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All Journal Techno.Com: Jurnal Teknologi Informasi Jurnal Terapan Abdimas Journal of Information System Sinkron : Jurnal dan Penelitian Teknik Informatika SISFOTENIKA Jurnal Abdimas BSI: Jurnal Pengabdian Kepada Masyarakat Jurnal Informatika IJCIT (Indonesian Journal on Computer and Information Technology) Jurnal Teknik Komputer AMIK BSI Paradigma Jurnal Pilar Nusa Mandiri JITK (Jurnal Ilmu Pengetahuan dan Komputer) Techno Nusa Mandiri : Journal of Computing and Information Technology JSiI (Jurnal Sistem Informasi) INTI Nusa Mandiri Jurnal Infortech Jurnal Teknologi Informatika dan Komputer Jurnal Sistem Informasi Bulletin of Computer Science Research Abditeknika - Jurnal Pengabdian Kepada Masyarakat
Dewi Ayu Nur Wulandari
Universitas Bina Sarana Informatika
Computer Science & IT Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering Environmental Science Library & Information Science Other

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Penerapan Naive Bayes Terhadap Sentimen Analisis Media Sosial Twitter Pengguna Commuter Line Sunarti, Sunarti; Handayanna, Frisma; Wulandari, Dewi Ayu Nur
Techno.Com Vol. 23 No. 4 (2024): November 2024
Publisher : LPPM Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/tc.v23i4.11497

Abstract

Commuter Line ialah salah satu sektor pelayanan publik dibidang transportasi banyak dipergunakan oleh masyarakat saat ini. Sesuai perkembangan teknologi banyak opini masyarakat terhadap pelayanan Commuter Line beredar secara online, untuk menyampaikan opininya masyarakat menggunakan media sosial seperti Twitter. Media Twitter dapat dijadikan sebagai bahan evaluasi peningkatan kualitas layanan. Twitter memudahkan pelanggan untuk menyampaikan keluhan serta pendapat terkait layanan seperti PT KAI Commuter. Untuk memahami komentar pelanggan, persepsi layanan publik, dan mendapatkan opini baru maka perlu dilakukan analisis sentimen terhadap pelayanan Commuter Line. Analisis sentimen ini bertujuan untuk mengklasifikasikan tweets masyarakat terhadap layanan Commuter Line ke dalam sentimen complain dan not complain dengan menggunakan metode Naive Bayes. Metode ini mempunyai tingkat akurasi paling tinggi dalam pengklasifikasian analisis sentimen. Data yang diunduh dari Twitter menggunakan aplikasi RapidMiner sebanyak 1.010 tweet dan data validasi sebanyak 1.003 tweets. Pada akhir tahap penelitian diperoleh accuracy 78,11%, precision 81,76%, recall 72,51%, dan AUC yang didapat sebesar 0,814.   Kata kunci: Analisis sentimen; Commuter Line; Twitter; Naive Bayes
Model Klasifikasi Risiko Stunting Pada Balita Menggunakan Algoritma CatBoost Classifier Pahlevi, Omar; Wulandari, Dewi Ayu Nur; Rahayu , Luci Kanti; Leidiyana, Henny; Handrianto, Yopi
Bulletin of Computer Science Research Vol. 4 No. 6 (2024): Oktober 2024
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v4i6.373

Abstract

Stunting is a significant health issue in Indonesia, affecting the growth and development of young children and influenced by various complex risk factors such as nutrition, environment, and access to healthcare services. The manual process of identifying stunting risks often requires considerable time, resources, and specialized expertise from medical professionals. This study aims to develop a stunting risk classification model for young children using machine learning through the CatBoost Classifier algorithm. This algorithm was chosen for its advantages in handling categorical variables without requiring complex encoding processes and its ability to manage imbalanced data, ultimately improving prediction accuracy. In the conducted case study, the model's prediction updates were illustrated by increasing the initial prediction from 0.25 to 0.27 after accounting for residual corrections in the first iteration, with a learning rate of 0.1. This process demonstrates CatBoost's iterative mechanism for improving model predictions through gradual updates. Evaluation results showed that the developed model achieved an accuracy of 98.47% and a ROC-AUC score of 1.00 for several classes, indicating a high capability in accurately classifying stunting risks. These findings suggest that the CatBoost algorithm is effective for stunting risk classification, capable of handling data complexity, and expected to contribute significantly to supporting stunting prevention efforts through improved early detection.
Co-Authors AA Sudharmawan, AA Ahmad Hendra Darmawan, Ahmad Hendra Anwar, Syafina Ardana, Mohamad Arfananda, Muhammad Ghifari Arfhan Prasetyo Bahar, Asyil Alfin Halani Bay, David Eko Octavian Daffa Aidhil Fitra Deva Nur Fauziah Eni Irfiani Fatiha, Syalysia Luna Fauzan Fadlurrohman Frisma Handayanna Hanum, Nur Alliza Pramudya Resita Helina Apriyani Henny Leidiyana Khaerul Azhar Lestari Yusuf Lestari, Hana Dwi Luci Kanti Rahayu Maharani Enggar Putri Maria Nining Triwidiastuti Maulana, Afik Maulida, Anjelika Muhammad Afif Muhammad Dika Atthariq Nisa, Navira Aura Omar Pahlevi Rahmawati - Rahmawati Rahmawati, Rahmawati Ratna Dewi Yulianti Ratnawaty Marginingsih Riski Annisa Rizal Amegia Saputra Salvana, Alsa Diva Siti Masripah Siti Masripah, Siti Sunarti Sunarti sunarti sunarti Syam, Amandasyifa Taat Kuspriyono, Taat Wahyu Dwi Nanda Winda Rahmawati Yopi Handrianto, Yopi Yusuf, Ghufran Faishal