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All Journal The Indonesian Journal of Business Administration Jurnas Nasional Teknologi dan Sistem Informasi Journal of Applied Geospatial Information Sinkron : Jurnal dan Penelitian Teknik Informatika JURNAL MEDIA INFORMATIKA BUDIDARMA Jurnal Eksplora Informatika Techne : Jurnal Ilmiah Elektroteknika EDUMATIC: Jurnal Pendidikan Informatika Building of Informatics, Technology and Science Abdimasku : Jurnal Pengabdian Masyarakat Jurnal Teknik Informatika (JUTIF) ProBisnis : Jurnal Manajemen Jurnal Minfo Polgan (JMP) Community: Jurnal Pengabdian Pada Masyarakat
Adhitya Nugraha
Sistem Informasi Fakultas Ilmu Komputer Universitas Dian Nuswantoro
Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Library & Information Science Mathematics Physics Social Sciences

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Marketing Strategy For Grenzelos Nugraha, Adhitya; Larso, Dwi
The Indonesian Journal of Business Administration Vol 4, No 2 (2015)
Publisher : The Indonesian Journal of Business Administration

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

Abstract

Abstract.Fitness industry in Indonesia is growing and becoming urban lifestyle, but there has not been a clothing designated for fitness activity. Grenzelos, engaged in fashion field, tries to capture this opportunity by offering premium-classed fitness wear products. However, as the new company Grenzelos perform operations using only owner intuition, so it cannot reach the market. This causes Grenzelos need to consider the marketing strategy to be done.Based on the external analysis, the external analysis shows the attractiveness of this new industry with its high demand. Internal analysis shows that the consumer segment for fitness clothing is a body builder. Additionally, Grenzelos has done test marketing in the form of beta testing done in two phases to determine the customers' response and demand levels. In a SWOT analysis found that Greenzelos fitness clothing is a superior product in terms of quality and design, but there is no clear standard price with a limited distribution. In addition, people's lifestyles that lead to a healthy life and good appearance, and the use of technology for shopping activity may be an opportunity that can be exploited by Grenzelos, while the capital strength of the nearest competitor and limited market can be a threat. This led the company still does not have a clear presence in the fitness apparel market that is prone to competitor products.IFAS - EFAS continued SWOT, 3.30 point for IFAS and 2.90 point. From IFAS-EFAS results, it can be seen that Grenzelos is in diversification quadrant. So, it needs a marketing strategy that can reach the market. So, Grenzelos need a marketing strategy that can reach the market. This marketing strategy using marketing mix that starts from the development of product design, sales made through cooperation with existing online stores, lower prices than major competitors and promotion system using the IMC (Integrated Marketing Communication) method.To implement the new strategy for Grenzelos, this final project generate the plan and budgeting for the company. These implementation plan and budgeting covers the proposed marketing mix activities for the period of one year with total cost of Rp 159 million.Keywords: Fitness, Beta Testing, Grenzelos, Marketing Strategy
Optimasi Logistic Regression untuk Deteksi Serangan DoS pada Keamanan IoT Primadya, Nauval Dwi; Nugraha, Adhitya; Luthfiarta, Ardytha; Fahrezi, Sahrul Yudha
Jurnal Eksplora Informatika Vol 13 No 2 (2024): Jurnal Eksplora Informatika
Publisher : Institut Teknologi dan Bisnis STIKOM Bali

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30864/eksplora.v13i2.1065

Abstract

Keamanan perangkat Internet of Things (IoT) merupakan prioritas utama karena potensi risiko kerusakan perangkat dan kebocoran data yang dapat berdampak serius. Perangkat IoT telah membawa manfaat signifikan ke berbagai sektor, seperti kesehatan, transportasi, dan industri, namun tingkat serangan terhadapnya terus meningkat. Dalam mengatasi tantangan ini, pendekatan machine learning digunakan dengan memanfaatkan dataset CIC IOT ATTACKS 2023 dari University of New Brunswick. Untuk menghasilkan data yang berkualitas, dilakukan random undersampling untuk mengatasi ketidakseimbangan data, dan seleksi fitur menggunakan Recursive Feature Elimination untuk mendapatkan fitur terbaik. Pemilihan Logistic Regression sebagai algoritma pemodelan dipilih dengan pertimbangan yang matang. Logistic Regression dipilih karena kemampuannya memberikan interpretasi yang jelas terhadap kontribusi relatif setiap fitur terhadap prediksi keamanan perangkat IoT. Selain itu, model ini efisien secara komputasional, mengatasi ketidakseimbangan data, dan tahan terhadap overfitting, yang semuanya merupakan faktor krusial dalam konteks keamanan IoT. Hasil penelitian menunjukkan bahwa penggunaan Logistic Regression bersamaan dengan seleksi fitur memberikan tingkat akurasi tertinggi mencapai 97%, dengan waktu pemrosesan yang efisien sekitar 11 detik. Dari hasil ini, dapat disimpulkan bahwa kombinasi teknik random undersampling dan seleksi fitur menggunakan Recursive Feature Elimination secara positif memengaruhi akurasi pada model Logistic Regression, menjadikannya pilihan yang sesuai untuk meningkatkan keamanan perangkat IoT.
Data-Driven Modeling of Human Development Index in Eastern Indonesia's Region Using Gaussian Techniques Empowered by Machine Learning Ganiswari, Syuhra Putri; Azies, Harun Al; Nugraha, Adhitya; Luthfiarta, Ardytha; Firmansyah, Gustian Angga
Journal of Applied Geospatial Information Vol 7 No 2 (2023): Journal of Applied Geospatial Information (JAGI)
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jagi.v7i2.6757

Abstract

The Human Development Index (HDI) is a statistical measure used to measure and evaluate the progress and quality of human life in a country. For the Government of Indonesia, HDI is important because it is used to create or develop effective policies and programs. In addition, HDI is also used as one of the allocators in determining the General Allocation Fund. The 2022 HDI data released by BPS shows that there has been an increase in the HDI in each district/city over the last 12 years, including in the regions of Eastern Indonesia. High and low HDI values are influenced by several factors, and there are indications that there is spatial diversity where surrounding areas tend to have HDI levels that are not far from the area. The Geographically Weighted Regression method is used in this study because it takes into account spatial aspects. However, the GWR model must be built repeatedly if there is regional expansion. Therefore, a GWR model that applies machine learning methods is needed where the model is built and tested using different datasets, namely training data and test data, so that the model can predict new data better. The results obtained are that the GWR model with test data has a better R-Square value when compared to the GWR model previously trained using training data, which is 0.9946702, based on the linear regression model shows the results that the most influential factor on HDI in Eastern Indonesia is expected years of schooling (X2).
Improving Data Embedding Capacity in LSB Steganography Utilizing LSB2 and Zlib Compression Kurniawan, Joshua Calvin; Nugraha, Adhitya; Prayogo , Ariel Immanuel; Novanto , The, Fandy
Sinkron : jurnal dan penelitian teknik informatika Vol. 8 No. 1 (2024): Articles Research Volume 8 Issue 1, January 2024
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v9i1.13185

Abstract

In an increasingly advanced era, the exchange of information through digital tools has become a common practice. With easy access and advancing facilities, securely and covertly exchanging data has become a challenging task. Therefore, the technique of steganography can be used as a solution for data hiding and protection, enabling safer data exchanges. Steganography is a method to conceal data within a transmission object, which can be an image, video, audio, and more. In this research, steganography will be performed using images as the transmission object. This study is done to offer a modification of the Least Significant Bit (LSB) steganography technique by utilizing the LSB-2 method, along with the utilization of the Zlib compression algorithm. The modification and use of the Zlib compression algorithm aim to increase the message capacity that can be embedded in the transmission object while preserving the image quality. The results of the experiments will be presented in tabular form by comparing the original image with the steganography-processed image using metrics such as Mean Square Error (MSE), Peak Signal-to-Noise Ratio (PSNR), and Structural Similarity Index (SSIM) as measures of image quality. The experiments conducted results in an increase of capacity of approximately 36.54%, an increase in PSNR value of approximately 4.72%, accompanied by a decrease in MSE value in average of 49.19%, and SSIM values constantly at 0,99999 thus proving the proposed method successfully increased the embedded massage capacity while preserving even enhance the quality of the stego image produced by the embedding process
Enhancing Least Significant Bit Steganography Image Fidelity Using Brotli Compression Prayogo , Ariel Immanuel; Nugraha, Adhitya; The, Fandy Novanto; Joshua Calvin Kurniawan
Sinkron : jurnal dan penelitian teknik informatika Vol. 8 No. 1 (2024): Articles Research Volume 8 Issue 1, January 2024
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v9i1.13186

Abstract

The rapid growth of technology has provided extensive convenience and openness in accessing information, yet this hasn't been balanced with an equivalent enhancement in information security. Steganography plays a crucial role in concealing and protecting data, with the Least Significant Bit (LSB) method being a commonly used algorithm that operates by substituting the least significant bits in the image pixels with the bits of the data to be hidden, aiming to preserve the image quality. This research aims to enhance the quality of the steganography result using LSB by employing the Brotli compression technique, coupled with increasing image's capacity. Brotli compression aims to reduce the size of the resulting stego image by combining embedded data that share identical values. Experiment results will be obtained by comparing the original image with the stego image using metrics like Mean Square Error (MSE), Peak Signal to Noise Ratio (PSNR), and Structural Similarity Index (SSIM). The experiments successfully demonstrated that the integration of LSB with Brotli compression outperformed the regular LSB method, showing a 6.64% increase in PSNR, followed by a decrease in MSE by approximately 63.79%, and an increase in SSIM by around 0.0039%. This was accompanied by a continuous increase in compression values depending on the input data size. These results indicate that the integration of LSB with Brotli compression was successfully implemented to enhance the fidelity of the stego image
Optimizing Digital Image Steganography through Hybridization of LSB and Zstandard Compression Novanto , Fandy; Nugraha, Adhitya; Joshua Calvin Kurniawan; Ariel Immanuel Prayogo
Sinkron : jurnal dan penelitian teknik informatika Vol. 8 No. 1 (2024): Articles Research Volume 8 Issue 1, January 2024
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v9i1.13187

Abstract

In response to the growing need for secure digital communication globally, this research delves into an innovative strategy for enhancing data transmission security through steganography. This inventive approach involves the integration of the conventional Least Significant Bit (LSB) method with Zstandard (Zstd) compression to elevate the quality of stego images. The study carefully explores how the synergistic use of LSB and Zstd contributes to an improved equilibrium between embedding capacity and visual quality in stego images. This hybrid methodology capitalizes on the efficiency of Zstd in reducing file size, thereby facilitating more effective data concealment using LSB. The experimental outcomes showcase a notable 51.2% increase in embedding capacity, a 4.70% elevation in PSNR value, accompanied by a substantial 51.03% decrease in MSE value. Additionally, SSIM values hover around 0.007%, indicating a perceptually minimal difference between the original and steganographically modified images. These compelling results underscore the efficacy of the proposed method, highlighting its proficiency in preserving and enhancing the quality of stego images generated through the embedding process. This research signifies a significant stride in the realm of secure digital communication, demonstrating a promising fusion of traditional LSB with advanced Zstd compression for optimizing digital image steganographic.
Komparasi Teknik Feature Selection Dalam Klasifikasi Serangan IoT Menggunakan Algoritma Decision Tree Setiawan, Dicky; Nugraha, Adhitya; Luthfiarta, Ardytha
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 8, No 1 (2024): Januari 2024
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v8i1.6987

Abstract

Presence of Internet of Things (IoT) has revolutionized how we interact with the world on our daily life by enabling various devices to connect the internet and transmit data. However, the increasingly widespread use of IoT technology also brings serious threats to cyber security and increases the number of IoT attacks. The need for robust classification models is becoming increasingly clear to anticipate these problems. This research focuses on developing an IoT attack classification model by comparing feature selection techniques that utilize data from the CIC IoT Dataset 2023. This research faces challenges such as data imbalance and the complexity of handling various features. To overcome these challenges, this research uses random undersampling techniques to balance the data and utilizes various feature selection methods, including filter based, wrapper based, and embedded based. Apart from that, this research also tries to use a decision tree algorithm. The findings reveal that the application of wrapper based techniques as feature selection together with a decision tree algorithm produces the highest accuracy of 87.32% in classifying IoT attack types. This emphasizes that the use of techniques and algorithms that are still rarely used can provide fairly good accuracy results.
Optimizing Performance of AdaBoost Algorithm through Undersampling and Hyperparameter Tuning on CICIoT 2023 Dataset Fahrezi, Sahrul Fahrezi; Nugraha, Adhitya; Luthfiarta, Ardytha; Primadya, Nauval Dwi
Techné : Jurnal Ilmiah Elektroteknika Vol. 23 No. 2 (2024)
Publisher : Fakultas Teknik Elektronika dan Komputer Universitas Kristen Satya Wacana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31358/techne.v23i2.467

Abstract

The increasing prevalence of the Internet of Things (IoT) in various sectors presents new challenges related to security and protection against cyberattacks. The connection of IoT devices to the Internet network makes them vulnerable to various types of attacks. One approach to attacking IoT devices is to perform analysis based on network traffic using machine learning algorithms such as AdaBoost. An IoT device attack prediction model was created for the purpose of predicting IoT device attacks based on network traffic. Based on research and discussion regarding optimization of the n_estimator value and algorithm in the AdaBoost algorithm on the CICIoT 2023 dataset that has been undersampled and using the grid search cv method, the most optimal n_estimator value is 500 and the most optimal algorithm value is SAMME with an accuracy rate of 0.78 and a recall value of 0.78. This optimization underscores the significance of finetuning parameters in machine learning algorithms to enhance the effectiveness of cybersecurity measures for IoT devices.
Komparasi Deteksi Single Shot Detector (SSD) Dengan YouLook (Yolov8) Menggunakan GhostFaceNet Untuk Pengenalan Wajah Pada Dataset Terbatas Salsabila, Pramesya Mutia; Luthfiarta, Ardytha; Nugraha, Adhitya; Muttaqin, Almas Najiib Imam; Zarifa, Yasmine
Building of Informatics, Technology and Science (BITS) Vol 6 No 3 (2024): December 2024
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v6i3.6225

Abstract

Face recognition has become a crucial topic in image processing and computer vision, particularly in university environments. This study explores the use of GhostFaceNet and YOLOv8 models to address the challenges of face recognition with a limited dataset, consisting of only one formal photo per individual. By applying image augmentation techniques, we improved the system's accuracy to 85%. GhostFaceNet excels in generating precise and detailed face embeddings, which are essential for accurate recognition. Meanwhile, YOLOv8 demonstrates superior speed in detecting faces under various lighting conditions and angles. Comparative results reveal that YOLOv8 achieves an accuracy of 81%, outperforming SSD, which only reaches 76%. Despite challenges related to the low quality of original images, the findings highlight the significant potential of deep learning-based face recognition systems. This research aims to compare SSD and YOLOv8 detection models using GhostFaceNet and contribute to the development of more effective and reliable face recognition methods in academic settings.
Peningkatan Akurasi Deteksi Dini Penyakit Parkinson melalui Pendekatan Ensemble Learning dan Seleksi Fitur Optimal Wulandari, Kang Andini; Nugraha, Adhitya; Luthfiarta, Ardytha; Nisa, Laila Rahmatin
Jurnal Pendidikan Informatika (EDUMATIC) Vol 8 No 2 (2024): Edumatic: Jurnal Pendidikan Informatika
Publisher : Universitas Hamzanwadi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29408/edumatic.v8i2.27788

Abstract

Early detection of Parkinson's disease (PD) is essential to enhance patient quality of life through timely intervention. This research aims to develop a predictive model using an ensemble learning approach and optimal feature selection. This experimental study employs three machine learning algorithms: random forest, XGBoost, and extra trees, optimized through hyperparameter tuning, feature selection techniques, and Kernel Principal Component Analysis (KPCA) for dimensionality reduction. The study utilizes the UCI Machine Learning Parkinson Dataset, which consists of 80 samples and 44 acoustic features extracted from patients' voices as they sustain the vowel sound "/a/" for five seconds. Results show that XGBoost achieved the highest accuracy at 88.93% after tuning and KPCA, followed by extra trees with 86.15%, and random forest with 85.47%. The application of KPCA successfully reduced data dimensionality without sacrificing accuracy, thereby improving modeling efficiency. These findings suggest that voice data holds significant potential for early PD detection and that selecting appropriate algorithms and dimensionality reduction techniques is crucial for optimizing data-driven diagnostic models.
Co-Authors Abu Salam Amanda, Nabila Rizka Ardytha Luthfiarta Ariel Immanuel Prayogo Astuti, Yani Parti Cahya, Leno Dwi Dheo Rimbano Dwi Larso Egia Rosi Subhiyakto, Egia Rosi Erick Irawadi Alwi, Erick Irawadi Erwin Yudi Hidayat Fahrezi, Sahrul Fahrezi Fahrezi, Sahrul Yudha Fahri Firdausillah Farandi, Muhammad Naufal Erza Fauzyah, Zahrah Asri Nur Firmansyah, Gustian Angga Gaffar, Andi Widya Mufila Ganiswari, Syuhra Putri Gustian, Dinda Aulia Hafiizhudin, Lutfi Azis Harun Al Azies Huda, Alam Muhammad Idayati, Irma Ika Novita Dewi Imam Muttaqin, Almas Najiib Joshua Calvin Kurniawan Krisna, Julius Immanuel Theo Kurniawan, Joshua Calvin Md. Mahadi Hasan, Md. Mahadi Miftahussilmi, Ray Ikhsandri Mumtaz, Najma Amira Muttaqin, Almas Najiib Imam Nisa, Laila Rahmatin Novanto , Fandy Novanto , The, Fandy Octaviani, Dhita Aulia Paramita, Cinantya Prayogo , Ariel Immanuel Primadya, Nauval Dwi Rachmad Hidayatullah, Rachmad Rumita, Dea Salsabila, Pramesya Mutia Setiawan, Dicky Sri Winarno Sundari Sundari Syarifah, Ulima Muna The, Fandy Novanto Wulandari, Kang Andini Wulandari, Kang, Andini Zarifa, Yasmine