Article Per Year (5 Year)
p-Index From 2021 - 2026
0.702
P-Index
This Author published in this journals
All Journal IKRA-ITH Informatika : Jurnal Komputer dan Informatika TIN: TERAPAN INFORMATIKA NUSANTARA
Nur Ariesanto Ramdhan
Universitas Muhadi Setiabudi, Brebes
Aerospace Engineering Arts Automotive Engineering Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Decision Sciences, Operations Research & Management Dentistry Earth & Planetary Sciences Economics, Econometrics & Finance Energy Engineering Environmental Science Health Professions Immunology & microbiology Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice Materials Science & Nanotechnology Mathematics Mechanical Engineering Medicine & Pharmacology Neuroscience Nursing Physics Social Sciences Veterinary Other

Published : 4 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 4 Documents
Search

 1 
Perbandingan Long Short-Term Memory dan Bidirectional Long Short-Term Memory pada Analisis Sentimen Ulasan Wisata Adam Malik; Nur Ariesanto Ramdhan; Otong Saeful Bachri
TIN: Terapan Informatika Nusantara Vol 7 No 1 (2026): June 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/tin.v7i1.10120

Abstract

Tourism reviews on Google Maps can be used to understand visitors’ perceptions of destination quality, including aspects that often become sources of satisfaction or complaints. This study analyzes reviews of Pantai Alam Indah because the destination has a large number of reviews, diverse text forms, and an imbalanced sentiment distribution. The purpose of this study is to compare the performance of Long Short-Term Memory and Bidirectional Long Short-Term Memory in tourism review sentiment analysis. The data were collected through Google Maps scraping and processed through case folding, cleaning, tokenizing, stopword removal, and stemming. The final dataset consisted of 2,702 reviews, including 2,024 positive reviews and 678 negative reviews. To reduce the effect of class imbalance, the training process applied class weighting by assigning a higher weight to the negative class. Both models used a 128-dimensional embedding layer, a 64-neuron dense layer, a sigmoid activation function in the output layer, Adam optimizer, batch size of 32, 10 epochs, and a validation split of 0.2. In addition to sentiment classification, the reviews were grouped into cleanliness, facilities, price, and general aspects using a keyword-based rule-based approach. The evaluation results show that Bidirectional Long Short-Term Memory achieved an accuracy of 75.79%, precision of 76.33%, recall of 75.79%, and F1-score of 76.04%, while Long Short-Term Memory achieved an accuracy of 75.05%, precision of 75.48%, recall of 75.05%, and F1-score of 75.25%. The performance difference between the two models was relatively small, so the results should be interpreted carefully. The aspect analysis shows that the general aspect dominated positive sentiment, while cleanliness had the highest number of negative sentiments.
Analisis Sentimen Ulasan Wisata Alun-Alun Brebes pada Google Maps Menggunakan Support Vector Machine Azkiyatul Maulida; Bambang Irawan; Nur Ariesanto Ramdhan
TIN: Terapan Informatika Nusantara Vol 6 No 8 (2026): January 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/tin.v6i8.8883

Abstract

The rapid development of information technology has encouraged the use of digital platforms as media for sharing opinions, including tourism reviews on Google Maps. Alun-Alun Brebes, as one of the most frequently visited public spaces, has generated thousands of reviews with diverse textual characteristics, making manual analysis inefficient and impractical. This study aims to analyze visitor sentiment toward Alun-Alun Brebes by applying a text mining approach using the Support Vector Machine algorithm. The dataset consists of 1,000 Google Maps reviews, including 327 reviews manually labeled as positive and negative sentiments and 673 unlabeled reviews. The research stages include data collection, text preprocessing, feature extraction using the Term Frequency–Inverse Document Frequency (TF-IDF) method, Support Vector Machine model training and testing, and automatic labeling of unlabeled data. Model performance was evaluated using a confusion matrix with accuracy, precision, recall, and F1-score metrics based on the manually labeled data. The results show that the Support Vector Machine model with a linear kernel achieved an accuracy of 100% with an F1-score of 1.00, indicating excellent sentiment classification performance. Furthermore, word cloud visualization reveals that positive sentiment is dominated by aspects related to comfort and facilities, while negative sentiment is associated with cleanliness, crowd density, and environmental management. These findings provide data-driven insights into key aspects that should be maintained and improved in managing Alun-Alun Brebes as a public space.
Evaluasi Kinerja Model Long Short-Term Memory Pada Prediksi Produksi Bawang Merah Kabupaten Brebes Ainan Zaky Nurrofiq; Nur Ariesanto Ramdhan; Otong Saeful Bachri
IKRA-ITH Informatika : Jurnal Komputer dan Informatika Vol. 10 No. 2 (2026): IKRAITH-INFORMATIKA Vol 10 No 2 Juli 2026
Publisher : Fakultas Teknik Universitas Persada Indonesia YAI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37817/ikraith-informatika.v10i2.6885

Abstract

Shallot production is one of the most important horticultural commodities that contributes to national food security and regional economic development, particularly in Brebes Regency, one of the largest shallot-producing areas in Indonesia. Production fluctuations caused by weather conditions and agricultural factors necessitate the use of accurate forecasting methods. This study aims to evaluate the performance of the Long Short-Term Memory (LSTM) method in predicting shallot production in Brebes Regency. The dataset consisted of production, harvested area, rainfall, and temperature data from 2016 to 2025, comprising 1,288 records. The research process included data preprocessing, feature engineering, Min-Max Scaling normalization, LSTM model development, and performance evaluation using Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and the coefficient of determination (R²). The proposed model employed two LSTM layers with 64 and 32 neurons, a dropout rate of 0.2, and a dense layer with 16 neurons. Experimental results showed that the model achieved an MAE of 9,478.55 quintals, an RMSE of 15,763.86 quintals, and an R² value of 0.8990. These results indicate that the model can explain 89.90% of the variation in shallot production data. Therefore, the LSTM model demonstrates excellent predictive performance and has the potential to support decision-making in agricultural production planning in Brebes Regency
Penerapan TOPSIS Multi-Kriteria untuk Pemilihan Motor Bekas Layak Jual Kembali Berdasarkan Multi-Kriteria Mohammad Umar Sasongko; Otong Saeful Bachri; Nur Ariesanto Ramdhan
IKRA-ITH Informatika : Jurnal Komputer dan Informatika Vol. 10 No. 2 (2026): IKRAITH-INFORMATIKA Vol 10 No 2 Juli 2026
Publisher : Fakultas Teknik Universitas Persada Indonesia YAI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37817/ikraith-informatika.v10i2.7265

Abstract

Selecting used motorcycles for resale inventory requires an objective assessment because each unit differs in price, age, mileage, engine capacity, type, and transmission. This study applies the multi-criteria Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) to prioritize used motorcycles suitable for resale. The dataset comprises 199 sales transactions recorded by Cahaya Emas Motor Showroom in 2025. Six criteria were evaluated: price, production year, odometer, engine capacity, motorcycle type, and transmission. Price and odometer were treated as cost criteria, while the remaining criteria were treated as benefit criteria. Their respective weights were 0.25, 0.20, 0.25, 0.10, 0.10, and 0.10. The calculation covered decision-matrix construction, normalization, weighting, determination of positive and negative ideal solutions, distance calculation, and preference scoring. The results placed a 2017 Honda Supra-X 125 first with a preference value of 0.89310533, followed by a Yamaha Vixion at 0.88259345 and a Honda Revo at 0.87931174. The findings indicate that favorable price and mileage profiles can offset differences in production year, engine capacity, type, and transmission when assessing resale suitability.
Co-Authors Adam Malik Ainan Zaky Nurrofiq Azkiyatul Maulida Bambang Irawan Mohammad Umar Sasongko Otong Saeful Bachri