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All Journal PROSISKO : Jurnal Pengembangan Riset dan observasi Rekayasa Sistem Komputer Journal of Artificial Intelligence and Engineering Applications (JAIEA)
Windy Aulia
Universitas Negeri Medan
Automotive Engineering Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Mechanical Engineering

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Implementation of IoT and Machine Learning for Monitoring and Prediction of Tank Water Levels Rizky Wahyudi; Dedy Kiswanto; Windy Aulia; Selfi Audy Priscilia
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.v5i2.1936

Abstract

The availability and quality of clean water in household storage tanks are essential yet often overlooked until problems such as depletion or contamination occur. Manual monitoring methods that rely on physical inspection tend to be inefficient, prone to delay, and unable to support predictive decision-making. This study proposes an automated monitoring solution by integrating Internet of Things (IoT) technology with Machine Learning-based analysis. The system is developed using an ESP32 microcontroller that continuously collects real-time data from an ultrasonic sensor to measure water level and a turbidity sensor to assess water clarity. The time-series data obtained is then analyzed using two algorithmic approaches. Linear Regression is employed to model the water depletion rate and generate predictions regarding the estimated remaining duration before the tank reaches an empty state. In parallel, Random Forest is applied as a comparative model to validate prediction accuracy under non-linear consumption patterns. Experimental results demonstrate that the combined IoT–Machine Learning framework provides accurate, timely, and informative insights for users. The proposed system improves water usage efficiency and strengthens early warning capabilities, making it a practical solution for supporting effective household water management.
OPTIMASI SELEKSI FITUR PREDIKSI SAHAM MENGGUNAKAN QUANTUM INSPIRED METAHEURISTIC DAN LIGHTGBM Rizky Wahyudi; Fauzan Azima Lubis; Windy Aulia; Adidtya Perdana
PROSISKO: Jurnal Pengembangan Riset dan Observasi Sistem Komputer Vol. 13 No. 1 (2026): Prosisko Vol. 13 No. 1 Maret 2026
Publisher : Pogram Studi Sistem Komputer Universitas Serang Raya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30656/prosisko.v13i1.11611

Abstract

Penelitian ini bertujuan untuk meningkatkan akurasi prediksi return harian saham perbankan Indonesia melalui penerapan metode seleksi fitur berbasis metaheuristik QIMA (Quantum-Inspired Multi-Objective Algorithm) pada model LightGBM. Permasalahan utama yang diangkat meliputi tingginya multikolinearitas antar-fitur teknikal, rendahnya validitas prediksi model baseline, serta mahalnya biaya komputasi saat menggunakan fitur penuh. Penelitian ini menggunakan 38 indikator teknikal yang kemudian direduksi menggunakan QIMA untuk memperoleh subset fitur optimal yang memaksimalkan akurasi sekaligus meminimalkan kompleksitas model. Hasil eksperimen menunjukkan bahwa model baseline mengalami overfitting dengan nilai R² negatif pada sebagian besar saham. Penerapan QIMA berhasil memperbaiki kinerja model menjadi positif pada BBCA (0,0006) dan BBNI (0,0173), serta menstabilkan performa BBRI dan BMRI. Selain itu, jumlah fitur berhasil direduksi sebesar 68,42% dengan waktu pelatihan yang lebih cepat hingga 6–7 kali lipat. Analisis fitur terpilih mengungkap bahwa volatilitas jangka pendek, return berbasis momentum, serta lag harga dan volume merupakan indikator yang paling relevan dalam memengaruhi pergerakan harga saham jangka pendek. Secara keseluruhan, studi ini membuktikan bahwa QIMA mampu meningkatkan validitas prediksi, efisiensi komputasi, dan interpretabilitas model dalam konteks pasar saham Indonesia.
Co-Authors Adidtya Perdana, Adidtya Dedy Kiswanto Fauzan Azima Lubis Rizky Wahyudi Selfi Audy Priscilia