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All Journal Jurnal Edukasi dan Penelitian Informatika (JEPIN) JIKO (Jurnal Informatika dan Komputer) JURNAL TEKNOLOGI DAN OPEN SOURCE JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Jurnal Informatika Kaputama (JIK) Jurnal JTIK (Jurnal Teknologi Informasi dan Komunikasi) Jurnal Bumigora Information Technology (BITe) Infotek : Jurnal Informatika dan Teknologi Jurnal Teknik Informatika (JUTIF) Information System Journal (INFOS) Jurnal SAINTIKOM (Jurnal Sains Manajemen Informatika dan Komputer) Jurnal Teknik Informatika Jurnal Nasional Teknologi Komputer Proceeding of International Conference Health, Science And Technology (ICOHETECH) Prosiding Seminar Nasional Teknologi Informasi dan Bisnis Innovative: Journal Of Social Science Research Dedikasi Saintek Jurnal Pengabdian Masyarakat SmartComp CSRID DEDIKASI SAINTEK Jurnal Pengabdian Masyarakat
Irawan, Ridwan Dwi
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Agriculture, Biological Sciences & Forestry Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice Mechanical Engineering Medicine & Pharmacology Nursing Physics Public Health Social Sciences Other

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Klasifikasi Sentimen dan Analisis Persepsi Pengguna Aplikasi myXL pada Google Play Store Menggunakan Naïve Bayes Classifier Ardiyanto, Marta; Irawan, Ridwan Dwi; Rifa'i, Ahmad
Jurnal Informatika Kaputama (JIK) Vol 10 No 2 (2026): Volume 10, Nomor 2, Juli 2026
Publisher : STMIK KAPUTAMA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59697/jik.v10i2.1349

Abstract

Penelitian ini dilakukan untuk menganalisis sentimen pengguna terhadap aplikasi myXL berdasarkan ulasan yang terdapat di Google Play Store. Aplikasi myXL merupakan layanan mandiri yang dirancang oleh XL Axiata, di mana ulasan penggunanya menjadi sumber data penting untuk mengetahui persepsi masyarakat terhadap kualitas layanan. Metode klasifikasi yang digunakan dalam penelitian ini adalah Naïve Bayes Classifier, dengan tahapan penelitian yang mengacu pada proses Knowledge Discovery in Database (KDD). Data yang digunakan berjumlah 1.000 ulasan yang dikumpulkan menggunakan teknik web scraping. Data ulasan tersebut dilabeli menjadi sentimen positif dan negatif secara otomatis berdasarkan rating bintang pengguna . Setelah melalui tahapan pra-pemrosesan teks, data diubah menjadi bentuk numerik menggunakan pembobotan Term Frequency-Inverse Document Frequency (TF-IDF). Pembagian dataset dilakukan dengan rasio 80% data latih dan 20% data uji. Berdasarkan hasil evaluasi pengujian, model Naïve Bayes Classifier menunjukkan performa yang baik dengan nilai accuracy sebesar 80,33%, precision 80,33%, recall 78,59%, dan F1-score 79,27%. Visualisasi Word Cloud menunjukkan bahwa kata yang dominan muncul dalam ulasan meliputi "kuota", "paket", "aplikasi", "login", dan "error". Hasil penelitian ini diharapkan dapat memberikan gambaran persepsi pengguna serta menjadi bahan evaluasi bagi pengembang untuk memperbaiki kendala teknis dan meningkatkan kualitas layanan.
Implementation Of A Personalized Healthy Food Menu Recommendation System Using Content-Based Filtering Based On User Profiles Dian Dian Dwi Setyawati; Pipin Widyaningsih; Ridwan Dwi Irawan
JURNAL TEKNOLOGI DAN OPEN SOURCE Vol. 9 No. 1 (2026): Jurnal Teknologi dan Open Source, June 2026
Publisher : Universitas Islam Kuantan Singingi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36378/jtos.v9i1.5794

Abstract

This study implemented a personalized healthy food menu recommendation system using content-based filtering and cosine similarity. The system translated user characteristics, dietary goals, preferences, allergies, and health-related restrictions into menu attributes. The pilot dataset contained 10 healthy menus represented by 15 binary attributes. Recommendation performance was evaluated offline using 10 controlled user-profile scenarios with realistic age, sex, weight, height, and activity values. Basal metabolic rate and total daily energy expenditure were calculated with the Mifflin-St Jeor equation. After allergen filtering, 94 user-menu candidate pairs were assessed. Reference relevance labels were determined before ranking based on meal-category compatibility, absence of allergen conflicts, and fulfillment of at least two target attributes. At a cut-off of three recommendations, the system achieved macro Precision@3 of 0.7000, Recall@3 of 0.9167, F1@3 of 0.7481, nDCG@3 of 0.8948, and Hit Rate@3 of 1.0000. All 10 functional test cases passed, and no allergen-conflicting menu appeared in the final top-three results. The findings indicated that the system produced relevant and traceable recommendations, although the small pilot dataset limited generalizability.
Co-Authors Abdul Harits, Muhammad Akbar Pratama, Hamzah Alva Hendi Muhammad Amelia, Dinda Anjarwati, Pipin Antoni DM, Nikolas Aprilisa Arum Sari Ardiyanto, Marta Artdelia Pingkan Salsabilla Ayu Setyowati, Sintiya Brilliant Firdaus, Azkha Choirul Ananda, Naufal Dian Dian Dwi Setyawati Eko Purwanto Eko Purwanto Ema Utami Fathoni, Syahrul Agung Febrian Fitiraldy Gilang Pramudito, Vincensius Ihza Yuzar Vianda, Rizqi Ika Putri Pujianti, Ika Putri Pujianti Imanuel, Christian Indra Kristiawan, Yohanes Intan Rahmawati Kresna Agung Yudhianto Maulana Akbar, Reza Maulindar, Joni Muh Adha Muh Adha Muhamad Paliya Sadana Muhammad Paliya Sadana Nailurrizqi, Adistya Nibras Faiq Muhammad Nugroh, Heri Nur Rohman, Sofyan Nurmalitasari Oktaviana Az Zahra, Erika Permatasari, Hanifah Pipin Widyaningsih Pramono Pramono Pratama, Dika Adi Rahmaning Umiadi, Amara Rifa'i, Ahmad Riyadsyah, Deprinico Rizqy Alfiansyah, Rafif Rudi Susanto Satria Yudha, Rafiq Satrio Atmojo, Fattah Setiawan, Stevania Shalikashilla Tiaramadhan, Faiza Sidik Prasetyo, Sidik Sofa Marwati Stevania Setiawan Suryanti, Ika Syarif , Muhammad Tri Hartanti, Ninik Viery, Andrean Yoga Saputra, Muchammad Zitnaa Dhiaaul Kusnaa Washilatul Arba’ah Zitnaa Dhiaaul Kusnaa Washilatul Arba’ah