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All Journal Jurnal Terapan Abdimas Kurawal - Jurnal Teknologi, Informasi dan Industri JATI (Jurnal Mahasiswa Teknik Informatika) Rainstek : Jurnal Terapan Sains dan Teknologi Jurnal Pengabdian Masyarakat Bhinneka BIMASAKTI
Nugraha, Danang Aditya
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Agriculture, Biological Sciences & Forestry Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Industrial & Manufacturing Engineering Mathematics Public Health Social Sciences Other

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PENERAPAN DATA MINING UNTUK MENGKLASIFIKASI PENERIMA BANTUAN PROGRAM KELUARGA HARAPAN MENGGUNAKAN METODE SUPPORT VECTOR MACHINE Jesika, Amelia; Budianto, Alexius Endy; Nugraha, Danang Aditya
Jurnal Fakultas Teknologi Informasi Vol 8 No 1 (2025): BIMASAKTI
Publisher : Prodi Teknik Informatika, Fakultas Sains dan Teknologi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21067/bimasakti.v8i1.12826

Abstract

The Family Hope Program (PKH) is a governmental initiative in Indonesia designed to decrease poverty and improve the welfare of families. However, the process of identifying eligible families frequently encounters difficulties. To address this, the study applies data mining techniques with the Support Vector Machine (SVM) method to classify prospective PKH recipients in Bangka Leleng Village. The research utilizes 1,039 data samples of recipients from 2019 to 2023, based on five key attributes: age, income, number of dependents, occupation, and home ownership status. Data processing was conducted using Python in the Google Colab environment. The research workflow involved data collection, preprocessing, splitting data for training and testing, analysis, and evaluation using a Confusion Matrix. The test results indicated that the SVM method is highly effective in classifying PKH recipients, achieving an accuracy rate of up to 96%. This optimal accuracy was obtained by employing the RBF kernel, which demonstrated superior performance compared to other kernels. It is anticipated that this research will provide a more efficient and transparent method for determining aid recipients, leading to a more precise distribution of assistance.
OPTIMASI RANDOM FOREST TERHADAP DATA PENYAKIT LIVER MENGGUNAKAN FIREFLYALGORITHM Sunarjo, Nemesius; Nugraha, Danang Aditya; Santoso, Heri
Jurnal Fakultas Teknologi Informasi Vol 8 No 2 (2026): BIMASAKTI
Publisher : Prodi Teknik Informatika, Fakultas Sains dan Teknologi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21067/bimasakti.v8i2.13041

Abstract

Liver disease is one of the most dangerous diseases for human survival. In an effort to find out liver disease early on, a classification method is needed. Researchers conducted testing and classification of lliver disease with the Random Forest algorithm which was then optimized with the Firefly algorithm. The purpose of this study is to learn how the application of the firefly algorithm in optimizing the accuracy of the random forest algorithm in liver disease. The data used is 1700 data with 11 attributes. The findings of this study with the Random Forest algorithm produced an accuracy of 87.24% while when optimized using the Firefly Algorithm produced an accuracy of 93.24%. The findings demonstrated a rise in the precision of the Random Forest algorithm and optimized using Firefly Algorithm.
PENERAPAN ALGORITMA LOGISTIC REGRESSION UNTUK KLASIFIKASI PENYAKIT STROKE Amelia, Rachel Trivica; Nugraha, Danang Aditya; Ahsan, Moh
Jurnal Fakultas Teknologi Informasi Vol 8 No 2 (2026): BIMASAKTI
Publisher : Prodi Teknik Informatika, Fakultas Sains dan Teknologi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21067/bimasakti.v8i2.13201

Abstract

Stroke is one of the leading causes of death worldwide, ranking after heart disease and cancer. Early detection of stroke risk is essential to enable faster and more accurate treatment. The purpose of this study is to apply the Logistic Regression algorithm to classify stroke cases based on several risk factors, including gender, age, hypertension, heart disease, marital status, occupation, residence type, average glucose level, body mass index (BMI), smoking status, and stroke status. The dataset used in this research was obtained from Kaggle and consists of 5,110 patient records. The research process involves several stages, including data cleaning, data transformation, and normalization using the Min-Max Scaler method, followed by splitting the data into training and testing sets with various proportions (90%-10%, 85%-15%, 80%-20%, 70%-30%, and 65%-35%). The evaluation was conducted using a Confusion Matrix with performance metrics such as accuracy, precision, recall, and F1-score. The analysis results show that the 90%-10% data split achieved the highest accuracy of 76.17%, with precision and recall values indicating that the model performs well in identifying non-stroke cases. However, performance on the minority class (stroke) remains relatively low, suggesting the need for improvement through data imbalance handling. Overall, the application of the Logistic Regression algorithm proved to be effective for initial stroke classification, although accuracy can still be improved through resampling techniques or advanced model optimization.
Co-Authors Abdul Aziz Abdul Aziz Afandi, Fajar Ahsan, Moh Akhmad Zaini, Akhmad Alvionita, Modesta Noviani Amanda, Kharisma Amelia, Rachel Trivica Anggri Sartika Wiguna Budianto, Alexius Endy Budianto, Alfa Wahyu Cahyani, Indah Ayu Djapalata, Anggreni Shinta Harianto, Wahyudi Heri Santoso Jesika, Amelia Luriansyah, Rizky Putra Maharani, Delvi Anjarita Miharja, Gangga Prakarsa Moh Ahsan Nage, Vensislaus Ramadhanti, Rizka Laela Saputri Sudiyono Sudiyono Sunarjo, Nemesius Tri Sulistiyo, Muhammad Priyono Zaini, Ahkmad