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All Journal Jurnal FASILKOM (teknologi inFormASi dan ILmu KOMputer) Jurnal Pendidikan dan Teknologi Indonesia
Pratama Putra, Daffa
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Computer Science & IT Decision Sciences, Operations Research & Management Education Engineering

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Perbandingan Model Regresi Machine Learning untuk Prediksi Skor Tingkat Stres Berdasarkan Pola Screen Time Tahun 2025 Pratama Putra, Daffa; Apriyadi, Apriyadi; Firmansyah, Zikri; Ditha Tania, Ken; Kurniawan, Dedy
Jurnal Pendidikan dan Teknologi Indonesia Vol 6 No 4 (2026): JPTI - April 2026
Publisher : CV Infinite Corporation

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jpti.1537

Abstract

Transformasi digital yang masif pada era modern telah mendorong peningkatan signifikan dalam durasi paparan layar (screen time), yang diidentifikasi sebagai salah satu faktor risiko utama terhadap kesehatan mental, khususnya peningkatan prevalensi stres psikologis. Metode diagnosis konvensional yang mengandalkan instrumen kuesioner mandiri dinilai kurang optimal karena rentan terhadap bias pelaporan dan bersifat subjektif. Penelitian ini bertujuan untuk membandingkan performa tiga algoritma machine learning, yaitu Random Forest, Support Vector Regression (SVR), dan XGBoost Regression, dalam memprediksi skor tingkat stres secara kontinu (skala 0–10) berdasarkan pola penggunaan perangkat digital. Tahapan penelitian meliputi akuisisi dataset "Screentime vs Mental Wellness Survey 2025" dari repositori publik, pra-pemrosesan data melalui imputasi statistik, normalisasi Min-Max Scaling, dan One-Hot Encoding, dilanjutkan dengan pembangunan model menggunakan evaluasi 10-fold cross-validation serta interpretasi model berbasis metode SHAP. Hasil evaluasi pada data uji menunjukkan bahwa XGBoost merupakan model dengan performa terbaik, mencapai nilai Mean Absolute Error (MAE) terendah sebesar 0,6502, Root Mean Squared Error (RMSE) sebesar 0,8253, dan koefisien determinasi (R²) sebesar 0,8367. Temuan ini mengindikasikan bahwa model mampu menjelaskan lebih dari 83% variasi tingkat stres pada data yang belum pernah dilatih sebelumnya. Analisis feature importance mengungkapkan bahwa indeks kesejahteraan mental dan produktivitas merupakan prediktor paling dominan, sedangkan durasi screen time berkontribusi relatif kecil, yang menunjukkan bahwa faktor psikologis internal lebih berpengaruh terhadap stres dibandingkan intensitas interaksi digital semata. Penelitian ini menyimpulkan bahwa pendekatan ensemble learning, khususnya XGBoost, efektif dalam memodelkan fenomena stres yang bersifat kompleks dan multidimensional sebagai dasar pengambilan keputusan klinis berbasis data.
Prediksi Lead Scoring untuk Optimasi Penjualan Menggunakan Random Forest dan Teknik SMOTE Pratama Putra, Daffa; Agil Kusuma, Dimas; Al Akbar, M. Rizki; Ibrahim, Ali; Fathoni, Fathoni
JURNAL FASILKOM Vol. 16 No. 1 (2026): Jurnal FASILKOM (teknologi inFormASi dan ILmu KOMputer)
Publisher : Unversitas Muhammadiyah Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37859/jf.v16i1.11292

Abstract

Accurate lead scoring systems have become a strategic necessity for organizations operating in data-driven marketing environments, as they enable systematic identification of high-value customer prospects to maximize sales conversion efficiency. A fundamental challenge confronting conventional classification models is the class imbalance inherent in real-world marketing data, which induces majority-class bias and substantially reduces sensitivity toward minority-class prospects. This study proposes a Random Forest (RF)-based lead scoring prediction model integrated with the Synthetic Minority Over-sampling Technique (SMOTE) to address this limitation systematically. The dataset employed is the Lead Scoring Dataset from Kaggle, comprising 9,240 customer prospect records from an educational company with a class imbalance ratio of 1.59:1. Preprocessing included missing value treatment, removal of attributes exceeding 40% data loss, mode-based imputation, and categorical feature encoding. Following an 80:20 stratified split, SMOTE was applied exclusively to the training set to produce a balanced class distribution and prevent data leakage. The RF model was configured with n_estimators = 100, max_features = 'sqrt', and class_weight = 'balanced'. The proposed RF+SMOTE model achieved accuracy of 88.80%, precision of 86.44%, recall of 84.13%, F1-Score of 85.27%, and AUC-ROC of 0.9453, outperforming the baseline across four of five evaluation metrics. The most notable improvement was observed in recall, with a gain of 1.26 percentage points. Stratified 5-Fold Cross-Validation confirmed robust generalization capability, with AUC-ROC values consistently ranging between 94% and 95%. These findings demonstrate that the hybrid RF+SMOTE approach effectively enhances high-potential prospect detection while maintaining overall model stability for real-world Customer Relationship Management (CRM) deployment.
Co-Authors Agil Kusuma, Dimas Al Akbar, M. Rizki Ali Ibrahim Apriyadi Apriyadi, Apriyadi Fathoni Fathoni Firmansyah, Zikri Ken Ditha Tania