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All Journal JATI (Jurnal Mahasiswa Teknik Informatika) Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics
Khoiriyah Harahap, Dayana
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Health Professions Materials Science & Nanotechnology

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PENDEKATAN KLASIFIKASI DALAM KNOWLEDGE DISCOVERY UNTUK ANALISIS SENTIMEN BERBASIS ASPEK PADA ULASAN BANDARA SULTAN MAHMUD BADARUDDIN II DI GOOGLE MAPS Nachwa, Syakillah; Khoiriyah Harahap, Dayana; Theresia Pardede, Eva; Nashiroh Ramadhani, Muthia; Anindya Putri, Salsa; Rositiani, Ely; Ditha Tania, Ken; Meiriza, Allsela
JATI (Jurnal Mahasiswa Teknik Informatika) Vol. 9 No. 3 (2025): JATI Vol. 9 No. 3
Publisher : Institut Teknologi Nasional Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36040/jati.v9i3.13776

Abstract

Sultan Mahmud Badaruddin II (SMB II) merupakan bandara utama di Sumatera Selatan yang berperan krusial untuk mobilitas regional. Kepuasan penumpang terhadap layanan bandara dapat dievaluasi melalui ulasan pengguna di Google Maps. Namun, volume ulasan yang besar menghadirkan tantangan dalam mengekstrak informasi yang bermakna. Studi ini menggunakan Aspect-Based Sentiment Analysis (ABSA) untuk melihat opini publik terhadap layanan Bandara SMB II menggunakan pendekatan klasifikasi dengan enam aspek yang dianalisis, yaitu check-in, keamanan, kenyamanan, lingkungan bandara, fasilitas dasar, dan mobilitas. Sebanyak 1.018 ulasan diperoleh dan diproses melalui tahapan preprocessing, data labeling, penyeimbangan data dengan SMOTE, ekstraksi fitur (TF-IDF), serta klasifikasi menggunakan algoritma Naïve Bayes, Support Vector Machine (SVM), dan Random Forest dengan skenario pembagian data 70:30, 80:20, dan 90:10. Hasil evaluasi menunjukkan bahwa skenario terbaik diperoleh pada perbandingan 90:10, yaitu model SVM mencapai kinerja terbaik dengan nilai accuracy sebesar 0.857, precision 0.839, recall 0.883, dan F1-score 0.860 melampaui Naïve Bayes dan Random Forest. Analisis sentimen telah menunjukkan bahwa aspek lingkungan bandara menerima sentimen positif tertinggi, tetapi aspek fasilitas dasar cenderung menerima ulasan negatif. Pengetahuan ini bisa menjadi dasar bagi pengelola Bandara SMB II untuk meningkatkan layanan mereka berdasarkan aspek-aspek yang paling berpengaruh terhadap pengalaman penumpang.
KOMPARASI KINERJA KNN DAN NAÏVE BAYES UNTUK KLASIFIKASI SENTIMEN PENGGUNA APLIKASI DEEPSEEK AI Fathoni, Fathoni; Khoiriyah Harahap, Dayana; Theresia Pardede, Eva; Nachwa, Syakillah; Ramadhani Maulizidan, Muammar; Ibrahim, Ali
JATI (Jurnal Mahasiswa Teknik Informatika) Vol. 9 No. 4 (2025): JATI Vol. 9 No. 4
Publisher : Institut Teknologi Nasional Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36040/jati.v9i4.13887

Abstract

Perkembangan Artificial Intelligence (AI), khususnya dalam Generative Artificial Intelligence (GenAI), telah menghasilkan berbagai model berbasis Large Language Model (LLM) yang mampu memahami dan menghasilkan teks menyerupai bahasa manusia. DeepSeek AI menjadi model yang baru-baru ini menarik perhatian publik. Penelitian ini menguji performa algoritma Naïve Bayes dan K-Nearest Neighbors (KNN) dalam analisis sentimen aplikasi DeepSeek AI berdasarkan accuracy, precision, recall, dan F1-score. Data dibagi menggunakan split data dengan rasio 70:30 dan 80:20, serta diterapkan Stratified K-Fold Cross Validation untuk memastikan generalisasi model yang optimal. Hasil terbaik diperoleh pada skema 70:30, di mana Naïve Bayes mencapai accuracy 81%, precision 87%, recall 50%, dan F1-score 64%, sedangkan KNN memperoleh accuracy 80%, precision 71%, recall 70%, dan F1-score 70%. Hasil cross validation menunjukkan bahwa rata-rata akurasi KNN mencapai 80.78%, sedangkan Naïve Bayes adalah 80.13%. Meskipun Naïve Bayes memiliki precision lebih tinggi, recall yang rendah mengurangi keseimbangan klasifikasi. Sebaliknya, KNN menunjukkan performa lebih stabil dengan keseimbangan precision dan recall, menjadikannya model yang lebih direkomendasikan untuk analisis sentimen ulasan aplikasi DeepSeek AI
Topic Mining-Based Knowledge Discovery of User Health Information Needs Khoiriyah Harahap, Dayana; Ditha Tania, Ken; Eka Sevtiyuni, Putri
Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol. 7 No. 4 (2025): November
Publisher : Jurusan Teknik Elektromedik, Politeknik Kesehatan Kemenkes Surabaya, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/ijeeemi.v7i4.270

Abstract

Understanding the user’s need for health information has become increasingly important as the use of digital health services continues to grow. However, the unstructured data of user-generated questions presents challenges in accurately capturing and analyzing these needs. This study contributes to addressing SDG 3 (Good Health and Well-being) by utilizing topic mining-based knowledge discovery to identify the primary topics emerging from user questions submitted through the “Tanya Dokter” feature on the Alodokter platform. A total of 8,550 questions were obtained through web scraping between July 2024 and June 2025. The collected data were preprocessed and subsequently analyzed using seven topic modeling approaches: Latent Dirichlet Allocation (LDA), Correlated Topic Model (CTM), Latent Semantic Analysis (LSA), Non-negative Matrix Factorization (NMF), BERTopic, Top2Vec, and ProdLDA. To assess model performance, the coherence metric (c_v) was employed to identify the most effective method. Among these techniques, NMF achieved the best results, producing the highest coherence score of 0.67 with six well-defined topics. The findings show six primary areas of concern: pregnancy; menstruation and contraceptive management; general health and minor ailments; infant care; dermatological conditions; and musculoskeletal and other physical complaints. General health-related issues occurred most frequently, particularly during seasonal transitions, while menstruation and contraceptive management received the least attention, despite menstruation contributing to women’s health risks and the use of contraceptives helping to reduce maternal mortality in Indonesia. These findings offer valuable insights for digital health platforms like Alodokter to enhance information delivery and health literacy, ultimately improving online health services and supporting the achievement of SDG 3
Co-Authors Ali Ibrahim Allsela Meiriza, Allsela Anindya Putri, Salsa Eka Sevtiyuni, Putri Fathoni Fathoni Ken Ditha Tania Nachwa, Syakillah Nashiroh Ramadhani, Muthia Ramadhani Maulizidan, Muammar Rositiani, Ely Theresia Pardede, Eva