Article Per Year (5 Year)
p-Index From 2021 - 2026
1.463
P-Index
This Author published in this journals
All Journal Jurnal TIMES Scientific Journal of Informatics Jurnal Informatika IT JOURNAL RESEARCH AND DEVELOPMENT Building of Informatics, Technology and Science International Journal of Engineering, Science and Information Technology TAMIKA: Jurnal Tugas Akhir Manajemen Informatika & Komputerisasi Akuntansi Jurnal INFOTEL
Kelvin Leonardi Kohsasih
Unknown Affiliation
Astronomy Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Physics Social Sciences Transportation

Published : 8 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 8 Documents
Search

 1 
Analisis Perbandingan Algoritma C4.5 dan Naïve Bayes Dalam Memprediksi Penyakit Cerebrovascular Kelvin Leonardi Kohsasih; Zakarias Situmorang
Jurnal Informatika Vol 9, No 1 (2022): April 2022
Publisher : LPPM Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (72.693 KB) | DOI: 10.31294/inf.v9i1.11931

Abstract

Cerebrovascular Disease atau stroke merupakan salah satu penyebab utama kematian di dunia. stroke adalah penyakit yang disebabkan oleh gangguan pada pembuluh darah yang mensuplai darah ke otak. Machine learning merupakan teknologi yang dapat digunakan untuk memprediksi  stroke. Salah satu algoritma klasifikasi machine learning yang dapat digunakan untuk melakukan prediksi adalah Algoritma Decision Tree C4.5 dan Algoritma Naive Bayes. Dalam penelitian ini, peneliti akan membandingkan akurasi dan kinerja dua algoritma untuk memprediksi  stroke. Berdasarkan hasil penelitian didapatkan bahwa algoritma C4.5 memperoleh tingkat akurasi yang lebih tinggi yaitu 0,953 sedangkan algoritma Naive Bayes memperoleh tingkat akurasi 0,913.
Classification SARS-CoV-2 Disease based on CT-Scan Image Using Convolutional Neural Network Kohsasih, Kelvin Leonardi; Hayadi, B. Herawan
Scientific Journal of Informatics Vol 9, No 2 (2022): November 2022
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v9i2.36583

Abstract

Purpose: Convolutional Neural Network (CNN) is one of the most popular and widely used deep learning algorithms. These algorithms are commonly used in various applications, including image processing in medical and digital forensics, speech recognition, and other academic disciplines. SARS-CoV-2 (COVID-19) is a disease that first appeared in Wuhan, China, and has symptoms similar to pneumonia. This study aims to classify the covid-19 virus by proposing a deep learning model to prevent infection rates.Methods: The dataset used in this study is a public dataset originating from a hospital in Sao Paulo, Brazil. The data images consisted of 1252 infected with covid and 1230 data classified as non-covid but have other lung diseases. The classification method proposed in this research is a CNN model based on Resnet 50.Result: The experimental results show that the proposed Resnet 50-based convolutional neural network model works well in classifying SARS-CoV-2 disease using CT-Scan images. Our proposed model obtains 95% accuracy, precision, recall, and f1 values on the Epoch 500.Novelty: In this experiment, we utilized the Resnet50-based CNN model to classify the SARS-CoV-2 (COVID-19) disease using CT-Scan images and got good performance.
A deep learning model to detect the brain tumor based on magnetic resonance images Kelvin Leonardi Kohsasih; Muhammad Dipo Agung Rizky; Rika Rosnelly; Willy Wira Widjaja
JURNAL INFOTEL Vol 14 No 3 (2022): August 2022
Publisher : LPPM INSTITUT TEKNOLOGI TELKOM PURWOKERTO

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20895/infotel.v14i3.793

Abstract

Deep learning techniques have been widely used in everything from analyzing medical information to tools for making medical diagnoses. One of the most feared diseases in modern medicine is a brain tumor. MRI is a radiological method that can be used to identify brain tumors. However, manual segmentation and analysis of MRI images is time-consuming and can only be performed by a professional neuroradiologist. Therefore automatic recognition is required. This study propose a deep learning method based on a hybrid multi-layer perceptron model with Inception-v3 to predict brain tumors using MRI images. The research was conducted by building the Inception-v3 and multilayer perceptron model, and comparing it with the proposed model. The results showed that the hybrid multilayer perceptron model with inception-v3 achieved accuracy, recall, precision, and fi-score of 92%. While the inception-v3 and multilayer perceptron models only obtained 66% and 56% accuracy, respectively. This research shows that the proposed model successfully predicts brain tumors and improves performance
Comparison of SVM, KNN, And Naïve Bayes Algorithms in Monkeypox Disease Classification Kohsasih, Kelvin Leonardi
TAMIKA: Jurnal Tugas Akhir Manajemen Informatika & Komputerisasi Akuntansi Vol 4 No 2(SEMNASTIK) (2024): TAMIKA: Jurnal Tugas Akhir Manajemen Informatika & Komputerisasi Akunt
Publisher : Universitas Methodist Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46880/tamika.Vol4No2(SEMNASTIK).pp168-174

Abstract

Advances in medical technology have enabled the application of machine learning for disease classification, including monkeypox. Monkeypox is a zoonotic disease caused by the monkeypox virus and can be detected through patient data. This study aims to compare the performance of Support Vector Machine (SVM), k-Nearest Neighbors (KNN), and Naïve Bayes algorithms in building a monkeypox classification model. The dataset used consists of 25,000 patient records. The results show that the SVM model with a linear kernel achieved the best accuracy compared to KNN and Naïve Bayes. These findings demonstrate that the SVM model with a linear kernel is highly effective in classifying monkeypox, offering great potential for further medical applications.
Hybrid Deep Fixed K-Means (HDF-KMeans) Zuhanda, Muhammad Khahfi; Kohsasih, Kelvin Leonardi; Octaviandy, Pieter; Hartono, Hartono; Kurnia, Dian; Tarigan, Nurliana; Ginting, Manan; Hutagalung, Manahan
International Journal of Engineering, Science and Information Technology Vol 5, No 3 (2025)
Publisher : Malikussaleh University, Aceh, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52088/ijesty.v5i3.913

Abstract

K-Means is one of the most widely used clustering algorithms due to its simplicity, scalability, and computational efficiency. However, its practical application is often hindered by several well-known limitations, such as high sensitivity to initial centroid selection, inconsistency across different runs, and suboptimal performance when dealing with high-dimensional or non-linearly separable data. This study introduces a hybrid clustering algorithm named Hybrid Deep Fixed K-Means (HDF-KMeans) to address these issues. This approach combines the advantages of two state-of-the-art techniques: Deep K-Means++ and Fixed Centered K-Means. Deep K-Means++ leverages deep learning-based feature extraction to transform raw data into more meaningful representations while employing advanced centroid initialization to enhance clustering accuracy and adaptability to complex data structures. Complementarily, Centered K-Means improve the stability of clustering results by locking certain centroids based on domain knowledge or adaptive strategies, effectively reducing variability and convergence inconsistency. Integrating these two methods results in a robust hybrid model that delivers improved accuracy and consistency in clustering performance. The proposed HDF-KMeans algorithm is evaluated using five benchmark medical datasets: Breast Cancer, COVID-19, Diabetes, Heart Disease, and Thyroid. Performance is assessed using standard classification metrics: Accuracy, Precision, Recall, and F1-Score. The results show that HDF-KMeans outperforms traditional K-Means, K-Means++, and K-Means-SMOTE algorithms across all datasets, excelling in overall accuracy and F1 Score. While some trade-offs are observed in specific precision or recall metrics, the model maintains a solid balance, demonstrating reliability. This study highlights HDF-KMeans as a promising and effective solution for complex clustering tasks, particularly in high-stakes domains like healthcare and biomedical analysis.
ANALISIS PERBANDINGAN ALGORITMA CONVOLUTIONAL NEURAL NETWORK DAN ALGORITMA MULTI-LAYER PERCEPTRON NEURAL DALAM KLASIFIKASI CITRA SAMPAH Kohsasih, Kelvin Leonardi; Agung Rizky, Muhammad Dipo; Fahriyani, Tasya; Wijaya, Veronica; Rosnelly, Rika
Jurnal TIMES Vol 10 No 2 (2021): Jurnal TIMES
Publisher : STMIK TIME

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (496.449 KB) | DOI: 10.51351/jtm.10.2.2021655

Abstract

Menurut laporan bank dunia sampah merupakan salah satu permasalahan yang dihadapi dunia. Image clasification adalah salah satu bidang machine learning yang mampu melakukan klasikasi sampah berdasarkan jenisnya. Salah satu algoritma klasifikasi yang populer dan banyak digunakan adalah algoritma CNN yang merupakan algoritma deep learning. Pada penelitian ini kami akan melakukan analisis perbandingan kinerja algoritma CNN dengan algoritma MLP dalam melakukan klasifikasi jenis sampah. Dari penelitian yang kami lakukan, CNN mendapatkan performa yang lebih baik dimana hasil precision, recall, f1-score, dan accuracy sebesar 0,98 dan model CNN lebih efektif dalam melakukan klasifikasi sampah berdasarkan kelasnya.
Enhancing Early Heart Disease Detection Through Comparative Analysis of Random Forest, Decision Tree, and K-NN Models Kohsasih, Kelvin Leonardi; Smith Sunario, Daniel; Alvin, Alvin; Laurendio, Fedro
IT Journal Research and Development Vol. 10 No. 2 (2025)
Publisher : UIR PRESS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25299/itjrd.2025.24703

Abstract

Heart disease is a leading cause of mortality worldwide and its rising prevalence challenges health systems. This study evaluates Decision Tree, k Nearest Neighbors, and Random Forest using the Heart Failure Prediction Dataset from Kaggle with 918 records and 12 demographic, clinical, and lifestyle features. The target variable indicates the presence of heart disease. Data preprocessing included cleaning, transformation, and scaling. Hyperparameters were tuned with stratified five fold cross validation to prevent data leakage. Performance was assessed using accuracy, precision, recall, F1 score, ROC AUC, PR AUC, Matthews Correlation Coefficient, and Brier score each estimated with 95 percent confidence intervals via bootstrap. k Nearest Neighbors achieved the highest accuracy at 90.2 percent, followed by Random Forest at 87.5 percent and Decision Tree at 85.3 percent. Calibration and decision curve analyses indicated that k Nearest Neighbors and Random Forest provided better calibrated probabilities and higher clinical utility across plausible thresholds. The study offers a reproducible evaluation pipeline and supports the use of machine learning for early detection of heart disease while encouraging future work on larger datasets and more advanced models.
Revisiting SMOTE in Balanced Medical Data: A Comparative Evaluation of SVM, Random Forest, and KNN Kohsasih, Kelvin Leonardi; Joni, Joni; Herman, Herman; Octaviandy, Pieter
Building of Informatics, Technology and Science (BITS) Vol 7 No 3 (2025): December 2025
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v7i3.8471

Abstract

Heart disease is one of the leading causes of death worldwide, making data-driven early detection crucial for supporting medical decision-making systems. A major challenge in developing heart disease prediction models is dataset quality, including the often imbalanced class distribution, which can impact the performance of classification algorithms. This study aims to analyze the effect of the Synthetic Minority Oversampling Technique (SMOTE) on the performance of three classification algorithms: Support Vector Classifier (SVC), Random Forest (RF), and K-Nearest Neighbor (KNN). The dataset used is heart_disease50.csv with 4,001 patient data consisting of 21 predictor attributes and one target variable (heart disease status: “Yes” or “No”) with a relatively balanced class distribution. The research process includes data preprocessing (data cleaning, normalization, and encoding), data partitioning using Stratified K-Fold Cross Validation (k=5), applying SMOTE to training data, building a classification model, and evaluation using accuracy, precision, recall, F1-score, and AUC-ROC metrics. The results showed that applying SMOTE did not always improve performance. The SVC model with SMOTE experienced a decrease in accuracy (0.4819) compared to the one without SMOTE (0.5106), while Random Forest remained relatively stable with insignificant differences (0.4669 without SMOTE and 0.4644 with SMOTE). KNN with SMOTE emerged as the best model with an accuracy of 0.5268 and a precision of 0.5271, although the AUC-ROC remained the same as KNN without SMOTE (0.5135). Overall, these results confirm that the effectiveness of SMOTE is highly dependent on dataset conditions, and in cases with relatively balanced data, SMOTE does not provide significant benefits. Therefore, improving the performance of heart disease prediction classification is recommended through hyperparameter optimization strategies, relevant feature selection, or the use of more sophisticated algorithms such as Gradient Boosting or Neural Networks.
Co-Authors Agung Rizky, Muhammad Dipo Alvin Alvin, Alvin B. Herawan Hayadi Fahriyani, Tasya Ginting, Manan Hartono Hartono Hutagalung, Manahan Joni Joni Kurnia, Dian Laurendio, Fedro Muhammad Dipo Agung Rizky Muhammad Khahfi Zuhanda Pieter Octaviandy, Pieter Rika Rosnelly Rika Rosnelly, Rika Smith Sunario, Daniel Tarigan, Nurliana Veronica Wijaya, Veronica Willy Wira Widjaja Zakarias Situmorang