Article Per Year (5 Year)
p-Index From 2021 - 2026
2.116
P-Index
This Author published in this journals
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
Unknown Affiliation
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Engineering Industrial & Manufacturing Engineering Mathematics

Published : 16 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 16 Documents
Search

 1   2 
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
Identifying Damage Types in Solar Panels Through Surface Image Analysis with Naive Bayes Wiliani, Ninuk; Abdul Rahman, Titik Khawa; Ramli, Suzaimah
Journal of Applied Research In Computer Science and Information Systems Vol. 2 No. 2 (2024): December 2024
Publisher : PT. BERBAGI TEKNOLOGI SEMESTA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61098/jarcis.v2i2.200

Abstract

The growing utilization of solar panels as a renewable energy source requires efficient maintenance solutions to guarantee their best functioning. Identifying and categorizing faults on solar panel surfaces is essential for maintenance, as these defects considerably affect energy output and system efficiency. This study investigates the utilization of statistical feature extraction methods alongside Bernoulli Naive Bayes (BNB) and Gaussian Naive Bayes (GNB) algorithms to categorize different defect types, such as cracks, scratches, spots, and non-defective surfaces, through digital image analysis. Statistical criteria, including recall, specificity, and area under the curve (AUC), are employed to assess model performance. The findings indicate that the GNB algorithm surpasses BNB, with a mean average precision (mAP) of 39.83% with an 85:15 training-test ratio, whereas BNB reaches a maximum mAP of 29.25% at a 90:10 ratio. Nonetheless, both models demonstrate constraints in precision, as indicated by a total AUC of 0.644. This work illustrates the potential of statistical feature extraction approaches for defect classification, while emphasizing the necessity for future improvements to boost the efficacy of feature extraction and classification techniques in practical applications
USABILITY APLIKASI RMS BOOKING BERBASIS WEB DENGAN METODE IMPORTANCE PERFORMANCE ANALYSIS. Wiliani, Ninuk; Endang; Fitria; Rachmalia Feta, Neneng
Innotech: Jurnal Ilmu Komputer, Sistem Informasi dan Teknologi Informasi Vol 1 No 1 (2024): Innotech Issue Januari 2024
Publisher : Universitas Siber Indonesia

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

Abstract

Online booking service will make it easier for users to rent meeting rooms, so that it can be done more efficiently in time and place, Bank BRI provides online booking facilities, namely RMS booking. Use can make it easier for users, especially the board of directors who want to use the room for meetings. The purpose of this study is to identify what indicators need to be improved based on the importance of performance analysis. The identification results show that there are no attributes in quadrant I "main priority", there are 4 attributes that are in quadrant II "maintain achievement", there are 4 attributes that are in quadrant III "low priority" and there is 1 attribute that is in quadrant IV " excessive". RMSBooking.com usability improvement focuses on 2 attributes that are close to quadrant I, namely (1) Users can easily understand how to use and (5) Users can easily operate Navigation.
SISTEM PENDUKUNG KELAYAKAN PEMBERIAN KREDIT ULTRA MIKRO DENGAN METODE PERBANDINGAN EKSPONENSIAL. Wiliani, Ninuk; Adi Saputra, Mulyana
Innotech: Jurnal Ilmu Komputer, Sistem Informasi dan Teknologi Informasi Vol 1 No 1 (2024): Innotech Issue Januari 2024
Publisher : Universitas Siber Indonesia

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

Abstract

The process of providing credit is now can be done easily through the presence of BRILink agents with additional facilities besides payment points namely as partners of ultra micro loans which are now popularly known as UMi Partners, which is BRILink agents can distribute micro loans with a loan range of 1 up to 5 million. The study used the Exponential Comparison Method (MPE) to determine lending decisions in order to optimize all existing information systems with the implementation of systems that can be used and applied by UMi partners to improve the process of granting creditworthiness to partners. The system used the NetBeans IDE with Java programming. The results of the calculations were precisely calculated by the system according to the manual calculations that have been carried out so that the results can be applied properly so as to produce creditworthiness which helps the loan granting process.
Peningkatan Kontras Pada PreProcessing Gambar Permukaan Solar Panel dengan Histogram wiliani, ninuk; Khawa, Titik; Ramli, Suzaimah
Innotech: Jurnal Ilmu Komputer, Sistem Informasi dan Teknologi Informasi Vol 2 No 1 (2025): Innotech Issue Januari 2025
Publisher : Universitas Siber Indonesia

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

Abstract

Contrast enhancement in solar panel surface images is a crucial step to support inspection and defect detection processes, such as cracks, scratches, and stains. This study aims to enhance the visibility of details in solar panel surface images using the Contrast Limited Adaptive Histogram Equalization (CLAHE) method. The evaluation was conducted by comparing visual results, histogram distribution, and image quality measurements. The results indicate that the CLAHE method effectively redistributes pixel intensity more evenly than traditional histogram methods and produces images with better contrast without losing essential information. This enhancement supports the identification of defect characteristics on solar panel surfaces. These findings significantly contribute to the development of image processing technologies for solar panel maintenance and other applications requiring improved visual quality
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