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All Journal JURNAL MEDIA INFORMATIKA BUDIDARMA JOURNAL OF APPLIED INFORMATICS AND COMPUTING JURNAL TEKNIK INFORMATIKA DAN SISTEM INFORMASI Jurnal Sisfokom (Sistem Informasi dan Komputer) Jiko (Jurnal Informatika dan komputer) Conference on Innovation and Application of Science and Technology (CIASTECH) Jurnal Nasional Pendidikan Teknik Informatika (JANAPATI) J-SAKTI (Jurnal Sains Komputer dan Informatika) Jurnal Teknologi Terpadu Building of Informatics, Technology and Science Technologia: Jurnal Ilmiah Jusikom: Jurnal Sistem Informasi Ilmu Komputer Jurnal Tekinkom (Teknik Informasi dan Komputer) JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) Journal of Computer Networks, Architecture and High Performance Computing Jurnal Teknik Informatika (JUTIF) Jurnal Pendidikan dan Teknologi Indonesia J-SAKTI (Jurnal Sains Komputer dan Informatika) Jurnal Informatika Teknologi dan Sains (Jinteks) VIDHEAS: Jurnal Nasional Abdimas Multidisiplin
Rohana, Tatang
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Religion Agriculture, Biological Sciences & Forestry Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Energy Engineering Law, Crime, Criminology & Criminal Justice Social Sciences

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PEREKAMAN OTOMATIS BERDASARKAN DETEKSI OBJEK MANUSIA PADA CCTV MENGGUNAKAN METODE YOU ONLY LOOK ONCE V3 (YOLOV3) Hakim, Mirwan Abdurrahman; Rohana, Tatang; Kusumaningrum, Dwi Sulistya
Conference on Innovation and Application of Science and Technology (CIASTECH) CIASTECH 2020 "Peranan Strategis Teknologi Dalam Kehidupan di Era New Normal"
Publisher : Universitas Widyagama Malang

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Abstract

CCTV selama ini memiliki kekurangan dalam hal penggunaan penyimpan, sementara pengawasan dilakukan penuh selama 1 × 24 jam, dan membuat kamera menulis frame terus menerus. Penyimpanan pun dengan drastis terpakai sehingga cepat penuh. Hal ini pun membuat storage seperti HDD, akan berkerja keras sehingga keawetan storage HDD tidak terjamin lama. Solusi yang bisa dilakukan adalah dengan memanfaatkan pengenalan objek sebagai kondisi. Frame hanya akan ditulis pada penyimpan ketika kamera mendeteksi adanya objek manusia. Metode Object Detection yang digunakan adalah YOLOv3, dataset dilatih dan menjadi model latih lalu diterapkan secara realtime. Model yang dilatih pada penelitian ini memiliki akurasi 100%, dan setiap objek yang terdeteksi berhasil menjadi kondisi kamera menulis frame pada penyimpan dengan ukuran video paling besar 1,6mb dengan durasi waktu 18 detik.
PENGENALAN SAMPAH PLASTIK DENGAN MODEL CONVOLUTIONAL NEURAL NETWORK Pratama, Irfan Nugraha; Rohana, Tatang; Al Mudzakir, Tohirin
Conference on Innovation and Application of Science and Technology (CIASTECH) CIASTECH 2020 "Peranan Strategis Teknologi Dalam Kehidupan di Era New Normal"
Publisher : Universitas Widyagama Malang

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Abstract

Edukasi tentang sampah yang rendah serta kultur lingkungan dan keluarga menjadi faktor yang mempengaruhi tingkat kepedulian masyarakat terhadap sampah, tercatat 72% masyarakat Indonesia tidak peduli dengan persoalan sampah. Padahal, masalah sampah dapat diselesaikan di level hulu jika masyarakat melakukan Gerakan 3R, yaitu reduce, reuse, dan recycle. Untuk itu, peneliti mencoba untuk mengimplementasikan metode Convolutional Neural Network (CNN) untuk mengklasifikasikan jenis sampah plastik (anorganik). Metode CNN digunakan karena pada penelitian sebelumnya mendapat akurasi sebesar 95%. Pada penelitian yang dilakukan, peneliti menguji setiap model yang disimpan saat proses pelatihan dan mendapatkan nilai akurasi sebesar 80% pada penyimpanan model ke 6000 dengan nilai rata-rata kerugian mencapai 0,03 dengan pembagian dataset untuk pelatihan sebesar 80% dan 20% untuk pengujian.
Implementasi Algoritma Convolutional Neural Network Untuk Klasifikasi Citra Kemasan Kardus Defect dan No Defect Antoni, Alan; Rohana, Tatang; Pratama, Adi Rizky
Building of Informatics, Technology and Science (BITS) Vol 4 No 4 (2023): March 2023
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v4i4.3270

Abstract

Packaging is an important aspect of a product, because packaging can affect the quality and competitiveness of the product. Damaged packaging can result in decreased product quality. One popular packaging used is corrugated cardboard type box. To visually distinguish defect and no defect cardboard packaging, there are tears, holes and dents on the defect cardboard packaging. Whereas the no defect cardboard packaging has a visual that there are no tears, holes or dents. To simplify the classification, technology is needed that can distinguish between defect and no-defect cardboard packaging. In this study the total images used as a dataset are 1300 images, which are then divided into 2 with a percentage of 80% for training data and 20% for test data. The dataset first goes through the preprocessing stage before being used. Preprocessing consists of cropping, augmentation and resizing. And also do the segmentation process using Grabcut method. Then feature extraction is also performed using Local Binary Pattern (LBP). This study uses the Convolutional Neural Network algorithm with a total of 3 convolution layers, namely 16.32 and 64 and the Adam optimizer. Four experiments were carried out by differentiating the hyperparameter epoch, the input image size and the learning rate. The results showed that the model produced with an epoch hyperparameter of 30, an input image size of 300x300 and a learning rate of 0.001 obtained the best performance with an accuracy value of 95.77%, 96% precision, 96% recall, 96% f1-score and loss of 0.1478.
Perbandingan Kinerja Klasifikasi Penyakit Ginjal Menggunakan Algoritma Support Vector Machine (SVM) dan Decision Tree (DT) Madani, Puja Milenia Sriwildan; Rohana, Tatang; Baihaqi, Kiki Ahmad; Fauzi, Ahmad
Building of Informatics, Technology and Science (BITS) Vol 6 No 1 (2024): June 2024
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v6i1.5206

Abstract

Chronic Kidney Disease is one of the deadliest diseases. In the early stages, the disease may go undetected, so patients tend to take it lightly, however, the disease can progress little by little and become serious without being detected. This can lead to complications of other diseases and can cause permanent damage to the kidney organs. Therefore, this study aims to classify individuals who are at risk of having Chronic Kidney Disease which can help medical personnel in an effort to reduce the number of people with the disease. This study uses Chronic Kidney Disease data obtained from the UCI Repository web. The data has 25 attributes with 400 rows. This research compares the Support Vector Machine and Decision Tree algorithms and uses the Confusion Matrix evaluation method. The results showed that the Support Vector Machine algorithm has superior accuracy, precision, recall, and f1-score results compared to the Decision Tree algorithm. The accuracy of the Support Vector Machine algorithm is 97.5, precision is 0.98, recall is 0.96, and f1-score is 0.97. While the Decision Tree algorithm obtained accuracy of 92.5, precision of 0.92, recall of 0.90, and f1-score of 0.91. with these results, this research can be continued into an application that can classify individuals at risk of Chronic Kidney Disease
Perbandingan Metode K-Means dan K-Medoids Untuk Clustering Jenis Kriminalitas Azizah, Nurul; Fauzi, Ahmad; Rohana, Tatang; Faisal, Sutan
Building of Informatics, Technology and Science (BITS) Vol 6 No 2 (2024): September 2024
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v6i2.5723

Abstract

Crime in Indonesia includes acts that violate the law, social norms and religion which cause economic and psychological losses as well as social tensions in society. Crimes such as theft, violence, fraud and drugs are often triggered by factors such as poverty and environmental conditions that support criminal behavior. This research needs to be carried out to overcome the complex and far-reaching crime problem in Indonesia, especially in Karawang Regency. With crimes such as theft, violence, fraud and drugs on the rise, often fueled by factors such as poverty and environmental conditions, a more effective approach is needed to understand and address these problems. This research uses data mining techniques, especially cluster analysis, to group types of crime. The aim is to identify existing crime patterns and understand the factors that influence their spread. Thus, the results of this research can help the authorities in developing more targeted crime prevention and handling strategies, so as to minimize the negative impact of crime in the area. Apart from that, this research also contributes to increasing knowledge regarding the most effective methods for analyzing crime data, which can be applied in other areas with similar problems. The results of the research show that the K-Means algorithm is more effective than K-Medoids in handling data variability, with a Silhouette Coefficient value of 0.482 and a Davies Bouldin Index of 0.915. It is hoped that the implementation of this algorithm will make it easier to identify and handle crimes in the area.
Pelatihan Penggunaan Literasi Digital Untuk Menunjang Pendidikan Dan Umkm Khaerudin, Muhammad; Tukino; Ratna Juwita, Ayu; Rohana, Tatang; Winarni
VIDHEAS: Jurnal Nasional Abdimas Multidisiplin Vol. 2 No. 1 (2024): Juni 2024
Publisher : VINICHO MEDIA PUBLISINDO

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61946/vidheas.v2i1.73

Abstract

The pandemic conditions have opened up opportunities for teenagers to do business. In conditions like this, it is hoped that teenagers can use their time positively and productively. Mental readiness and skills need to be prepared before someone enters the world of work. Meanwhile, applications in the field of information technology have a big impact in various areas of life, one of which is in the creative industry such as advertising, billboards, graphic design and digital image processing. One of them is supporting skills for the younger generation. The method of community service carried out by a team of lecturers at Bhayangkara University, Greater Jakarta this time is in the form of training in graphic design skills using computers and using CorelDraw and Photoshop software. This activity aims to increase the knowledge and skills of youth in improving the quality of their abilities in creating attractive graphic designs so that participants can compete to meet the demand for employment and also towards entrepreneurship. The training given to participants includes creating and completing product designs for advertising or printing needs. The results of graphic design skills training activities show that participants can design logos, business cards, invitations, leaflets, banners and other forms of advertising.
Implementasi Algoritma K-Means dan K-Medoids Dalam Klasterisasi Kasus Kekerasan Terhadap Perempuan Kamilah, Nur Azizah; Rohana, Tatang; Rahmat, Rahmat; Fauzi, Ahmad
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 8, No 2 (2024): April 2024
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v8i2.7558

Abstract

The number of women's violence in Indonesia is increasing. In West Java alone, 58,395 cases of violence against women were recorded. Violence against women that occurs in West Java is among the most common compared to other provinces. This high number shows that violence against women is still not being handled seriously. Therefore, clustering is carried out to achieve a more structured solution so that it can assist the government in providing appropriate and appropriate responses to the conditions of each region, so that case handling can be more focused. The aim of this research is to group districts or cities in West Java in cases of violence against women using the K-Means and K-Medoids algorithms into two clusters, namely, high and low. In this research, data grouping was carried out using 2 methods, namely the K-Means and K-Medoids algorithms to find out which comparison between the two algorithms is more optimal. It is hoped that this research will produce the best cluster, the results of this cluster can help the government and related agencies to determine which districts or cities should be prioritized in handling cases of violence against women in West Java. The results of this research produced 2 clusters. Cluster 0 (high) and cluster 1 (low). The number of cluster 0 (high) is 14 districts and cities, while cluster 1 (low) is 13 districts and cities. Comparing the clustering evaluation between K-Means and K-Medoids, the best cluster evaluation value was obtained using the K-Medoids Algorithm with a Silhoutte Coefficient evaluation of 0.43, while the Davies Bouldin Index evaluation results showed the best cluster results using the K-Means Algorithm with a DBI value of 0.95.
Application Of Yolo V8 For Product Defect Detection In Manufacturing Companies Jamal, Malikil; Faisal, Sultan; Kusumaningrum, Dwi Sulistya; Rohana, Tatang
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

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Abstract

One important aspect in the production process is maintaining product quality and avoiding defects that could harm the company. This research aims to improve quality and avoid product defects that are detrimental to the company, especially defects in the form of bubbles in the product, by using YOLOv8. The dataset consists of 100 data which is divided into 80 for training and 20 testing data with an epoch value of 100. To obtain optimal bubble detection results, this research chose the latest version of YOLOv8 and compared several models, namely YOLOv8n, YOLOv8s, YOLOv8m, YOLOv8l, and YOLOv8x. The research results show that YOLOv8m achieves the highest accuracy among other models with a mAP value of 0.712, precision of 0.764, recall of 0.659, and F1-score of 0.708. This research highlights the potential of detection models that can detect bubbles precisely and accurately. Keywords: Kecacatan Produk, Deteksi Gelembung, Perusahaan Manufaktur, Model YOLOv8
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
PUBLIC SENTIMENT ANALYSIS ON ELECTRIC CARS USING MACHINE LEARNING ALGORITMS Damaiarta Tejayanda, Rigger; Prasetyo, Bayu; Faisal, Muhamad Agus; Abigael, Rakha; Rohana, Tatang; Sukmawati, Cici Emilia
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.2024.5.4.2141

Abstract

The presence of electric vehicles has generated diverse opinions among the public, as widely discussed on social media. The lack of understanding about electric vehicle innovation can influence their perception. Issues such as infrastructure, high prices, pollution concerns, and adaptation to new technology present challenges for automotive companies in their innovation efforts. This study aims to analyze public sentiment towards electric vehicles through comments on the TikTok platform, which can serve as a reference for companies in evaluating and developing electric vehicle innovations. Six different classification algorithms were tested to determine the most effective and accurate one. The methods used include data collection of comments, pre-processing, data processing through stemming, tokenization, and stopwords removal techniques, as well as labeling and modeling stages. The results of the study show that Support Vector Machine are the most superior algorithms with the highest accuracy of 90%. This research provides new insights into public perception of electric cars and the effectiveness of various sentiment analysis algorithms in the context of social media.
Co-Authors Abigael, Rakha Adi Susilo Adinata, Abdul Rohim Agustin, Rachmayanti Tri Antoni, Alan Awal, Elsa Elvira Azhari, Febrian Akbar Baihaqi, Kiki Ahmad Bayu Prasetyo, Bayu Bunga Tiara, Vira Butar Butar, Naomi Nova Meylica Damaiarta Tejayanda, Rigger Faisal, Muhamad Agus Faisal, Sultan Faisal, Sutan Farah Maulida, Oxana Fauzi Ahmad Muda Fauzi, Ahmad Fauzi, Rifqi Arul Hakim, Mirwan Abdurrahman Hartini, Dina Indra, Jamaludin Irawan, Muhamad Anggi Jamal, Malikil Juwita, Ayu Ratna Kamilah, Nur Azizah Karyanto, Dony Dwi Kusumaningrum, Dwi Sulistya Kusumaningrum, Dwi Sulistya Kusumaningrum Lestari, Santi Arum Puspita Madani, Puja Milenia Sriwildan Maulana, Moch Sigit Rizky Mudzakir, Tohirin Al Muhammad Khaerudin Niendha Biell Binna Novita, Hilda Yulia Nurlaelasari, Euis Nurmayanti, Trisya Nursyawalni, Reva NURUL AZIZAH Pratama, Adi Rizky Pratama, Irfan Nugraha Purnomo, Indarto Aditya Rahmat Rahmat Ramadhan, Muchamad Fachrul Ramadhan, Rizky Agung Ratna Juwita, Ayu Siregar, Amril Mutoi Sukmawati, Cici Emilia tukino, tukino Wahiddin, Deden Winarni Yana Cahyana Yulia Novita, Hilda