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All Journal International Journal of Artificial Intelligence Research JOURNAL OF APPLIED INFORMATICS AND COMPUTING Building of Informatics, Technology and Science Jusikom: Jurnal Sistem Informasi Ilmu Komputer Techno Xplore : Jurnal Ilmu Komputer dan Teknologi Informasi Jurnal Tekinkom (Teknik Informasi dan Komputer) Abdi Kami : Jurnal Pengabdian Kepada Masyarakat Journal of Computer Networks, Architecture and High Performance Computing Jurnal Teknik Informatika (JUTIF) Jurnal Pendidikan dan Teknologi Indonesia Bulletin of Computer Science Research Jurnal Algoritma
Wahiddin, Deden
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Religion Agriculture, Biological Sciences & Forestry Humanities Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Languange, Linguistic, Communication & Media Library & Information Science Other

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Klasifikasi Kadar Hidrasi Tubuh Berdasarkan Warna Urine dengan Metode Ekstraksi Fitur Citra dan Euclidean Distance Wahiddin, Deden
Techno Xplore : Jurnal Ilmu Komputer dan Teknologi Informasi Vol 5 No 1 (2020): Techno Xplore: Jurnal Ilmu Komputer dan Teknologi Informasi
Publisher : Teknik Informatika, Fakultas Teknik dan Ilmu Komputer, Universitas Buana Perjuangan Karawang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36805/technoxplore.v5i1.887

Abstract

Dehidrasi merupakan salah satu masalah kesehatan yang terjadi akibat ketidakseimbangan jumlah cairan atau air dalam tubuh. Dehidrasi sering luput dari perhatian karena tidak menimbulkan efek signifikan secara langsung bagi tubuh, oleh karena itu diperlukan adanya sistem deteksi dini terhadap tingkat dehidrasi tubuh untuk mencegah gangguan kesehatan yang lebih parah. Salah satu alat sederahana yang dapat digunakan untuk mengukur tingkat hidrasi tubuh adalah warna urine. Pada bidang kesehatan warna urine diklasifikasikan menjadi tabel derajat warna urine untuk mengukur kadar cairan dalam tubuh yang akan menentukan kadar hidrasi. Metode pengukuran dehidrasi dilakukan menggunakan nomor skala yang menunjukkan rentang warna urine mulai dari jernih dengan skala 1 hingga yang pekat (coklat kehijauan) dengan skala 8. Pada penelitian ini dilakukan pengambilan citra warna urine yang diproses pada pengolahan citra digital dengan menggunakan metode ekstraksi fitur warna dan euclidean distance untuk kemudian dilakukan pengecekan tingkat kemiripan warna dengan tabel derajat warna urine untuk proses klasifikasi kadar hidrasi tubuh. Berdasarkan hasil pengujian pada 20 sampel citra uji, didapatka tingkat akurasi sebesar 75%.
SOSIALISASI APLIKASI UNTUK MELAKUKAN DETEKSI DINI KECANDUAN PERMAINAN ONLINE PADA SISWA SMK N 1 KLARI KARAWANG Masruriyah, Anis Fitri Nur; Wahiddin, Deden; Novita, Hilda Yulia; Awal, Elsa Elvira
ABDI KAMI: Jurnal Pengabdian Kepada Masyarakat Vol 5 No 2 (2022): (Oktober 2022)
Publisher : LPPM Institut Agama Islam (IAI) Ibrahimy Genteng Banyuwangi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.69552/abdi_kami.v5i2.1470

Abstract

The global COVID-19 pandemic has an impact on people's activities in the world, including in Indonesia. The policies of each country to overcome this condition also vary, one of which is the Indonesian government which imposes limited face-to-face activities offline. Many activities must be carried out online to minimize the transmission of COVID-19. Finally, this has an impact on many people who spend time with their gadgets to play permainans with cellphones, laptops or other electronic media. Playing permainans has benefits for relaxation from the fatigue of online activities, but if this continues it will result in permainan addiction. So that community service activities for the socialization of permainan addiction detection applications are carried out, so that users are able to control the use of devices when playing permainans. So, if an addiction is detected, you can ask experts for help, for school children you can have an initial consultation with a Counseling Guidance teacher.
Comparison of K-Nearest Neighbors and Convolutional Neural Network Algorithms in Potato Leaf Disease Classification Nurmayanti, Trisya; Hartini, Dina; Rohana, Tatang; Lestari, Santi Arum Puspita; Wahiddin, Deden
Jurnal Sistem Informasi dan Ilmu Komputer Vol. 8 No. 1 (2024): JUSIKOM: JURNAL SISTEM INFROMASI ILMU KOMPUTER
Publisher : Fakultas Teknologi dan Ilmu Komputer Universitas Prima Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34012/jurnalsisteminformasidanilmukomputer.v8i1.5337

Abstract

tatang.rohana@ubpkarawang.ac.id3, santi.arum@ubpkarawang.ac.id4, deden.wahiddin@ubpkarawang.ac.id5ABSTRACTPotato production in Central Java was recorded to have decreased by 10.77% by the Central Statistics Agency (BPS), from 278,717 tons in 2022 to 248,700 tons in 2023. This decline is due to the fact that potatoes are susceptible to diseases such as late blight and dry spot (early blight) which can significantly reduce yields. This study aims to evaluate the performance of Convolutional Neural Network (CNN) with VGG16 architecture and K-Nearest Neighbors (KNN) to find the best method for potato late blight classification. The dataset used consists of 1500 potato leaf images divided into training, validation, and testing. This research uses pre- processing including resizing, rescaling, and data augmentation. The results show that CNN with the VGG16 model is superior in classifying potato leaf diseases compared to KNN with the MobileNetV2 model. CNN produced an accuracy of 96% while KNN with the MobileNetV2 model obtained an accuracy of 93%. These results can be used as a powerful tool in supporting potato leaf disease identification. This model makes a significant contribution to the development of disease identification techniques through digital image processing.Keywords: Potato Leaf Disease, Convolutional Neural Network, VGG16, K-Nearest
PERSONAL PROTCTIVE EQUIPMENT DETECTION FOR OCCUPATIONAL SAFETY AND HEALTH USING YOLOV8 IN MANUFACTURING COMPANIES: DETEKSI ALAT PELINDUNG DIRI (APD) UNTUK KESELAMATAN DAN KESEHATAN KERJA MENGGUNAKAN YOLOV8 Gapur, Abdul; Wahiddin, Deden; Mudzakir, Tohirin Al; Indra, Jamaludin
Jurnal Teknik Informatika (Jutif) Vol. 5 No. 4 (2024): JUTIF Volume 5, Number 4, August 2024
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2024.5.5.2619

Abstract

According to data from BPJS Keltelnagakelrjaan, 265,333 cases of work accidents were recorded in 2022. The use of personal protective equipment (PPE) is very important in reducing and preventing work accidents in the company. Although PPE cannot eliminate all risks, it is possible to minimise the number of work accidents in manufacturing companies. The aim of this research is to automatically select Personal Protective Equipment (PPE) in the form of hard hats and vests and to improve the accuracy results using the YOLOv8 model. With a dataset of 500 helmet and velst images for deltelksi which will be categorised into 4 classes namely hellelm, velst, no-hellelm, no-velst. The dataset used is 500 data, which is then divided into three datasets, namely: training data as much as 70%, validation data as much as 20%, and telst data as much as 10%, from the dataselt telrselbut the best results of testing data values from 50 dataselt the accuracy results obtained are 0.98. It is hoped that with the use of Meltode and accuracy results using Yolo v8, it can be used in companies by detecting Personal Protective Equipment (PPE) with fast and accurate results, so that it can be applied in monitoring the use of PPE in manufacturing companies to reduce the risk of work accidents in manufacturing companies
Development of Health Mask Identification Using YOLOv5 Architecture Fauzi, Ahmad; Ajie, Prasetyo; Nur Masruriyah, Anis Fitri; Wahiddin, Deden; Hikmayanti, Hanny; Hananto, April Lia
International Journal of Artificial Intelligence Research Vol 6, No 1.1 (2022)
Publisher : STMIK Dharma Wacana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29099/ijair.v6i1.1.573

Abstract

Coronavirus Disease 2019 (COVID-19) causes the state to suffer losses, especially in the health sector. WHO calls for controlling COVID-19 with health protocols that must be obeyed, one of which is wearing a mask. The use of masks can reduce the transmission of COVID-19. But there are still many people who ignore the protocol to use masks properly. So a system was created to detect the use of masks properly using the YOLOv5 architecture. Aiming to help regulate the use of masks in public areas or open places. The process of this research begins with data collection in the form of images. The collected image data will later be used as a dataset and model training will be carried out using the YOLOv5s model. The accuracy results obtained from this study reached 90.37%
Klasterisasi Tingkat Kemiskinan Kabupaten/Kota di Indonesia Menggunakan Algoritma K-Means dan K-Medoids Pratiwi, Gita Risky; Wahiddin, Deden; Awal, Elsa Elvira; Fauzi, Ahmad
Jurnal Algoritma Vol 21 No 2 (2024): Jurnal Algoritma
Publisher : Institut Teknologi Garut

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Kemiskinan adalah ketika sebuah masyarakat memiliki akses fisik yang terbatas terhadap lingkungan dasar. Kondisi permukiman miskin ini seringkali jauh di bawah standar kelayakan dan menyebabkan orang-orang di sana kesulitan mendapatkan uang untuk hidup. Fokus penelitian ini adalah untuk menentukan tingkat kemiskinan di Kab/Kota di Indonesia. Karena ada peningkatan angka kemiskinan di Indonesia, clustering diperlukan untuk pemerintah dapat memberikan bantuan yang tepat kepada mereka yang paling membutuhkan. Metode yang digunakan adalah algoritma K-Means dan K-Medoids. Hasil dari pengelompokkan ini menghasilkan cluster 0 menunjukkan tingkat kemiskinan yang relatif rendah yaitu 250 kab/kota, cluster 1 menunjukkan tingkat kemiskinan yang tinggi yaitu 330 kab/kota , Sedangkan algoritma K-Medoids menghasilkan tiga klaster dengan tingkat kemiskinan rata-rata yang berbeda: cluster 0 menunjukkan tingkat kemiskinan yang relatif rendah yaitu 270 kab/kota, cluster 1 menunjukkan tingkat kemiskinan yang tinggi yaitu 310 kab/kota. Hal ini dapat menjadi referensi bagi pemerintah untuk meningkatkan perhatian wilayah dengan tingkat kemiskinan tinggi dalam upaya mengurangi tantangan ekonomi yang sedang berlangsung. Dengan menggunakan skor sillhouette, untuk membagi tingkat kemiskinan evaluasi algoritma K-Means dan K-Medoids dilakukan. Algoritma k-means menghasilkan nilai K = 0.284 sedangkan algoritma K-Medoids menghasilkan nilai K = 0.224.
PEMODELAN INSPEKSI PAINTING DEFECT PADA MOBIL MENGGUNAKAN CONVOLUTIONAL NEURAL NETWORK (CNN) Ramadhan, Muchamad Fachrul; Fauzi, Ahmad; Wahiddin, Deden; Rohana, Tatang
Jurnal Tekinkom (Teknik Informasi dan Komputer) Vol 7 No 2 (2024)
Publisher : Politeknik Bisnis Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37600/tekinkom.v7i2.1519

Abstract

Quality control is an important process carried out at the last stage of the production process, this activity is carried out by checking a product. Painting defects on cars are a problem that must be considered in the car production process at car companies. The perfection of a product is important to increase the level of customer satisfaction. These checking activities are still carried out manually with human power, which can still cause defective products to be missed in a production process that occurs as a result of human error. The use of artificial intelligence can be used to detect image and video objects, used to overcome the problem of human error in carrying out checks. Convolutional Neural Networks (CNN) is an algorithm that can be used in product defect inspection, image recognition, and image classification. The study focuses on modeling the inspection and detection of painting defects in cars using CNN, emphasizing the importance of quality control in ensuring product quality. The CNN model is trained with image data of normal car paint and defective car paint, and evaluated using a confusion matrix for optimal parameters. The results show quite high accuracy in detecting car paint defects of 98% with the help of the ResNet50 transfer learning CNN architecture.
PENERAPAN ALGORITMA SUPPORT VECTOR MACHINES DAN RANDOM FOREST DALAM ANALISIS SENTIMEN ULASAN APLIKASI IDENTITAS KEPENDUDUKAN DIGITAL Ramadhan, Rizky Agung; Rohana, Tatang; Mudzakir, Tohirin Al; Wahiddin, Deden
Jurnal Tekinkom (Teknik Informasi dan Komputer) Vol 7 No 2 (2024)
Publisher : Politeknik Bisnis Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37600/tekinkom.v7i2.1595

Abstract

The Digital Population Identity (IKD) application, developed by the Directorate General of Population and Civil Registration, aims to streamline access to digital documents and reduce reliance on printed KTPs. Despite its benefits, user reviews from the Play Store highlight significant issues. This research aims to analyze user sentiment towards the IKD application using Support Vector Machines (SVM) and Random Forest algorithms. The study employed these models to classify sentiment in user reviews and used word cloud analysis to further understand the feedback. Results indicate that both the Random Forest and SVM models struggled with accuracy, achieving only 19.25% and 18% respectively. The word cloud analysis revealed a high prevalence of negative reviews, reflecting the app's low rating. These findings suggest that the current sentiment analysis methods are insufficient for capturing the public's opinion on the IKD application, providing crucial insights for improving future digital population identity management strategies.
BITCOIN PRICE PREDICTION USING LONG SHORT TERM MEMORY ALGORITHM Fauzi, Rifqi Arul; Rohana, Tatang; Nurlaelasari, Euis; Wahiddin, Deden
Journal of Computer Networks, Architecture and High Performance Computing Vol. 7 No. 3 (2025): Articles Research July 2025
Publisher : Information Technology and Science (ITScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/cnahpc.v7i3.5945

Abstract

Bitcoin a digital asset with the largest market capitalization in the world and shows high price volatility, attracting the interest of researchers to make accurate price predictions. The research aims to build a Bitcoin price prediction model use Long Short-Term Memory (LSTM) algorithm by utilizing closing price data and technical indicator variables, Moving Average (MA) and Exponential Moving Average (EMA). Dataset obtained from Yahoo Finance with a time range of January 1, 2015 to January 1, 2024 as much as 3287 data. The LSTM model is designed in multivariate form with an input sequence of 30 with several test scenarios at the epoch number 50, 100 and 200. Model evaluation is based on 4 metrics, namely Root Mean Squared Error (RMSE), Mean Squared Error (MSE), Mean Absolute Error (MAE), and Mean Abso-lute Percentage Error (MAPE). Model evaluation results show that the model is capable of providing a good prediction value with an MSE value of 0.0001, RMSE of 0.0117, MAE of 0.0081, and MAPE of 2.21% at epoch 200. The use of technical indicators proved to be helpful in improving the performance of the model compared to using only closing price data.
Model Regresi Linear Berganda untuk Prediksi Tingkat Pengangguran di Provinsi Jawa Barat Halif, Jenny; Wahiddin, Deden; Sanjaya, Iman; Faisal, Sutan
Jurnal Algoritma Vol 22 No 1 (2025): Jurnal Algoritma
Publisher : Institut Teknologi Garut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33364/algoritma/v.22-1.2312

Abstract

The Open Unemployment Rate (TPT) in West Java has been the highest nationally in recent years. This study aims to predict the TPT in 2025 using the Multiple Linear Regression (RLB) algorithm with variables such as inflation, GRDP, HDI, and population. Secondary data from 2013-2024 was analyzed through preprocessing, PCA, and training-test data division methods. The model was evaluated using RMSE and R-squared, with the results of RMSE 0.0148 and R² 0.5716. Multiple Linear Regression was chosen because it is able to handle many variables at once and provide a quantitative estimate of the contribution of each factor, in contrast to the individual approach which only looks at the influence of one variable separately. These results can serve as the basis for unemployment reduction policies at the regional level.
Co-Authors AA Sudharmawan, AA Abdul Gapur Ajie, Prasetyo April Lia Hananto Awal, Elsa Elvira Baihaqi, Kiki Ahmad Enjelia, Lola Faisal, Sutan Fauzi, Ahmad Fauzi, Farras Ahmad Fauzi, Rifqi Arul Fitri Nur Masruriyah, Anis Gozali, Gozali Halif, Jenny Hanny Hikmayanti Handayani Hartini, Dina Hastari, Nazwa Putri Iman Sanjaya Indra, Jamaludin Juwita, Ayu Ratna Lestari, Santi Arum Puspita Mudzakir, Tohirin Al Novita, Hilda Yulia Nurlaelasari, Euis Nurmayanti, Trisya Nursyawalni, Reva Pratiwi, Gita Risky Rahmat Ramadhan, Muchamad Fachrul Ramadhan, Rizky Agung Ridho Pratama, Ilham Rohana, Tatang Sukmawati, Cici Emilia Yana Cahyana