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All Journal Jurnal Infra JOIV : International Journal on Informatics Visualization Performance Jurnal Ilmu Hukum, Humaniora dan Politik (JIHHP)
Albert Kurniawan, Albert
Program Studi Teknik Informatika
Computer Science & IT Education Law, Crime, Criminology & Criminal Justice

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Analisa Risiko pada bidang Software Acquisition,Implementation,Maintenance PT. Z Kurniawan, Albert; Wibowo, Adi; Gunawan, Ibnu
Jurnal Infra Vol 3, No 2 (2015)
Publisher : Jurnal Infra

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (78.005 KB)

Abstract

PT.Z is a printing company based in Sidoarjo. PT.Z handle various customers both domestic and abroad. Information technology has been used to support nearly in all processes in PT.Z, but they has never done a risk analysis before so that the company do not know anything about IT risks that can occur. Therefore, it takes a risk analysis so that the company can determine what risks may occur and how to respond to those risks.In this thesis, risk assessment performed in the process of software acquisition, implementation, and maintenance. The steps used in performing the risk assessment are measuring the level of maturity of the IT using the Capability Maturity Model Integration (CMMI), then perform mapping of CMMI to COBIT 4.1, and using the OWASP Risk Rating Methodology as a guide in the calculation of risk. Some of these risk factors include the lack of monitoring process based on clear value of metrics, no identification of IT processes that have great impact on the companys business process, there is no verification of value in the result of monitoring data collection.
FENG SHUI BASED MARKETING: STRATEGI MEMENANGKAN KONSUMEN BERBASIS PEMAHAMAN NILAI-NILAI KEPERCAYAAN TIONGHOA Kurniawan, Albert
Performance Vol 21 No 1 (2015): Performance
Publisher : Faculty of Economics and Business Universitas Jenderal Soedirman

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Abstract

Feng shui is an ancient chinese wisdom about “chi” or positive energy. In reality,most of feng shui’s belief could be explained by modern science. In contemporary practice,feng shui incorporates a wide range of concepts considered to affect a person’s lu ck.These include traditional ideas about feng shui’s five elements, site selection, buildingdesign, as well a belief about certain numbers. Focusing on feng shui’s belief, this paperwill intepret how to utilize feng shui in interest of marketing. Combining marketing mixwith feng shui to attract more attention from the consumers.
Automated Detection of Molting Crabs Using YOLO: Enhancing Efficiency in Soft-Shell Crab Aquaculture Saputra, Dany Eka; Rangkuti, Abdul Haris; Dwi Putra, Sulistyo Emantoko; Daru Kusuma, Purba; Kurniawan, Albert; Gabriela, Melanie
JOIV : International Journal on Informatics Visualization Vol 9, No 5 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.5.3468

Abstract

Crab molting detection is a crucial process in aquaculture, particularly to produce soft-shell crabs, which are considered a delicacy in many markets. Traditional methods of manually monitoring crabs for molting are labor-intensive and susceptible to human error. To address this challenge, this study examines the application of the YOLO (You Only Look Once) object detection model for automating the detection of molting crabs. YOLO is renowned for its capability to perform real-time object detection, making it an ideal choice for this application. Our research focuses on developing a YOLO-based system that accurately identifies molting crabs from videos or images captured in farming environments. The model was trained on a comprehensive dataset comprising images of crabs at various stages of molting, ensuring robustness against environmental variations and different lighting conditions commonly encountered in aquaculture settings. The results indicate that the YOLO model achieves high accuracy in detecting molting crabs, significantly enhancing the efficiency and reliability of the detection process compared to manual observation and other machine learning approaches. These advancements facilitate timely intervention and harvesting, which are critical for optimizing the quality and yield of soft-shell crabs. In our experiments, the recognition of the crab molting process was categorized into three classes: the molting crab, the crab skin, and the newly molted crab. Overall, the YOLOv8 and YOLOv11 models demonstrated impressive performance, achieving an average accuracy of 96% to 98%. This research on molting crab detection has proven successful and can be further extended to include other types of crabs.
Perlindungan Hukum bagi Pemegang Hak Atas Merek dalam Sengketa Merek Kurniawan, Albert; Rahaditya, R.
Jurnal Ilmu Hukum, Humaniora dan Politik Vol. 4 No. 4 (2024): (JIHHP) Jurnal Ilmu Hukum, Humaniora dan Politik (Mei - Juni 2024)
Publisher : Dinasti Review Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.38035/jihhp.v4i4.2110

Abstract

Tujuan dari adanya penelitian ini adalah mencari wawasan terkait perlindungan hukum yang diperoleh pemilik hak atas merek dan upaya hukum yang dapat dilakukan dalam sengketa merek. Meningkatnya tren penjualan yang ada di Indonesia mengakibatkan kenaikan penggunaan merek oleh pelaku usaha untuk produk dan jasa yang dimilikinya. Menggunakan dan mendaftarkan merek dapat membuat pelaku usaha memperoleh banyak keuntungan. Namun di luar keuntungan yang ada, banyak kejadian pelanggaran terhadap pemilik yang sah atas merek sehingga menghadirkan kerugian bagi pelaku usaha. Dengan menggunakan metode penelitian hukum normatif yang bersifat deskriptif analisis untuk mendalami pembuatan Undang – Undang Nomor 20 Tahun 2016 tentang Merek dan Indikasi Geografis, dapat ditemukan bahwa pemerintah memberikan perlindungan hukum terhadap pemilik merek yang sah dengan syarat – syarat pendaftaran merek yang dapat mencegah terjadinya pelanggaran terhadap merek. Namun hal tersebut tidak menutup kemungkinan tetap terjadi pelanggaran oleh pihak yang tidak bertanggung jawab. Oleh karena itu pemerintah menyediakan upaya hukum berupa upaya hukum perdata, pidana, maupun administratif yang bisa ditempuh oleh pelaku usaha maupun pihak yang dirugikan melalui sengketa dalam pengadilan
Improving Accuracy in Deep Learning-Based Mushroom Image Classification through Optimal Use of Classification Techniques Kerta, Johan Muliadi; Rangkuti, Abdul Haris; Lun Lau, Sian; Kurniawan, Albert; Gabriela, Melanie; Tandianto, Alicia
JOIV : International Journal on Informatics Visualization Vol 9, No 3 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.3.2820

Abstract

The primary purpose of this research is to address the existing knowledge gap surrounding various lesser-known types of edible mushrooms. A common understanding exists that mushrooms are edible and possess numerous health benefits. This research is intended to advance that understanding by deploying AI technology and deep learning models specifically designed to recognize and identify various fungi. During this research, we have developed a unique derivative of deep learning. This involved testing several Convolutional Neural Network (CNN) models aimed at automatically identifying and detecting different types of mushrooms and understanding the benefits associated with each type. The research methodology was divided into several stages: Collection of mushroom images, Preprocessing of images, Feature extraction, and Classification. The preprocessing involved adjustments such as scale, image rotation, and setting the brightness range. The goal of selecting and training the CNN model was to enhance the classification accuracy of mushroom images within each class. The data was divided into training, testing, and validation sets for the experimental stage. The purpose was to process image data from test and validation images based on the training images that have been processed. We evaluated the classification layer to be shorter, but it demonstrated excellent accuracy in assessing similarity performance. Based on several experiments conducted using different CNN models, DenseNet, MobileNetV2, and InceptionResNetV2 models achieved an accuracy of more than 90%, specifically 95%, 94%, and 92%, respectively. The most accurately recognized mushroom types include Snow, Dried Shitake, King Oyster, Straw, Button, and Truffle; some CNN models could identify these up to 100%. Overall, the models and algorithms used in this research successfully facilitated the identification and detection of various types of fungi. They were fast and displayed high accuracy performance. Hopefully, this research can be extended to process images of even more diverse types of mushrooms, particularly in terms of shape, color, and texture characteristics. This will enhance the depth and breadth of knowledge in this field and further advance our understanding of the beneficial properties of various mushrooms.
Co-Authors A.A. Ketut Agung Cahyawan W Dwi Putra, Sulistyo Emantoko Gabriela, Melanie Ibnu Gunawan Johan Muliadi Kerta, Johan Muliadi Lun Lau, Sian Purba Daru Kusuma Rahaditya, R. Rangkuti, Abdul Haris Saputra, Dany Eka Tandianto, Alicia