Article Per Year (5 Year)
p-Index From 2021 - 2026
3.372
P-Index
This Author published in this journals
All Journal PROSIDING SENATEK FAKULTAS TEKNIK UMP INTENSIF: Jurnal Ilmiah Penelitian dan Penerapan Teknologi Sistem Informasi Jurnal SOLMA JMM (Jurnal Masyarakat Mandiri) JURNAL TEKNIK INFORMATIKA DAN SISTEM INFORMASI METHOMIKA: Jurnal Manajemen Informatika & Komputerisasi Akuntansi Jiko (Jurnal Informatika dan komputer) Multitek Indonesia : Jurnal Ilmiah KOMPUTEK bit-Tech JATI (Jurnal Mahasiswa Teknik Informatika) Journal of Computer Networks, Architecture and High Performance Computing GANESHA: Jurnal Pengabdian Masyarakat Jurnal Pustaka Data : Pusat Akses Kajian Database, Analisa Teknologi, dan Arsitektur Komputer Prosiding SEMNAS INOTEK (Seminar Nasional Inovasi Teknologi) Jurnal Sains Komputer dan Sistem Informasi Jurnal Pengabdian Kepada Masyarakat Bersinergi Inovatif
Yovi Litanianda, Yovi
Program Studi Teknik Elektro, Fakultas Teknik, Universitas Muhammadiyah Ponorogo Jl. Budi Utomo No 10 Ponorogo, 63471
Agriculture, Biological Sciences & Forestry Humanities 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 Industrial & Manufacturing Engineering Mathematics Mechanical Engineering Social Sciences Transportation Other

Published : 26 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 26 Documents
Search

 1   2   3 
Implementation of Fuzzy Logic for Chili Irrigation Integrated with Internet of Things Prasetyo, Angga; Yusuf, Arief Rahman; Litanianda, Yovi; Sugianti; Masykur, Fauzan
Journal of Computer Networks, Architecture and High Performance Computing Vol. 5 No. 2 (2023): Article Research Volume 5 Issue 2, July 2023
Publisher : Information Technology and Science (ITScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/cnahpc.v5i2.2518

Abstract

Chili, mustard greens, and tomatoes have always been farmers' favored crops, despite their high water and labor demands. Adapt to these conditions by utilizing smart agriculture systems (SAS) agricultural techniques that involve technology such as automatic irrigation that regulates watering based solely on routine, regardless of land conditions. This type of control during the transitional season can lead to root rot and fungisarium disease on chile plants. In the form of an embedded system with internet of things (IoT) monitoring, a system incorporating artificial intelligence such as fuzzy logic is proposed as a solution. Fuzzy logic will regulate irrigation based on the land's humidity and temperature using computational mathematics. Beginning with the fuzzyification stage to map the sensor's temperature and humidity input values, fuzzy logic is applied. The creation of an inference engine in the NodeMcu 8266 microcontroller to interpret fuzzy rule statements in the form of aggregation of minimum conditions with the AND operator, followed by the combination of a single set value of 0 and 1 in the fuzzy system to produce an appropriate actuator response After the entire system has been prototyped, testing is conducted to determine the responsiveness of the fuzzy program code to changes in the simulated agricultural cultivation land ecosystem. This study found that the fuzzy logic program code embedded in the nodeMCU8266 microcontroller effectively controls the spraying duration of the pump in response to various simulated environmental conditions within 3.6 seconds.
Integration Of Open CV LBF Model To Detect Masks In Health Protocol Surveillance Systems Litanianda, Yovi; Setyawan, Moh Bhanu; Fajaryanto C, Adi; Abdurrozzaq Z, Ismail; Aditya, Charisma Wahyu
Journal of Computer Networks, Architecture and High Performance Computing Vol. 6 No. 1 (2024): Article Research Volume 6 Issue 1, January 2024
Publisher : Information Technology and Science (ITScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/cnahpc.v6i1.3460

Abstract

The Corona Viruses Diseases pandemic that was rife in early 2020 and hit many countries caused discipline to be applied to health protocols. The prevention of physical contact between humans gave rise to new traditions in aspects of human life. Almost all public facilities in Indonesia require visitors to wear masks as a means of preventing exposure to viruses in the air. However, this advice is often ignored by some people. In addition to endangering many people, this condition also makes public facility managers need extra resources in the form of time, energy and costs to ensure this health protocol is implemented. The existence of these problems triggers the emergence of innovations to present a system that provides assurance and convenience in ensuring compliance with health protocols for the use of masks through creative and effective methods. This method is done by utilizing CCTV cameras or webcams at the entrance equipped with an Artificial Intelligence program designed to be able to detect the use of masks on visitors to public facilities, and without the need for other sensors. The detection system is built on the concept of facial biometrics and utilizes the OpenCV LBF model to detector landmarks on a person's face. Based on tests conducted through several scenario, it can be said that the open CV LBF model successfully identified the use of masks within 35 seconds, increasing the reading distance to 2 meters making the process longer. In addition, in indoor lighting conditions, the system experienced 1 detection error with a process time of 18 seconds, while for well-light outdoor conditions the system managed to detect all objects within 10 seconds.
CLASSIFICATION OF DURIAN LEAF IMAGES USING CNN (CONVOLUTIONAL NEURAL NETWORK) ALGORITHM Fitriani, Lely Mustikasari Mahardhika; Litanianda, Yovi
JIKO (Jurnal Informatika dan Komputer) Vol 7, No 2 (2024)
Publisher : Universitas Khairun

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33387/jiko.v7i2.8576

Abstract

This research investigates the classification of durian leaf images using Convolutional Neural Network (CNN) algorithms, specifically focusing on the architectures AlexNet, InceptionNetV3, and MobileNet. The study begins with the collection of a dataset comprising 1604 images for training, 201 images for validation, and 201 images for testing. The dataset includes five classes of durian leaves: Bawor, Duri Hitam, Malica, Montong, and Musang King, chosen for their varied characteristics such as taste, texture, and aroma. Data preprocessing involved several steps to ensure the images were suitable for model training. These steps included data augmentation to increase variability, pixel normalization to standardize the images, and resizing to 150x150 pixels to match the input requirements of the CNN models. After preprocessing, the CNN models were implemented and trained using deep learning frameworks such as TensorFlow and PyTorch. Model performance was evaluated using a Confusion Matrix, which provided detailed insights into classification accuracy, precision, sensitivity, specificity, and F-score. The results indicated that InceptionNetV3 and AlexNet achieved near-perfect classification accuracy, with no misclassifications, demonstrating their robustness and precision in identifying durian leaf images. The training accuracy for both models rapidly approached 100% within the first few epochs and stabilized, while the loss values decreased sharply, indicating effective learning without overfitting. In contrast, MobileNet, while showing high accuracy and low loss during training, exhibited several misclassifications across all classes. The training accuracy of MobileNet also approached 100%, but the presence of misclassifications suggested that further tuning and improvements were necessary. Specifically, MobileNet's Confusion Matrix revealed errors in correctly identifying samples from each class, indicating potential areas for enhancement in the model's architecture or preprocessing techniques. In conclusion, InceptionNetV3 and AlexNet proved to be highly efficient and accurate architectures for classifying durian leaf images, making them suitable for practical applications. MobileNet, although performing well, requires further refinement to achieve the same level of accuracy and reliability. This study highlights the importance of selecting appropriate CNN architectures and the need for thorough preprocessing to optimize model performance in image classification tasks.
Pemberdayaan Petani Kopi Desa Ngrayun Kabupaten Ponorogo melalui Pelatihan Budidaya, Pascapanen, dan Pemasaran dalam Upaya Penguatan Sentra Kopi Unggulan Yuli Astuti, Arin; Kurniawan, Edy; Winangun, Kuntang; Winardi, Yoyok; Sugianti, Sugianti; Ardika, Rizki Dwi; Litanianda, Yovi
Jurnal SOLMA Vol. 14 No. 3 (2025)
Publisher : Universitas Muhammadiyah Prof. DR. Hamka (UHAMKA Press)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22236/solma.v14i3.20999

Abstract

Background: Desa Ngrayun, yang terletak di Kecamatan Ngrayun, Kabupaten Ponorogo, memiliki banyak potensi dalam pengembangan komoditas kopi. Namun, potensi tersebut belum dimanfaatkan secara optimal karena keterbatasan pengetahuan teknis budidaya, rendahnya efisiensi pengolahan pascapanen, lemahnya strategi pemasaran, serta keterbatasan bibit unggul. Kegiatan Pengabdian kepada Masyarakat (PkM) ini bertujuan untuk memperkuat kemampuan para petani kopi melalui kegiatan pelatihan budidaya berkelanjutan serta penerapan teknologi yang sesuai dan efisien dan penguatan strategi pemasaran digital. Metode: Metode yang digunakan meliputi pendidikan masyarakat, pendekatan fungsional, dan pendampingan berkelanjutan. Kegiatan dilaksanakan dalam bentuk pelatihan teknis, demonstrasi pengolahan kopi dengan metode full washed, honey, dan natural process, serta pengenalan mesin pengupas dan penggiling kopi sebagai inovasi teknologi efisiensi produksi. Hasil: Hasil pelaksanaan menunjukkan peningkatan signifikan terhadap pengetahuan dan keterampilan petani. Sekitar 80% peserta berhasil menerapkan teknik budidaya yang baik, sementara 70% lainnya telah memahami metode pengolahan kopi modern, 60% mulai menerapkan strategi pemasaran digital melalui marketplace, dan 100% bibit unggul telah ditanam di lahan percontohan. Rata-rata capaian keberhasilan program mencapai 78%, dengan dampak nyata berupa peningkatan kualitas produk kopi dan kesadaran terhadap keberlanjutan usaha tani. Implikasi program ini mencakup perlunya pembentukan koperasi petani kopi, pelatihan lanjutan cupping test, serta pengembangan jejaring pemasaran kopi spesialti. Kesimpulan: Dengan kolaborasi antara perguruan tinggi, pemerintah daerah, dan kelompok tani, Desa Ngrayun memiliki potensi untuk berkembang menjadi salah satu sentra kopi unggulan di Kabupaten Ponorogo sekaligus menjadi contoh pemberdayaan masyarakat yang berbasis pada potensi lokal secara berkelanjutan.
Fuzzy Method Design for IoT-Based Mushroom Greenhouse Controlling Prasetyo, Angga; Setyawan, Moh. Bhanu; Litanianda, Yovi; Sugianti, Sugianti; Masykur, Fauzan
INTENSIF: Jurnal Ilmiah Penelitian dan Penerapan Teknologi Sistem Informasi Vol 6 No 1 (2022): February 2022
Publisher : Universitas Nusantara PGRI Kediri

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (490.632 KB) | DOI: 10.29407/intensif.v6i1.16786

Abstract

The ideal conditions for the oyster mushrooms growth are at a humidity of 65-75% and 29-31C during incubation, while the growth of stems should be at a humidity of 70-90% 29-32C. This ideal ecosystem is maintained by aeration and manual watering. Still, the results are not optimal in preventing damage to the mycelium during the incubation period, resulting in a decrease in crop yields. Automatic control has not created ideal conditions because air temperature and humidity regulation are only based on fans and sprayers that do not directly affect air conditions. Therefore, we need a method to manipulate the mushroom greenhouse space ecosystem, namely fuzzy logic, the application of fuzzy logic integrated with sensors, actuators, and microcontrollers with the Internet of Things to solve this problem. The results of the installation of fuzzy logic in the mushroom's greenhouse in this system can be seen from the fan's modulation response and the pump's duration. The test results of this control feature can manipulate temperature and humidity. Therefore, the oyster mushroom greenhouse produces an ideal state of 29.8C, the humidity of 68.97% RH, and the production has been proven to be optimal with an average daily harvest of 3.8kg.
Pengendalian Suhu dan Kelembapan Kumbung Jamur Dengan Metode Fuzzy Terintegrasi Internet of Things Prasetyo, Angga; Litanianda, Yovi; Setyawan, Moh. Bhanu; Masykur, Fauzan; Sugianti, Sugianti; Sumaji, Sumaji
Prosiding SEMNAS INOTEK (Seminar Nasional Inovasi Teknologi) Vol. 5 No. 1 (2021): Prosiding Seminar Nasional Inovasi Teknologi Tahun 2021
Publisher : Universitas Nusantara PGRI Kediri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29407/inotek.v5i1.841

Abstract

Jamur tiram atau dalam bahasa latin volvariella volvacea budidaya jamur tiram ini, membutuhkan akurasi dan toleransi kepresisian dalam mengendalikan suhu serta kelembapan yang menyerupai ekosistem habitat jamur tiram sebenarnya, fase inkubasi yang membutuhkan suhu udara 23-28C dengan kelembapan 60- 70%, Fase pembentukan Tubuh dan buah membutuhkan suhu udara 28-32C dengan kelembapan 70-90%. Pengelolaan suhu udara dan kelembapan oleh pembudidaya jamur tiram dilakukan dengan cara penyemprotan serta aerasi kumbung yang masih manual, sehingga pada tahapan fase inkubasi dan fase pembentukan tubuh jamur, belum optimal. Akibatnya hasil panen jamur menurun karena banyak miselium yang rusak saat fase inkubasi. perancangan system akan dilakukan dalam dua tahapan, fase pertama pembuatan wiring perangkat keras, kemudian fase kedua pengintegrasian logika fuzzy di perangkat lunak yang secara keseluruhan akan berupa internet of things (IoT) guna memudahkan dalam proses monitoring. Kinerja logika fuzzy pada sistem ini dilihat dari respon PWM kipas, durasi pompa dan kualitas jaringan pada koneksi internetnya. Hasil pengujian menunjukkan nilai PWM kipas berhasil merespon berbagai kondisi suhu. Durasi penyalan pompa juga bisa merespon perubahan kelembaban ruangan jamur. Sedangkan kualitas jaringan dari hasil percobaan diperoleh nilai konektifitas berupa nilai jitter buffering data 0,72 ms, nilai ping jaringan saat kondisi transmitter(Tx) dan received (Rx) 0,29 ms, dan delay sebesar 0,97 ms atau secara keseluruhan rata-ratanya kurang dari 1ms merupakan kondisi yang termasuk baik untuk penyelenggaraan sistem IoT.
Co-Authors Abdurrozaq Zulkarnain, Ismail Abdurrozzaq Z, Ismail Abdurrozzaq, Ismail Aditya, Charisma Wahyu Alfiansyah, Nanda Safiq Angga Prasetyo April Riyanto, David Apristawijaya, Oceu Arifqi, Miftahul Asmaul Khusna, Zakia Access Asrofi Buntoro, Ghulam Astuti, Arin Yuli Bimantoro, Alfin Dien Edy Kurniawan, Edy Fadila, Daras Fajaryanto C, Adi Fajaryanto Cobantoro, Adi Fajaryanto, Adi Fauda Pradana, Yofhi Fauzan Masykur Fikrotul Ulya As Siddieq, Muhammad Firdaus, Usamah Putra Fitriani, Lely Mustikasari Mahardhika Ishaq Kaffah Imanuddin M R Santosa Karaman, Jamilah KUNTANG WINANGUN Masykur , Fauzan Mohammad Bhanu Setyawan Muhammad Ridwan Hidayatulloh, Muhammad Ridwan Nashikhuddin, Ahmad Yahya Rahman Yusuf, Arief Renaldy, Aldi Rizki Dwi Ardika Rizki, Rizki Dwi Ardika Safiq Alfiansyah, Nanda Setyawan, Moh Bhanu Setyawan, Moh. Bhanu Sopian sopian Sugianti Sugianti, Sugianti Sumaji Tama, Okti Liksyan Nita Wijayanti, Zahrotul Ilmi Winardi, Yoyok Yoga, Robis Fahma Yuli Astuti , Arin Yuli Astuti, Arin Yusuf, Arief Rahman Zulkarnain, Ismail Abdurrazzaq Zulkarnain, Ismail Abdurrozzaq