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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 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) Jurnal Warta Desa (JWD) Jurnal Teknoif Teknik Informatika Institut Teknologi Padang Jurnal Pepadu JEITECH (JOURNAL OF ELECTRICAL ENGINEERING AND INFORMATION TECHNOLOGY)
Misbahuddin, Misbahuddin
Universitas Mataram
Religion Agriculture, Biological Sciences & Forestry 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 Social Sciences Other

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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.
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.
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 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 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 Misbahuddin Muhamad Syamsu Iqbal Muhamat Taufik, Muhamat Muhammad Amjad Syahrastany Muhammad Irwan Muhammad Rafli Mutia, Baiq Hana Nabilah, Nuha Ni Made Seniari Ni Wayan Krisnitha Putri Nuriman Nuriman, Nuriman Nurmahsya, Guruh Khedar Paniran Paniran Pikrianto, Riki Putra Rahmat Ramadhan Putu Aditya Wiguna Raflin, Muhammad Rafli Randa Maulana Rosmaliati, Rosmaliati Sudi Mariyanto Al Sasongko Sukartono Suksmadana, I Made Budi Supiani Supiani Suryani Kazrina Syafaruddin Syafaruddin Taufik Taufik Wira Wawantoro Yunisa Afriani Yunita Sari Yusron Rizki Ardiansyah ZA, Baiq Ayu Rizka Amalia Zamroni, Sulthon