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All Journal METHODIKA: Jurnal Teknik Informatika dan Sistem Informasi JSI (Jurnal sistem Informasi) Universitas Suryadarma JATI (Jurnal Mahasiswa Teknik Informatika) Jurnal Dinamika Informatika (JDI) Jurnal Informatika Teknologi dan Sains (Jinteks) Prosiding Seminar Nasional Sisfotek (Sistem Informasi dan Teknologi Informasi) Journal of Artificial Intelligence and Engineering Applications (JAIEA) Jurnal Sistem Informasi dan Teknologi (SINTEK) AMMA : Jurnal Pengabdian Masyarakat Sintek Kuwera Miracle Get Journal Bianglala Informatika
Martanto
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Humanities Automotive Engineering Chemistry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Law, Crime, Criminology & Criminal Justice Materials Science & Nanotechnology Nursing Public Health

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Comparative Performance Analysis of Multilayer Perceptron and Long Short-Term Memory for Daily Demand Forecasting in E-Commerce Delivery Platforms Unari, Ica; Martanto; Dana, Raditya Danar; Rifa'i, Ahmad; Hamongan, Ryan
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 5 No. 2 (2026): February 2026
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59934/jaiea.v5i2.1846

Abstract

This study compares the performance of two deep learning architectures—Multilayer Perceptron (MLP) and Long Short-Term Memory (LSTM)—for daily demand forecasting on an e-commerce delivery platform. The dataset consists of 1,827 daily observations from 2020 to 2024 and includes operational, temporal, and behavioral features such as holiday indicators, promotion signals, active customers, and delivery time. Data preprocessing includes cleaning, feature engineering, scaling, and sequence generation using a 30-day sliding window. Both models were trained and evaluated using consistent experimental settings and performance metrics. The results show that the LSTM model achieves better accuracy than the MLP model, with an RMSE of 811.81 compared to 830.15, while the difference in MAE between the two models remains minimal. LSTM demonstrates superior capability in capturing temporal dependencies and reacting to rapid demand fluctuations, whereas both models face challenges when predicting sudden demand spikes. These findings indicate that memory-based models such as LSTM are more effective for highly volatile time-series forecasting in e-commerce operations. However, performance can be further improved with the addition of external variables such as real-time promotions, weather conditions, and multivariate features.
Predicting Student Academic Performance Based on Learning Habits Using XGBoost and SHAP Latifah, Siti; Martanto; Dana, Raditya Danar; Dikananda, Fatihanursari; Hayati, Umi
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 5 No. 2 (2026): February 2026
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59934/jaiea.v5i2.1860

Abstract

This study developed a model for predicting student academic achievement based on learning habits using the XGBoost algorithm and SHAP interpretability techniques. The secondary dataset contains 1,000 entries and 16 variables (for example, hours of study per day, mental health, frequency of exercise, social media use, hours of sleep) pre-processed including cleaning, imputation, encoding, and normalization before being divided into train–test (80:20) and validated using 5-fold CV. Three models were tested: Linear Regression, Random Forest, and XGBoost. Evaluation using RMSE, MAE, and R² showed that XGBoost achieved RMSE = 0.335, MAE = 0.266, and R² = 0.882, while Linear Regression showed the best performance according to R² in certain configurations (R² = 0.888; RMSE = 0.326). SHAP analysis revealed that the most influential features were hours of study per day, mental health scores, exercise frequency, duration of social media use, and hours spent watching Netflix. The findings confirm that students' study habits and psychological conditions are the main determinants of academic achievement variation; the use of interpretable features strengthens the readability of the model for education stakeholders. Research recommendations include testing the model on longitudinal datasets, integrating socioeconomic factors, and implementing data privacy procedures before institutional-scale implementation.
KLASIFIKASI PENYAKIT DAUN PADI MENGGUNAKAN TRANSFER LEARNING DENGAN ANALISIS PENGARUH VARIASI DIMENSI CITRA PADA KINERJA MODEL Akhmad Taukhid; Martanto; Yudhistira Arie Wijaya; Heliyanti Susana; Nana Suarna
METHODIKA: Jurnal Teknik Informatika dan Sistem Informasi Vol. 12 No. 1 (2026): Volume 12 Nomor 1 Tahun 2026
Publisher : Universitas Methodist Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46880/mtk.v12i1.4940

Abstract

Penelitian ini berfokus pada deteksi dini penyakit daun padi untuk meningkatkan produktivitas pertanian dan mengurangi kesalahan diagnosis yang sering terjadi pada identifikasi manual. Meskipun berbagai penelitian telah menerapkan deep learning untuk klasifikasi penyakit tanaman, pengaruh resolusi citra terhadap kinerja model klasifikasi penyakit daun padi, khususnya pada skenario data terbatas, masih jarang dikaji secara sistematis. Penelitian ini bertujuan menganalisis kinerja model klasifikasi penyakit daun padi berbasis transfer learning dengan arsitektur VGG16 pada citra beresolusi 224×224 piksel, sekaligus menilai efisiensi proses komputasi pelatihan dan pengujian yang dilakukan. Data yang digunakan berupa 320 citra daun padi dari dataset publik “Daun Padi Sultra (Sulawesi Tenggara)” di Kaggle yang komprehensif menjadi data latih, validasi, dan uji dengan perbandingan 60:20:20. Tahapan penelitian utama meliputi eksplorasi karakteristik dan distribusi data, pra-pemrosesan citra (pengubahan ukuran ke 224×224, normalisasi, dan augmentasi terbatas), serta pembangunan model transfer learning dengan VGG16 sebagai ekstraktor fitur yang membekukan dan kepala klasifikasi kustom. Model dibor menggunakan optimizer Adam dengan mekanisme EarlyStopping dan ModelCheckpoint, kemudian dievaluasi menggunakan akurasi, presisi, recall, F1-score, dan konfusi matriks. Hasil pengujian menunjukkan bahwa model mencapai akurasi uji sebesar 98,44% dengan loss 0,1815, serta nilai rata-rata makro dan rata-rata tertimbang untuk presisi, recall, dan F1-score yang mendekati 0,98 dengan hanya satu kesalahan klasifikasi pada data uji. Proses pelatihan dan penyelesaian dapat diselesaikan dengan beban komputasi yang masih moderat pada lingkungan GPU Google Colab, sehingga konfigurasi VGG16 dengan resolusi 224×224 piksel berpotensi menjadi baseline yang efektif dan efisien untuk klasifikasi penyakit daun padi pada skenario data terbatas.
Co-Authors Ade Irma Purnama Sari Adi Putra, Panji Ahmad Faqih Ahmad Rifai Ahmad Rifai Akhmad Taukhid Arif Budi Setiawan Arif Rifaldi Dikananda Arif Rinaldi Dikananda Arif Rinaldi Dikanda Artoti, Azzahra Rizky Ayu Febrian Lesmana, Alfira Azzahra Moudy Fajria Bagus Hermawan Bisma Mahendra Candra, Candra Rahmawati Dadang Sudrajat Daisy Debora Grace Pangemanan Dede Rohman Denni Pratama Dian Ade Kurnia Dikananda, Arif Rinaldi Dikananda, Fatihanursari Fachry Abda El Rahman Fahmi Naufal Fatha Mudzhaffar, Mochammad Fatihanursari Dikananda Hamongan, Ryan Hanifah Nur Aulia Hayati, Umi Heliyanti Susana Hery Widijanto Ica, Ica Pandia Ika Riantika Indra Wiguna Marthanu Irfan Ali Jannah, Eka Roehatul Jayawarsa, A.A. Ketut Khaerul Anam Laylatunna'imah Maluw, Fandel Masdarul Rizqi maulana faz'rin, rama Muchlisinalahuddin Muhamad Abdul Fatah Muhammad Hilman Mulyawan Nana Suarna Nasakh Nisa Dienwati Nuris Nuraeni Pamungkas, Vicky Puji Pramudya Marta Raditya Danar Dana Rai Fatkaozi, Ahmad Ramadan, Muhamad Firly Rano Renaldi , Betran Ridho Nugraha Rifa'i, Ahmad Rinaldi Dikananda, Arif Rohayati, Eti Rohman, Dede Rudi Kurniawan Satria Turangga Siti Latifah Unari, Ica Venny Yusiana Wahyudin, Edi Wiratriyana Yudhistira Arie Wijaya