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All Journal JITK (Jurnal Ilmu Pengetahuan dan Komputer) INTI Nusa Mandiri Jurnal Manajemen Informatika dan Sistem Informasi JIKA (Jurnal Informatika) Computer Science (CO-SCIENCE) JUSTIAN - Jurnal Sistem Informasi Akuntansi INSANtek – Jurnal Inovasi dan Sains Teknik Elektro Jurnal Komputer Teknologi Informasi Sistem Komputer (JUKTISI) Jurnal Penelitian dan Pengabdian Masyarakat Jotika Fusion : Journal of Research in Engineering, Technology and Applied Sciences semanTIK Indonesian Community Service Journal of Computer Science (IndoComs) Jurnal Infortech Jurnal Nasional Komputasi dan Teknologi Informasi
Muhammad Iqbal
Universitas Bina Sarana Informatika
Humanities Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering Environmental Science Materials Science & Nanotechnology Other

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Development of a Web-Based Smart Ecosystem Platform for Sustainable Public Service Automation Shoffan Saifullah; Muhammad Iqbal; Lisnawanty Lisnawanty; Weiskhy Steven Dharmawan; Fahmi Raditya
Jurnal Infortech Vol. 8 No. 1 (2026): June 2026
Publisher : LPPM Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/infortech.v8i1.12830

Abstract

Public service delivery in Indonesia continues to face fundamental challenges including inefficient manual administrative processes, error-prone document validation, and the absence of real-time tracking systems. This research aims to develop the PANDU (Pelayanan Publik Digital Terpadu) platform as a web-based smart ecosystem that automates public services sustainably. The platform is built using the Waterfall development method with Model-View-Controller architecture based on Laravel 12 framework, Filament 4.0 administration panel, and Tailwind CSS 4.0 responsive interface. Four main smart features are integrated: automatic document validation, duplicate request detection within a 30-day window, category-based related service recommendations, and automatic priority calculation using multi-criteria scoring algorithm. The platform produces three separate panels for citizens, officers, and administrators, equipped with real-time tracking system through public API and configurable multi-step approval workflows. Black box testing results using equivalence partitioning technique demonstrate one hundred percent functional success rate, while usability evaluation using System Usability Scale yields an average score in the Excellent category with Acceptable acceptability level. The PANDU platform successfully bridges the gap between smart government theoretical frameworks and operational implementation, providing significant contribution to accelerating sustainable digital transformation of public services in Indonesia.
Optimasi Hyperparameter Gradient Boosting Menggunakan RandomizedSearchCV untuk Prediksi Harga Rumah di Wilayah Jabodetabek Baka Dayla Mahaga Br Tarigan; Muhammad Iqbal; Mia Rosmiati
Jurnal Komputer Teknologi Informasi Sistem Komputer (JUKTISI) Vol. 5 No. 2 (2026): September 2026
Publisher : LKP KARYA PRIMA KURSUS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62712/juktisi.v5i2.1338

Abstract

Backlog perumahan di Jabodetabek yang tembus 2,93 juta unit bikin kebutuhan sistem prediksi harga rumah yang akurat jadi makin penting, supaya masyarakat dan pengembang bisa ambil keputusan jual-beli properti dengan lebih terukur. Penelitian ini mencoba membandingkan performa enam algoritma machine learning, yaitu Ridge Regression, Random Forest, Gradient Boosting, XGBoost, Artificial Neural Network (ANN) Backpropagation, dan Deep Neural Network (DNN), untuk memprediksi harga rumah di Jabodetabek menggunakan dataset open source dari Kaggle berisi 3.553 data. Tahapan penelitian meliputi eksplorasi data, penanganan missing value, penghapusan outlier dengan metode Interquartile Range (IQR), rekayasa fitur, encoding, standardisasi, pelatihan model dengan validasi silang 10-fold, serta penyetelan hyperparameter menggunakan Randomized Search. Hasil pengujian pada 571 data uji (20%) menunjukkan model Gradient Boosting yang sudah disetel (tuned) memberikan performa paling bagus dengan R² 93,06%, MAE Rp265.951.001, RMSE Rp480.524.642, dan MAPE 13,42%, mengungguli XGBoost (R² 92,33%), Random Forest (R² 91,23%), ANN Backpropagation (R² 86,70%), DNN (R² 86,37%), dan Ridge Regression (R² 85,65%). Hasil ini juga lebih tinggi dibandingkan penelitian-penelitian acuan sebelumnya yang memakai algoritma serupa pada dataset yang sama. Penelitian ini memberi kontribusi berupa perbandingan yang lebih lengkap antara algoritma berbasis pohon keputusan, regresi linear teregularisasi, dan jaringan saraf tiruan untuk kasus prediksi harga properti di kawasan urban Indonesia
Detection of Rupiah Nominal Values Based on Computer Vision and OCR for Low Vision Accessibility Doucoure Mohammed Hakeem; Trisna Almuti; Syahbil Afriza Baharaji; Muhammad Iqbal; Albert Riyandi
Fusion : Journal of Research in Engineering, Technology and Applied Sciences Vol. 3 No. 1 (2026): Fusion - April
Publisher : PT. Faaslib Serambi Media

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.66341/fusion.v3i1.338

Abstract

The ability to recognize banknotes' nominal value is a fundamental skill in daily economic transactions. However, for low-vision individuals, this simple task poses a major challenge, risking transaction errors and fraud. This study aims to build a web-based application capable of detecting Rupiah currency nominals in real-time by integrating computer vision and Optical Character Recognition (OCR) as an independent accessibility feature. The method combines a custom object detection model based on the YOLO architecture via the Roboflow platform and Tesseract OCR for nominal text verification, which is then integrated with the Web Speech API for voice-based output (Text-to-Speech). The system test results indicate that the combined "Roboflow + OCR" approach significantly improves detection reliability compared to using the object model alone. The system achieved a classification accuracy rate of 94.5% under optimal lighting conditions, with an average Text-to-Speech response latency of 1.8 seconds. This implementation proves that the synergy of image processing and OCR can provide an effective and inclusive assistive technology solution for visually impaired groups in Indonesia.
Implementasi Smart Parking System Berbasis Computer Vision Menggunakan IP Camera dan Integrasi Java Library Pada PT. Tri Alfa Sinar Mandiri Nanda Diaz Arizona; Deasy Purwaningtias; Muhammad Iqbal
Jurnal Sistem Informasi Akuntansi Vol 7 No 1 (2026): : Periode Maret 2026
Publisher : LPPM Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/justian.v7i1.12148

Abstract

PT. Tri Alfa Sinar Mandiri, as a public facility management company, faces challenges in parking management efficiency, where the previous system had limitations in real-time visual monitoring and relied heavily on manual operator input. This manual process often leads to queues and is prone to errors in recording physical vehicle data. This research aims to implement a Smart Parking System based on Computer Vision using IP Cameras directly integrated with a Java-based desktop application. Unlike previous developments that used the Waterfall method, this study applies the Prototyping method to facilitate iterative testing of camera hardware integration and image processing library accuracy. The system is designed to automatically capture video streams when a vehicle is detected, perform visual validation without physical interaction, and store image data into the database. Test results demonstrate that the integration of Java Libraries with IP Cameras successfully displays real-time monitoring and accelerates transaction processes at entry and exit posts. This implementation provides a modern solution that significantly improves security and operational efficiency compared to conventional systems
PEMODELAN PREDIKSI TSUNAMI DENGAN MACHINE LEARNING MENGGUNAKAN PYCARET PADA DATA HISTORIS GEMPA Muhammad Iqbal; Siti Nurdiani; Lisnawanty Lisnawanty; Muhammad Fahmi Julianto
Jurnal Informatika Vol 10 No 1 (2026): JIKA (Jurnal Informatika)
Publisher : University of Muhammadiyah Tangerang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31000/jika.v10i1.15571

Abstract

AbstractIndonesia faces high tsunami risk due to its position on the Pacific Ring of Fire. This study analyzes machine learning implementation using PyCaret AutoML framework for tsunami prediction based on earthquake parameters. The dataset consists of 782 earthquake records with 13 features. Methodology includes automated preprocessing with outlier removal (16.88%), 80:20 train-test split, 10-fold cross-validation, and comprehensive evaluation. Results show XGBoost achieved best performance (93.95% accuracy, 97.19% AUC, 90.89% F1-score), LightGBM highest AUC (97.35%), Random Forest highest recall (93.11%), and SVM lowest performance (75.81% accuracy). Detailed analysis of PyCaret's automated workflow validates ensemble boosting superiority for tsunami early warning systems in Indonesia.Keywords: tsunami, machine learning, PyCaret, XGBoost, early warningAbstrakIndonesia menghadapi risiko tsunami tinggi karena posisinya di jalur Cincin Api Pasifik. Penelitian ini menganalisis implementasi machine learning menggunakan framework PyCaret AutoML untuk prediksi tsunami berdasarkan parameter gempa bumi. Dataset terdiri dari 782 rekaman gempa dengan 13 fitur. Metodologi mencakup preprocessing otomatis dengan penghapusan outlier (16,88%), pembagian data 80:20, cross-validation 10-fold, dan evaluasi komprehensif. Hasil menunjukkan XGBoost mencapai performa terbaik (akurasi 93,95%, AUC 97,19%, F1-score 90,89%), LightGBM AUC tertinggi (97,35%), Random Forest recall tertinggi (93,11%), dan SVM performa terendah (akurasi 75,81%). Analisis detail workflow otomatis PyCaret memvalidasi keunggulan ensemble boosting untuk sistem peringatan dini tsunami di Indonesia.Kata Kunci: tsunami, machine learning, PyCaret, XGBoost, peringatan dini 
PERBANDINGAN PENERAPAN ALGORITMA DEEP LEARNING DALAM PREDIKSI HARGA EMAS Muhammad Fahmi Julianto; Muhammad Iqbal; Wahyutama Fitri Hidayat; Yesni Malau
INTI Nusa Mandiri Vol. 19 No. 1 (2024): INTI Periode Agustus 2024
Publisher : Lembaga Penelitian dan Pengabdian Pada Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/inti.v19i1.5559

Abstract

Digital investment is trending because advancements in information technology make access easy through smartphones. Various digital investment instruments attract much interest from the public. Post COVID-19 pandemic, the economic impact of the pandemic is still felt until the end of 2022, requiring people to be smart in managing their finances. Gold investment is considered profitable due to its high value and tendency to increase, unlike the fluctuating stocks. Although easily accessible, investments carry risks, so investors must have sufficient knowledge to maximize profits. This research aims to predict gold prices using several deep learning models, namely Artificial Neural Network (ANN), Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), and Long Short-Term Memory (LSTM). The dataset used was taken from the Kaggle website, which includes historical gold price data. In this research, various deep learning models were applied and evaluated to determine the best model for predicting gold prices. The results show that the CNN model with Adam optimization and Mean Squared Error (MSE) loss function provides the best performance. The CNN model achieved the lowest Mean Absolute Error (MAE) of 0.004848717761305338, the lowest MSE of 4.3451079619612133, and the lowest Root Mean Squared Error (RMSE) of 0.006591743291392053. These results indicate that the CNN model is more effective in predicting gold prices compared to the ANN, RNN, and LSTM models on the used dataset.
Co-Authors Abdul Hamid Abdul Hamid Achmad Rifai Albert Riyandi Ali Mustopa, Ali Baka Dayla Mahaga Br Tarigan Destiana Putri Doucoure Mohammed Hakeem Eri Bayu Pratama Fahmi Raditya Hendri Mahmud Nawawi Henny Destiana Ikram Surya Ramadhan Iqbal Ananda Rizqy Nasution Irwan Herliawan Irwan Herliawan Jajang Jaya Purnama Lisnawanty M. Rangga Ramadhan Saelan Martias Martias Mia Rosmiati Muhammad Fahmi Julianto Muhammad Ifan Rifani Ihsan Muhammad Rezki Muhammad Sony Maulana Nanda Diaz Arizona Purwaningtias, Deasy Rabiatus Sa’adah Reza Maulana Ricki Sastra Ridwansyah Ridwansyah Ridwansyah Ridwansyah Shoffan Saifullah Siti Nurdiani Sri Rahayu Sugiono Syahbil Afriza Baharaji Trisna Almuti Umi Khultsum Wahyu Nugraha Wahyutama Fitri Hidayat Weiskhy Steven Dharmawan Windu Gata Windu Gata Yesni Malau Yudhistira Yudhistira Yudhistira Yudhistira