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All Journal International Journal of Electrical and Computer Engineering Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) KLIK (Kumpulan jurnaL Ilmu Komputer) (e-Journal) Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Bianglala Informatika : Jurnal Komputer dan Informatika Akademi Bina Sarana Informatika Yogyakarta JURNAL MEDIA INFORMATIKA BUDIDARMA Faktor Exacta Conference on Innovation and Application of Science and Technology (CIASTECH) Petir Jurnal Nasional Pendidikan Teknik Informatika (JANAPATI) Jurnal Telematika Progresif: Jurnal Ilmiah Komputer Techno Xplore : Jurnal Ilmu Komputer dan Teknologi Informasi Jurnal Accounting Information System (AIMS) INTERNAL (Information System Journal) Computer Science and Information Technologies TIN: TERAPAN INFORMATIKA NUSANTARA Jurnal Teknik Informatika (JUTIF) Scientific Student Journal for Information, Technology and Science Jurnal Informatika Teknologi dan Sains (Jinteks) Jurnal Buana Pengabdian Jurnal Accounting Information System (AIMS) INTERNAL (Information System Journal) Scientific Journal of Informatics
Siregar, Amril Mutoi
Teknik Informatika, Universitas Buana Perjuangan Karawang
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Implementasi Clustering Menggunakan Algoritma K-Means dan K-Medoids pada Kerusakan Tempat Tinggal Akibat Bencana di Jawa Barat Nurani Khoerunnisa; Amril Mutoi Siregar; Yana Cahyana
Scientific Student Journal for Information, Technology and Science Vol. 6 No. 1 (2025): Scientific Student Journal for Information, Technology and Science
Publisher : Scientific Student Journal for Information, Technology and Science

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Abstract

Bencana alam adalah rangkaian peristiwa yang mengganggu dan mengancam keselamatan serta menyebabkan kerugian materiil dan nonmateriil, terutama di Provinsi Jawa Barat. Dampak dari bencana alam tersebut menyebabkan banyak masyarakat kehilangan tempat tinggal mereka. Hal ini menimbulkan kekhawatiran masyarakat akan keamanan daerah tempat tinggal mereka. Berdasarkan permasalahan tersebut, penelitian ini menghitung cluster kerusakan tempat tinggal di Jawa Barat menggunakan algoritma K-Means dan K-Medoids Clustering untuk mengelompokkan kabupaten atau kota di Jawa Barat. Sebanyak 27 kabupaten atau kota di Provinsi Jawa Barat dikelompokkan ke dalam 2 cluster, yaitu cluster Tinggi (rawan) dan cluster Rendah (aman), berdasarkan dataset yang diperoleh dari situs web Badan Penanggulangan Bencana Daerah (BPBD) dengan jumlah data sebanyak 1.620. Hasil penelitian menunjukkan bahwa algoritma K-Means lebih optimal, dengan jumlah daerah dalam cluster Rendah (aman) sebanyak 14 dan dalam cluster Tinggi (rawan) sebanyak 13. Sementara itu, algoritma K-Medoids menghasilkan 15 daerah dalam cluster Rendah (aman) dan 12 daerah dalam cluster Tinggi (rawan). Evaluasi menggunakan silhouette coefficient menunjukkan bahwa algoritma K-Means lebih unggul dengan nilai 59% (0.59), dibandingkan dengan algoritma K-Medoids yang memiliki nilai 58% (0.58).
Penerapan Algoritma K-Medoids dan K-Means untuk Pemetaan Penyebaran Guru Tingkat SMP Seluruh Kabupaten/Kota di Indonesia Lilis Kartika; Amril Mutoi Siregar; Dwi Sulistya Kusumaningrum
Scientific Student Journal for Information, Technology and Science Vol. 6 No. 1 (2025): Scientific Student Journal for Information, Technology and Science
Publisher : Scientific Student Journal for Information, Technology and Science

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Abstract

Guru memiliki peran penting dalam pendidikan, jasanya mampu menciptakan generasi-generasi yang berkualitas, baik secara intelektual maupun akhlaknya. Tenaga pengajar Indonesia belum tersebar dengan baik di pelosok negeri, sesuai dengan Roadmap Pengelolaan Aparatur Sipil Negara (ASN) dan Perencanaan Formasi tahun 2014. Oleh karena itu, perlu dilakukan pemetaan persebaran tenaga pengajar di berbagai wilayah dan kota di Indonesia. Dalam riset ini, pengelompokkan data memakai Algoritma K-Medoids dan K-Means dengan dataset yaitu jumlah guru, jumlah peserta didik, dan jumlah sekolah jenjang SMP. Algoritma K-Medoids menghasilkan cluster 1 yang memiliki kekurangan guru sebanyak 302 Kabupaten/Kota, pada cluster 2 yang memiliki kelebihan guru sebanyak 77 Kab/Kota, sedangkan cluster 3 yang memiliki cukup guru sebanyak 135 Kab/Kota. Sedangkan Algoritma K-Means menghasilkan cluster 1 yang memiliki kekurangan guru sebanyak 363 Kabupaten/Kota, cluster 2 yang memiliki cukup guru sebanyak 125 Kabupaten/Kota, sedangkan cluster 3 yang memiliki kelebihan guru sebanyak 26 Kabupaten/Kota. Manfaat dari penelitian ini sebagai penunjang keputusan pemerataan guru seluruh Kabupaten/Kota di Indonesia yang masih kekurangan atau kelebihan guru.
Ketahanan Pembelajaran Mesin terhadap Adversarial examples: Metodologi dan Pertahanan Kurniawan, Ade; Aprilia, Ely; Aulia, Achmad Indra; Siregar, Amril Mutoi; Goeirmanto, Leonard
Faktor Exacta Vol 18, No 2 (2025)
Publisher : LPPM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30998/faktorexacta.v18i2.26078

Abstract

This paper examines the vulnerability of machine learning models to adversarial examples: inputs that are subtly manipulated to deceive a model into making incorrect predictions. Although deep learning has demonstrated remarkable performance across various tasks, the security of these models remains a significant challenge. This study provides a comprehensive review of various methods for generating adversarial examples, a classification of attack techniques, and corresponding defense strategies, including both active and passive approaches. The findings indicate that a combination of several defense techniques is significantly more effective in enhancing model robustness compared to any single approach. This research is expected to provide a foundation for the development of more secure and reliable machine learning models for critical applications.
Optimasi Algoritma Machine Learning Menggunakan Seleksi Fitur Xgboost Untuk Klasifikasi Kanker Payudara Ramadhan, Naufal Cahya; H, Hanny Hikmayanti; Rohana, Tatang; Siregar, Amril Mutoi
TIN: Terapan Informatika Nusantara Vol 5 No 2 (2024): July 2024
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/tin.v5i2.5408

Abstract

This research analyzes the performance of the K-Nearest Neighbors (KNN), Naïve Bayes, and Random Forest algorithms in the classification of breast cancer diagnosis using the Wisconsin Breast Cancer dataset. The problem discussed is how to improve the accuracy of breast cancer diagnosis classification through appropriate preprocessing techniques. The research objective is to evaluate and compare the performance of the three algorithms after the application of preprocessing which includes data cleaning, handling missing values, data duplication, and outliers, as well as feature selection using XGBoost and SMOTE oversampling. application of feature selection to identify the most relevant features and SMOTE to balance the class distribution in the dataset. Performance evaluation results using a confusion matrix show that Random Forest has the best performance with high accuracy, precision, recall, and F1-score, reaching an AUC of 98% after the application of SMOTE. The combination of feature selection and SMOTE was shown to significantly improve model performance, although KNN showed a decrease in performance with SMOTE, while Naïve Bayes experienced a considerable improvement. This study demonstrates the importance of preprocessing techniques in the development of machine learning models for medical applications, emphasizing that appropriate techniques can significantly improve classification performance and result in more accurate diagnoses.
Optimization of Machine Learning Models with Segmentation to Determine the Pose of Cattle Siregar, Amril Mutoi; Hartono Wijaya, Sony; Fauzi, Ahmad; Sen, Tjong Wan; Faisal, Sutan; Tukino, Tukino; Cahyana, Yana
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol. 9 No. 3 (2023): September
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v9i3.26750

Abstract

Image pattern recognition poses numerous challenges, particularly in feature recognition, making it a complex problem for machine learning algorithms. This study focuses on the problem of cow pose detection, involving the classification of cow images into categories like front, right, left, and others. With the increasing popularity of image-based applications, such as object recognition in smartphone technologies, there is a growing need for accurate and efficient classification algorithms based on shape and color. In this paper, we propose a machine learning approach utilizing Support Vector Machine (SVM) and Random Forest (RF) algorithms for cow pose detection. To achieve an optimal model, we employ data augmentation techniques, including Gaussian blur, brightness adjustments, and segmentation. The proposed segmentation methods used are Canny and Kmeans. We compare several machine learning algorithms to identify the optimal approach in terms of accuracy. The success of our method is measured by accuracy and Receiver Operating Characteristic (ROC) analysis. The results indicate that using the Canny segmentation, SVM achieved 74.31% accuracy with a testing ratio of 90:10, while RF achieved 99.60% accuracy with the same testing ratio. Furthermore, testing with SVM and K-means segmentation reached an accuracy of 98.61% with a test ratio of 80:20. The study demonstrates the effectiveness of SVM and Random Forest algorithms in cow pose detection, with Kmeans segmentation yielding highly accurate results. These findings hold promising implications for real-world applications in image-based recognition systems. Based on the results of the model obtained, it is very important in pattern recognition to use segmentation based on color even though shape recognition.
Optimized Machine Learning Performance with Feature Selection for Breast Cancer Disease Classification Koirunnisa, Koirunnisa; Siregar, Amril Mutoi; Faisal, Sutan
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol. 9 No. 4 (2023): December
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v9i4.27527

Abstract

The prevalence of breast cancer is relatively high among adults worldwide. Particularly in Indonesia, according to the latest data from the World Health Organization (WHO), breast cancer accounts for 1.41% of all deaths and continues to increase. In order to address this growing issue, a proactive approach becomes essential. Therefore, the objective of this study is to classify the diagnosis of breast cancer into two categories: Benign and Malignant. Moreover, this classification pattern can serve as a benchmark for early detection and is expected to reduce mortality and cancer rates in breast cancer cases. The dataset used in this study is obtained from Kaggle and consists of 569 rows with 32 attributes. Various machine learning algorithms, such as Artificial Neural Network (ANN), Support Vector Machine (SVM), Decision Tree (DT), Random Forest (RF), Logistic Regression (LR), K-Nearest Neighbors (KNN), and Naïve Bayes (NB), are employed for the classification analysis in this disease. . This study uses Principal Component Analysis (PCA) for optimized feature selection techniques with dimension reduction are employed on the dataset prior to modeling the data. Our highest accuracy model is the Support Vector Machine (SVM) with an RBF kernel, utilizing c-value selection. Additionally, the Logistic Regression (LR) model achieves an accuracy of 97.3%. However, it is worth noting that the precision and recall of the SVM model are both 100%. Moreover, the Receiver Operating Characteristic (ROC) curve indicates that the SVM graph surpasses the LR graph, which can be attributed to the results obtained from the confusion matrix calculation, where the False Positive Rate is found to be 0. Consequently, the overall performance evaluation of the SVM model with an RBF kernel, along with the utilization of the c-value selection approach, is significantly superior. This is primarily due to the fact that the SVM model does not make any incorrect predictions by classifying something as positive when it is actually negative.
ANALYSIS AND IMPLEMENTATION OF AES-128 ALGORITHM IN SUKAHARJA KARAWANG VILLAGE SERVICE SYSTEM Fariz Duta Nugraha; Kiki Ahmad Baihaqi; Hilda Yulia Novita; Siregar, Amril Mutoi
Jurnal Teknik Informatika (Jutif) Vol. 5 No. 3 (2024): JUTIF Volume 5, Number 3, June 2024
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2024.5.3.2038

Abstract

Data security in databases is needed in the industrial era 4.0 to prevent attacks and unwanted things from happening, one of the biggest cases that has been widely reported is data leakage, in this study aims to implement and analyze the Advanced Encryption Standard Algorithm, one of the data security algorithms with a block chiper type that has 4 transformations (SubByte, ShiftColumn, MixColumn, AddRoundKey), or what we usually call the Cryptography method. Cryptography is a method that is often used to secure important data in databases, in this article the Advanced Encryption Standard Algorithm is used to secure citizen data and family card data in the Sukaharja Karawang Village service system. The method in this research is the observation method, the data is obtained from each head of the neighborhood in Sukaharja Karawang Village with the permission of the head of Sukaharja Karawang Village. Citizen data and family cards were encrypted and analyzed for resource requirements in storing encryption results and time in returning and displaying original data. The results of the analysis obtained the amount of resources required 1.5MB to store family card data, which before encryption required 352KB. Citizen data requires a resource of 6.5MB, before encryption it takes 1.5MB. As for the AES resilience test stage using the Bruteforce attack method with the help of Hashcat software version 6.2.5 with 4 trial processes, One encrypted address data was taken for this test, but out of 4 attempts none of them showed that the data could be cracked.
IMPROVING HEART DISEASE PREDICTION ACCURACY USING PRINCIPAL COMPONENT ANALYSIS (PCA) IN MACHINE LEARNING ALGORITHMS Jayidan, Zirji; Siregar, Amril Mutoi; Faisal, Sutan; Hikmayanti, Hanny
Jurnal Teknik Informatika (Jutif) Vol. 5 No. 3 (2024): JUTIF Volume 5, Number 3, June 2024
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2024.5.3.2047

Abstract

This study aims to improve the accuracy of heart disease prediction using Principal Component Analysis (PCA) for feature extraction and various machine learning algorithms. The dataset consists of 334 rows with 49 attributes, 5 classes and 31 target diagnoses. The five algorithms used were K-nearest neighbors (KNN), Logistic Regression (LR), Random Forest (RF), Support Vector Machine (SVM), and Decision Tree (DT). Results show that algorithms using PCA achieve high accuracy, especially RF, LR, and DT with accuracy up to 1.00. This research highlights the potential of PCA-based machine learning models in early diagnosis of heart disease.
OPTIMIZATION OF MACHINE LEARNING MODEL ACCURACY FOR BRAIN TUMOR CLASSIFICATION WITH PRINCIPAL COMPONENT ANALYSIS Maulana, Indra; Siregar, Amril Mutoi; Rahmat, Rahmat; Fauzi, Ahmad
Jurnal Teknik Informatika (Jutif) Vol. 5 No. 3 (2024): JUTIF Volume 5, Number 3, June 2024
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2024.5.3.2058

Abstract

The main issue in brain tumor classification is the accuracy and speed of diagnosis through medical imaging. This study aims to improve the accuracy of machine learning models for brain tumor classification by using Principal Component Analysis (PCA) for dimensionality reduction. The research methods include image preprocessing, feature scaling, PCA application, and the implementation of machine learning algorithms such as Logistic Regression, Random Forest, Support Vector Machine (SVM), K-Nearest Neighbor (KNN), and Naive Bayes. The dataset consists of 3,264 images divided into training and testing sets. The results show that the use of PCA has varying impacts on different algorithms. PCA increases the accuracy of the SVM algorithm from 81% to 83% and KNN from 68% to 71%, but decreases the accuracy of Logistic Regression from 77% to 69% and Naive Bayes from 49% to 42%. Evaluation is performed using the Confusion Matrix and AUC-ROC to measure model performance. In conclusion, selecting the appropriate algorithm and preprocessing method is crucial in medical image classification, and the use of PCA should be considered based on the characteristics of the data and the algorithms used. This study also encourages the exploration of alternative dimensionality reduction methods for medical image analysis.
Classification Model of Public Sentiments About Electric Cars Using Machine Learning Romadoni, Nurul; Siregar, Amril Mutoi; Kusumaningrum, Dwi Sulistya; Rohana, Tatang
Scientific Journal of Informatics Vol. 11 No. 2: May 2024
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v11i2.1309

Abstract

Purpose: This research compared the accuracy level of six algorithms based on the ROC method and the Confusion Matrix evaluation on data regarding public sentiments towards electric cars. Methods: Data collection was conducted for data sourced from TikTok. Next, the data underwent text preprocessing (data cleaning and case folding) and text processing (stemming, tokenizing, stopword removal, word frequency, word relation, TF-IDF, scoring, and labeling). Modeling was then conducted using supervised (labeled) algorithms consisting of the Support Vector Machine (SVM), Decision Tree, Naive Bayes, Random Forest, K-Neighbor, and Logistic Regression. Finally, an evaluation was conducted (confusion matrix and ROC). Result: The results revealed that the Decision Tree algorithm with the Confusion Matrix and ROC evaluation obtained the highest result of 87%. The algorithm with the lowest result is KNN, which has an accuracy of 56%. The classification result for the neutral sentiment has a percentage of 57.1%, followed by negative sentiment at 26.8% and positive sentiment at 16.1%. The KNN algorithm is suitable for large and low-dimensional data, SVM is suitable for data with many features and clear separation between classes, and Naive Bayes is efficient for large datasets with many low-quality features. Additionally, the Random Forest algorithm could overcome overfitting and unbalanced data. Logistic regression is also suitable for linear data without assuming a certain distribution. The Decision Tree algorithm is good for complex data as it provides a visual explanation of predictions. In this study, the Decision Tree algorithm obtained high results because it has the best characteristics and is a linear technique. Novelty: This study found that based on the ROC method and the Confusion Matrix evaluation conducted, the Decision Tree algorithm is more accurate than the other algorithms studied.
Co-Authors Abda Abda Abdul Mufti Ahmad Fauzi Ahmad Fauzi Alma Hidayanti Alya Nabilah Andri Juliyanto Anton Romadoni Junior Aprilia, Ely Ariesta, Eliza ARIF, SITI NOVIANTI NURAINI Aulia, Achmad Indra Baihaqi, Kiki Ahmad Basuni, Nursela Bunga Tiara, Vira Citra Nur Napiah Deden Wahiddin Dwi Sulistya Kusumaningrum Dwi Sulistya Kusumaningrum Dwi Vina Wijaya Faisal, Sutan Fariz Duta Nugraha Farkhina Dwi Utari Fauzi Ahmad Muda Favian Jarsi Taufiqqurakhman Fitri Nur Masruriyah, Anis Goeirmanto, Leonard Hanny Hikmayanti Handayani Hartono Wijaya, Sony Hexsel Aldoegasha Hilda Yulia Novita Indi Nurul Hassanah Indra Maulana` Indra Maulana Indra, Jamaludin Jaman, Jajam Haerul Jayidan, Zirji Juwita, Ayu Ratna Koirunnisa, Koirunnisa Kurniawan, Ade kurniawan, Rifky Kusumaningrum, Dwi Sulistya Kusumaningrum, Dwi Sulistya Kusumaningrum Lestari, Santi Arum Puspita Lilis Kartika Lutfiah Adeliana Maulana Abdur Rofik Maulana, Ikhsan Muhammad Fathir Fahlevi Mulya Cahya Ramadanty Murniasih nabila, putri Nahrowi Nahrowi Nahrowi Nilam Atsirina Krisnaputri Nofita Sari Nur Dava Kurniawan Nur Davi Kurniawan Nusaibah Nusaibah Permana, Tedi Pratama, Adi Rizky Priyatna, Bayu Rahmad Nahar Siregar Rahmat Rahmat Ramadhan, Naufal Cahya Rizqi Fahrozi Rohana, Tatang Romadoni, Nurul Salsa Desmalia Samsul Arifin Santi Arum Puspita Lestari Sekar Wuni Sinta Candra Dewi Sinung Suakanto SITI NURJANAH Siti Silvia Arifin Sony Hartono Wijaya Sony Hartono Wijaya Sukamto, Ika Sumiyarsi Surjandy Sutan Faisal Sutan Faisal Sutan Faisal Tatang Rohana Taufiqqurahman Hutri Tia Astiyah Hasan Tiawan Tjong Wan Sen Tjong Wan Sen Tohirin Al Mudzakir Tohirin Al Mudzakir Tria Pratiwi Sutriyani Tukino Tukino Tukino, Tukino Wilda Amalia Y Aris Purwanto Yana Cahyana Yana Cahyana Yana Cahyana Cahyana Yholanda Maldini Yogi Firman Alfiansyah Yusuf Khoiruddin