In recent years, Solar power plants are currently developed rapidly, where solar power plants don't cause environmental damage. This generator utilizes sunlight as its input source which environmentally friendly, when sunlight is converted into direct current and voltage that can be stored in batteries. Therefore, the solar energy industry needs to have high efficiency, competitive pricing, and durability. With the right information technologies and developing application systems, it can help to optimize business processes and integrating information systems. But the fact there are many industries that do not apply information technology correctly into their business. In this research, provide to the best solution for applying the enterprise architecture framework correctly. The purpose of this paper is to design a manufacturing industry information system that is in accordance with the business model canvas and enterprise architecture. The design also discusses the development of a large and integrated information system with other systems. The study of Business Model Canvas is a business model that can explain and focus on nine business aspects with a solid strategy. Studies of the ArchiMate core Framework is the framework used to classify elements of the ArchiMate core language. These two methods are expected to be able to design an enterprise information system architecture consisting of business architecture, application architecture, information architecture and technology architecture as a result of the study. The result is a more complete system design to meet system requirements for users, integrated with existing system modules and no system duplication occurs.

Currently adoption of mobile phones and mobile applications based  on Android operating system is increasing rapidly. Many companies and emerging startups are carrying out digital transformation by using mobile applications to provide disruptive digital services to replace existing old styled services. This transformation prompted the attackers to create malicious software (malware) using sophisticate methods to target victims of Android mobile phone users. The purpose of this study is to identify Android APK files by classifying them using Artificial Neural Network (ANN) and Non Neural Network (NNN). The ANN is Multi-Layer Perceptron Classifier (MLPC), while  the NNN are KNN, SVM, Decision Tree, Logistic Regression and Naïve Bayes methods. The results show that the performance using NNN has decreasing accuracy when training using larger datasets. The use of the K-Nearest Neighbor algorithm with a dataset of 600 APKs achieves an accuracy of  91.2% and dataset of 14170 APKs achieves an accuracy of 88%. The using of the Support Vector Machine algorithm with the 600 APK dataset has an accuracy of 99.1% and the 14170 APK dataset has an accuracy of 90.5%. The using of the Decision Tree algorithm with the 600 APK dataset has an accuracy of  99.2%, the 14170 APK dataset has an accuracy of 90.8%. The experiment using the Multi-Layer Perceptron Classifier has increasing with the 600 APK dataset reaching 99%, the 7000 APK dataset reaching 100% and the 14170 APK dataset reaching 100%.

Object recognition in images is one of the problems that continues to be faced in the world of computer vision. Various approaches have been developed to address this problem, and end-to-end object detection is one relatively new approach. End-to-end object detection involves using the CNN and Transformer architectures to learn object information directly from the image and can produce very good results in object detection. In this research, we implemented ResNet-50 in an End-to-End Object Detection system to improve object detection performance in images. ResNet-50 is a CNN architecture that is well-known for its effectiveness in image recognition tasks, while DETR utilizes Transformers to study object representations directly from images. We tested our system performance on the COCO dataset and demonstrated that ResNet-50 + DETR achieves a better level of accuracy than DETR models that do not use ResNet-50. In addition, we also show that ResNet-50 + DETR can detect objects more quickly than similar traditional CNN models. The results of our research show that the use of ResNet-50 in the DETR system can improve object detection performance in images by about 90%. We also show that using ResNet-50 in DETR systems can improve object detection speed, which is a huge advantage in real-time applications. We hope that the results of this research can contribute to the development of object detection technology in images in the world of computer vision.

The development of Deep Learning technology is very good at detecting Objects. One of them is detection on the vehicle number plate. This method can be applied to Computer Vision to process images using DensetNet121, NasNetLarge, VGG16 and VGG19 methods. The most basic difference between Machine Learning and Deep Learning is the inclusion of a Hidden Layer and what distinguishes the Deep Learning process using neurons as a process from input, process to output. Feature extraction is done directly with the Deep Learning process. In terms of time, training models with Deep Learning are very long, when compared to Machine Learning. The dataset comes from Kaggle, then training is carried out with four Deep Learning models, resulting in a model. There are differences in conducting the training process. Before carrying out the Training process, a pre-paration process from the Image Dataset is carried out. The dataset is divided into two parts, the Training Dataset and the Testing Dataset. After the training model is completed, it is continued with the Testing process and measuring the performance of the model's accuracy. The accuracy of the four models resulting from Deep Learning training is also presented

Dewasa ini masyarat banyak memanfaatkan teknologi Internet, untuk berbagai kebutuhan. Mulai dari berbelanja, transportasi dan dunia pendidikan memanfaatkan Internet sebagai layanan digital. Peralatan dalam mengakses Internet pun banyak dan sangat beragam, mulai dari personal komputer, laptop sampai perangkat komunikasi seperti perangkat seluler. Perangkat seluler saat ini yang cukup banyak variasinya dan digunakan masyarakat adalah perangkat seluler berbasis sistem operasi Android. Dalam situasi ini mendorong pihak-pihak tertentu memanfaatkan celah untuk mencari keuntungan, salah satunya pembuatan Malicious Software (Malware). Keberadaan Malware sangat meresahkan, dimana pertumbuhan malware sangat cepat. Fenomena Malware yang terus bertumbuh inilah yang menjadikan peneliti berfokus untuk menganalisa Malware dengan memanfaatkan teknologi kecerdasan buatan. Tujuan dari penelitian ini adalah menganalisa file-file APK Android dengan metode anlisa statis dan melakukan klasifikasi keluarga Malware dan bukan Malware atau file APK Normal. File-file APK Malware dan bukan Malware di unduh dari Canadian Institute for Cyber Security, Google Play dan APK Pure. File-file tersebut dilakukan fitur ekstraksi untuk digenerate dan disimpan menjadi Malware dataset. Malware dataset tersebut dilakukan training menggunakan algoritma pembelajaran mesin. Pembelajaran mesin yang digunakan adalah Naïve Bayes, K-Nearest Neighbor dan Decision Tree. Pengukuran performansi akurasi dan perbandingan antara Naïve bayes, K-Nearest Neighbor dan Decision Tree yang merupakan bagian dari Pembelajaran Mesin.

Currently, the development of mobile phones and mobile applications based on the Android operating system is increasing rapidly. Many new companies and startups are digitally transforming by using mobile apps to provide disruptive digital services to replace existing old-fashioned services. This transformation prompted attackers to create malicious software (malware) using sophisticated methods to target victims of Android phone users. The purpose of this study is to identify Android APK files by classifying them using Artificial Neural Network (ANN) and Non-Neural Network (NNN). ANN is a Multi-Layer Perceptron Classifier (MLPC), while NNN is a method of KNN, SVM, Decision Tree. This study aims to make a comparison between the performance of the Non-Neural Network and the Neural Network. Problems that occur when classifying using the Non-Neural Network algorithm have problems with decreasing performance, where performance is often decreased if done with a larger dataset. Answering the problem of decreasing model performance, the solution is used with the Artificial Neural Network algorithm. The Artificial Neural Network Algorithm selected is Multi_layer Perceptron Classifier (MLPC). Using the Non-Neural Network algorithm, K-Nearest Neighbor conducts training with the 600 APK dataset achieving 91.2% accuracy and training using the 14170 APK dataset decreases its accuracy to 88%. The use of the Support Vector Machine algorithm with the 600 APK dataset has 99.1% accuracy and the 14170 APK dataset has decreased accuracy to 90.5%. The use of the Decision Tree algorithm to conduct training with a dataset of 600 APKs has an accuracy of 99.2% and training with a dataset of 14170 APKs has decreased accuracy to 90.8%. Experiments using the Multi-Layer Perceptron Classifier have increased accuracy performance with the 600 APK dataset achieving 99% accuracy and training using the 14170 APK dataset increasing the accuracy reaching 100%.

Rice is a staple food for people in tropical countries. Indonesia is a country that needs a lot of rice for its people in providing food. This country has implemented various ways to plant rice properly. Many agricultural fields have implemented harvests up to three times a year, due to the role of technology which has helped a lot in agriculture. Planting to harvest already uses advanced technology and tools. A good rice harvest can improve the welfare of the surrounding community. Meanwhile with lots of rice products because many rice plants produce with lots of rice. The type of rice from different regions of origin, the yield of rice is also different from other regions of origin. But with advances in technology, it is possible to plant rice whose types of plants come from other regions. The rice sold to the public varies, so that people who are unfamiliar with the types of rice find it difficult to detect the types of rice. Machine learning is present in detecting various kinds of rice. Machine learning, especially deep learning can make better detection, because one of the deep learning methods works similar to the human brain. In the human brain there are millions or even billions of neurons. This research uses neural networks in experiments using public datasets. Experiments using Artificial Neural Networks achieve an training accuracy of 98.2%, loss: 0.2351. It takes about 10 minutes of training. Testing accuracy reaches accuracy: 96%, loss: 0.6641. By conducting experiments using the Convolution Neural Network, it achieves an accuracy of 99.3% and the training time requires around 18 hours. The purpose of this research is to classify the rice image dataset and detect the rice image.

<p><em>To support the bamboo processing manufacturing operations, this company uses the Enterprise Resource Planning Application System. Starting from sales operations, production, inventory to financial application systems, everything is controlled with Enterprise Resource Planning (ERP). The ERP system has actually been implemented for more than 1 year. The company wants to measure the performance of the system. Then measurements were made using the COBIT 5 Framework, with the aim of knowing between real conditions and targets (expectations). Of course there will be a gap or gap. The GAP is used as a measuring tool to improve ERP system services. Before analyzing the maturity level measurement using COBIT 5, first conduct a survey to obtain data. After getting the data, then assessing and determining the maturity level for Enterprise Resource Planning in bamboo processing manufacturing companies.</em></p><p><strong><em>Keywords:</em></strong><em> Enterprise Resource Planning; COBIT 5; </em><em>GAP</em><em>; Bamboo Processing Manufacturing; Maturity level</em></p><p><strong>Abstrak.</strong> Untuk memberikan dukungan terhadap operasional manufaktur pengolahan bambu, perusahaan ini menggunakan sistem aplikasi <em>Enterprise Resource Planning</em>. Mulai dari operasional penjualan, produksi, <em>inventory</em> sampai sistem aplikasi keuangan, semua dilakukan kontrol dengan <em>Enterprise Resource Planning</em> (ERP). Sistem ERP sebenarnya sudah diterapkan lebih dari 1 tahun. Perusahaan ingin melakukan pengukuran performansi dari sistem tersebut. Maka dilakukan pengukuran menggunakan <em>Framework COBIT</em> 5, dengan tujuan untuk mengetahui antara kondisi riil dengan target (harapan). Tentunya akan terjadi GAP atau kesenjangan. GAP tersebut dijadikan alat ukur untuk meningkatkan pelayanan sistem ERP. Sebelum melakukan analisa pengukuran tingkat kematangan menggunakan COBIT 5, terlebih dari dahulu melakukan survey untuk mendapatkan data. Setelah mendapatkan data kemudian melakukan penilaian dan penetapan tingkat kematangan (<em>maturity level</em>) untuk <em>Enterprise Resource Planning</em> pada perusahaan manufaktur pengolahan bambu.</p><p><strong>Kata kunci</strong><strong>:</strong> <em>Enterprise Resource Planning; COBIT 5; GAP; Manufaktur Pengolahan Bambu; Tingkat kematangan.  </em><em></em></p>