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All Journal IT JOURNAL RESEARCH AND DEVELOPMENT Jusikom : Jurnal Sistem Komputer Musirawas JURNAL TEKNIK INFORMATIKA DAN SISTEM INFORMASI JURNAL TEKNOLOGI DAN OPEN SOURCE Digital Zone: Jurnal Teknologi Informasi dan Komunikasi Building of Informatics, Technology and Science Zonasi: Jurnal Sistem Informasi Journal of Applied Data Sciences Mitra Mahajana: Jurnal Pengabdian Masyarakat Jurnal J-PEMAS Jurnal Algoritma SATIN - Sains dan Teknologi Informasi The Indonesian Journal of Computer Science
Edwar Ali
Tenik Informatika, STMIK Amik Riau
Agriculture, Biological Sciences & Forestry Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Public Health Social Sciences

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Journal : Jurnal Algoritma

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Perbandingan Kinerja Xgboost Dan Lightgbm Dalam Klasifikasi Depresi Pada Mahasiswa Berdasarkan Faktor Demografi Dan Akademik Pratama, Farhan; Ali, Edwar; Rahmaddeni; Agustin, Wirta
Jurnal Algoritma Vol 22 No 2 (2025): Jurnal Algoritma
Publisher : Institut Teknologi Garut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33364/algoritma/v.22-2.2439

Abstract

Depresi merupakan salah satu gangguan mental yang umum dialami mahasiswa, sehingga berdampak signifikan terhadap kesejahteraan psikologis dan performa akademik mereka. faktor-faktor seperti jenis kelamin, usia, tekanan finansial, tekanan belajar, kepuasan studi, dan waktu belajar yang tidak proporsional diketahui berkontribusi dalam memengaruhi kondisi tersebut. penelitian ini bertujuan untuk membandingkan kinerja algoritma XGBoost dan LightGBM dalam mengklasifikasikan risiko depresi pada mahasiswa, serta mengembangkan model melalui teknik tuning parameter menggunakan RandomizedSearchCV untuk meningkatkan akurasi prediksi. dataset yang digunakan berasal dari platform Kaggle yang terdiri dari 502 baris data. evaluasi performa dilakukan menggunakan metrik akurasi, precision, recall, f1-score, dan AUC-ROC, pada skenario pembagian data 80:20 dan 70:30, baik dengan parameter default maupun setelah tuning. hasil penelitian menunjukkan bahwa model XGBoost dengan tuning pada pembagian data 80:20 memberikan performa terbaik dengan akurasi 82,18%, precision 85,11%, recall 78,43%, f1-score 81,63%, dan AUC-ROC sebesar 0,8973. terbaik kemudian diimplementasikan dalam bentuk aplikasi web menggunakan Streamlit, guna memberikan prediksi risiko depresi secara otomatis dan interaktif, sehingga memudahkan pengguna non-teknis dalam mendeteksi kondisi tersebut secara praktis.
Optimasi Klasifikasi Tingkat Obesitas Pada Remaja Berdasarkan Pola Hidup Menggunakan SVM Dengan Teknik Smote Setiawan, Andri; Yanti, Rini; Ali, Edwar; Yenni, Helda
Jurnal Algoritma Vol 22 No 2 (2025): Jurnal Algoritma
Publisher : Institut Teknologi Garut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33364/algoritma/v.22-2.2509

Abstract

Obesity is a condition caused by an imbalance between energy intake and expenditure, characterized by excessive fat accumulation in the body. Obesity is influenced by four factors, namely genetics, economics, lack of activity, and diet. The purpose of this study is to analyze the effectiveness of the SMOTE method in improving the accuracy of classification in the Support Vector Machine method and to compare the accuracy of the Support Vector Machine method with the SMOTE and non-SMOTE techniques on adolescent obesity data. The dataset used was obtained from the Kaggle website, which contained 2,111 records. The model evaluation used a confusion matrix with accuracy, precision, recall, and F1-score measurements and used 80:20 data splitting. The results showed that the SVM model using Smote performed well with an accuracy of 88% for Linear SVM, 82% for RBF SVM, and 93% for Polynomial SVM, while the SVM model without Smote achieved an accuracy of 88% for Linear SVM, 79% for RBF SVM, and 91% for Polynomial SVM. The best classification model was then implemented into a Streamlit-based web application to facilitate the process of automatically predicting obesity levels, thereby helping to raise awareness of the potential risks of obesity.
Prediksi Dukungan Publik Terhadap Program Makan Bergizi Gratis (MBG) Menggunakan Analisis Sentimen Berbasis Long Short-Term Memory (LSTM) Novfuja, Elma; Efrizoni, Lusiana; Ali, Edwar; Susanti, Susanti
Jurnal Algoritma Vol 22 No 2 (2025): Jurnal Algoritma
Publisher : Institut Teknologi Garut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33364/algoritma/v.22-2.2690

Abstract

The Free Nutritious Meal Program (MBG) is a public policy that requires evaluation based on public opinion. This study developed a Long Short-Term Memory (LSTM) model to classify public sentiment from 13,923 X reviews, collected using the tweet-harvest library. The data was processed with Word2Vec weighting and Lexicon-Based labeling, resulting in 73.4% positive sentiment and 26.6% negative sentiment. The model was tested with train-test split ratios of 60:40, 70:30, 80:20, and 90:10, with the best performance at a ratio of 80:20 (91.71% accuracy, 89% precision, 90% recall, 89% F1-score). The model architecture includes Embedding, LSTM (128 units), Dropout (70%), and Dense layers, optimized with categorical_crossentropy and Adam. The confusion matrix evaluation shows the effectiveness of the model, despite weak negative classes due to data imbalance. The results provide insights for improving MBG implementation, with LSTM excelling at capturing text patterns compared to SVM and BERT.
Co-Authors Adellia Meiriza Agnes Chrisnalia Agustin Agustin Andri Setiawan Asparizal Asri - Asri -, Asri Azmi , Muhammad Baehaqi Didik Sudyana Efrizoni, Lusiana Hadi Asnal, Hadi Hamdani - Hasibuan, Fadliansyah Helda Yenni, Helda Herwin Herwin Herwin, Herwin Junadhi Junadhi Junadhi, Junadhi Khairani Djahara Khairani Djahara, Khairani Koko Harianto Lestari, Fika Ayu Mardainis Meiriza, Adellia Nova Indriyani Novfuja, Elma Pradewanta, Rian Pratama, Farhan Rahmaddeni , Rahmaddeni Rahmaddeni Rahmaddeni Rahmiati Rahmiati Rahmiati Rahmiati Rian Pradewanta Rini Yanti, Rini Saputra Wijaya , Yansyah Setiawan, Hengki Sharon - Sharon -, Sharon Sigit, Rapel Aprilius Susandri, Susandri SUSANTI Susanti Susanti Susanti, Susanti Triyani Arita Fitri Unang Rio Wirta Agustin Yurnalis