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Universitas Bina Sarana Informatika
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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.
Analisis Komparatif Sentimen Negatif Pengguna Platform E-Commerce Shopee dan Tokopedia selama Periode Diskon Faris Syahrendra; Cahyani Ayu Sulistyawati; Ginting Wibi Prasetyo; Sumanto Sumanto; Roida Pakpahan; Imam Budiawan
IJAI (Indonesian Journal of Applied Informatics) Vol 10, No 1 (2025)
Publisher : Universitas Sebelas Maret

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20961/ijai.v10i1.110824

Abstract

Abstrak : Fenomena potongan harga besar pada platform jual beli online sering kali menimbulkan kekecewaan bagi pengguna karena masalah dalam layanan, harga, dan pengiriman. Studi ini bertujuan untuk menganalisis dan membandingkan perasaan pengguna terhadap Shopee dan Tokopedia selama masa promosi dengan cara menggunakan pendekatan machine learning. Data ulasan diambil dari Google Play Store, yang terdiri dari 929 ulasan untuk Shopee dan 1.111 ulasan untuk Tokopedia. Dua algoritma untuk klasifikasi sentimen, yaitu Naive Bayes dan Neural Network, diimplementasikan dan dievaluasi dengan metode validasi silang 10-fold. Temuan yang berasal dari penilaian analitis menunjukkan bahwa model Naive Bayes menunjukkan tingkat akurasi dan presisi tertinggi yaitu 91,0%, sementara Neural Network memperoleh 83,9%. Selain itu, ulasan positif mendominasi sentimen terhadap Shopee (70%), sedangkan Tokopedia lebih banyak diwarnai oleh sentimen negatif (60%). Penemuan ini menandakan bahwa pengguna lebih puas dengan pengalaman diskon di Shopee dan memberikan masukan strategis untuk peningkatan layanan e-commerce.===============================================Abstract :Large-scale discount events on e-commerce platforms often lead to user disappointment due to issues with service, pricing, and delivery. This study aims to analyze and compare user sentiment towards Shopee and Tokopedia during promotional periods using a machine learning approach. Review data were sourced from the Google Play Store, consisting of 929 reviews for Shopee and 1,111 for Tokopedia. Two algorithms for sentiment classification, namely Naive Bayes and Neural Network, were implemented and evaluated using the 10-fold cross-validation method. Findings from analytical assessments indicate that the Naive Bayes model demonstrates the highest level of accuracy and precision at 91.0%, while the Neural Network obtained 83.9%. Furthermore, positive reviews dominated the sentiment towards Shopee (70%), whereas Tokopedia was largely characterized by negative sentiment (60%). These findings indicate that users are more satisfied with the discount experience on Shopee and provide strategic input for the improvement of e-commerce services.
Perbandingan Algoritma Machine Learning untuk Klasifikasi Risiko Penyakit Paru Berdasarkan Data Diagnostik Pasien Alwan Kapi Muntaha; Kevin Dwi Satria; Desiana Nuranudin Putri; Sumanto Sumanto; Roida Pakpahan; Imam Budiawan
IJAI (Indonesian Journal of Applied Informatics) Vol 10, No 1 (2025)
Publisher : Universitas Sebelas Maret

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20961/ijai.v10i1.110867

Abstract

Abstrak : Penyakit paru-paru termasuk salah satu faktor utama penyebab tingginya angka kematian di seluruh dunia. Kondisi ini terjadi karena penyakit paru-paru sering kali sulit terdeteksi pada tahap awal akibat gejalanya yang tidak spesifik. Perkembangan teknologi machine learning memberikan peluang untuk membantu proses diagnosis secara otomatis dengan memanfaatkan data diagnostik pasien. Penelitian ini bertujuan untuk mengklasifikasikan risiko penyakit paru menggunakan berbagai algoritma machine learning pada aplikasi Orange3, serta menentukan model dengan akurasi terbaik. Dataset yang digunakan terdiri dari 5.000 data pasien dengan 18 atribut yang mencakup faktor demografis, gaya hidup, riwayat medis, dan kondisi klinis seperti kadar oksigen, tingkat stres, dan kebiasaan merokok. Lima algoritma diuji, yaitu iDecision Tree, Naïve Bayes, Support Vector Machine (SVM), k-Nearest Neighbor (kNN), dan Neural Network. Hasil pengujian menunjukkan bahwa Neural Network menghasilkan nilai akurasi tertinggi sebesar 89,15%, diikuti oleh Decision Tree (85,12%) dan Naïve Bayes (83,63%). Temuan ini membuktikan bahwa Neural Network lebih unggul dalam mengenali pola kompleks antarvariabel dan mampu memberikan prediksi yang lebih akurat. Dengan demikian, penelitian ini menegaskan potensi penerapan machine learning berbasis data diagnostik non-citra sebagai sistem pendukung keputusan untuk diagnosis dini penyakit paru.=================================================Abstract : Lung disease is a major contributing factor to high mortality rates worldwide. This is because lung disease is often difficult to detect in its early stages due to its nonspecific symptoms. The development of machine learning technology provides an opportunity to assist the automated diagnosis process by utilizing patient diagnostic data. This study aims to classify the risk of lung disease using various machine learning algorithms in the Orange3 application, and determine the model with the best accuracy. The dataset used consists of 5,000 patient data with 18 attributes covering demographic factors, lifestyle, medical history, and clinical conditions such as oxygen levels, stress levels, and smoking habits. Five algorithms were tested: Decision Tree, Naïve Bayes, Support Vector Machine (SVM), k-Nearest Neighbor (kNN), and Neural Network. The test results showed that Neural Network produced the highest accuracy value of 89.15%, followed by Decision Tree (85.12%) and Naïve Bayes (83.63%). These findings prove that Neural Network is superior in recognizing complex patterns between variables and is able to provide more accurate predictions. Thus, this study confirms the potential of applying machine learning based on non-image diagnostic data as a decision support system for early diagnosis of lung disease.
Co-Authors Adriyanichsan Adriyanichsan Ahcmad Anwar Asyirri Alghifar Firgiawan Alwan Kapi Muntaha Alya Avisa Andika Amansyah Arnata Nur Rasyid Asmawati Asmawati Asriani Asriani Astri Rezky Jumarah Audy Aulia Azzahra Bambang Junadi Cahyani Ayu Sulistyawati Desiana Nuranudin Putri Dinda Aprillia Erlangga Rizki Ekaptra Fajar Yoga Adiansyah Farhan Fadhilah Faris Syahrendra Fauzan Nawwir Andriansyah Ginting Wibi Prasetyo Handini Widyastuti Imam Budiawan Imam Wahyudi Indra Zaman Iqro Mukti Arto Joseph Melchior Nababan Kevin Dwi Satria Mahdiyyah Mahdiyyah Muhammad Raviansyah Nindya Dwi Lestari Nurul Afni Paulus Paulus Prasetyo Adi Suwignyo Putra Satria Raihan Naufal Ramadhan Rifki Nur Hidayat Putra Roni Saputra Pratama Ryehan Alfiansyah Sumanto, Sumanto Tarmidzi Ibrahim Vemi Januar Pratama Widya Viona Septi Tanjung Yoesgiantoro, Donny Yuni Fitriani Yuni Fitriani Yuni Fitriani Zahwa Asfa Rabbani