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All Journal Jurnal Teknologi Informasi dan Ilmu Komputer Jurnal Informatika dan Teknik Elektro Terapan Jurnal Ilmiah KOMPUTASI Sinkron : Jurnal dan Penelitian Teknik Informatika JOURNAL OF APPLIED INFORMATICS AND COMPUTING Jurnal Sisfokom (Sistem Informasi dan Komputer) ILKOM Jurnal Ilmiah Jurnal Infomedia MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer IJISTECH (International Journal Of Information System & Technology) JOURNAL OF SCIENCE AND SOCIAL RESEARCH JOURNAL OF INFORMATICS AND COMPUTER SCIENCE Jurnal Sistem Komputer dan Informatika (JSON) Journal of Computer Science, Information Technology and Telecommunication Engineering (JCoSITTE) IJISTECH Rambideun : Jurnal Pengabdian Kepada Masyarakat International Journal of Engineering, Science and Information Technology jeti JURNAL ILMIAH GLOBAL EDUCATION JUSTIN (Jurnal Sistem dan Teknologi Informasi) TECHSI - Jurnal Teknik Informatika Sisfo: Jurnal Ilmiah Sistem Informasi Jurnal Teknologi Terapan and Sains 4.0 Jurnal Kemitraan Responsif untuk Aksi Inovatif dan Pengabdian Masyarakat Journal Of Artificial Intelligence And Software Engineering Journal of Advanced Computer Knowledge and Algorithms Applied Engineering, Innovation, and Technology
Munirul Ula
Universitas Malikussaleh
Agriculture, Biological Sciences & Forestry Humanities Astronomy Automotive Engineering Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Physics Public Health Social Sciences Transportation Other

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Journal : Journal Of Artificial Intelligence And Software Engineering

 1 
Comparison of the Performance of Fuzzy Tsukamoto and Fuzzy Mamdani in an Internet of Things Based Grape Greenhouse Control System Rusadi, Athirah; Ula, Munirul; Daud, Muhammad; Nurdin, Nurdin; Hasibuan, Arnawan
Journal of Artificial Intelligence and Software Engineering Vol 5, No 2 (2025): Juni On-Progress
Publisher : Politeknik Negeri Lhokseumawe

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30811/jaise.v5i2.6936

Abstract

The application of Internet of Things in agriculture, particularly in grape greenhouses, enables automated environmental control to enhance efficiency and crop yield. This study compares the performance of two fuzzy logic methods, Fuzzy Mamdani and Fuzzy Tsukamoto, in a temperature and humidity control system based on IoT using the DHT22 sensor. The system is designed to automate irrigation via actuators based on sensor data. Performance evaluation was conducted using RMSE, MAE, and standard deviation metrics. The results show that the Tsukamoto method achieved lower RMSE 2.6928, MAE 2.2625, and standard deviation 1.1080 compared to the Mamdani method, which recorded RMSE of 2.9039, MAE of 2.3947, and standard deviation of 1.9268. However, a paired t-test yielded a p-value of 0.0690 0.05, indicating no statistically significant performance difference. Thus, while Fuzzy Tsukamoto appears superior in metrics, both methods are considered equally effective for controlling environmental conditions in grape greenhouses.The application of Internet of Things in agriculture, particularly in grape greenhouses, enables automated environmental control to enhance efficiency and crop yield. This study compares the performance of two fuzzy logic methods, Fuzzy Mamdani and Fuzzy Tsukamoto, in a temperature and humidity control system based on IoT using the DHT22 sensor. The system is designed to automate irrigation via actuators based on sensor data. Performance evaluation was conducted using RMSE, MAE, and standard deviation metrics. The results show that the Tsukamoto method achieved lower RMSE 2.6928, MAE 2.2625, and standard deviation 1.1080 compared to the Mamdani method, which recorded RMSE of 2.9039, MAE of 2.3947, and standard deviation of 1.9268. However, a paired t-test yielded a p-value of 0.0690 0.05, indicating no statistically significant performance difference. Thus, while Fuzzy Tsukamoto appears superior in metrics, both methods are considered equally effective for controlling environmental conditions in grape greenhouses.
Enhancing Resource Efficiency in Urban Agriculture: A GA-Fuzzy Logic IoT-Based Smart Hydroponic Greenhouse Ula, Munirul; Rusadi, Athirah; Daud, Muhammad
Journal of Artificial Intelligence and Software Engineering Vol 5, No 3 (2025): September
Publisher : Politeknik Negeri Lhokseumawe

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30811/jaise.v5i3.7799

Abstract

Pertanian presisi berbasis Internet of Things (IoT) menawarkan solusi inovatif terhadap tantangan ketahanan pangan dan keterbatasan lahan di daerah perkotaan. Penelitian ini bertujuan merancang dan mengevaluasi sistem rumah kaca cerdas berbasis hidroponik untuk budidaya tumpang sari anggur dan selada  menggunakan Nutrient Film Technique (NFT). Metodologi penelitian mengintegrasikan Pengendali Logika Fuzzy yang dioptimalkan dengan Algoritma Genetika (GA-FLC) untuk kontrol real-time enam parameter lingkungan: suhu, kelembapan, pH, konduktivitas listrik, intensitas cahaya, dan konsentrasi CO₂. Sistem menggunakan mikrokontroler ESP32 dengan array sensor presisi tinggi dan platform cloud (ThingSpeak, Firebase) untuk monitoring dan kontrol otomatis. Eksperimen dilaksanakan menggunakan Randomized Complete Block Design dengan dua faktor (sistem kontrol GA-FLC vs konvensional; monokultur vs tumpang sari) selama 120 hari di kondisi iklim tropis Bireuen, Aceh. Hasil menunjukkan sistem GA-FLC superior dalam akurasi kontrol dengan Mean Absolute Error suhu 0,7°C (61% lebih baik), response time aktuator 47-53% lebih cepat, dan efisiensi energi 25-30% lebih tinggi. Produktivitas anggur meningkat 27,8% (2,48 kg/tanaman) dan selada 23,7% (245 g/tanaman) dibandingkan sistem konvensional. Efisiensi sumber daya menunjukkan penghematan air 33,3%, energi 32,6%. Water Use Efficiency mencapai 12,4 kg/m³ dengan Energy Productivity 1,85 kg/kWh. Sistem ini memberikan kontribusi signifikan untuk pertanian perkotaan berkelanjutan dengan produktivitas tinggi, efisiensi sumber daya optimal, dan viabilitas ekonomi yang menarik untuk implementasi komersial di daerah tropis.
Analisis Perbandingan Kinerja Algoritma You Only Look Once (YOLOv8) Dan Single Shot Detector (SSD) dalam Pengenalan Nominal Uang Kertas Ulfah, Julia; Ula, Munirul; Fajriana, Fajriana; Nurdin, Nurdin
Journal of Artificial Intelligence and Software Engineering Vol 5, No 4 (2025): Desember (On Progress)
Publisher : Politeknik Negeri Lhokseumawe

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30811/jaise.v5i4.7471

Abstract

The advancement of technology in the field of image recognition has significantly facilitated and improved the effectiveness of object detection in computer-based banknote recognition systems. This study aims to automatically identify banknotes based on their denominations, with the objective of minimizing human errors—such as lack of concentration, fatigue, and other factors—and enabling its application in ATMs and automated payment systems. This research compares the accuracy levels and detection success rates between the YOLO and SSD algorithms in recognizing the denominations of banknotes. The YOLO model operates by dividing the image into grids and predicting bounding boxes along with object classes in a single step, resulting in fast and consistent detection. In contrast, the SSD model employs a multi-scale approach by utilizing feature maps from multiple levels to generate predictions. The parameters used in this study include 7 classes of Indonesian banknotes: Rp1,000, Rp2,000, Rp5,000, Rp10,000, Rp20,000, Rp50,000, and Rp100,000. A total of 353 images were used in the dataset, and three images from each class were selected for testing purposes. The results of the study indicate a significant performance difference. The YOLO algorithm achieved a 100% accuracy rate under both normal and low-light conditions, while the SSD algorithm achieved an accuracy rate of 87.2% under normal lighting and 91.4% under low-light conditions.
Co-Authors Abdullah, ⁠Dahlan Afif, Muhammad Athallah Afridah, Rita Agustriya, Manda Al-Ghiyats, Said Ananda Faridhatul Ulva Andreansyah, Sabda Arnawan Hasibuan Azzikri, Affan Syafiq Bustami Bustami Cut Agusniar Dahlan Abdullah Dhani, Saniah Dinda, Dinda Fadillah, Rizky Fajriana, F Fajriana, Fajriana Farhiyah, Dara Fasdarsyah Fasdarsyah Fidyatun Nisa Fikhri, Aditya Aziz Fitri, Anisa Amelia Fuddin, Mudhya Hasan Dalimunthe, Amir Imanda, Nanda Kamaruzzaman, Hilda Zulfira Khalid, M David Kiram, Saiful KURNIAWATI - Kurniawati Kurniawati Lidya Rosnita Lubis, Syahrul Andika Melani, Siska Amelia Muhammad Daud Muhammad Fauzan Muhammad Ikhwanus Muhammad Muhammad Mutammimul Ula Nasution, Wahidatunnisa Nurdin Nurdin Nurdin Nurdin Nurul Aula Putri, Nazirah Allisya Rahman, Ashri Nurhajizah Ridha, Ridha Rini Meiyanti Rizal Rizal Rizal S.Si., M.IT, Rizal Rizki Suwanda Rizky Putra Fhonna Rizky Putra Phonna Rizky, Rahmat Rozzi Kesuma Dinata Rusadi, Athirah Said Fadlan Anshari Salimuddin, Salimuddin Sayed Fachrurrazi Sayuti, Muhammad Siagian, Tania Annisa Sinambela, Reza Syahputra Sitepu, Yuni SariBr Siti Aminah Sudarti, Atrida Sujacka Retno Susanti Susanti Taufiq Taufiq Ulfah, Julia Veri Ilhadi Yasin, Fijri Ahmad Yessi Apprilia Yesy Afrillia Yulisda, Desvina Zailani Mohamed Sidek Zara Yunizar