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All Journal Jurnal Pilar Nusa Mandiri Jurnal Sains dan Informatika JITK (Jurnal Ilmu Pengetahuan dan Komputer) Jurnal Riset Informatika BRITech : Jurnal Ilmiah Ilmu Komputer, Sains dan Teknologi Terapan Teknomatika: Jurnal Informatika dan Komputer Innotech Journal of Applied Research In Computer Science and Information Systems
Wiliani, Ninuk
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Engineering Industrial & Manufacturing Engineering Mathematics

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OPTIMALISASI JARINGAN WIRELESS DENGAN METODE WIRELESS DISTRIBUTION SYSTEM (WDS) Arafat, Fadhilah; Sani, Asrul; Wiliani, Ninuk; Budiyantara, Agus
BRITech, Jurnal Ilmiah Ilmu Komputer, Sains dan Teknologi Terapan Vol 1 No 2 (2020): Periode Januari
Publisher : Institute Teknologi dan Bisnis Bank Rakyat Indonesia

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

Abstract

Jaringan wireless merupakan sekumpulan komputer yang saling terhubung antara satu dengan lainnya sehingga terbentuk sebuah jaringan computer menggunakan media sinyal radio. Teknologi ini merupakan perkembangan yang memungkinkan efisiensi dalam implementasi dan pengembangan jaringan komputer karena dapat meningkatkan mobilitas user dan mengingat keterbatasan dari teknologi jaringan komputer menggunakan media kabel apabila terdapat tempat ? tempat yang sulit dijangkau. WDS merupakan sistem untuk mengembangkan jaringan internet wireless tanpa harus menggunakan kabel sebagai backbone untuk access point melainkan memanfaatkan jalur wireless dari access point tersebut. Dengan Wireless Distribution System (WDS) maka user ketika sudah terkoneksi ke jaringan ketika berpindah dari tempat satu ke tempat lannya tidak perlu melakukan koneksi berulang-ulang karena masih dalam cakupan sinyal wireless yang sama. Tujuan dari kegiatan penelitian ini adalah untuk mengimplementasikan jaringan Wireless Distribution System (WDS) dan untuk melakukan analisis performa jaringan Wireless Distribution System (WDS) dengan metode Quality Of Service (QoS) pada proses koneksi wireless hotspot ketika pengguna melakukan pindah lokasi dari satu tempat ke tempat lainnya maka tidak mengalami putus koneksi.
APPLICATION OF MACHINE LEARNING FOR BITCOIN EXCHANGE RATE PREDICTION AGAINST US DOLLAR Wiliani, Ninuk; Hesananda, Rizki; Rahmawati, Nidya Sari; Prianggara, Erdham Hestiadhi
JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) Vol 7 No 2 (2022): JITK Issue February 2022
Publisher : LPPM Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1590.378 KB) | DOI: 10.33480/jitk.v7i2.2880

Abstract

Predicting a currency Exchange rate and performing analysis is an action to try to determine the price valuation of a currency or other financial instrument traded on an exchange platform. Bitcoin is a consensus network that enables new payment systems and fully digital money. Bitcoin is the first decentralized peer to peer payment network that is fully controlled by its users without any central authority or intermediary. From the user's point of view, Bitcoin is like cash in the internet world. Bitcoin can also be viewed as the most prominent triple bookkeeping system in existence today. The change in Bitcoin's behavior against the US dollar is influenced by many factors. Basic or economic factors that may be affected include inflation rates and money supply. In this study, data was collected by obtaining all data through the API provided by binance.com and labeled with the specified attribute. The modeling is done by using the rapidminer application. The process begins by taking training data that has been provided previously. The next stage is the data testing process, all operators that have been previously determined are connected and tested using the Linear Regression operator. The purpose of testing this data is to predict stock prices from the testing data that has been made by the Split Data operator, which is 19% of the total data that has been prepared.
MEASUREMENT OF READINESS AND INFORMATION TECHNOLOGY ADOPTION BASED ON ORGANIZATIONAL CONTEXT AMONG SMEs Sani, Asrul; Nawangtyas, Nur; Budiyantara, Agus; Wiliani, Ninuk
Jurnal Pilar Nusa Mandiri Vol 16 No 2 (2020): Pilar Nusa Mandiri : Journal of Computing and Information System Publishing Peri
Publisher : LPPM Universitas Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/pilar.v16i2.1642

Abstract

The importance of using information technology forces organizations to switch to using this technology in the daily activities of the organization in running a business and this cannot be separated from the SMEs organization. This research was conducted to measure the readiness level of a Small, and Medium Enterprise (SMEs) organization in the use and adoption of information technology based on the organizational context. This research uses quantitative methods by conducting surveys and interviews with policymakers organized by SMEs to avoid inaccurate information. Surveys and interviews were conducted in the Jabodetabek area. Data will be processed using PLS-SEM software for statistical analysis and inferential analysis, while for descriptive analysis using SPSS and spreadsheets. The results obtained indicate a significant relationship between the readiness level variable and the IT adoption variable.
CLOTH BAG OBJECT DETECTION USING THE YOLO ALGORITHM (YOU ONLY SEE ONCE) V5 Hesananda, Rizki; Natasya, Desima; Wiliani, Ninuk
Jurnal Pilar Nusa Mandiri Vol 18 No 2 (2022): Pilar Nusa Mandiri : Journal of Computing and Information System Publishing Peri
Publisher : LPPM Universitas Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/pilar.v18i2.3019

Abstract

The use of plastic in modern life is increasing rapidly, causing the number of people who use plastic to increase, one of which is when shopping. The function of plastic bags as packaging for luggage is not comparable to the impact caused by plastic waste in the years to come. Plastic bags take a long time, even hundreds to thousands of years, to completely decompose. In order to support the government's program to reduce the use of plastic bags, this study will discuss how to detect cloth bags as a substitute for plastic bags. In this research, a system will be implemented to detect the use of cloth bags with Roboflow and Yolo v5. After carrying out all stages of the research, it can be concluded that the goodie bag detection model has been successfully created. The detection model was created using the YOLOV5 algorithm. The dataset used consists of 102 goodie bag images. The process model uses 100 epochs with the training result mAP@0.5 is 89.8%. So, in other words, it can be said that YOLO v5 can detect goodie bags very well.
TREND ANALYSIS AND CORRELATION OF TOURIST, RESTAURANT AND HOTEL VISITS IN KUNINGAN REGENCY Hesananda, Rizki; Trihandoyo, Agus; Wiliani, Ninuk; Rahmawati, Nidya Sari
Jurnal Pilar Nusa Mandiri Vol. 20 No. 2 (2024): Pilar Nusa Mandiri : Journal of Computing and Information System Publishing Pe
Publisher : LPPM Universitas Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/pilar.v20i2.4618

Abstract

This study conducts an in-depth analysis of the tourism sector in Kuningan Regency, focusing specifically on hotel stays, tourist arrivals, and restaurant visits. Utilizing forecasting models and correlation analyses, the research aims to uncover trends and interdependencies within the sector. The primary objective is to identify actionable insights that can inform data-driven decision-making. The study employs the FBProphet algorithm for forecasting future trends and conducts Kendall correlation analysis to examine relationships among key variables. Data collected spans a time series of 84 months, from January 2016 to December 2022. FBProphet accurately predicts trends in hotel stays, while variations exist in predictions for tourist arrivals and restaurant visits. Mean values for hotel stays, tourist arrivals, and restaurant visits are 21,098.67, 135,647.33, and 130,660.83, respectively. Kendall correlation analysis reveals a moderate positive correlation (0.214, p-value = 0.004) between tourist arrivals and restaurant visits, a strong positive correlation (0.324, p-value = 1.291e-05) between tourist arrivals and hotel stays, and a weaker positive correlation (0.176, p-value = 0.019) between restaurant visits and hotel stays. These findings underscore the intricate dynamics of Kuningan Regency's tourism sector, providing stakeholders with critical insights for strategic planning. The research contributes significantly to sustainable growth initiatives by guiding stakeholders in leveraging the interconnected elements of tourism and making well-informed decisions.
Perbandingan Deteksi Objek Kemeja Putih dan Hitam menggunakan ANN dan CNN.: Indonesia Jane Arnecia, Zahra; Wiliani, Ninuk
Jurnal Teknomatika Vol 17 No 2 (2024): TEKNOMATIKA
Publisher : Fakultas Teknik dan Teknologi Informasi, Universitas Jenderal Achmad Yani Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30989/teknomatika.v17i2.1552

Abstract

This study discusses the comparison of object detection of white shirts and black shirts using the Artificial Neural Network and Convolutional Neural Network methods. The purpose of this study is to analyze the performance of the two algorithms in recognizing color differences in objects and characteristics of shirts. The dataset used is a dataset of white and black shirts from various angles. In this study, it is known that the CNN method is superior in detecting black and white shirts with an accuracy of 41% compared to ANN, which reaches an accuracy of 29%.
Perbandingan Perbandingan Kinerja ANN dan CNN dalam Tugas Klasifikasi Citra Berbasis Pembelajaran Mesin Akbar Nugroho, Faathir; Wiliani, Ninuk
Jurnal Teknomatika Vol 18 No 1 (2025): TEKNOMATIKA
Publisher : Fakultas Teknik dan Teknologi Informasi, Universitas Jenderal Achmad Yani Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30989/teknomatika.v18i1.1561

Abstract

Advances in machine learning have brought great impact on image recognition through Artificial Neural Network (ANN) and Convolutional Neural Network (CNN) approaches. This study compares the performance of both algorithms in image classification with a dataset of two classes, namely Green and Red Keychains. The dataset consists of 100 images processed through augmentation and data division of 65% for training and 35% for testing. The evaluation results show that CNN has higher accuracy, which is 88.24% to 93.94%, compared to ANN which reaches 62.12% to 67.65%. CNN is also more efficient in training time. The advantage of CNN lies in its ability to extract spatial features through convolution layers, while ANN is more suitable for simple data. This study concludes that CNN is superior for color-based image classification, although further research is needed with larger datasets.
Analisis Akurasi Perbandingan Jumlah Layer Deteksi Warna Objek Menggunakan Algoritma Convulutional Neural Network Prasetyo, Dio; Wiliani, Ninuk
Jurnal Teknomatika Vol 18 No 1 (2025): TEKNOMATIKA
Publisher : Fakultas Teknik dan Teknologi Informasi, Universitas Jenderal Achmad Yani Yogyakarta

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Abstract

This study evaluates the impact of variations in the number of layers on the implementation of the Convolutional Neural Network (CNN) algorithm in a color-based object identification and categorization system, using python language supported by the TensorFlow/Keras framework. The data used is a collection of visual data in the form of red and white cups divided into a proportion of 90% training data and 10% testing data in the dataset in this study which amounted to 62 red cup data and 59 white cup data. Testing was carried out by comparing three different convolution layer configurations of 1, 2, and 3 layers, where each configuration was integrated with a max pooling and fully connected layer. The results of the study showed an accuracy of 92%, precision of 93%, recall of 92%, and f1-score of 92%. On the other hand, the application of two and three convolution layers actually showed a significant decline with an accuracy of only 46%.
Pendekatan Deep Learning Untuk Klasifikasi Kematangan Tempe Mendoan Menggunakan Convolutional Neural Network Chusna, Nuke L; Sampoerno, Ahmad RIzqi; Wiliani, Ninuk
Jurnal Sains dan Informatika Vol. 11 No. 1 (2025): Jurnal Sains dan Informatika
Publisher : Teknik Informatika, Politeknik Negeri Tanah Laut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34128/jsi.v11i1.1245

Abstract

Tempe mendoan dikenal dengan makanan yang memiliki kematangan yang berbeda dalam tiap jenisnya. Penelitian ini bertujuan untuk mengembangkan sistem klasifikasi tingkat kematangan tempe mendoan menggunakan algoritma Convolutional Neural Network (CNN). Dataset yang digunakan terdiri dari 400 data citra tempe mendoan yang dikategorikan ke dalam empat level kematangan: Level 1 (6 jam pertama), Level 2 (12 jam), Level 3 (18 jam), dan Level 4 (24 jam). Berbagai arsitektur CNN diuji dalam penelitian ini, dan hasil terbaik diperoleh menggunakan arsitektur VGG16 dengan nilai AUC sebesar 0,94 atau 95%, menunjukkan kemampuan klasifikasi yang sangat baik. Sistem ini dirancang untuk membantu produsen, seperti karyawan dan penjual tempe mendoan, dalam menentukan tingkat kematangan tempe secara tepat. Dengan sistem ini, tempe yang dihasilkan memiliki kualitas kematangan optimal, sehingga dapat meningkatkan daya tarik produk dan minat konsumen. Penelitian ini memberikan kontribusi pada penerapan teknologi berbasis deep learning untuk meningkatkan kualitas produksi dalam industri makanan tradisional.
Enhancing Ulos Batik Pattern Recognition through Machine Learning: A Study with KNN and SVM Chusna, Nuke L.; Wiliani, Ninuk; Abdillah, Achmad Feri
Jurnal Riset Informatika Vol. 6 No. 3 (2024): June 2024
Publisher : Kresnamedia Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34288/jri.v6i3.311

Abstract

This research aims to develop an automated classification system to accurately identify and classify Ulos batik patterns using K-Nearest Neighbors (K-NN) and Support Vector Machine (SVM) techniques. The method is based on computer vision technology and texture analysis using the Gray-Level Co-occurrence Matrix (GLCM). The dataset consists of 1,800 images of Ulos fabric categorized into six main motif classes. The preprocessing process involves converting images to grayscale and extracting features with GLCM. Two classification algorithms, K-NN and SVM, were used for modeling, with evaluation using confusion matrix metrics and Area Under Curve (AUC). Evaluation results show that the K-NN model has an accuracy of 82%, while SVM has an accuracy of 57%. The analysis also highlights the superiority of K-NN in distinguishing Ulos fabric patterns. This research contributes to cultural preservation and the development of the creative industry by introducing an effective automated classification system for Ulos fabric patterns.
Co-Authors Abdillah, Achmad Feri Abdul Rahman, Titik Khawa Adi Saputra, Mulyana Agus Trihandoyo Akbar Nugroho, Faathir Andiani Andiani, Andiani Arafat, Fadhilah Asrul Sani Budiyantara, Agus Chusna, Nuke L Chusna, Nuke L. Endang Fitria Hesananda, Rizki Jane Arnecia, Zahra Khawa, Titik Natasya, Desima Nawangtyas, Nur Prasetyo, Dio Prianggara, Erdham Hestiadhi Rachmalia Feta, Neneng Rahmawati, Nidya Sari Ramli, Suzaimah Sampoerno, Ahmad RIzqi Setiaji, Hari Simanjuntak, Yoel Tri Hartati, Tri Wahyono, Herry