Garuda - Garba Rujukan Digital

Penerapan Digitalisasi Data Umkm Berbasis Website Untuk Monitoring UMKM Di Desa Saroka Eka Mala Sari Rochman; Rachmad, Aeri; Setiawan, Wahyudi
Jurnal Pengabdian dan Pemberdayaan Banyuwangi (Jurnal Abdiwangi) Vol 1 No 1 (2023): Jurnal Abdiwangi
Publisher : Pusat Penelitian dan Pengabdian Kepada Masyarakat, Politeknik Negeri Banyuwangi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57203/abdiwangi.v1i1.2023.54-64

Potensi ekonomi lokal pedesaan dapat menjadi salah satu faktor pendukung pembangunan desa yang dapat dimanfaatkan oleh masyarakat untuk menciptakan nilai tambah. Salah satu cara yang dapat membangun ekonomi masayarakat pedesaan adalah dengan mendirikan usaha mikro, kecil dan menengah atau disebut dengan UMKM. sektor UMKM berperan penting untuk memajukan perekonomian masyarakat maupun negara. Desa memiliki peran untuk mendukung pembangunan pada sektor tersebut. salah satu fungsi desa adalah memberikan sarana prasarana terhadap masyarakat desa salahsatunya dukungan terhadap UMKM yang ada didesa. Saat ini desa masih mengalami kesulitan untuk melakukan pendataan UMKM karena tumbuhnya usaha mikro masyarakat tersebut seringkali tidak melibatkan desa. Dengan demikian desa masih belum memiliki data induk UMKM hingga data perkembangan UMKM tersebut secara realtime. Hal ini menyebabkan desa tidak dapat mengambil keputusan secara tepat untuk memberikan dukungan pada UMKM

Salt Sales Prediction Using the Moving Average Method (A Case Study of Madura-Indonesia Salt) Syakur, Muhammad Ali; Negara, Yudha Dwi Putra; Rachmad, Aeri; Rochman, Eka Mala Sari
Elinvo (Electronics, Informatics, and Vocational Education) Vol. 8 No. 2 (2023): November 2023
Publisher : Department of Electronic and Informatic Engineering Education, Faculty of Engineering, UNY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21831/elinvo.v8i2.56279

Forecasting is a term used to forecast or predict the business that we run to see the direction in the future which uses historical data as the main reference. An appropriate strategy is needed to manage the production of salt raw materials properly, namely through sales forecasting. PT Budiono Madura Bangun Persada is a company engaged in salt processing with the brands "Anak pintar (AP)" and "Kapal Container (KC)" where the amount of production experiences uncertainty, namely an increase or decrease, this results in an uncertain amount of raw materials. This study aims to predict the exact amount of salt production in a certain period. The amount of data used is the period November 2020 - April 2021 as much as. The final result of this forecasting model is the best using predictions on day 6 for both AP salt and KC salt, with an MSE value of 290.71 for KC salt and an MSE value of 843.08 for AP salt

Application based of Tourist Attraction Selection with Fuzzy Tahani Sari Rochman, Eka Mala; Pratama, Ifan; -, Husni; Rachmad, Aeri
Jurnal Pekommas Vol 5 No 2 (2020): October 2020
Publisher : Sekolah Tinggi Multi Media “MMTC” Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56873/jpkm.v5i2.2417

Madura is one of the islands located in the Province of East Java, Indonesia, which has a lot of tourism potency but there has been no system to accommodate this. As a result, the tourists have difficulty of getting the official information about tourism objectives within Madura. Therefore, in this study was conducted with the aim of creating a decision support system for tourism object selection in Madura so that the prospective tourists find it easier to choose tourism attractions that that suits their desires. In connection with these objectives, this study applies the Fuzzy Tahani logic method as the decision support model. It is used because its main equipment is a functional hierarchy that its main input is predetermined criteria. The results of this study are expected to facilitate prospective tourists in making decisions in choosing attractions in Madura. By using the visitor criterias, the distance from the city center, and the reviews of the tourists on the attractions, the decision support system is able to provide recommendations for tourist attractions to be selected. The result of the functionality test showed determined that the system can 100% run.

PENERAPAN METODE MOORA DALAM SISTEM PENDUKUNG KEPUTUSAN UNTUK MENENTUKAN DOSEN PEMBIMBING TUGAS AKHIR Utomo, Yudo Bismo; Kurniadi, Harso; Anam, Syaiful; Rachmad, Aeri
Electro Luceat Vol 10 No 2 (2024): Elektro Luceat- November 2024
Publisher : LPPM Poltek ST Paul

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32531/jelekn.v10i2.854

The Computer Engineering program at UNISKA Kediri is a study program focused on information technology. Despite being an IT-based program, the process of determining Final Project a lecture is carried out through a coordination meeting mechanism with the lecturers. In this meeting, a lecture matched based on their expertise and the topic of the Final Project proposed by each student. This discussion process requires a significant amount of time, making student services less efficient in terms of time management. To address this issue, a decision support system is needed to help the program coordinator expedite the lecture assignment process without compromising the accuracy of matching lecturer expertise to the students Final Project topics. This research uses the MOORA method (Multi-Objective Optimization on the Basis of Ratio Analysis). This method effectively conducts multi-criteria selection by considering various factors. The results of this study, based on data from 10 trials using the MOORA method, showed an accuracy of 90%, meaning that students were assigned a lecture in line with their respective areas of interest.

KLASIFIKASI PNEUMONIA DENGAN METODE CONVOLUTIONAL NEURAL NETWORK Nabil Dzul Afkar, Ahmad; Rachmad, Aeri; Mala Sari Rochman, Eka
JATI (Jurnal Mahasiswa Teknik Informatika) Vol. 9 No. 4 (2025): JATI Vol. 9 No. 4
Publisher : Institut Teknologi Nasional Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36040/jati.v9i4.13938

Pneumonia adalah infeksi akut pada paru-paru yang disebabkan oleh mikroorganisme seperti virus, bakteri, jamur, dan parasit. Penyakit ini dapat menyerang berbagai usia, terutama balita dan orang tua, namun, balita dan orang tua yang paling sering terdampak. Diagnosis pneumonia masih bergantung pada tenaga medis yang berpengalaman, sehingga diperlukan metode otomatis yang dapat meningkatkan akurasi dan efisiensi dalam deteksi pneumonia. Dengan memanfaatkan pendekatan Deep Learning, khususnya Convulution Neural Network (CNN) yang menjadi pilihan popular dalam klasifikasi gambar dan analisi visual, Penelitian ini bertujuan mengembangkan model CNN berbasis ResNet50 untuk mengklasifikasikan gambar rontgen dalam mendeteksi pneumonia. Model ini dapat memberikan solusi otomatis yang lebih efisian dalam membantu tenaga medis, serta meningkatkan akurasi diagnosis penyakit pneumonia menggunakan ResNet50. Dalam penelitian ini klasifikasi pneumonia menggunakan dataset Chest X-Ray Images yang di ambil dari kaggle dengan format JPG. Dataset berisi citra x-ray dada normal dan pneumonia. Data berjumlah 5.856 gambar yang terbagi kedalam 2 kelas yakni, 1.583 normal dan 4.273 pneumonia. Hasil eksperimen menunjukkan bahwa model ResNet50 dengan optimizer yang digunakan adalah Stochastic Gradient Descent Momentum (SGD-M) dengan learning rate 0.1 menghasilkan penelitihan data train di dapat akurasi sebesar 95.43%, sedangkan tahap pelatihan data test mendapatkan akurasi sebesar 92.25% tingkat akurasi sudah cukup layak.

Integration of Concatenated Deep Learning Models with ResNet Backbone for Automated Corn Leaf Disease Identification imam sudianto, Achmad; Sigit Susanto Putro; Eka Mala Sari; Ika Oktavia Suzanti; Aeri Rachmad; Wildan Surya Wijaya
BEST Vol 7 No 2 (2025): BEST
Publisher : Universitas PGRI Adi Buana Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36456/3kct9e57

Corn is one of Indonesia's food commodities, which is an alternative food that supports food diversification in Indonesia. However, leaf infections in corn plants often cause significant yield losses and threaten food security. Early detection of this disease is very important, especially for small farmers, because conventional diagnostic methods that rely on agronomists are expensive and time-consuming. Recent advances in Agricultural Artificial Intelligence (AI) and image processing have facilitated automatic plant disease recognition through Convolutional Neural Networks (CNN), with ResNet as the main backbone combined through concatenation with MobileNetV3, DenseNet161, and GoogleNet. The dataset consists of 4,000 images divided into 2,560 training data, 640 validation data, and 800 test data, with image sizes adjusted to 224×224 pixels. The dataset consists of 4,000 images distributed across four categories: gray leaf spot, common rust, northern leaf blight, and healthy leaf. The testing was conducted using three different optimizers, namely Adam, RMSprop, and SGD, with a learning rate of 0.01. The experimental results showed that the SGD optimizer provided the best performance with a loss value of 0.2275, accuracy of 0.9513, precision of 0.9536, recall of 0.9513, and F1-score of 0.9512. These findings confirm that the combination of ResNet, MobileNetV3, DenseNet161, and GoogleNet architectures with the SGD optimizer can significantly improve the accuracy of corn leaf disease detection, making it a potential application for automatic detection systems in support of smart farming practices.

PENERAPAN METODE MOORA DALAM SISTEM PENDUKUNG KEPUTUSAN UNTUK MENENTUKAN DOSEN PEMBIMBING TUGAS AKHIR Utomo, Yudo Bismo; Kurniadi, Harso; Anam, Syaiful; Rachmad, Aeri
Electro Luceat Vol 10 No 2 (2024): Elektro Luceat- November 2024
Publisher : LPPM Poltek ST Paul

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32531/jelekn.v10i2.854

The Computer Engineering program at UNISKA Kediri is a study program focused on information technology. Despite being an IT-based program, the process of determining Final Project a lecture is carried out through a coordination meeting mechanism with the lecturers. In this meeting, a lecture matched based on their expertise and the topic of the Final Project proposed by each student. This discussion process requires a significant amount of time, making student services less efficient in terms of time management. To address this issue, a decision support system is needed to help the program coordinator expedite the lecture assignment process without compromising the accuracy of matching lecturer expertise to the students Final Project topics. This research uses the MOORA method (Multi-Objective Optimization on the Basis of Ratio Analysis). This method effectively conducts multi-criteria selection by considering various factors. The results of this study, based on data from 10 trials using the MOORA method, showed an accuracy of 90%, meaning that students were assigned a lecture in line with their respective areas of interest.

Comparison of Support Vector Machine (SVM), K-Nearest Neighbor (K-NN), and Stochastic Gradient Descent (SGD) for Classifying Corn Leaf Disease based on Histogram of Oriented Gradients (HOG) Feature Extraction Solihin, Firdaus; Syarief, Muhammad; Rochman, Eka Mala Sari; Rachmad, Aeri
Elinvo (Electronics, Informatics, and Vocational Education) Vol. 8 No. 1 (2023): Mei 2023
Publisher : Department of Electronic and Informatic Engineering Education, Faculty of Engineering, UNY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21831/elinvo.v8i1.55759

Image classification involves categorizing an image's pixels into specific classes based on their unique characteristics. It has diverse applications in everyday life. One such application is the classification of diseases on corn leaves. Corn is a widely consumed staple food in Indonesia, and healthy corn plants are crucial for meeting market demands. Currently, disease identification in corn plants relies on manual checks, which are time-consuming and less effective. This research aims to automate disease identification on corn leaves using the Support Vector Machine (SVM), K-Nearest Neighbor (K-NN) with K=2, and Stochastic Gradient Descent (SGD) algorithms. The classification process utilizes the Histogram of Oriented Gradients (HOG) feature extraction method with a dataset of corn leaf images. The classification results achieved an accuracy of 71.44%, AUC of 79.16%, precision of 70.08%, recall of 71.44%, and f1 score of 67.11%. The highest accuracy was obtained by combining HOG feature extraction with the SGD algorithm.

PENGARUH REDUKSI DIMENSI PADA CLUSTERING CITRA DAUN TOMAT MENGGUNAKAN DARKNET19 DAN K-MEANS Rachmad, Aeri; Holilur Rohman, Muhammad; Setiawan, Wahyudi
NERO (Networking Engineering Research Operation) Vol 8, No 1 (2023): Nero - 2023
Publisher : Jurusan Teknik Informatika Fakultas Teknik Universitas Trunojoyo Madura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21107/nero.v8i1.21473

Image Clustering adalah pengelompokan citra dari kelas tanpa label sebelumnya. Pada penelitian ini menggunakan citra clustering dengan data daun tomato leaf panthogen sebanyak 900 citra yang terdiri dari 3 cluster yaitu Bacterial Spot, Yellow Leaf Curl Virus dan Healthy. Pada setiap cluster memiliki 300 citra. Langkah awal yang dilakukan adalah ekstraksi fitur menggunakan DarkNet19. DarkNet19 menerapkan beberapa parameter seperti epoch sebanyak 160 dengan menggunakan Stochastic Gradient Descent, Learning Rate dimulai dari 0.01, weight decay 0.0005, dan augmentasi data termasuk random crops, rotations, hue, saturation, and exposure shifts. Selain ekstraksi fitur, pada penelitian ini juga melakukan pengurangan dimensi menggunakan Principal Component Analysis (PCA). Selanjutnya, proses clustering menggunakan K-Means Clustering. Penelitian ini bertujuan untuk mengetahui tingkat akurasi dalam proses Clustering penyakit daun tomat menggunakan metode CNN DarkNet19, PCA dan K-Means. Hasil klastering yang terbaik menggunakan K-Means Clustering dengan PCA 20 yang menghasilkan accuracy 97.7%, precission 97.73%, dan recall 97.67% dengan waktu komputasi 1 menit 16 detik.Kata Kunci: Image Clustering, Penyakit Daun Tomat, DarkNet19, Principal Component Analysis, K-Means Clustering.

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