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All Journal INFORMATIKA Jurnal Informatika Universitas Pamulang Journal of Artificial Intelligence and Innovative Applications (JOAIIA) KLIK: Kajian Ilmiah Informatika dan Komputer Jurnal Nasional Teknik Elektro dan Teknologi Informasi JAMAIKA: Jurnal Abdi Masyarakat Jurnal Komputer Teknologi Informasi Sistem Komputer (JUKTISI) Abdi Jurnal Publikasi LOGIC : Jurnal Ilmu Komputer dan Pendidikan Biner : Jurnal Ilmu Komputer, Teknik dan Multimedia OKTAL : Jurnal Ilmu Komputer dan Sains AMMA : Jurnal Pengabdian Masyarakat Jurnal Ilmiah Multidisiplin Indonesia Indonesian Journal of Infomatics
Rinna Rachmatika
Universitas Pamulang
Agriculture, Biological Sciences & Forestry Humanities Astronomy Chemistry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Materials Science & Nanotechnology Nursing Physics Social Sciences Transportation Other

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Penerapan Algoritma XGBoost untuk Prediksi Tingkat Pemahaman Siswa pada Praktikum Laboratorium Komputer Kecitaan Harefa; Rinna Rachmatika; Oktafpianus Gea; Arojasa Harefa
Jurnal Komputer Teknologi Informasi Sistem Komputer (JUKTISI) Vol. 5 No. 2 (2026): September 2026
Publisher : LKP KARYA PRIMA KURSUS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62712/juktisi.v5i2.1582

Abstract

Evaluasi tingkat pemahaman siswa pada praktikum laboratorium komputer masih sering dilakukan secara manual berdasarkan nilai dan observasi guru, sehingga membutuhkan waktu dan berpotensi menimbulkan subjektivitas. Penelitian ini bertujuan menerapkan algoritma Extreme Gradient Boosting (XGBoost) sebagai solusi untuk memprediksi tingkat pemahaman siswa secara objektif, cepat, dan berbasis data. Penelitian menggunakan pendekatan kuantitatif dengan 120 data siswa yang terbagi seimbang ke dalam kategori rendah, sedang, dan tinggi. Variabel prediktor meliputi nilai kuis, nilai praktikum, kehadiran, waktu pengerjaan, keaktifan, dan observasi guru. Tahapan penelitian mencakup pengumpulan dan prapemrosesan data, pengodean label, serta pembagian data secara terstratifikasi dengan komposisi 80% data latih dan 20% data uji. Model kemudian dilatih menggunakan XGBoost dan dievaluasi melalui matriks konfusi, akurasi, presisi, recall, dan F1-score. Hasil pengujian menunjukkan bahwa model berhasil mengklasifikasikan 23 dari 24 data uji dengan benar, dengan akurasi sebesar 95,83%, presisi 95,62%, recall 95,83%, dan F1-score 95,69%. Analisis kepentingan fitur menunjukkan bahwa nilai kuis merupakan variabel paling berpengaruh. Dengan demikian, XGBoost efektif digunakan sebagai pendukung evaluasi pembelajaran untuk membantu guru mengidentifikasi tingkat pemahaman siswa secara lebih akurat dan efisien.
PELATIHAN PEMANFAATAN TEKNOLOGI EVALUASI PEMBELAJARAN UNTUK MENINGKATKAN KETERAMPILAN PRAKTIKUM KOMPUTER Rinna Rachmatika; Kecitaan Harefa; Joko Priambodo; Michelle Aniela Wijaya; Ivana Aulia Sadiyah; Alvia Cinta Aprillia Wijaya
Abdi Jurnal Publikasi Vol. 5 No. 1 (2026): September
Publisher : Abdi Jurnal Publikasi

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

Abstract

This community service activity addressed the limited use of digital tools for learning evaluation at SDN Pamulang Timur 01, where practicum assessment was still done manually and had not yet drawn on a Learning Management System (LMS) or an analytics dashboard. The program, originally aimed at students, was redirected to teachers after a needs assessment indicated a more urgent need at that level, following a multiplier-effect rationale. The activity aimed to strengthen teachers' capacity to design and apply technology-based evaluation instruments for computer practicum. Using a participatory training approach combining concept exposure, demonstration, hands-on practice, and personal mentoring across five stages, needs identification, module preparation, a concept workshop, hands-on practice, and post-training consultation, the two-day program (11-12 May 2026) involved 21 teachers with full school support. Day one covered the shift to objective, real-time, data-driven evaluation, plus three tools, Google Forms, Quizizz, and Kahoot!, and the use of artificial intelligence (AI). Day two focused on building instruments in Google Forms, since limited time did not allow equal practice across all three tools. Technical constraints such as unstable internet and inactive Google accounts were addressed through personal mentoring. Results are qualitative, based on observation and documentation. Every teacher completed at least one instrument, and the team left a reusable template with the school for continued, independent use of technology-based evaluation.
Co-Authors Achmad Bisri Achmad Faisal Achmad Khoirur Roziqin Agung Apriansyah Agung Laksana Ahmad Al Khaidar Ahmad Faisal Alvia Cinta Aprillia Wijaya Ardianto Moenir Arojasa Harefa Azmi Mubarok Astripat Azmi Muslimah Madaniah Bima Sakti Nurdewantoro Dewi Meidiyanti Dicky Prasetyo Eko Heriyanto Fachrur Rozi Fahmi Taqiudin Fathi Rizki Ammar Fauzi Maulana Akbar Hafidz Alamin Ivana Aulia Sadiyah Joko Priambodo Karina Mawardah Kecitaan Harefa Khotibul Umam Michelle Aniela Wijaya Mohammad Syibro Malisie Muhammad Ahqaaf Rahman Muhammad Akbar Ny.Ayni Suwarni Herry Oktafpianus Gea Orisa Satifa Perani Rosyani Rawinda Maulia Rendyka Purnama Rio Agung Pangestu Rizaludin Pahlevi Utomo Rizki Adi Pangestika Rizky Maulana Salvia Anggraini Siva Fauziyah Syauqi Tamzik Willy Arianto Yanto Hermawan Yogi Pratama Zainul Janariandana