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All Journal Jiko (Jurnal Informatika dan komputer) bit-Tech Jurnal Impresi Indonesia Journal of Information Systems and Technology Research
Aviolla Terza Damaliana
Universitas Pembangunan Nasional Veteran Jawa Timur
Religion Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Education Electrical & Electronics Engineering Law, Crime, Criminology & Criminal Justice Social Sciences

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COMPARISON OF DECISION TREE AND RANDOM FOREST METHODS IN THE CLASSIFICATION OF DIABETES MELLITUS Nova Auliyatul Maulidiyyah; Trimono Trimono; Aviolla Terza Damaliana; Dwi Arman Prasetya
JIKO (Jurnal Informatika dan Komputer) Vol 7 No 2 (2024)
Publisher : Program Studi Teknik Informatika Universitas Khairun

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33387/jiko.v7i2.8316

Abstract

Diabetes mellitus is a deadly disease caused by the failure of the pancreas to produce enough insulin. Indonesia ranks fifth in the world with the number of people with diabetes in 2021 at around 19.47 million, and this number continues to increase. One of the main challenges in diabetes management is to make the right classification between type 1 and type 2 diabetes, as misdiagnosis can result in inappropriate treatment and worsen the patient's condition. This study uses a machine learning approach to compare Decision Tree and Random Forest methods in classifying type 1 and type 2 diabetes mellitus. The goal is to identify the most effective model in predicting the type of diabetes based on medical record data. The comparison was done using k-fold cross validation and confusion matrix. The results showed that Random Forest provided an average accuracy of 94%, while Decision Tree reached 93% during cross validation testing. Although both models were able to perform well in classification, Random Forest showed a more stable performance and a slight edge in accuracy over Decision Tree. Evaluation with the confusion matrix showed that the Decision Tree model achieved 93% accuracy compared to Random Forest's 91%. In addition, the Decision Tree model also had a lower number of prediction errors, 7, compared to 9 for Random Forest. The most influential variables in classification also differed between the two models, showing the unique advantages and characteristics of each approach.
Price Dynamics and Financial Risk Analysis A Neural Hierarchical Time-Series Forecasting Approach Vannesa Nathania; Aviolla Terza Damaliana; Shindi Shella May Wara
Journal of Information Systems and Technology Research Vol. 5 No. 2 (2026): May 2026
Publisher : Ali Institute or Research and Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55537/jistr.v5i2.1583

Abstract

The highly volatile nature of cryptocurrency prices often causes conventional predictive models to fail in capturing complex nonlinear patterns. This study integrates the Neural Hierarchical Interpolation for Time Series Forecasting (N-HiTS) deep learning model with nonparametric Historical Simulation Value-at-Risk (VaR) method for price forecasting and risk analysis. Using univariate data on daily Ethereum closing prices from January 1, 2021, to January 31, 2025 (N = 1,491 observations), the out-of-sample evaluation was executed using a rolling cross-validation scheme initiated testing from a cut-off point in April 2024 through December 2024, where each evaluation window was set for the next 30 days. The research results show that the N-HiTS model can predict price dynamics with high accuracy, achieving an MAPE of 3.25%, an MAE of 107.825, an RMSE of 136.83, and directional accuracy of 48.28%. Risk analysis using historical simulation yielded a VaR of -6.23% at a 95% confidence level.
Pendekatan Time Series Decomposition (STL) Dalam Prediksi Kecelakaan Berbasis Kepadatan Lalu Lintas Sebagai Dasar Kebijakan Di Tol Surabaya-Gempol Rakha Rizky Mahendra; Aviolla Terza Damaliana; I Gede Susrama Mas Diyasa
Jurnal Impresi Indonesia Vol. 4 No. 5 (2025): Jurnal Impresi Indonesia
Publisher : Riviera Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58344/jii.v4i5.6491

Abstract

Kecelakaan lalu lintas di jalan tol tetap menjadi masalah kritis yang mempengaruhi keselamatan publik dan stabilitasekonomi. Penelitian ini mengusulkan penggunaan dekomposisi Seasonal-Trend menggunakan LOESS (STL) untukmemprediksi risiko kecelakaan berdasarkan data volume lalu lintas di jalan tol Surabaya-Gempol. Data dari Januari 2022hingga Desember 2023, termasuk volume lalu lintas harian dan laporan kecelakaan, diuraikan menjadi komponen tren,musiman, dan residu untuk mengidentifikasi pola. Korelasi positif sedang (r = 0,4882) ditemukan antara volume lalulintas dan frekuensi kecelakaan. Analisis STL mengungkapkan puncak musiman mingguan yang konsisten di akhir pekan,terutama hari Sabtu. Model prediktif yang dikembangkan berhasil mengidentifikasi 11 hari berisiko tinggi pada Januari2024. Berdasarkan temuan tersebut, delapan rekomendasi kebijakan berbasis waktu dirumuskan, termasuk manajemenlalu lintas dinamis, pemantauan real-time, dan peningkatan pengawasan selama periode puncak. Penelitian ini menyumbangkan kerangka kerja berbasis data baru untuk manajemen keselamatan lalu lintas, menggabungkandekomposisi deret waktu dengan panduan kebijakan yang dapat ditindaklanjuti. Tidak seperti penelitian sebelumnya yanghanya berfokus pada prediksi volume, atau pada konteks jalan non-tol, penelitian ini memajukan penerapan STL untukidentifikasi risiko real-time di jalan tol Indonesia. Implikasinya menekankan integrasi sistem lalu lintas cerdas dan potensiprakiraan berbasis STL sebagai fondasi strategi keselamatan jalan nasional.
Bus Passenger Demand Forecasting Using A Hybrid ARIMA–MLP Model Naufal Baihaqi Moerrin; Aviolla Terza Damaliana; I Gede Susrama Mas Diyasa
bit-Tech Vol. 8 No. 3 (2026): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i3.3549

Abstract

Accurate passenger demand forecasting is crucial for operational planning and service reliability in public transportation systems. Despite the effectiveness of traditional models, existing approaches often struggle with nonlinear fluctuations in demand, which limits their ability to adapt to real-world variability. This study proposes a hybrid forecasting framework that combines the Autoregressive Integrated Moving Average (ARIMA) model with a Multi-Layer Perceptron (MLP) neural network for short-term passenger demand prediction. By using ARIMA to capture linear components like trend, seasonality, and autocorrelation, and MLP to model the residuals that contain nonlinear patterns, the proposed approach integrates the strengths of both models. This hybrid method addresses gaps in current forecasting techniques by improving adaptability and precision. Empirical analysis was conducted using daily passenger count data from Bus Trans Jatim during 2023–2024. Data preprocessing included exploratory time series analysis, variance stabilization, and outlier assessment to ensure compatibility with the modeling assumptions. Forecast performance was evaluated using the Mean Absolute Percentage Error (MAPE). The results show that the hybrid ARIMA–MLP model achieved a MAPE of 4.95%, outperforming the standalone ARIMA model in providing more adaptive and accurate short-term forecasts. These findings have practical implications for public transportation planning, enabling more responsive and efficient operations, particularly for forecasting demand fluctuations.
Co-Authors Dwi Arman Prasetya I Gede Susrama Mas Diyasa Naufal Baihaqi Moerrin Nova Auliyatul Maulidiyyah Rakha Rizky Mahendra Shindi Shella May Wara Trimono, Trimono Vannesa Nathania