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All Journal Jurnal ELTIKOM : Jurnal Teknik Elektro, Teknologi Informasi dan Komputer JURNAL TEKNOLOGI DAN ILMU KOMPUTER PRIMA (JUTIKOMP) Jusikom: Jurnal Sistem Informasi Ilmu Komputer
Atika, Syarifah
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Aerospace Engineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering

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Analisis Akurasi Algoritma K-Nearest Neighbor Untuk Diagnosis Penyakit Jantung Pada Lansia Sipayung, David Sebastian; Syarifah Atika
Jurnal Sistem Informasi dan Ilmu Komputer Vol. 8 No. 2 (2025): JUSIKOM: JURNAL SISTEM INFROMASI ILMU KOMPUTER
Publisher : Fakultas Teknologi dan Ilmu Komputer Universitas Prima Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Penyakit jantung adalah salah satu penyebab utama kematian di dunia, terutama pada populasi lansia, yang sering kali sulit dideteksi pada tahap awal karena gejala yang tidak spesifik. Oleh karena itu, diperlukan metode diagnosis yang lebih cepat dan efisien, seperti penerapan algoritma pembelajaran mesin. Penelitian ini bertujuan untuk mengevaluasi akurasi algoritma K-Nearest Neighbor (KNN) dalam mendiagnosis penyakit jantung pada lansia, dengan menggunakan dataset yang diperoleh dari Kaggle dan terdiri dari 918 data pasien. Data tersebut disaring untuk usia lansia (60 tahun ke atas), menghasilkan 253 data yang digunakan dalam klasifikasi. Empat nilai k (3, 5, 7, dan 9) diuji untuk menentukan nilai k terbaik dalam mengklasifikasikan penyakit jantung. Hasil evaluasi menunjukkan bahwa model dengan k = 9 memiliki performa terbaik dengan nilai recall tertinggi (0.93) dan F1-Score sebesar 0.81, meskipun dengan akurasi yang sedikit lebih rendah (0.68). K = 5 memberikan keseimbangan terbaik antara precision (0.72) dan recall (0.85), dengan F1-Score 0.78. Berdasarkan hasil ini, K = 9 lebih efektif untuk aplikasi medis yang mengutamakan deteksi lebih banyak kasus positif, meskipun mengorbankan sedikit precision. Penelitian ini dapat memberikan kontribusi untuk pengembangan sistem diagnosis penyakit jantung yang lebih cepat, efisien, dan akurat pada lansia, dengan harapan dapat meningkatkan deteksi dini penyakit jantung.
Recent Trends and Innovations in Elementary School Educational Game Development: A Literature Review Atika, Syarifah
JURNAL TEKNOLOGI DAN ILMU KOMPUTER PRIMA (JUTIKOMP) Vol. 8 No. 1 (2025): Jurnal Teknologi dan Ilmu Komputer Prima (JUTIKOMP)
Publisher : Fakultas Teknologi dan Ilmu Komputer Universitas Prima Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34012/jutikomp.v8i1.6660

Abstract

Educational games have emerged as interactive learning media that enhance elementary students’ motivation, engagement, and understanding. This study analyzes recent trends in academic game development over the past five years by reviewing 15 peer-reviewed articles published between 2021 and 2025, sourced from Google Scholar. The analysis reveals that 60% of the studies focused on mobile-based games, particularly Android applications developed using Unity and Construct 2, due to their high accessibility and engaging interactive features. Additionally, web-based games such as Wordwall and desktop-based visual novel games developed with TyranoBuilder were found to improve students’ concept mastery by up to 30%, especially in language and mathematics learning. However, key challenges remain, including limited platform compatibility, the absence of adaptive learning features, and weak integration with formal curriculum standards. To enhance their effectiveness, future educational games should prioritize cross-platform accessibility, implement adaptive learning mechanisms, and ensure strong alignment with academic curricullum.
Application of Natural Language Processing and LSTM in A Travel Chatbot for Medan City Atika, Syarifah; Bengi, Mahara; Sardeng, Shekainah Kim A.
Jurnal ELTIKOM : Jurnal Teknik Elektro, Teknologi Informasi dan Komputer Vol. 9 No. 1 (2025)
Publisher : P3M Politeknik Negeri Banjarmasin

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31961/eltikom.v9i1.1481

Abstract

The tourism sector plays a vital role in economic growth and regional development. Medan, a major city in North Sumatra, offers rich religious, historical, and cultural attractions. However, fragmented and inconsistent information presents challenges for both tourists and destination managers, often complicating travel planning. To address this issue, this study proposes the development of an AI-based chatbot aimed at enhancing the tourism experience in Medan. By integrating Natural Language Processing (NLP) and Long Short-Term Memory (LSTM), the chatbot is designed to deliver accurate, contextual, and conversational responses tailored to users' tourism-related queries. It was trained on a comprehensive dataset compiled from various sources concerning Medan’s tourism. The training ran over 100 epochs, achieving an accuracy of 84.31% and a loss of 0.7594. Validation testing yielded an accuracy of 77.14% and a loss of 2.4233, indicating good generalization to unseen data. End-to-end testing with 312 queries covering all defined intents resulted in a testing accuracy of 75.64%, confirming the model’s practical effectiveness. The findings demonstrate that the chatbot can accurately interpret user input, classify information, and enhance user interaction. supports the digital transformation of Medan’s tourism sector by introducing a reliable, AI-driven tool for seamless travel planning and engagement.
Co-Authors Febrina Rizky Husna Hidayati Mahara Bengi Sardeng, Shekainah Kim A. Sipayung, David Sebastian