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Imam Budiawan
Universitas Bina Sarana Informatika
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Analisis Perbandingan Algoritma Random Forest, SVM, dan Logistic Regression untuk Menentukan Model Terbaik Prediksi Penyakit Diabetes Alghifar Firgiawan; Fauzan Nawwir Andriansyah; Raihan Naufal Ramadhan; Sumanto Sumanto; Imam Budiawan; Roida Pakpahan
Jurnal Teknik Informatika dan Teknologi Informasi Vol. 5 No. 3 (2025): Desember: Jurnal Teknik Informatika dan Teknologi Informasi
Publisher : Lembaga Pengembangan Kinerja Dosen

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55606/jutiti.v5i3.6213

Abstract

Diabetes is a chronic metabolic disorder characterized by elevated blood glucose levels caused by the body’s inability to produce or effectively respond to insulin. The increasing prevalence of diabetes in Indonesia requires accurate data-driven early detection systems to assist the diagnostic process. This study aims to compare the performance of three machine learning algorithms—Support Vector Machine (SVM), Random Forest, and Logistic Regression—in predicting diabetes disease based on patient clinical data. The dataset used was obtained from the Kaggle repository titled 100,000 Diabetes Clinical Dataset. The research process was conducted using the Orange Data Mining software through several stages, including data preprocessing, One-Hot Encoding transformation, model training, and evaluation using the 10-Fold Cross Validation method. The results show that the Random Forest algorithm achieved the best performance with an accuracy of 97.1%, followed by Logistic Regression at 96.0% and SVM at 92.3%. These findings indicate that ensemble-based methods such as Random Forest outperform others in producing stable and accurate predictions for diabetes diagnosis
Komparasi Algoritma Machine Learning (Random Forest, Gradient Boosting, dan Ada Boosting) untuk Prediksi Tingkat Penyakit Alzheimer Muhammad Raviansyah; Andika Amansyah; Farhan Fadhilah; Sumanto Sumanto; Imam Budiawan; Roida Pakpahan
Jurnal Teknik Informatika dan Teknologi Informasi Vol. 5 No. 3 (2025): Desember: Jurnal Teknik Informatika dan Teknologi Informasi
Publisher : Lembaga Pengembangan Kinerja Dosen

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55606/jutiti.v5i3.6227

Abstract

Alzheimer’s disease is one of the most common forms of progressive dementia and has become a major global health challenge as the aging population continues to increase. Early detection of this disease is crucial to mitigate its social, economic, and health impacts. In this context, data-driven approaches using machine learning algorithms can be utilized to predict Alzheimer’s risk more accurately. This study aims to compare the performance of three ensemble learning algorithms—Gradient Boosting, Random Forest, and AdaBoost—in predicting the risk level of Alzheimer’s disease using the public Alzheimer’s Disease Dataset, which includes demographic, clinical, and lifestyle data. The research process involved several stages, including data preprocessing, splitting data into training and testing sets, model training using cross-validation, and performance evaluation based on accuracy, precision, recall, F1-score, and AUC metrics. The experimental results show that the Gradient Boosting algorithm achieved the best performance with an accuracy of 0.956, an F1-score of 0.956, and an AUC of 0.985, demonstrating its ability to capture complex non-linear relationships among features such as age, MMSE score, and lifestyle factors. Meanwhile, Random Forest and AdaBoost achieved competitive yet slightly lower performance. These findings indicate that ensemble boosting approaches, particularly Gradient Boosting, hold great potential for medical decision-support systems in the early detection of Alzheimer’s disease and can serve as a foundation for developing more accurate and adaptive predictive models in the future.
Analisis Pola Pergerakan dan Prediksi Harga Emas Menggunakan Regresi Linear serta Model Time Series ARIMA dan VAR Roni Saputra Pratama; Ryehan Alfiansyah; Prasetyo Adi Suwignyo; Sumanto Sumanto; Imam Budiawan; Roida Pakpahan
Jurnal Teknik Informatika dan Teknologi Informasi Vol. 5 No. 3 (2025): Desember: Jurnal Teknik Informatika dan Teknologi Informasi
Publisher : Lembaga Pengembangan Kinerja Dosen

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55606/jutiti.v5i3.6233

Abstract

Gold is one of the most popular investment instruments due to its stable value and ability to protect assets against inflation. However, its price tends to fluctuate significantly, influenced by macroeconomic factors such as exchange rates, interest rates, and global geopolitical conditions. This study aims to analyze the movement patterns and predict gold prices based on historical data from 2019 to 2024 using the Linear Regression method and Time Series models, namely ARIMA and VAR. The analysis process was carried out using Orange Data Mining software, which enables the application of machine learning algorithms through a visual and interactive interface without manual coding. The dataset used consists of daily gold closing prices, processed and tested to evaluate model accuracy using Root Mean Square Error (RMSE) and Correlation Coefficient (R) indicators. The results indicate that the Linear Regression model effectively captures the general trend of gold prices, while ARIMA and VAR models produce more accurate forecasts based on historical fluctuations. The integration of regression and time series approaches improves prediction reliability. Overall, this research contributes to the development of financial data analysis and provides insights for investors in making more informed and data-driven investment decisions.
Penerapan Algoritma K-Means untuk Pengelompokan Kerentanan Wilayah terhadap Kasus DBD di Kota Bandung Zahwa Asfa Rabbani; Alya Avisa; Paulus Paulus; Sumanto Sumanto; Imam Budiawan; Roida Pakpahan
Jurnal Teknik Informatika dan Teknologi Informasi Vol. 5 No. 3 (2025): Desember: Jurnal Teknik Informatika dan Teknologi Informasi
Publisher : Lembaga Pengembangan Kinerja Dosen

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55606/jutiti.v5i3.6239

Abstract

Dengue Hemorrhagic Fever (DHF) is an infectious disease caused by the dengue virus and transmitted through bites of the Aedes aegypti mosquito. This illness remains a major public health concern in Indonesia, particularly in urban regions like Bandung City, where population density and environmental variations contribute to disease transmission. The purpose of this study is to apply the K-Means Clustering algorithm to group areas based on their level of vulnerability to DHF spread in Bandung City. The dataset, obtained from the Bandung Open Data portal covering the 2016–2024 period, was processed using the Orange Data Mining application. The analysis began with data preprocessing, which included cleaning, attribute selection, and normalization to ensure optimal clustering performance. The data were then grouped into three primary clusters representing high, medium, and low risk zones. The findings indicate that the K-Means algorithm effectively detects the spatial and temporal distribution of DHF cases and presents it through scatter plot visualizations that illustrate yearly patterns. High-risk regions are typically characterized by dense population, poor sanitation, and limited environmental management. These findings provide essential insight for local health authorities to design more targeted prevention and control strategies. Furthermore, this research can serve as a foundation for developing a decision support system that aids in monitoring, evaluating prevention efforts, and optimizing health resource allocation to reduce the incidence of DHF in the future.
Penerapan dan Perbandingan Algoritma SVM, Naive Bayes, dan Gradient Boosting dalam Prediksi Stroke Joseph Melchior Nababan; Iqro Mukti Arto; Putra Satria; Sumanto Sumanto; Imam Budiawan; Roida Pakpahan
Jurnal Teknik Informatika dan Teknologi Informasi Vol. 5 No. 3 (2025): Desember: Jurnal Teknik Informatika dan Teknologi Informasi
Publisher : Lembaga Pengembangan Kinerja Dosen

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55606/jutiti.v5i3.6254

Abstract

Stroke is a major cardiovascular disease that significantly contributes to global mortality and disability rates. Early detection through stroke risk prediction is essential in reducing its impact. This study focuses on evaluating and comparing the performance of three machine learning algorithms—Support Vector Machine (SVM), Naive Bayes (NB), and Gradient Boosting (GB)—in predicting stroke occurrence. The research utilizes the Healthcare Stroke Dataset, which contains 5,109 records and 11 predictor variables. Modeling was performed using Orange Data Mining software, with 70% of the data allocated for training and 30% for testing. The results show that the SVM algorithm achieved the highest performance, obtaining an AUC score of 0.919 and an accuracy of 96.0%, followed by Gradient Boosting with an AUC of 0.885 and accuracy of 95.2%, and Naive Bayes with an AUC of 0.803 and accuracy of 88.2%. Therefore, SVM is identified as the most effective algorithm for predicting stroke risk within this dataset.
Klastering Penyakit Diabetes Melitus dengan Algoritma K-Means berdasarkan Karakteristik Klinis Audy Aulia Azzahra; Fajar Yoga Adiansyah; Erlangga Rizki Ekaptra; Sumanto Sumanto; Imam Budiawan; Roida Pakpahan
Jurnal Teknik Informatika dan Teknologi Informasi Vol. 5 No. 3 (2025): Desember: Jurnal Teknik Informatika dan Teknologi Informasi
Publisher : Lembaga Pengembangan Kinerja Dosen

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55606/jutiti.v5i3.6281

Abstract

Diabetes Mellitus is a complex and progressive chronic metabolic disorder that requires a personalized management strategy tailored to each individual’s clinical, physiological, and lifestyle characteristics. Addressing this challenge, the present study aims to apply the K-Means algorithm to identify clustering patterns among diabetic patients using the Knowledge Discovery in Databases (KDD) framework. The dataset was obtained from the Kaggle repository, consisting of 769 patient medical records with key variables such as glucose levels, body mass index (BMI), blood pressure, age, and other metabolic parameters relevant to the diagnosis of Diabetes Mellitus. The research methodology includes several stages: data selection, preprocessing to handle missing values, duplication, and normalization to ensure the dataset is properly structured for analysis. The implementation of the K-Means algorithm was carried out using Orange Data Mining software to produce optimal clustering patterns. The analysis identified three primary clusters (C1, C2, C3) that demonstrated significant differences, particularly based on glucose levels as the dominant variable in cluster formation. The scatter plot visualization revealed clear separations among clusters, with high intra-cluster homogeneity and strong inter-cluster heterogeneity. These findings confirm the effectiveness of the K-Means algorithm as an unsupervised learning method capable of uncovering hidden patterns within clinical diabetes data. The results are expected to serve as a foundation for developing more adaptive and precise clinical decision support systems, assisting healthcare professionals in designing targeted management and intervention strategies aligned with each patient’s risk profile.       
Penerapan Metode Logistic Regression untuk Memprediksi Potensi Penyakit Liver pada Pasien Tarmidzi Ibrahim; Imam Wahyudi; Vemi Januar Pratama; Sumanto Sumanto; Imam Budiawan; Roida Pakpahan
Jurnal Teknik Informatika dan Teknologi Informasi Vol. 5 No. 3 (2025): Desember: Jurnal Teknik Informatika dan Teknologi Informasi
Publisher : Lembaga Pengembangan Kinerja Dosen

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55606/jutiti.v5i3.6284

Abstract

Liver disease is a major global health concern that often goes undiagnosed in its early stages due to the absence of specific symptoms. Implementing data-driven approaches for early detection can significantly enhance diagnostic accuracy and improve clinical outcomes. This study aims to develop a predictive model using the Logistic Regression algorithm to identify individuals at high risk of liver disease. The data analysis process was conducted visually through data mining software, encompassing several stages such as data loading, feature selection, exploratory data analysis, and model evaluation. The dataset includes various clinical and laboratory attributes of patients, such as blood test results, liver function indicators, and demographic factors. The model’s performance was assessed using multiple evaluation metrics, with a focus on Classification Accuracy (CA) and the Area Under the ROC Curve (AUC) to measure predictive precision and classification ability. The results show that the Logistic Regression model achieved an accuracy of 71.8% and an AUC score of 0.746. These findings indicate that the model demonstrates good predictive performance and effectively identifies early-stage liver disease cases. However, further optimization is necessary to improve overall model efficiency and ensure more robust predictive capabilities in clinical applications.
Co-Authors Alghifar Firgiawan Alwan Kapi Muntaha Alya Avisa Andika Amansyah Audy Aulia Azzahra Cahyani Ayu Sulistyawati Desiana Nuranudin Putri Erlangga Rizki Ekaptra Fajar Yoga Adiansyah Farhan Fadhilah Faris Syahrendra Fauzan Nawwir Andriansyah Ginting Wibi Prasetyo Imam Wahyudi Iqro Mukti Arto Joseph Melchior Nababan Kevin Dwi Satria Muhammad Raviansyah Paulus Paulus Prasetyo Adi Suwignyo Putra Satria Raihan Naufal Ramadhan Roida Pakpahan Roni Saputra Pratama Ryehan Alfiansyah Sumanto Sumanto Tarmidzi Ibrahim Vemi Januar Pratama Zahwa Asfa Rabbani