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All Journal Jurnal Edukasi Elektro Transmisi: Jurnal Ilmiah Teknik Elektro Teknika Techno.Com: Jurnal Teknologi Informasi CALYPTRA : Jurnal Ilmiah Mahasiswa Universitas Surabaya Jurnal Informatika dan Teknik Elektro Terapan Jurnal Ampere Multitek Indonesia : Jurnal Ilmiah Jurnal Teknologi Informasi dan Terapan (J-TIT) Indonesian Journal of Business Intelligence (IJUBI) Jurnal Teknik Elektro dan Komputasi (ELKOM) G-Tech : Jurnal Teknologi Terapan Jurnal Teknik Industri Terintegrasi (JUTIN) Community Empowerment Jurnal Amplifier: Jurnal Ilmiah Bidang Teknik Elektro dan Komputer JEEE-U (Journal of Electrical and Electronic Engineering-UMSIDA) Fidelity : Jurnal Teknik Elektro Jurnal Media Teknologi JUSTI (Jurnal Sistem dan Teknik Industri) Scientia Business Law Review Indonesian Vocational Research Journal JIM: Jurnal Ilmiah Mahasiswa Pendidikan Sejarah SmartComp Economic and Finance in Indonesia Jurnal Teknik Elektro: Ilmu dan Teknologi
Denny Irawan
Universitas Surabaya
Arts Humanities Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice Mechanical Engineering Public Health Social Sciences

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IMPLEMENTASI SMOTE DAN ALGORITMA MACHINE LEARNING UNTUK MENINGKATKAN AKURASI REKOMENDASI HOTEL Agustina, Candra; Rahmawati, Eka; Irawan, Denny; Tristanti, Vriska wahyu
Indonesian Journal of Business Intelligence (IJUBI) Vol 7 No 2 (2024): Indonesian Journal of Business Intelligence (IJUBI)
Publisher : Universitas Alma Ata

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21927/ijubi.v7i2.5141

Abstract

Pariwisata memiliki peran penting dalam perekonomian global, dengan destinasi seperti Candi Borobudur menarik berbagai jenis pengunjung. Untuk meningkatkan pengalaman wisatawan, rekomendasi hotel yang akurat menjadi sangat penting. Namun, data yang tidak seimbang, seperti ulasan positif yang terlalu dominan, sering kali mengurangi kinerja model machine learning yang digunakan untuk rekomendasi. Penelitian ini bertujuan untuk mengatasi masalah tersebut dengan menerapkan Synthetic Minority Over-sampling Technique (SMOTE) guna menyeimbangkan dataset dan meningkatkan akurasi rekomendasi hotel. Beragam algoritma machine learning, termasuk Random Forest, Support Vector Machines, dan Neural Networks, diterapkan dan dievaluasi. Hasil penelitian menunjukkan bahwa penerapan SMOTE secara signifikan meningkatkan kinerja semua model, dengan Random Forest memberikan hasil terbaik. Studi ini menunjukkan bahwa SMOTE, dalam kombinasi dengan teknik machine learning, memberikan solusi yang kuat terhadap ketidakseimbangan kelas pada sistem rekomendasi hotel, sehingga menghasilkan rekomendasi yang lebih andal dan relevan bagi wisatawan. Temuan ini memiliki implikasi penting bagi manajemen hotel dan sektor pariwisata secara keseluruhan.
IoT-Based Automatic Irrigation System with Reinforcement Learning for Water Optimization Baihaqi, Naufal Arief; Irawan, Denny
G-Tech: Jurnal Teknologi Terapan Vol 10 No 1 (2026): G-Tech, Vol. 10 No. 1 January 2026
Publisher : Universitas Islam Raden Rahmat, Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70609/g-tech.v10i1.8376

Abstract

Water efficiency in agriculture plays a crucial role in addressing climate change and freshwater scarcity challenges. Conventional irrigation systems often result in excessive water usage due to non-adaptive watering schedules. This study proposes an adaptive automatic irrigation system based on the Internet of Things (IoT) integrated with the Reinforcement Learning (RL) Q-Learning algorithm to optimize water usage. The system utilizes an ESP32 microcontroller connected to DHT22 and YL-69 sensors for real-time monitoring of temperature, humidity, and soil moisture, with data transmitted to the Firebase Realtime Database. The system was experimentally tested for 30 days under three soil moisture conditions with repeated measurements, resulting in an average sensor accuracy of 97.9% and a 70% reduction in daily water consumption compared to manual irrigation. The implementation of RL enables the system to autonomously adjust irrigation decisions based on environmental dynamics while providing remote monitoring via a web dashboard. The results demonstrate that the proposed IoT-RL solution offers an effective, intelligent, and sustainable approach for improving agricultural water resource management.
Develompent of Machine Learning Model to Predict Hotel Room Reservation Cancellations Rahmawati, Eka; Nurohim, Galih Setiawan; Agustina, Candra; Irawan, Denny; Muttaqin, Zainal
Jurnal Teknologi Informasi dan Terapan Vol 11 No 2 (2024): December
Publisher : Jurusan Teknologi Informasi Politeknik Negeri Jember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25047/jtit.v11i2.431

Abstract

The frequent cancellations of hotel room reservations have become a pressing issue for the hospitality industry, especially in high-tourism areas such as Borobudur, Indonesia. This research develops a predictive machine learning (ML) model to identify cancellation probabilities to support proactive decision-making for hotel management. Using datasets from Borobudur-based hotels, key variables such as booking lead time, arrival month, and reservation outcomes were analyzed. Random Forest demonstrated the best performance, achieving an accuracy of 86.36% with a precision of 88.06%, recall of 93.65%, and F1-score of 90.77%. Logistic Regression demonstrated moderate effectiveness, while Bayesian Networks underperformed, highlighting the importance of robust algorithms for such tasks. The findings underscore the potential of ML models, particularly Random Forest, to reduce financial losses and enhance operational efficiency in the hospitality sector by anticipating cancellations and facilitating better resource allocation
Co-Authors Abda Abda Al Faritsi, Muhammad Izzi Al Sarfini, Achmad Asrori Alkhaqiqi, Fashoma Yudha Anggana Anwar, Irfan Anwar, Sofiandy Arifian, Raafi' Yanuar Purnama Astutik, Rini Puji Audi Amzar, Vilza Baihaqi, Naufal Arief Bramasta, Geni Cahyani, Dwi Ovelita Candra Agustina eka rahmawati Fahlevi, Rezza Agung Fakhruddin, Anas Galih Setiawan Nurohim Handika, Muhammad Fery Hidayah, Alief Hidayatullah, Novaltino Syarif Imawan, Moh. Rizki Indra Maulana` Indra Maulana Maliki, M Sayid Alawi Mubiin, Illal Balaaghul Muhammad Taufiqur Rahman, Muhammad Taufiqur Muhammad Yusuf nugraha, jihan Nuryudin, Akhmad Pamungkas, Moch Perdana, Adzriel Thoriq Helyo Purnama Arifian, Raafi' Yanuar Putra, Muhammad Alif Arya Raden Mohamad Herdian Bhakti Rahmatullah*, Zahrul Ulum Raka Trialviano Bagus Ramadhan, Laode Abdikarso Panca Ramadhan, Muhammad Irvan Rhomadhoni, Muhammad Ronal Rohman, Muhammad Aqifur Rosyidi, Muhammad Dawwam Suryo, Yoedo Ageng Tristanti, Vriska wahyu Utomo, Muhammad Febri Prasetyo Winarno, Hendra Ari Zainal Muttaqin