 Indonesian-language comments collected through a web scraping process and processed through text preprocessing and TF-IDF–based feature extraction. The model was trained using a 70% training data and 30% testing data split. The evaluation results show that the C4.5 model achieved an accuracy of 78%. For the positive class, the model obtained a very high recall of 0.99 with an F1-score of 0.83, indicating strong performance in identifying positive comments. In contrast, the negative class achieved a recall of 0.51 with an F1-score of 0.67, despite having a high precision of 0.97. The disparity in performance between classes is influenced by the data distribution, which is not fully balanced, with positive comments being more dominant than negative ones, causing the model to be more sensitive to the majority class. Overall, the results indicate that the Decision Tree C4.5 algorithm is sufficiently effective for Indonesian-language Bitcoin sentiment classification, although it still has limitations in recognizing the minority class. Future research may explore the application of data imbalance handling techniques or more advanced algorithms to improve the balance of classification performance across classes.
Analisis Sentimen Masyarakat Mengenai Relokasi Penduduk Rempang pada Media Sosial X Menggunakan Metode Naïve Bayes Classifier Taufiq, Muhammad; Haerani, Elin; Syafria, Fadhilah
Jurnal Teknik Indonesia Vol. 4 No. 2 (2025): Jurnal Teknik Indonesia
Publisher : Publica Scientific Solution

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58860/jti.v4i2.711

Abstract

Media sosial X telah menjadi salah satu sarana utama bagi masyarakat dalam menyampaikan opini terhadap isu publik, termasuk kebijakan relokasi penduduk Pulau Rempang sebagai bagian dari Proyek Strategis Nasional (PSN). Permasalahan yang muncul adalah opini publik yang bersifat tidak terstruktur, beragam, dan tersebar luas sulit untuk diklasifikasikan secara manual dan objektif. Oleh karena itu, penelitian ini bertujuan untuk mengembangkan sistem klasifikasi sentimen otomatis terhadap opini masyarakat dengan pendekatan kombinasi leksikal dan pembelajaran mesin. Sebanyak 1.000 tweet relevan dikumpulkan melalui proses crawling dan disaring menggunakan kriteria tertentu. Pelabelan sentimen dilakukan secara otomatis menggunakan InSet Lexicon, sedangkan representasi fitur teks dilakukan dengan metode TF-IDF. Algoritma Naïve Bayes Classifier digunakan sebagai model klasifikasi dan dievaluasi menggunakan confusion matrix, classification report, dan 10-fold cross-validation. Hasil evaluasi menunjukkan bahwa model mampu mengklasifikasikan sentimen pro dan kontra secara efektif, dengan akurasi tertinggi pada data uji sebesar 81,00% (rasio 90:10), dan akurasi validasi silang tertinggi sebesar 80,03% (rasio 80:20). Precision tertinggi diperoleh pada kelas pro (hingga 93%), sedangkan recall tertinggi pada kelas kontra (hingga 89%). Pendekatan ini terbukti efisien dan akurat untuk menganalisis opini publik berbasis media sosial, serta memiliki potensi untuk diterapkan pada isu-isu sosial lainnya yang relevan.
Co-Authors -, Jasril Abdul Haris Kurnia Sandi Harahap Afrianti, Liza Afriyanti, Liza Akbar, Lionita Asa Alfaridzy, M. Audi Amelia, Felina Ananda, Rahmadan Alam Ardan Anugrah, Wendy Baehaqi Cahyadi, Wahyu Dede Fadillah Elsilaturrahmi, Elsilaturrahmi Elvia Budianita Fadhilah Syafria Fahmi Rizal Febi Yanto Fitri Insani Fitri Wulandari Fitria Novitasari Gusti, Gogor Putra Hafi Puja Gusti, Siska Kurnia Habibi, M. Ilham hariansyah, Aldi Ikhsan, Tomi Indrizal, Habibi Putra Irsyada, Amelia Israldi, Tino Iwan Iskandar Jasril Jasril Kasman Rukun kurnia, fitra Mai Candra , Reski Maulani, Tedi Muhammad Affandes Muhammad fauzan Muhammad Fikry Muhammad Irsyad Muhammad Taufiq Muhammad Yusuf Fadhillah Muhammad, Raja Allifin Nazir, Alwis Novriyanto Novriyanto Okfalisa Okfalisa Okfalisa Oktavia, Lola Prananda, Alga Pranata, Joni Rahman, Muhammad Taufikur Ramadhani, Siti Rendi Ibnu Syahputra Sapitri, Janaria Surya Agustian Suwanto Sanjaya Syafria, Fadhillah Syah Utama, Isruel Vitriani, Yelfi Vitriani, Yelvi Wahyuni, Ayu Sri Yusra, Yusra zikrillah aulia, said rizki