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All Journal Jurnal Pengabdian Masyarakat Bumi Raflesia Khazanah Informatika : Jurnal Ilmu Komputer dan Informatika
Gusti Chandra Kurniawan
Universitas Ahmad Dahlan
Humanities Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Education Languange, Linguistic, Communication & Media Other

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PROGRAM PENINGKATAN PENGETAHUAN DAN KEAHLIAN GURU DAN SISWA SEKOLAH MENENGAH PERTAMA TERHADAP SEARCH ENGINE OPTIMIZATION Herman; Imam Riadi; Gusti Chandra Kurniawan; Basit Adhi Prabowo
Jurnal Pengabdian Masyarakat Bumi Rafflesia Vol 7 No 2 (2024): AGUSTUS: Jurnal Pengabdian Masyarakat Bumi Rafflesia
Publisher : Universitas Muhammadiyah Bengkulu

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Mahasiswa Magister Informatika Universitas Ahmad Dahlan melakukan Program Pemberdayaan Umat (PRODAMAT) dengan tema Search Engine Optimization (SEO) di Sekolah Menengah Pertama (SMP) Muhammadiyah 1 Berbah Sleman. Salah satu problem di dalam melakukan dakwah melalui dunia maya adalah konten tidak muncul di halaman pertama mesin pencari meskipun konten dibuat lebih dulu. PRODAMAT ini bertujuan untuk meningkatkan keahlian menulis judul dan konten menerapkan SEO sehingga memperbesar peluang konten muncul di halaman pertama mesin pencari. Pelatihan ini diikuti 17 guru, 10 siswa dan satu karyawan SMP Muhammadiyah 1 Berbah Sleman secara luring di laboratorium komputer sekolah. Pretest dilakukan sebelum melakukan pelatihan. Sesi pertama memberikan materi tentang faktor-faktor yang mempengaruhi indexing mesin pencari agar konten muncul di halaman pertama yang ditampilkan oleh mesin pencari berdasarkan kata kunci. Sesi pertama juga memberikan materi tentang cara menulis konten dengan template yang menerapkan SEO. Sesi kedua mempraktikkan pembuatan template yang menerapkan SEO dan menulis konten menggunakan template yang sudah dibuat. Posttest dilakukan setelah pelatihan usai. Hasil tes pretest dan post test menunjukkan bahwa nilai persentase guru meningkat 26%, persentase siswa meningkat 31% dan persentase karyawan meningkat 45%. Secara keseluruhan, nilai persentase meningkat 28%. Hasil ini mengindikasikan adanya peningkatan yang signifikan dalam pengetahuan wawasan dan soft skill tentang penerapan SEO tanpa menggunakan tools SEO.Kata Kunci: SEO, Template, Konten
Comparison of Backward Elimination Features on Diabetes Disease Classification Accuracy using K-Nearest Neighbor and Random Forest Algorithms Gusti Chandra Kurniawan; Imam Riadi; Anton Yudhana
Khazanah Informatika : Jurnal Ilmu Komputer dan Informatika Vol. 11 No. 2 (2025): October 2025
Publisher : Universitas Muhammadiyah Surakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23917/khif.v11i2.10429

Abstract

The critical need for faster, more accurate diagnosis of diabetes mellitus by machine learning classification models is highlighted by the exponential growth in the global incidence of the disease. The predominance of clinical traits and an unequal class distribution, among other problems, usually make it difficult for these models to forecast results. This research investigates the comparative performance of K-Nearest Neighbor (KNN) and Random Forest (RF) algorithms in diagnosing diabetes using a unified pipeline. The methodology encompasses several stages: data preprocessing (including the treatment of missing values, normalization, and encoding), feature selection via backward elimination, class rebalancing using the Synthetic Minority Over-sampling Technique (SMOTE), and performance assessment through a 5-fold cross-validation protocol. The dataset from Mendeley Data was separated into two categories: those with diabetes and those without.   Based on the results, the RF algorithm achieved 97.10% accuracy, 99.52% precision, 98.26% F1-score, and 99.50% ROC-AUC, outperforming KNN in most evaluation criteria.   However, KNN also shows a competitive ability to categorize data with a high precision rate of 99.15%.   These results show that feature selection and data balance techniques should be part of every classification pipeline if we want more consistent and accurate models.   The research concludes that Random Forest is more appropriate for clinical diabetes classification and suggests the systematic use of structured preprocessing in developing systems for early detection of chronic illnesses.
Co-Authors Anton Yudhana Basit Adhi Prabowo Herman Imam Riadi