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All Journal Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) Jurnal Teknologi dan Sistem Komputer Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Jurnal Biologi Tropis Jurnal Pengabdian Magister Pendidikan IPA Dielektrika : Jurnal Ilmiah Kajian Teori dan Aplikasi Teknik Elektro Jurnal Sistem Komputer & Kecerdasan Buatan International Journal of Electrical, Energy and Power System Engineering (IJEEPSE) Journal of Computer System and Informatics (JoSYC) EEICT (Electric, Electronic, Instrumentation, Control, Telecommunication) Jurnal Warta Desa (JWD) Jurnal Teknoif Teknik Informatika Institut Teknologi Padang Prisma Sains: Jurnal Pengkajian Ilmu dan Pembelajaran Matematika dan IPA IKIP Mataram Jurnal Pepadu Journal of Artificial Intelligence and Digital Business ARMADA : Jurnal Penelitian Multidisiplin JEITECH (JOURNAL OF ELECTRICAL ENGINEERING AND INFORMATION TECHNOLOGY)
Misbahuddin, Misbahuddin
Universitas Mataram
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IoT-Driven Solutions for Improved Plant Care in Terrariums Diva Septiawan; Misbahuddin; Wiriasto, Giri Wahyu
International Journal of Electrical, Energy and Power System Engineering Vol. 8 No. 1 (2025): The International Journal of Electrical, Energy and Power System Engineering (I
Publisher : Electrical Engineering Department, Faculty of Engineering, Universitas Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31258/ijeepse.8.1.72-85

Abstract

The rapid advancement of Internet of Things (IoT) technology has revolutionized various sectors, including smart agriculture. This study explores an IoT-driven solution to enhance plant care in terrariums by automating maintenance and optimizing growth conditions. The proposed system monitors key environmental parameters temperature, humidity, and soil moisture while automating irrigation using an ESP32 microcontroller, DHT11 and YL-69 sensors, a relay, and a mini DC pump. An Android application, developed with Android Studio and Arduino IDE, integrates the system via Firebase for real-time data access. A 14-day observation of Rombusa plant growth revealed that the optimal soil moisture level ranges between 60%–70%, averaging 65%. The findings confirm that IoT-driven plant care enhances growth efficiency and simplifies maintenance, offering a more effective alternative to traditional methods.
PEMANFAATAN HASIL LAUT MELALUI INOVASI PRODUK OLAHAN SAMBAL IKAN TONGKOL DAN STRATEGI USAHA BERKELANJUTAN DI DESA KUTA KECAMATAN PUJUT LOMBOK TENGAH Amri, Septia Bahrul; Hisan, Khaeratun; ZA, Baiq Ayu Rizka Amalia; Mutia, Baiq Hana; Fatmalasari, Desi; David, Muhammad; Nabilah, Nuha; Nuriman, Nuriman; Kusniati, Pipit; Pikrianto, Riki; Misbahuddin, Misbahuddin
Jurnal Pepadu Vol 6 No 1 (2025): Jurnal Pepadu
Publisher : Universitas Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/pepadu.v6i1.7244

Abstract

Desa Kuta, Kecamatan Pujut, Lombok Tengah, memiliki potensi besar di sektor kelautan, terutama dengan melimpahnya ikan tongkol (Euthynnus affinis). Namun, pemanfaatannya masih terbatas pada konsumsi segar. Kegiatan KKN-PMD Universitas Mataram bertujuan meningkatkan nilai tambah melalui inovasi sambal ikan tongkol dan strategi usaha berkelanjutan. Melalui sosialisasi dan pelatihan yang melibatkan Dinas Perindustrian dan UMKM, masyarakat Desa Kuta dibekali pengetahuan dan keterampilan praktis dalam pengolahan dan pengemasan sambal ikan tongkol. Pembentukan kelompok usaha berkelanjutan diharapkan mendorong kemandirian ekonomi dan kesejahteraan masyarakat. Produk ini berpotensi menjadi oleh-oleh khas yang meningkatkan pendapatan masyarakat.
IMPLEMENTATION OF FEEDFORWARD NEURAL NETWORK FOR CARDIOVASCULAR DISEASE PREDICTION WITH PERFORMANCE EVALUATION Muhammad Rafli; Misbahuddin; Bulkis Kanata; Raflin, Muhammad Rafli
Jurnal Teknoif Teknik Informatika Institut Teknologi Padang Vol 13 No 2 (2025): TEKNOIF OKTOBER 2025
Publisher : ITP Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21063/jtif.2025.V13.2.97-104

Abstract

Disease is crucial to prevent more serious complications. This study implemented a Feedforward Neural Network (FNN) algorithm to build a cardiovascular disease risk prediction model using patient clinical data. The dataset used was sourced from open sources and underwent preprocessing stages such as one-hot encoding and normalization. The model architecture consists of two hidden layers with ReLU and dropout activation functions, and an output layer with a sigmoid function for binary classification. Training was conducted for 100 epochs with a data split ratio of 80:20. Evaluation was carried out using accuracy, precision, recall, F1-score, and confusion matrix metrics. The evaluation results showed that the model achieved a training accuracy of 92% and a testing accuracy of 88%, with an average F1-score of 87.2%. The Confidence Factor value also indicated a high level of confidence in each prediction. These results indicate that the FNN model is effective for cardiovascular disease risk prediction and has the potential to be used as a tool for rapid and accurate medical decision-making.
Integrasi Smart Agriculture untuk Peningkatan Penyimpanan Air dan Mitigasi Kekeringan Wahyuti, Putri Yunita; Misbahuddin, Misbahuddin; Sukartono, Sukartono; Mayantika, Husnitalia
RIGGS: Journal of Artificial Intelligence and Digital Business Vol. 4 No. 4 (2026): November - January
Publisher : Prodi Bisnis Digital Universitas Pahlawan Tuanku Tambusai

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/riggs.v4i4.4707

Abstract

Perubahan iklim menyebabkan ketidakstabilan curah hujan dan peningkatan risiko kekeringan yang berdampak serius terhadap produktivitas pertanian di Indonesia. Tantangan tersebut diperparah oleh rendahnya daya infiltrasi tanah dan penggunaan sistem irigasi konvensional yang belum efisien dalam pemanfaatan air. Penelitian ini bertujuan untuk mengkaji integrasi sistem Smart Agriculture berbasis Internet of Things (IoT) sebagai solusi peningkatan kapasitas penyimpanan air dan mitigasi dampak kekeringan pada lahan pertanian. Penelitian ini menggunakan metode penelitian kepustakaan (library research) dengan mengumpulkan data sekunder dari berbagai literatur ilmiah nasional dan internasional yang diperoleh melalui portal seperti Google Scholar, ScienceDirect, ResearchGate, SpringerLink, dan ProQuest. Hasil kajian menunjukkan bahwa penerapan sistem Smart Agriculture dengan dukungan sensor kelembapan tanah, sensor cuaca, sistem irigasi otomatis, dan algoritma pengambilan keputusan adaptif mampu meningkatkan efisiensi penggunaan air hingga 40–58% tanpa menurunkan produktivitas tanaman. Selain itu, teknologi konservasi air seperti embung mikro dan aplikasi biochar terbukti meningkatkan infiltrasi dan retensi air tanah pada lahan kering. Namun, implementasi Smart Agriculture di Indonesia masih menghadapi hambatan berupa keterbatasan infrastruktur digital, biaya investasi awal yang tinggi, serta rendahnya literasi digital petani. Oleh karena itu, diperlukan dukungan pemerintah dalam bentuk subsidi perangkat, pelatihan teknis, dan pengembangan sistem berbasis open-source untuk memperluas adopsi teknologi ini. Integrasi antara teknologi digital, konservasi sumber daya air, dan penguatan kapasitas sumber daya manusia menjadi strategi kunci dalam mewujudkan pertanian berkelanjutan.
RadReader: An Enhanced AlexNet-Based GUI Application for Pneumonia Prediction in Thoracic X-Ray Images Wiriasto, Giri Wahyu; Hipzi, Ahdiat Aunul; Suksmadana, I Made Budi; Misbahuddin; Kinasih, Indira puteri; Wiguna, Putu Aditya
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 9 No 6 (2025): December 2025
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v9i6.7023

Abstract

Recent advancements in radiology applications have led to user-friendly interfaces, improving pneumonia diagnosis by accurately differentiating between viral and bacterial pneumonia from thoracic X-rays. This approach enhances diagnostic precision and efficiency while offering intuitive real-time interaction for radiologists. This study aims to achieve two objectives: (i) developing a desktop-based radiology reader application, and (ii) modifying the alexNet architecture for classifying pneumonia based on thoracic X-ray datasets with the output encompassing pneumonia and normal cases. The desktop application assists radiologists in efficient image analysis and is developed using python–Tkinter. Integrate enhanced of AlexNet models which has been modified to better differentiate. The modified alexNet includes changes like adding max pooling in specific blocks and adjusting hidden layer neuron count. The dataset consists of 7442 images, with 4484 positive pneumonia and 2958 normal images obtained from the Mendeley websites. The enhanced alexNet (EAM) model achieves impressive results: 95.36% accuracy, 95.34% precision, 95.28% recall, and 95.31% F1-score for classifying bacterial pneumonia.
System IoT-AI Based on Microclimate Disease Risk Index for Early Detection of Vanilla Plant Diseases Muh. Hayatullah; Raodatul Putri; Nur Safitri; Misbahuddin; Muhammad Husni Idris
ARMADA : Jurnal Penelitian Multidisiplin Vol. 4 No. 3 (2026): ARMADA : Jurnal Penelitian Multidisplin, March 2026
Publisher : LPPM Sekolah Tinggi Ilmu Ekonomi 45 Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55681/armada.v4i3.1940

Abstract

The vanilla plant (Vanilla planifolia) is a high-value commodity, but it is highly susceptible to microclimatic fluctuations and disease attacks, especially stem and root rot closely related to Fusarium oxysporum f. sp. vanillae. A review of the literature also shows that temperature, humidity, and shade conditions affect vanilla growth, whereas conventional monitoring approaches often detect disease risk too late. This paper presents a systematic literature study with the help of Google Scholar-based Publish or Perish (PoP), enriched by targeted searches on ScienceDirect and Web of Science, and reported to follow the principles of PRISMA 2020. The synthesis results show that the integration of IoT, microclimate sensors, and AI has the potential to form a more precise Early Warning System through the MDRI index, which is a weighted risk score that collects parameters of temperature, relative humidity, VPD, light intensity, soil moisture, and history of daily conditions. Conceptually, MDRI can be applied to edge devices to provide early warnings, recommendations for cultivation actions, and the basis for data-driven decision-making. This paper emphasizes that the IoT–AI approach is not just a monitoring tool, but the foundation of an adaptive and sustainable vanilla disease risk management system.
PENINGKATAN AKURASI SENSOR ETANOL DENGAN KALMAN FILTER UNTUK PENDETEKSIAN KADAR ALKOHOL PADA HEMBUSAN NAPAS MANUSIA BERBASIS IOT Imam Abdul AZIS
EEICT (Electric, Electronic, Instrumentation, Control, Telecommunication) Vol 9, No 1 (2026)
Publisher : Universitas Islam Kalimantan Muhammad Arsyad Al Banjari Banjarmasin

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31602/eeict.v9i1.22967

Abstract

Penelitian ini bertujuan untuk meningkatkan akurasi dan kestabilan pembacaan sensor MQ-3 dalam mendeteksi kadar alkohol pada hembusan napas manusia dengan menerapkan metode Kalman  Filter  serta  mengintegrasikannya ke dalam sistem berbasis Internet of Things (IoT). Permasalahan utama pada sensor MQ-3 adalah adanya fluktuasi pembacaan dan lonjakan nilai (spike) akibat noise pengukuran,  terutama  pada  variasi  jarak dan sudut pengujian. Pada penelitian ini, sensor MQ-3 digunakan untuk mendeteksi konsentrasi uap etanol dalam satuan Parts Per Million (PPM), sedangkan ESP8266 berfungsi sebagai mikrokontroler dan modul komunikasi untuk pengiriman data secara real-time. Algoritma Kalman Filter satu dimensi diterapkan dengan mempertimbangkan parameter process noise (Q), measurement noise (R), dan error covariance (P) untuk mereduksi noise dan menghasilkan estimasi yang lebih optimal. Pengujian dilakukan pada variasi sudut 90° dan 45° serta jarak 5 cm dan 2,5 cm menggunakan alkohol dengan konsentrasi 10%, 70%, dan 96%. Hasil penelitian menunjukkan bahwa pembacaan tanpa Kalman     Filter     mengalami fluktuasi signifikan dan lonjakan hingga 2000 PPM, sedangkan setelah diterapkan Kalman Filter, data menjadi lebih halus, stabil, dan representatif terhadap kondisi sebenarnya. Dengan demikian, penerapan Kalman Filter terbukti efektif dalam meningkatkan kualitas dan keandalan sistem pendeteksi kadar alkohol berbasis IoT.
Ambulance Siren Audio Classification Using Convolutional Neural Network for Medical Emergency Detection Paninggalih, Ramadhan; Prihasto, Bima; Pratama, Maryo Inri; Ramadhana, Rizky Irswanda; Misbahuddin, Misbahuddin; Anshari, Buan; Akbar, Lalu Ahmad Syamsul Irfan; Wiriasto, Giri Wahyu
Prisma Sains : Jurnal Pengkajian Ilmu dan Pembelajaran Matematika dan IPA IKIP Mataram Vol. 14 No. 2: April 2026
Publisher : Universitas Pendidikan Mandalika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33394/j-ps.v14i2.20099

Abstract

The rapid detection of emergency vehicle sirens is critical for enhancing road safety and traffic management. This study proposes an automated classification system for ambulance sirens using a Convolutional Neural Network (CNN). The method utilizes Mel-Frequency Cepstral Coefficients (MFCC) to transform audio signals into 2D feature maps, allowing the model to capture distinct spectral and temporal patterns. The dataset was preprocessed using a stratified split to ensure balanced class distribution and prevent data leakage. Experimental results demonstrate that the CNN model achieves a high performance with an accuracy of 0.95, significantly outperforming baseline models such as Multi-Layer Perceptron (MLP) and XGBoost. Detailed evaluation through a confusion matrix indicates a consistent precision, recall, and F1-score of 0.95, proving the model’s robustness in distinguishing sirens from complex urban noise. The implementation of the Adam optimizer and early stopping mechanism ensured stable convergence and prevented overfitting. These findings suggest that the proposed CNN-MFCC framework provides a reliable solution for real-time emergency signal detection, offering a substantial contribution to intelligent transportation systems.
The Potential Application of IoT and Multispectral UAV Soil Sensor Technology in Sorgum (Sorgum bicolor L. Moench) Cultivation in Dry Land in Pujut District, Central Lombok Regency Pramesthi, Ardi Yoga; Safitri, Auliya; Misbahuddin, Misbahuddin; Idris, Muhammad Husni
Jurnal Biologi Tropis Vol. 26 No. 2 (2026): April - Juni
Publisher : Biology Education Study Program, Faculty of Teacher Training and Education, University of Mataram, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/jbt.v26i2.11867

Abstract

Sorghum (Sorghum bicolor L. Moench) is a drought-tolerant crop with significant potential for dryland cultivation in Pujut District, Central Lombok Regency, Indonesia. This study reviews the potential application of Internet of Things (IoT) and multispectral Unmanned Aerial Vehicle (UAV) technologies for precision sorghum cultivation in dryland ecosystems. A qualitative descriptive literature review was conducted, synthesizing 13 peer-reviewed studies on IoT sensor networks, UAV-based remote sensing, machine learning algorithms, and their integration in precision agriculture. The results indicate that IoT soil sensors can continuously monitor soil moisture, temperature, pH, and nutrient levels in Vertisol soils, while multispectral UAVs capture vegetation indices (NDVI, NDRE, CWSI) for biomass estimation, drought stress assessment, and yield prediction. The integration of both technologies, combined with machine learning approaches including ensemble learning and transfer learning, produces comprehensive crop health maps and site-specific management recommendations. The dryland characteristics of Pujut District, with Vertisol soils (pH 6.5–8.4) and limited water availability, are highly suitable for sorghum cultivation and would benefit substantially from precision agriculture interventions. A five-stage implementation framework is proposed, encompassing baseline survey, monitoring, analytics, precision management, and evaluation. Despite challenges including initial investment costs and technical capacity requirements, the long-term benefits of improved productivity and resource efficiency make IoT-UAV integration a viable strategy for sustainable dryland sorghum farming.
Co-Authors A. Sjamsjiar Rachman A. Syamsu Irfan Akbar Abdul Amar Gifari Abdul Kholid Ade Safarudin Madani Akbar, L A Syamsul Irfan Amri, Septia Bahrul Anisaturahman Arsy Saefatullah Baiq Irma Fitriani Baiq Juliati Buan Anshari Bulkis Kanata David, Muhammad Dena Prihatiningsih Dinda Ayu Rizqia Diva Septiawan Djul Fikri Budiman Djul Fikry Budiman, Djul Fikry Elmy Ericka Stywati Fatmalasari, Desi Faturrahman Giri Wahyu Wiriasto Gunawan Gunawan Hazi, Khaerul Helmi Ilzam Fadholi Hipzi, Ahdiat Aunul Hisan, Khaeratun I Gusti Putu Muliarta Aryana I Made Ari Nrartha Ibzani Ilham Shagianto Indira Puteri Kinasih Irfan Akbar, L. Ahmad Syamsul Jafar, Sitti Rusnah Januarman Maulana Putra Jurnal Pepadu Kusniati, Pipit L Ahmad L. Syamsul Irfan Akbar L.A.S. Irfan Akbar Lalu Jazuly Khaerul Hady Made Sutha Yadnya Mathildes Inna Kamuri Mayantika, Husnitalia Muh. Hayatullah Muhamad Syamsu Iqbal Muhamat Taufik, Muhamat Muhammad Amjad Syahrastany Muhammad Husni Idris Muhammad Husni Idris Muhammad Irwan Muhammad Rafli Mutia, Baiq Hana Nabilah, Nuha Ni Made Seniari Ni Wayan Krisnitha Putri Nur Safitri Nuriman Nuriman, Nuriman Nurmahsya, Guruh Khedar Paninggalih, Ramadhan Paniran Paniran Pikrianto, Riki Pramesthi, Ardi Yoga Pratama, Maryo Inri Prihasto, Bima Putra Rahmat Ramadhan Putu Aditya Wiguna Raflin, Muhammad Rafli Ramadhana, Rizky Irswanda Randa Maulana Raodatul Putri Rosmaliati, Rosmaliati Safitri, Auliya Sudi Mariyanto Al Sasongko Sukartono Sukartono Sukartono Suksmadana, I Made Budi Supiani Supiani Suryani Kazrina Syafaruddin Syafaruddin Taufik Taufik Wahyuti, Putri Yunita Wira Wawantoro Yunisa Afriani Yunita Sari Yusron Rizki Ardiansyah ZA, Baiq Ayu Rizka Amalia Zamroni, Sulthon