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Mufti, Nabilah
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering

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Klasifikasi Sentimen Ulasan Aplikasi Gojek Berbasis Decision Tree Dengan Optimasi Grid Search Mufti, Nabilah; Apriano Putri, Aurahaqqi; Gustriansyah, Rendra
Jurnal Sarjana Teknik Informatika Vol. 14 No. 1 (2026): Februari
Publisher : Program Studi Informatika, Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/jstie.v14i1.31092

Abstract

Penelitian ini bertujuan untuk mengklasifikasikan sentimen ulasan pengguna aplikasi Gojek secara otomatis menggunakan algoritma Decision Tree dengan optimasi hyperparameter GridSearchCV dan teknik penyeimbangan data SMOTE. Ulasan pengguna dari Google Play Store mencerminkan persepsi terhadap layanan, sehingga diperlukan metode analisis yang efisien. Dataset terdiri dari 44.950 ulasan yang diperoleh dari Kaggle dan diproses melalui tahapan tokenisasi, stopword removal, stemming, dan representasi numerik menggunakan TF-IDF. Model Decision Tree awal menghasilkan akurasi 87,59%. Setelah penerapan GridSearchCV dan SMOTE, akurasi model pada data seimbang menjadi 83,5% dengan F1-score 0,84. Namun, performa terhadap kelas minoritas (netral) masih rendah. Sebagai pembanding, model Random Forest menunjukkan hasil lebih baik dengan akurasi 86,5% dan F1-score 0,86. Hasil penelitian menunjukkan bahwa kombinasi TF-IDF dan Decision Tree efektif untuk klasifikasi sentimen mayoritas, tetapi kurang optimal pada kelas minoritas. Penggunaan model yang lebih kompleks seperti Random Forest disarankan untuk hasil klasifikasi yang lebih merata.
Analisis Struktur Tulisan Tangan melalui Deteksi Zona Spasi Antarkata Menggunakan CNN Mendalam Mufti, Nabilah; Heriansyah, Rudi; Irfani, Muhammad Haviz
SMARTICS Journal Vol 12 No 1 (2026): Journal SMARTICS (April 2026)
Publisher : Universitas PGRI Kanjuruhan Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21067/smartics.v12i1.13937

Abstract

Advances in image processing and deep learning technology enable more accurate handwriting analysis, including the detection of interword spacing, which exhibits high complexity due to variations in writing styles. This study aims to implement a Convolutional Neural Network (CNN) algorithm using the You Only Look Once version 11 (YOLOv11) framework to detect and classify interword spacing zones into three classes: Narrow Word Spacing (NWS), Medium Word Spacing (MWS), and Wide Word Spacing (WWS). The dataset comprises 150 handwritten images with a total of 4.117 annotated interword spacing objects. The research methodology involves testing the model across variations of learning rates (0.1, 0.01, 0.001, and 0.0001) and data split ratios (70:30, 80:20, and 90:10). Model performance was evaluated using Precision, Recall, F1-Score, and mean Average Precision (mAP) metrics. Based on 12 experimental trials, the best configuration was achieved with a learning rate of 0.001 and a 90:10 data split. This configuration produced an mAP@50 of 0.455, an mAP@50–95 of 0.261, and an F1-Score of 0.49. These results indicate that the YOLOv11 model is capable of detecting interword spacing zones with reasonably good performance, despite remaining classification errors due to visual similarities between classes.
Co-Authors Apriano Putri, Aurahaqqi Muhammad Haviz Irfani Rendra Gustriansyah Rudi Heriansyah, Rudi