Article Per Year (5 Year)
p-Index From 2021 - 2026
6.857
P-Index
This Author published in this journals
All Journal Information Technology and Telematics Dinamik Jupiter Publikasi Eksternal Jurnal Buana Informatika Pixel : Jurnal Ilmiah Komputer Grafis JUITA : Jurnal Informatika Proceeding SENDI_U Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) JURNAL ILMIAH INFORMATIKA JURNAL TEKNIK INFORMATIKA DAN SISTEM INFORMASI Jurnal Teknik Informatika UNIKA Santo Thomas INTECOMS: Journal of Information Technology and Computer Science Building of Informatics, Technology and Science Jurnal Teknologi Informasi dan Terapan (J-TIT) Jurnal Manajemen Informatika dan Sistem Informasi Jurnal JTIK (Jurnal Teknologi Informasi dan Komunikasi) Jurnal Teknik Elektro dan Komputasi (ELKOM) JATI (Jurnal Mahasiswa Teknik Informatika) Aiti: Jurnal Teknologi Informasi Dinamika Informatika: Jurnal Ilmiah Teknologi Informasi Jurnal Teknik Informatika (JUTIF) JURPIKAT (Jurnal Pengabdian Kepada Masyarakat) International Journal of Social Learning (IJSL) Jurnal Teknik Informatika Unika Santo Thomas (JTIUST) Jurnal Teknoif Teknik Informatika Institut Teknologi Padang Jurnal Informatika Teknologi dan Sains (Jinteks) Maritime Park: Journal Of Maritime Technology and Socienty Jurnal Pengabdian Masyarakat Intimas (Jurnal INTIMAS): Inovasi Teknologi Informasi Dan Komputer Untuk Masyarakat Eduvest - Journal of Universal Studies Seminar Nasional Teknologi dan Multidisiplin Ilmu Kesatria : Jurnal Penerapan Sistem Informasi (Komputer dan Manajemen) INOVTEK Polbeng - Seri Informatika
Eri Zuliarso
Universitas Stikubank
Aerospace Engineering Agriculture, Biological Sciences & Forestry Arts Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice Library & Information Science Mechanical Engineering Neuroscience Public Health Social Sciences Other

Published : 74 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 3 Documents
Search
Journal : INOVTEK Polbeng - Seri Informatika

 1 
Application of Machine Learning in Analyzing Bandwidth Usage Patterns for Internet Service Providers Nurmakhlufi, Alfin Hilmy; Zuliarso, Eri
INOVTEK Polbeng - Seri Informatika Vol. 10 No. 3 (2025): November
Publisher : P3M Politeknik Negeri Bengkalis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35314/h2p5s858

Abstract

This study aims to address bandwidth management challenges faced by Internet Service Providers (ISP) through the application of machine learning techniques for analyzing usage patterns and forecasting future demand. A key novelty of this research lies in the combined use of K-Means clustering for dynamic customer segmentation based on real-time utilization patterns, followed by accurate short-term forecasting using Random Forest regression, specifically tailored for corporate client bandwidth planning. Data was collected from 12 corporate customers over a three-month period (January–March 2025) at five-minute intervals using the PRTG Network Monitor. The analytical workflow included data preprocessing, customer segmentation using K-Means clustering, and short-term bandwidth prediction using Random Forest regression. The clustering results classified customers into three main categories: underutilized, optimal, and overutilized, with a silhouette score of 0.663 indicating good cluster separation. The regression model achieved a coefficient of determination (R²) of 0.931, a Mean Absolute Error (MAE) of 0.036 Mbps, and a Root Mean Square Error (RMSE) of 0.062 Mbps, demonstrating high predictive accuracy for operational planning. This study is limited by the relatively short observation period and the exclusion of external variables in the modeling process. For future work, the use of deep learning methods such as Long Short-Term Memory (LSTM) or Temporal Convolutional Networks (TCN) is recommended, along with the integration of external features such as time-based traffic anomalies and customer profiles, to enhance model robustness, accuracy, and generalization.
Decade Rainfall Prediction Using Prophet Algorithm and LSTM (Case Study in Banjarnegara Regency) sulis, Sulistiyowati; Eri Zuliarso
INOVTEK Polbeng - Seri Informatika Vol. 10 No. 3 (2025): November
Publisher : P3M Politeknik Negeri Bengkalis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35314/j3mbxq89

Abstract

Hydrometeorological disasters such as floods and landslides in Banjarnegara Regency are closely related to fluctuating rainfall variability. This study aims to predict decadal (10-day) rainfall by comparing the performance of the Prophet algorithm and the Long Short-Term Memory (LSTM) model. The dataset comprises daily rainfall records from 14 observation stations spanning the period 2005–2024. The research stages included preprocessing, modelling, hyperparameter optimization using Optuna, and evaluation with Root Mean Square Error (RMSE) and Mean Absolute Error (MAE). The results indicate that the Prophet model outperformed LSTM in most locations, with an average RMSE of 69.55 and MAE of 53.05, lower than LSTM, which recorded 73.03 and 55.72, respectively. The ensemble averaging model produced competitive results at several locations, although it was less responsive to sharp fluctuations in rainfall. These findings confirm that Prophet is more effective in capturing seasonal patterns and long-term trends, thus providing significant potential to support climate-based disaster mitigation systems in vulnerable areas such as Banjarnegara
Rainfall Prediction using the SARIMAX and LSTM Methods in Semarang City Rudi setyo P; Zuliarso, Eri
INOVTEK Polbeng - Seri Informatika Vol. 10 No. 3 (2025): November
Publisher : P3M Politeknik Negeri Bengkalis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35314/6sg7m889

Abstract

The purpose of this study is to predict the decade rainfall in Semarang City using two main methods, namely Seasonal Autoregressive Integrated Moving Average with Exogenous Variables (SARIMAX) and Long Short Term Memory (LSTM). The methodology of this study begins with data preprocessing, which includes data deletion analysis using dropna and data normalization using Min-Max Scaling to reduce the scale to between 0 and 1. The dataset is then divided into 80% training data and 20% test data. The validity of the data (X_test, Y_test) using the best 56-epoch data validation (val_loss) is better than the validity of the training data (loss). On the other hand, SARIMAX uses the (2,1,2), (2,1,2,36) model, and its validation techniques include Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Coefficient of Determination (R2). Specifically, the RMSE of the LSTM model is 19.6, and the RMSE of the SARIMAX is 31.05. The MAE of LSTM is 15.0, SARIMAX is 24.5, the R2 of LSTM is 0.814, and SARIMAX is 0.52. Lower RMSE and MAE values indicate lower prediction errors, but a higher R2 value of 1 indicates that LSTM can explain 81% of the actual data variation, which is better than SARIMAX, which is only about 52%. The main finding of this study is that the LSTM model performs better when recommending rainfall datasets.
Co-Authors . Sulastri . Suwarsito Aditya Bobby Rizki Agus Prasetyo Utomo Ahmad Fathoni Allaam, Ekananda Naufal Amalina, Hana Anefia Mutiara Atha ariadi, hastomo Arief Jananto Ariyani, Dewi Ayu Arya Sena Setyanegara Astuti, Vivi Rizki Indri Bambang Wiranto, Joko budi hartono Cahyono Purbomartono Dewi Handayani Untari Ningsih Dian Kristiawan Nugroho Dwi Agus Diartono Dwi Budi Santoso Edy Winarno Fathoni, Aliffatul Majid Februarianti, Herny Fidiniari, Fathia Gunadi, Ilham Hastomo Ariadi Hermawan, Taufan Herny Februariyanti Herny Februariyanti Hersatoto Listiyono Hindayati Mustafidah Indah Widhi Prastika Indriani, Cheryllista Isworo Nugroho Khabib Mustofa Kogoya, Erminus Kusuma Satria, Hafiyan Nafan Mardi Siswo Utomo Munna, Aliyatul Nur Rohim Nurmakhlufi, Alfin Hilmy Priambodo, Wisnu Putra Alva, Ilyasa Garuda Putri, Indah Lissiana R. Soelistijadi Radyanto, Mohammad Riza Rara Sri Artati Redjeki Ratmoko, Hari Rezal Arminto, Edo Rina Candra NS Rizky Abdul Malik Rosyida, Elviana Rudi setyo P Ruslana, Zauyik Nana S Sunardi Safra, Icha Adellia Safra, Kyla Kaneshia Sariyun Naja Anwar Sri Eniyati Sri Hartati Sudiantoro, Adhi Viky Sugiyamto Sugiyamto, Sugiyamto Sulastri Sulastri Sulastri Sulastri Sulatri sulis, Sulistiyowati Sunardi Sunardi Toman, Sarah Husain Velamentosa, Desvio Wahyu Prasetyo Wibowo, Sayogo Wismarini T.D. Wismarini, Th Dwiati Wiwien Hadikurniawati Yassin Achmad Nur Aziz Yohanes Suhari Yunus Anis Yunus Anis, Yunus Yuwan, Ridho Pangestu Zulfa Febriana Dewi Mellinia