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All Journal TEKNOLOGI INFORMASI Jurnal Saintekom : Sains, Teknologi, Komputer dan Manajemen
Maharani, Hamidah Lutfiyanti
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Computer Science & IT Decision Sciences, Operations Research & Management

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Implementasi Metode Random Forest untuk Peningkatan Efisiensi Penilaian Status Uji Kelayakan Kendaraan Bermotor di Kota Malang Maharani, Hamidah Lutfiyanti; Zaman, Syahiduz
Jurnal Saintekom : Sains, Teknologi, Komputer dan Manajemen Vol 15 No 1 (2025): Maret 2025
Publisher : STMIK Palangkaraya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33020/saintekom.v15i1.751

Abstract

The growth of vehicle volume in Malang City presents challenges in the form of increased accident risks, especially if the technical condition of the vehicles does not meet standards. As a preventive measure, the Motor Vehicle Feasibility Testing (KIR Test) is conducted to ensure that vehicles comply with safety standards. However, manual assessments in this process are prone to human error, necessitating a more efficient and accurate system. This study implements the Random Forest method to classify the eligibility status of motor vehicles, focusing on two main categories: public and private vehicles. This implementation is expected to improve the efficiency and accuracy of the KIR test process. Among the data split ratios tested, a 60% training data and 40% test data ratio yielded the best results with an accuracy of 86.94% and an OOB error rate of 13.03%, indicating the model's error rate on data not used during training. These results indicate that the Random Forest method effectively identifies the eligibility status of motor vehicles with an optimal data configuration.
Penerapan Long Short-Term Memory dan TF-IDF dalam Mengklasifikasikan Sentimen Publik terhadap Kebijakan Efisiensi Anggaran 2025 Maharani, Hamidah Lutfiyanti; Hariyadi, Mokhamad Amin; Abidin, Zainal
Teknologi Informasi : Teori, Konsep, dan Implementasi : Jurnal Ilmiah Vol 16 No 2 (2025): JURNAL TEKNOLOGI INFORMASI: Teori, Konsep dan Implementasi Vol 16 No 2 Tahun 202
Publisher : STMIK PPKIA Pradnya Paramita

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36382/jti-tki.v16i2.605

Abstract

Tanggapan masyarakat terhadap suatu kebijakan seringkali bervariasi dan perlu dilakukan klasifikasi untuk memahami gambaran keseluruhan respons publik. Salah satu contohnya adalah kebijakan Efisiensi Anggaran 2025, yang menjadi fokus penelitian ini. Dilakukan analisis tweet yang terkait dengan kebijakan tersebut menggunakan model LSTM untuk mengklasifikasikan sentimen publik menjadi dua kategori: positif dan negatif. Pembobotan kata dilakukan menggunakan metode TF-IDF, dan hasilnya menunjukkan nilai akurasi 94,38%, precision 93,75%, recall 89,44%, dan F1-score 91,55% pada epoch 10 dan batch size 32 pada rasio pembagian data 80:20. Dari hasil tersebut model LSTM terbukti baik dalam mengklasifikasikan data teks.
Co-Authors Mokhamad Amin Hariyadi Syahiduz Zaman Zainal Abidin