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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 Jurasik (Jurnal Riset Sistem Informasi dan Teknik Informatika) Teknomatika: Jurnal Informatika dan Komputer Jurnal Rekayasa Informasi Innotech Journal of Informatics and Advanced Computing Journal of Applied Research In Computer Science and Information Systems
Ninuk Wiliani
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Engineering Industrial & Manufacturing Engineering Mathematics

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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%.
Penerapan Rapidminer Dengan K-Means Cluster Pada Daerah Terjangkit AIDS Ninuk Wiliani; Anang Martoyo; Angel Tiarma Sipahutar; Alfi Prabowo; Eksa Manda Pramaswari; Novita Maharani Suparta
Journal of Informatics and Advanced Computing (JIAC) Vol 2 No 2 (2021): Journal of Informatics and Advanced Computing
Publisher : Universitas Pancasila

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35814/jiac.v2i2.3255

Abstract

Aids merupakan sekumpulan gejala akibat kekurangan atau kelemahan sistem kekebalan tubuh yang dibentuk setelah manusia lahir. Aids disebabkan oleh virus yang disebut HIV atau Human Immunodeficiency Virus. Apabila seseorang terkena HIV, maka tubuh manusia akan mencoba menyerang infeksi. Sistem kekebalan manusia yang disebut dengan antibody akan menyerang HIV tersebut. Pada kasus AIDS terdapat banyak data mentah yang dapat diolah dan dikembangkan untuk membantu melihat daerah mana yang mempunyai potensi AIDS terburuk. Metode yang digunakan pada penelitian ini menggunakan metode Klustering K-Mean untuk memperoleh gambaran dari setiap wilayah di Indonesia.
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.
Perbandingan Model CNN dan SVM untuk Klasifikasi Jenis Footwear pada Dataset Alas Kaki Berbasis Citra Gina Annisa; Ninuk Wiliani
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.v18i1.1564

Abstract

The classification of footwear types, such as boots, sandals, and shoes, is a significant challenge in the development of image recognition systems powered by artificial intelligence. This study aims to compare the performance of two popular classification models, namely Convolutional Neural Network (CNN) and Support Vector Machine (SVM), in recognizing footwear types. The dataset used is the Footwear-Shoe vs Sandal vs Boot Image Dataset, consisting of 3000 images for each category with a resolution of 136x102 pixels in RGB format. The methodology includes training and testing both models using optimized parameters to measure accuracy, precision, and computational efficiency. The results show that CNN achieves an accuracy of 98%, while SVM reaches an accuracy of 96%. The findings indicate that CNN is more suitable for applications requiring high accuracy, while SVM is an effective alternative in resource-constrained scenarios. This study offers significant contributions to understanding model performance in image-based footwear classification using machine learning.
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

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

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.
Performance Assessment of ARIMA and LSTM Models in Prediction Using Root Mean Square Error (RMSE) Andiani, Andiani; Simanjuntak, Yoel; Wiliani, Ninuk
Journal of Applied Research In Computer Science and Information Systems Vol. 2 No. 1 (2024): June 2024
Publisher : PT. BERBAGI TEKNOLOGI SEMESTA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61098/jarcis.v2i1.181

Abstract

Cryptocurrency is a digital financial asset that serves as a medium of exchange, with its ownership guaranteed using decentralized cryptographic technology, and it has become a growing investment tool. Solana is one of the highly sought-after Cryptocurrencies by investors. The market price of Solana exhibits highly volatile movements, which are considered risky for investment purposes, as it offers both high potential profits and losses. In this regard, time series data prediction models are used to analyze and forecast the price movements of Solana. By comparing the performance of ARIMA and LSTM models in predicting the closing price of Solana using RMSE as a testing metric, the aim is to determine the efficiency level of both ARIMA and LSTM models. The research results show that the ARIMA model with an order of (2,1,3) achieves an RMSE of 0.019 (1.9%) with an accuracy of 98.1%, while the LSTM model with a data training ratio of 70:30%, a batch size of 64, and 500 epochs has an RMSE of 0.075 (7.5%) with an accuracy of 92.5%. The conclusion drawn from the conducted experiments is that, in the case of using time series data samples from Solana, the ARIMA method demonstrates higher accuracy compared to the LSTM method.
K-Means Clustering for Identifying Traffic Accident Hotspots in Depok City Wahyono, Herry; Setiaji, Hari; Hartati, Tri; Wiliani, Ninuk
Journal of Applied Research In Computer Science and Information Systems Vol. 2 No. 1 (2024): June 2024
Publisher : PT. BERBAGI TEKNOLOGI SEMESTA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61098/jarcis.v2i1.182

Abstract

This study applies the K-Means clustering algorithm to support decision-making processes related to identifying traffic accident-prone areas in Depok City over a three-year period (2020-2022). Secondary data was obtained from the Traffic Accident Unit of the Depok Metro Police, encompassing monthly traffic accident recapitulations for each district. The data underwent preprocessing steps, including integration and selection of relevant attributes. Using RapidMiner, the data was clustered into three distinct groups, with the optimal number of clusters determined by the Davies-Bouldin Index (DBI), which yielded a score of 0.896, indicating a satisfactory clustering result. The findings reveal that four districts—Beji, Cimanggis, Pancoran Mas, and Sukmajaya—are identified as high-risk areas for traffic accidents. These results are expected to assist local authorities in implementing targeted safety measures. The study demonstrates that the K-Means clustering method is a viable tool for analyzing traffic accident data and can significantly contribute to improving road safety in urban areas
Co-Authors Abdillah, Achmad Feri Abdul Rahman, Titik Khawa Adi Saputra, Mulyana Agus Trihandoyo Akbar Nugroho, Faathir Alfi Prabowo Anang Martoyo Andiani Andiani, Andiani Angel Tiarma Sipahutar Anita Putri Valeria Dhiu Lusi Arafat, Fadhilah Asrul Sani Aulia Fahmi Budiyantara, Agus Chusna, Nuke L Chusna, Nuke L. Eksa Manda Pramaswari Endang Fitria Gina Annisa Herry Wahyono Heru Abrianto Hesananda, Rizki Jane Arnecia, Zahra Khawa, Titik Latifah Maspiyanti, Febri Michael Valiant Mulyana Adi Supatra Natasya, Desima Nawangtyas, Nur Novita Maharani Suparta Nur Hikmah Prasetyo, Dio Prianggara, Erdham Hestiadhi Rachmalia Feta, Neneng Rahmawati, Nidya Sari Ramli, Suzaimah Rizki Hesananda Sampoerno, Ahmad RIzqi Setiaji, Hari Shabrina Putri Simanjuntak, Yoel Tanuwidjaya, Michael Keyva Valiant Tri Hartati, Tri Wahyono, Herry Yunita Achsanty