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All Journal Jurnal Pendidikan Indonesia Jurnal Informatika dan Teknik Elektro Terapan Creative Information Technology Journal INTEGER: Journal of Information Technology Pendas : Jurnah Ilmiah Pendidikan Dasar Best Journal (Biology Education, Sains and Technology) Jurnal Computer Science and Information Technology (CoSciTech) Journal of Soft Computing Exploration Jurnal Informatika Terpadu International Journal of Community Service Journal of Innovation Research and Knowledge Jurnal Riset Rumpun Ilmu Teknik (JURRITEK) Jurnal Riset Rumpun Ilmu Kesehatan (JURRIKES) Jurnal Ilmiah Teknik Informatika dan Komunikasi Innovative: Journal Of Social Science Research Indonesian Vocational Research Journal Neptunus: Jurnal Ilmu Komputer dan Teknologi Informasi IJENSET Jurnal Teknik Informatika dan Teknologi Informasi
Mufti Ari Bianto
Universitas Muhammadiyah Lamongan
Religion Agriculture, Biological Sciences & Forestry Humanities Automotive Engineering Biochemistry, Genetics & Molecular Biology 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 Health Professions Immunology & microbiology Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Mechanical Engineering Medicine & Pharmacology Nursing Public Health Social Sciences Other

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The Monitoring System for Water Quality Based on The Internet of Things (IoT) and Uses A TDS Sensor Zafi, Ali; Saputra, Bagus Dwi; Bianto, Mufti Ari
Indonesian Journal of Engineering, Science and Technology Vol. 1 No. 2 (2024): VOL. 01 NO. 02 (DECEMBER 2024)
Publisher : Universitas Muhammadiyah Lamongan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.38040/ijenset.v1i2.1015

Abstract

This research focuses on designing an Internet of Things (IoT)-based water quality monitoring system for aquaculture ponds, utilizing Total Dissolved Solids (TDS) sensors and the ESP32 microcontroller. The system is developed to monitor water quality in real-time by measuring the concentration of dissolved solids in the pond water. Data from the TDS sensor is collected by the ESP32 microcontroller, which is connected to a WiFi network and subsequently transmitted to the cloud, where it is displayed on a website. The study shows that the system can categorize water quality into three statuses: safe, alert, and poor. These categories are based on predefined TDS threshold values. Daily collected data is processed to provide accurate information on water quality status. This system enables continuous monitoring, facilitating pond management. Users can easily access data through a web page that presents information in an easily understandable format. The research demonstrates the effectiveness of using the ESP32 microcontroller and TDS sensors in an IoT-based monitoring application, as well as the system's capability to provide clear and timely indications of water quality status. Keywords--  ESP32 Microcontroller; Total Dissolved Solids (TDS); Water Quality Sensor  
Decision Support System for Prioritizing Road Repairs with Simple Additive Weighting Method Ramadhan, Bayu Putra; Ardiansyah, Heri; Bianto, Mufti Ari; Saputra, Bagus Dwi; Widodo, Aris
Indonesian Journal of Engineering, Science and Technology Vol. 2 No. 1 (2025): VOL. 02 NO. 01 (JUNE 2025)
Publisher : Universitas Muhammadiyah Lamongan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.38040/ijenset.v2i1.1040

Abstract

The Decision Support System (DSS) is a technology utilized to address the issue of determining road improvement priorities. In this context, DSS will be used to integrate various road assessment criteria and provide recommendations for repair priorities based on proven methods. The aim of this study is to design a decision support system for prioritizing road repairs in Lamongan Regency and to implement this system effectively. The Simple Additive Weighting (SAW) method was chosen for its ability to handle multiple criteria and provide measurable evaluations of alternative solutions. The criteria used in this research include road condition, traffic volume, and socio-economic impact. The results of this system demonstrate a prioritization order for road repairs that can assist in more efficient decision-making, focusing on the most urgent needs. This research is expected to contribute to improving the efficiency of road infrastructure management and to aid authorities in the planning and execution of road repairs. Keywords-- Decision Support System; Road Repair Priority; Simple Additive Weighting.
Sistem Pendukung Keputusan Identifikasi Daerah Potensi Banjir Dengan Metode Multi Attribute Utility Theory (Studi Kasus: Kabupaten Lamongan) Bianto, Mufti Ari; Aprillya, Mala Rosa
INTEGER: Journal of Information Technology Vol 8, No 2 (2023): September
Publisher : Fakultas Teknologi Informasi Institut Teknologi Adhi Tama Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31284/j.integer.2023.v8i2.5024

Abstract

Penelitian ini bertujuan untuk membangun Sistem Pendukung Keputusan (SPK) yang dapat memberikan informasi sebaran daerah rawan banjir secara online pada masing-masing daerah di Kabupaten Lamongan. Penelitian ini menggunakan beberapa kriteria antara lain intensitas curah hujan, kemiringan lereng, jenis tanah, dan jarak ke sungai. Dalam penelitian ini data diperoleh dari seluruh kecamatan yang ada di Kabupaten Lamongan yang berjumlah 27 kecamatan. Tahapan dalam pengembangan sistem ini dimulai dengan mengumpulkan data terkait yang meliputi intensitas curah hujan, kemiringan lereng dan jenis tanah di setiap kecamatan. Proses penghitungan daerah potensi banjir menggunakan metode Multi Attribute Utility Theory. Langkah selanjutnya adalah membangun sistem berbasis web dengan menggunakan bahasa pemrograman PHP. Hasil kepuasan responden (pemangku kepentingan) terhadap system rata-rata 80%
Implementasi Convolutional Neural Network (CNN) untuk Klasifikasi Citra Batik Nusantara Zufar Faiil Haq; Mufti Ari Bianto; Afifah Agustin; Moch. Ryan Nurfebrianto
Jurnal Teknik Informatika dan Teknologi Informasi Vol. 5 No. 1 (2025): April: Jurnal Teknik Informatika dan Teknologi Informasi
Publisher : Lembaga Pengembangan Kinerja Dosen

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55606/jutiti.v5i1.5421

Abstract

Batik is a cultural heritage of the nation, with each batik having a unique and diverse pattern motif. The batik culture is very strong in Indonesia, so batik can be found in all regions of the archipelago. Each batik has its own characteristics and traits to distinguish itself in each area. However, many people find it difficult to differentiate the types of batik motif patterns, one of which is the Nusantara Megamendung batik. Therefore, this research aims to introduce the classification process of Nusantara batik motif patterns using one of the Deep Learning methods, namely Convolutional Neural Network (CNN), to differentiate the types of batik motif patterns in each region. The dataset is taken from the numeric representations of Red, Green, and Blue (RGB) values of each pixel, which are used as model learning features to study color patterns and textures. From the results of the experiments conducted, the batik image classification using the CNN method has a high level of accuracy The batik classification model achieved an accuracy of 85%, demonstrating a fairly good ability to identify batik images, one of which is the Mega Mendung batik. The Mega Mendung and Keraton classes showed perfect performance, with precision, recall, and F1-score close to 1.00. However, the Bali class was the main weak point, with a recall of only 60%, indicating that 40% of Bali Batik samples were misclassified, primarily as Keraton.
DESIGN OF AN AI-INTEGRATED RENEWABLE ENERGY SMART ELECTRIC FENCE FOR RAT PEST MITIGATION Bianto, Mufti Ari; Ihsan, M. Nurul; Umam, Khairul
Pendas : Jurnal Ilmiah Pendidikan Dasar Vol. 10 No. 04 (2025): Volume 10 No. 04 Desember 2025 Published
Publisher : Program Studi Pendidikan Guru Sekolah Dasar FKIP Universitas Pasundan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23969/jp.v11i01.43196

Abstract

Rat infestations in rice crops in Indonesia cause losses of approximately 5% of total national production, equivalent to 4 million tons per year, with an estimated value of IDR 18 trillion. Conventional methods such as chemical poisons and electric traps have limitations and pose risks to the environment and human safety. This study develops a Smart Electric Fence powered by renewable energy and integrated with Artificial Intelligence for safe and sustainable rat pest mitigation. The human and rat detection system applies a Convolutional Neural Network (CNN) approach using the YOLOv8 algorithm, implemented on a Raspberry Pi to automatically control the electric fence relay. The system is powered by solar panels. A dataset of 7,712 images was divided into training, validation, and testing sets. Evaluation results show 64.4% precision, 100% recall, and 64.4% accuracy, enabling real-time object detection.
Co-Authors A, Dwi Putra Adam Rizky Al’insani Adhani, Hamka Lukmanul Hakim AF Suryaning Ati MZ Afifah Agustin Ahmad Abdullah Fahmi Arbiansyah, Lutfi Ardian Hudi Ramadhani Aris Widodo Badriyatul Musyarofah Bagus Dwi Jauhari Bagus Dwi Saputra Bufa Al badi Eko Handoyo Fiqrul Labib Abdullah Hanif Azhar Ramadhan Heri Ardiansyah Hidayah, Septina Alfiani Ihsan, M. Nurul Khairul Umam Kusrini Kusrini Kusrini, Kusrini M. Cahyo Kriswantoro Mala Rosa Aprillya Moch. Ryan Nurfebrianto Mohammad Hisyam Romadhon Mohammad Huda Adi Sanjaya Mubaarok, Ahmad Husni Muhammad Syaifuddin Alamsyah Mutsna, Mutsna Pratama, Alif Nanda Ramadhan, Bayu Putra Rifki Noor Rohman Rofiatun Solekha Ryky Ardiansyah Salasun Nisah SAPUTRA, BAGUS DWI Setiyowati, Putri Ayu Ika Sholikha, Hidayatin Sudarmawan Sudarmawan Tsalits Wildan Hamid Uswatun Chasanah Vico Tegar Rawo Wijoyo Wijayanti, Lusi Salsabilla Yuni Furoida Maknuna Zafi, Ali Zamani, Sevian Nadi Zufar Faiil Haq