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All Journal JUSTIN (Jurnal Sistem dan Teknologi Informasi) NU-JST
Nugraha, Danang Satya
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Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Engineering Materials Science & Nanotechnology

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Evaluasi Metode Pelabelan Sentimen Berbasis Leksikon terhadap Ulasan Aplikasi Sekuritas di Google Play Store Thoib, Imam; Candra, Beda Puspita; Sururi, Nafis; Nugraha, Danang Satya; Kholifah, Binti
JUSTIN (Jurnal Sistem dan Teknologi Informasi) Vol 13, No 4 (2025)
Publisher : Jurusan Informatika Universitas Tanjungpura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26418/justin.v13i4.93039

Abstract

Penelitian ini bertujuan untuk mengevaluasi persepsi pengguna terhadap aplikasi sekuritas di Indonesia melalui pendekatan analisis sentimen berbasis leksikon dan mengevaluasi performa klasifikasi menggunakan algoritma Random Forest. Data penelitian berupa 130.905 ulasan pengguna dari sepuluh aplikasi sekuritas populer di Google Play Store. Dua pendekatan leksikal yang digunakan adalah InSet Lexicon dan SentiWords_ID untuk memberi label sentimen pada ulasan tersebut. Hasil analisis menunjukkan bahwa aplikasi Ajaib memperoleh proporsi sentimen positif tertinggi dan paling representatif dengan jumlah ulasan terbesar dibandingkan aplikasi lain, sedangkan aplikasi MOST menunjukkan proporsi sentimen negatif tertinggi menurut kedua pendekatan. Pemodelan klasifikasi sentimen dilakukan menggunakan ekstraksi fitur TF-IDF dan algoritma Random Forest, yang dievaluasi melalui metrik akurasi, precision, recall dan f1-score. Hasil evaluasi menunjukkan bahwa SentiWords_ID memberikan performa klasifikasi yang lebih unggul dan stabil dibandingkan InSet Lexicon, khususnya dalam mengidentifikasi sentimen positif dan netral.
Exploratory Data Analysis dan Machine Learning dalam Memprediksi Employee Attrition: Exploratory Data Analysis and Machine Learning in Predicting Employee Attrition Kholifah, Binti; Firmansyah, Fendy Bayu; Sururi, Nafis; Nugraha, Danang Satya
Nusantara Journal of Science and Technology Vol 1 No 2 (2024): Published in November of 2024
Publisher : Lembaga Penelitian dan Pengabdian pada Masyarakat (LPPM) Universitas Nahdlatul Ulama Kalimantan Selatan

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

Abstract

Employee attrition is one of the main challenges faced by organizations in retaining competent human resources. This study aims to explore data patterns and predict employee attrition using the Exploratory Data Analysis (EDA) approach and Machine Learning algorithms such as Logistic Regression, Support Vector Machine (SVM), and Naive Bayes. The analysis was conducted on a dataset that includes various factors such as demographics, job satisfaction, and employee performance. The research findings indicate that Logistic Regression achieved an accuracy of 87%, but the model has weaknesses in detecting the positive class (attrition), as reflected by its low recall score. SVM, with an accuracy of 85%, provided high precision for the positive class but performed poorly in detecting actual attrition cases. Conversely, Naive Bayes, with an accuracy of 85%, demonstrated more balanced performance with a higher weighted average F1-score compared to the other models, although there is still room for improvement, particularly in predicting the positive class. Based on the results, Naive Bayes stands out as a more reliable model for predicting employee attrition with more balanced performance compared to Logistic Regression and SVM. To enhance prediction performance, it is recommended to address the class imbalance in the dataset through techniques such as oversampling, undersampling, class weighting, or specialized algorithms.
Co-Authors Candra, Beda Puspita Firmansyah, Fendy Bayu Kholifah, Binti Sururi, Nafis Thoib, Imam