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All Journal PINTER : Jurnal Pendidikan Teknik Informatika dan Komputer Jurnal Ilmiah Informatika dan Komputer
Moh. Syaiful Anam
Universitas Islam Kadiri
Computer Science & IT Education Engineering Library & Information Science

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DETEKSI DINI RISIKO PENYAKIT KARDIOVASKULAR MENGGUNAKAN TEKNIK STACKING ENSEMBLE LEARNING DENGAN PENDEKATAN CRISP-DM whilli; Yudo Bismo Utomo; Moh. Syaiful Anam; Harso Kurniadi
PINTER : Jurnal Pendidikan Teknik Informatika dan Komputer Vol. 9 No. 2 (2025): Jurnal PINTER
Publisher : PTIK Fakultas Teknik UNJ

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21009/pinter.9.2.10

Abstract

Penyakit kardiovaskular, termasuk penyakit jantung dan stroke, menjadi salah satu kasus penyakit tertinggi di Indonesia dalam daftar penyakit tidak menular (PTM). Penelitian ini bertujuan pada pengembangan model prediksi risiko penyakit kardiovaskular dan mengimplementasikannya dalam bentuk aplikasi web berbasis framework Streamlit yang diperuntukkan bagi pengguna umum sebagai alat deteksi dini, dengan output berupa probabilitas risiko kardiovaskular yang dihasilkan oleh model prediksi. Model prediksi dikembangkan menggunakan teknik Stacking Ensemble Learning. Penelitian ini menggunakan metode CRISP-DM sebagai kerangka kerja yang terstruktur dan sesuai untuk pengembangan model pembelajaran mesin. Hasil penelitian menunjukkan bahwa model prediksi menghasilkan akurasi sebesar 97% pada data uji. Model prediksi berhasil di-deploy ke dalam aplikasi web berbasis Streamlit yang memungkinkan pengguna memasukkan data kesehatan untuk memperoleh estimasi probabilitas risiko kardiovaskular. Selanjutnya, aplikasi diujikan kepada pengguna melalui kuesioner, yang menunjukkan tingkat penerimaan yang baik dengan skor 83,47%.
Developing a Content-Based Book Recommendation System for School E-Libraries Using TF-IDF and Cosine Similarity Muhammad Fatkul Roziq; Riska Nurtantyo Sarbini; Halimahtus Mukminna Halimahtus; Moh. Syaiful Anam
Jurnal Ilmiah Informatika dan Komputer Vol. 3 No. 1 (2026): Juni 2026 (In Progress)
Publisher : CV.RIZANIA MEDIA PRATAMA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.69533/informatech.volume3number1.543

Abstract

The rapid growth of e-libraries has increased the availability of digital book collections, making it difficult for users to identify relevant reading materials. This study develops an automated book recommendation system for the SMKN 1 Semen e-library using Content-Based Filtering with TF-IDF for term weighting and Cosine Similarity for similarity measurement. The dataset consisted of 35 book records containing metadata and book descriptions collected from the SMKN 1 Semen e-library. Text data were processed through case folding, tokenization, stopword removal, and stemming before recommendation generation. The system was developed using the Laravel framework and a MySQL database following the Research and Development (R&D) method with the Waterfall model. Recommendation performance was evaluated using a Top-5 recommendation scenario, where the recommended books were compared with manually identified relevant books based on content similarity using Precision and Recall metrics, while system functionality was verified through Black Box Testing. The experimental results achieved a Precision of 80% and a Recall of 57.14%, indicating that the proposed approach effectively generates relevant book recommendations. This study contributes by demonstrating that the integration of Content-Based Filtering, TF-IDF, and Cosine Similarity provides an effective recommendation approach for school e-libraries with limited user interaction data, enabling personalized book recommendations and improving the efficiency of book discovery in small-scale digital library environments.
Co-Authors Halimahtus Mukminna Halimahtus Harso Kurniadi Muhammad Fatkul Roziq Riska Nurtantyo Sarbini whilli Yudo Bismo Utomo