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All Journal JURIKOM (Jurnal Riset Komputer) Jurnal Ilmiah Wahana Pendidikan
Firman Edi
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Evaluasi Kinerja Tim Penjaminan Mutu Pendidikan Sekolah (TPMPS) pada Masa Pandemi Covid19 Firman Edi
Jurnal Ilmiah Wahana Pendidikan Vol 7 No 7 (2021): Jurnal Ilmiah Wahana Pendidikan
Publisher : Peneliti.net

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (244.613 KB) | DOI: 10.5281/zenodo.5724418

Abstract

Tujuan dari penelitian ini adalah untuk menilai kinerja tim penjaminan mutu pendidikan di Balaraja, Tangerang, selama masa pandemi COVID-19. Anggota Tim Penjamin Mutu Pendidikan Sekolah (TPMPS) yang terdiri dari kepala sekolah, guru, dan komite sekolah ikut serta dalam penelitian ini. Teknik pengumpulan data meliputi wawancara, observasi, dan dokumentasi. Berdasarkan temuan tersebut, kinerja penjaminan mutu pendidikan sekolah (TPMPS) TIM Balaraja termasuk dalam kategori kurang baik. Kegiatan koordinasi juga termasuk dalam kategori kurang baik, demikian pula kegiatan pembinaan dan pendampingan. Kegiatan pemetaan mutu pendidikan masuk dalam kategori kurang baik, sedangkan kegiatan monitoring dan evaluasi masuk dalam kategori kurang baik, dan kegiatan rekomendasi strategis masuk dalam kategori kurang baik. Rekomendasi berdasarkan hasil penelitian; Diharapkan TMTPS mengembangkan berbagai strategi untuk meningkatkan kualitas pendidikan di sekolah, seperti dengan memasukkan teknologi informasi ke dalam proses belajar mengajar, melakukan pengawasan, dan berkoordinasi dengan berbagai pihak di masa pandemi COVID-19.
Prediksi Harga Mobil Global Menggunakan Machine Learning dengan Algoritma Naive Bayes Hts, Dedek Indra Gunawan; Firman Edi; Ratna Sri Hayati; Hendro Sutomo Ginting
JURNAL RISET KOMPUTER (JURIKOM) Vol. 12 No. 6 (2025): Desember 2025
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v12i6.9320

Abstract

Determining car prices is one of the major challenges in the global automotive industry because it is influenced by various factors such as technical specifications, vehicle condition, and market dynamics. This issue becomes more complex as the volume of available data increases, requiring methods capable of performing fast and accurate analysis. This study aims to predict car price levels based on vehicle specifications using a Machine Learning approach, with the Naive Bayes algorithm selected as a solution to simplify the price classification process on large-scale data. The dataset used is the Global Car Sales Analysis from the Kaggle platform, which includes attributes such as Manufacturer, Model, Engine size, Fuel type, Year of manufacture, Mileage, and Price. The research methodology consists of data preprocessing, label encoding for categorical attributes, splitting the dataset into training and testing sets, and applying the Naive Bayes algorithm to classify car prices into three categories: Low, Medium, and High. The results indicate that Naive Bayes is capable of predicting car prices with very strong performance, achieving an accuracy of 96%, precision of 0.97, recall of 0.96, and an F1-score of 0.96. The model performs best on the Low category with an F1-score of 0.98, although performance decreases for the Medium and High categories due to imbalanced class distribution. Further analysis also reveals that Engine size, Year of manufacture, and Mileage are the most influential attributes in determining price. Overall, this study demonstrates that Naive Bayes is an effective method for predicting car prices using global automotive data.
Co-Authors Hendro Sutomo Ginting Hts, Dedek Indra Gunawan Ratna Sri Hayati