Article Per Year (5 Year)
p-Index From 2020 - 2025
1.633
P-Index
This Author published in this journals
All Journal INFORMAL: Informatics Journal Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control IT JOURNAL RESEARCH AND DEVELOPMENT Jurnal Riset Rekayasa Elektro Prosiding Seminar Nasional Sains Teknologi dan Inovasi Indonesia (Senastindo) Abdimas Indonesian Journal Jurnal Pengabdian Masyarakat
Dwiretno Istiyadi Swasono
Teknologi Informasi, Universitas Jember
Religion 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 Mechanical Engineering Social Sciences Other

Published : 8 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 8 Documents
Search

 1 
Implementasi Fuzzy Logic Control Untuk Pemberi Pakan Ayam Otomatis Pada Ayam Broiler Dengan Menggunakan Teknologi IoT Husyainus Sobri; Yanuar Nurdiansyah; Dwi Retno Istiyadi; Ardian Infantono
Prosiding Seminar Nasional Sains Teknologi dan Inovasi Indonesia (SENASTINDO) Vol 3 (2021): Prosiding Seminar Nasional Sains Teknologi dan Inovasi Indonesia (Senastindo)
Publisher : Akademi Angkatan Udara

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (443.488 KB) | DOI: 10.54706/senastindo.v3.2021.159

Abstract

Implementation of Fuzzy Logic Control Method for Automatic Feeding of Broiler Chickens Using Internet of Things Technology; Husyainus Sobri, 172410102013, 2021, Information Technology Study Program, Faculty of Computer Science, University of Jember. Broiler chickens or also known as broilers are superior breeds resulting from crosses from chicken nations that have high productivity, especially in producing chicken meat. Broiler chickens are the result of cross-breeding and sustainable systems so that the genetic quality can be said to be good. Good genetic quality will appear optimally if the chicken is given supportive environmental factors, for example giving good feed so that chickens avoid death and lack of chicken weight. The use of technology in feeding chickens is an effective way to improve the quality of good chickens and avoid chicken mortality. The technology used can set the feeding time automatically and feed accordingly. To make technology work automatically, a decision-making method is needed. One suitable method is Fuzzy Logic Control. The Fuzzy Logic Control method can overcome the diversity of feeding parameters to make decisions.
Sistem Kontrol Kualitas Air pada Akuaponik Ikan Nila dan Cabai Rawit Berbasis Embedded System menggunakan Fuzzy Logic Satrio Priambodo; Anang Andrianto; Dwiretno Istiyadi Swasono
INFORMAL: Informatics Journal Vol 7 No 3 (2022): Informatics Journal (INFORMAL)
Publisher : Faculty of Computer Science, University of Jember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.19184/isj.v7i3.32958

Abstract

Aquaponics is a cultivation method that combines aquaculture and hydroponics which can produce two products, namely plants and fish at a time and on relatively narrow land. The important thing in implementing aquaponics is to maintain water quality so that it remains at the standard of the two products. Automatic water quality control can make it easier for cultivators to maintain water quality more efficiently. Therefore we need an aquaponic water quality control system that focuses on the parameters of dissolved oxygen (DO), total dissolved solids (TDS), and hydrogen power (PH). DO control uses a pump that functions to increase DO levels. TDS control uses a pump that circulates AB mix nutrients to increase TDS and the addition of water volume to lower TDS. PH control uses a pump that circulates PH up and down PH. In its development, the arithmetic method used to process data is fuzzy. The output of this calculation is the duration of time the pump is running. The system is applied to a concrete pond measuring 4 x 1.5 x 1 meter with aquaponic products in the form of 100 fish and 20 cayenne pepper plants.
Klasifikasi Penyakit pada Citra Buah Jeruk Menggunakan Convolutional Neural Networks (CNN) dengan Arsitektur Alexnet Dwiretno Istiyadi Swasono; Mohammad Abuemas Rizq Wijaya; Muhamad Arief Hidayat
INFORMAL: Informatics Journal Vol 8 No 1 (2023): Informatics Journal (INFORMAL)
Publisher : Faculty of Computer Science, University of Jember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.19184/isj.v8i1.38563

Abstract

Citrus fruit is a plant that is very susceptible to disease. Diseases that often attack citrus fruits are usually in the form of spots on the fruit. Diagnostics of citrus fruit diseases are usually carried out by experts manually which can cause the results to be subjective. Not all farmers are experts in diagnosing citrus fruit diseases. Therefore, this study proposes a system for diagnosing citrus fruit diseases using computer vision based on deep learning. So that the model can be used on computers with limited resources, this study proposes the Alexnet model, which is relatively light but has proven excellent accuracy in classifying several datasets. The dataset used is citrus fruit disease images of 1790 images which are divided into 4 classes, namely blackspot, canker, grenning, and healthy fruit. The best results achieved with a scenario of 90% training data and 10% validation data are with an accuracy of 94,34%, a precision of 93,0%, a recall of 94,0%, and an F1-score of 95,0%. The best results are obtained with a combination of dropout, batch normalization, and fully-connected layer scenarios in the classifier layers section.
Identifikasi Kinerja Arsitektur Transfer Learning Vgg16, Resnet-50, Dan Inception-V3 Dalam Pengklasifikasian Citra Penyakit Daun Tomat Fathur Rozi, Muhammad Iqbal; Adiwijaya, Nelly Oktavia; Swasono, Dwiretno Istiyadi
Jurnal Riset Rekayasa Elektro Vol 5, No 2 (2023): JRRE VOL 5 NO 2 DESEMBER 2023
Publisher : PROGRAM STUDI TEKNIK ELEKTRO

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30595/jrre.v5i2.18050

Abstract

Tomat merupakan salah satu tumbuhan holtikultura sekaligus tanaman musiman yang banyak dikonsumsi di Indonesia. Produksi tanaman tomat sering kali terancam oleh serangan hama dan penyakit, sehingga diperlukannya campur tangan teknologi dalam pengidentifikasian penyakitnya. Teknologi untuk mengidentifikasi penyakit yang terfokus pada daun tomat ini menggunakan pengolahan citra dengan metode CNN. Penelitian ini diharapkan dapat menghasilkan perbandingan arsitektur CNN yang terbaik secara akurasi. Terdapat tiga arsitektur CNN yang dibandingkan dalam penelitian ini yaitu, arsitektur VGG16, ResNet50 dan Inception-V3. Dalam pengimplementasiannya, ketiga arsitektur tersebut diberikan perlakuan yang sama seperti penggunaan input piksel, penambahan layers model, dan lain sebagainya. Penelitian ini menghasilkan tingakt akurasi yang berbeda beda. Arsitektur Inception-V3 mendapatkan nilai akurasi dan validasi akurasi sebesar 0.9551 dan 0.9544. Arsitektur ResNet50 mendapatkan nilai akurasi sebesar 0.9578 dan nilai validasi akurasi sebesar 0.9467. Dan nilai akurasi tertinggi didapat dengan nilai akurasi sebesar 0.9754 dan nilai validasi akurasi tertinggi pada 0.9778 menggunakan arsitektur VGG16.
Penguatan Pengelola Lahan Kelengkeng di Perkebunan Sentool melalui Teknologi Berbasis IoT Maududie, Achmad; Swasono, Dwiretno Istiyadi; Adiwijaya, Nelly Oktavia
JURNAL PENGABDIAN MASYARAKAT (JPM) Vol 4 No 2 (2024)
Publisher : Institut Teknologi dan Sains Mandala

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31967/jpm.v4i2.1231

Abstract

Sentool Plantation is one of the plantations in Jember Regency that is currently promoting longan as one of its superior products. From the results of previous observations, two problems were found, namely workers having difficulty in monitoring the land and inefficient use of water resources. To overcome these problems, an appropriate technology for controlling irrigation and monitoring land conditions based on the Internet of Things (IoT) is prepared that can help Sentool plantation workers in managing the longan plantation area. The implementation of this activity is divided into three stages, namely: development of irrigation control system and monitoring of land conditions, system integration, and implementation and socialization to plantation employees as the use of the system. This activity has succeeded in realizing the intended system in the form of hardware and software to control the irrigation system and monitoring with four required indicators, namely soil moisture, air humidity, air temperature, wind speed, and rainfall measurements. At the socialization stage, the enthusiasm of the Sentool plantation employees can be seen from the liveliness in participating in the socialization stage of the use of the system to assist land management. Currently, the plantation employees also know how to operate the system so that they can reduce the constraints of the irrigation system and can see the condition of the longan fields through the application.
Pelatihan Teknologi Drone untuk Pemetaan Pertanian Berkelanjutan Kelompok Tani Kemiri Santoso Desa Kalibaru Manis Arief, M. Habibullah; Segara, Akbar Pandu; Kartiko, Erik Yohan; Maududie, Achmad; Auliya, Yudha Alif; El Maidah, Nova; Swasono, Dwiretno Istiyadi
Abdimas Indonesian Journal Vol. 4 No. 2 (2024)
Publisher : Civiliza Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59525/aij.v4i2.533

Abstract

This community service program aims to overcome the low efficiency of agricultural land management in Kalibaru Manis Village, Banyuwangi, by focusing on increasing farmers' knowledge in utilizing drone technology for mapping. This training provides theory and practice of drone operation and processing aerial image data using Geographic Information System (GIS) software. The implementation method includes preparation, training, and evaluation stages. Participants were trained to operate drones, retrieve image data, and analyze it to produce land maps. A collaborative approach between lecturers, students, and practitioners was applied to ensure the success of the program. As a result, participants are able to use drones independently and utilize the data for more effective land management. This program increases agricultural productivity and supports environmental sustainability through the application of modern technology.
Detecting Acute Lymphoblastic Leukemia in Blood Smear Images using CNN and SVM Adiwijaya, Nelly Oktavia; Ardiansyah, Sultan; Swasono, Dwiretno Istiyadi
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Vol. 10, No. 1, February 2025
Publisher : Universitas Muhammadiyah Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22219/kinetik.v10i1.2027

Abstract

Acute Lymphoblastic Leukemia (ALL) is a common and aggressive subtype of leukemia that predominantly affects children. Accurate and timely diagnosis of ALL is critical for successful treatment, but it is hindered by the limitations of manual examination of peripheral blood smear images, which are prone to human error and inefficiency. This study proposes an improved diagnostic approach by integrating the EfficientNet architecture with a Support Vector Machine (SVM) classifier to enhance classification accuracy and address the performance inconsistencies of standalone EfficientNet models. Additionally, a novel CNN-based model with a reduced number of parameters is developed and evaluated. A dataset comprising 3.256 peripheral blood smear images across four classes (benign, early, pre and pro) was used for training and testing. The EfficientNet-SVM models achieved a peak accuracy of 97.35% using the EfficientNet-B3 architecture, surpassing previous studies. The improved CNN model achieved the highest accuracy of 99.18% while reducing parameters by 59.5% compared to the best prior models, with a negligible accuracy decrease of only 0.67%. These findings highlight the potential of combining EfficientNet with SVM and the efficiency of the improved CNN model for automated ALL detection, paving the way for more reliable, cost-effective, and scalable diagnostic tools.
Forecasting Used Car Prices Using Machine Learning Khotimah, Eni Khusnul; Swasono, Dwiretno Istiyadi; Fajarianto, Gama Wisnu
IT Journal Research and Development Vol. 9 No. 2 (2025)
Publisher : UIR PRESS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25299/itjrd.2025.18031

Abstract

In an increasingly competitive era, it is crucial for car dealers and retailers to address the challenges of accurately determining the prices of used cars. To tackle these challenges, this study implements Machine Learning models to predict used car prices accurately. By applying the Artificial Neural Network (ANN) and Random Forest Regression algorithms, this research aims to evaluate the performance of these methods in predicting used car prices. The used car price data was obtained from the Kaggle repository, consisting of 14,657 data entries that provide comprehensive information about used cars. The analysis focuses on six main columns, including Brand, Model, Variant, Year, and Mileage, to estimate used car prices. Model evaluation was conducted using Mean Absolute Error (MAE) as the primary metric. The results show that the ANN model achieved a lower MAE (0.035) compared to the Random Forest Regression (0.047), indicating better performance in predicting used car prices. These findings demonstrate the effectiveness of ANN in handling data complexity and the non-linear relationships between variables involved in forecasting used car prices. Additionally, this contributes to the implementation of more accurate used car price predictions, enabling automotive companies to improve operational efficiency and provide greater benefits to the community.
Co-Authors Achmad Maududie Akbar Pandu Segara Anang Andrianto Ardiansyah, Sultan El Maidah, Nova Fathur Rozi, Muhammad Iqbal Gama Wisnu Fajarianto Husyainus Sobri Infantono, Ardian Kartiko, Erik Yohan Khotimah, Eni Khusnul M. Habibullah Arief Mohammad Abuemas Rizq Wijaya Muhamad Arief Hidayat Oktavia, Nelly Satrio Priambodo Yanuar Nurdiansyah, Yanuar Yudha Alif Auliya, Yudha Alif