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All Journal International Journal of Electrical and Computer Engineering Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) Jurnas Nasional Teknologi dan Sistem Informasi 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 Jurnal Teknologi Informasi dan Pendidikan Progresif: Jurnal Ilmiah Komputer Techno Xplore : Jurnal Ilmu Komputer dan Teknologi Informasi Buana Ilmu Jurnal Accounting Information System (AIMS) INTERNAL (Information System Journal) Computer Science and Information Technologies BERNAS: Jurnal Pengabdian Kepada Masyarakat TIN: TERAPAN INFORMATIKA NUSANTARA Jurnal Teknik Informatika (JUTIF) Scientific Student Journal for Information, Technology and Science Jurnal Buana Pengabdian Jurnal Informatika: Jurnal Pengembangan IT 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.
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.
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.
Comparison Model Optimal Machine Learning Model With Feature Extraction for Heart Attack Disease Classification Salsa Desmalia; Amril Mutoi Siregar; Kiki Ahmad Baihaqi; Tatang Rohana
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.v11i2.4561

Abstract

Purpose: The purpose of this study is to classify the number of people affected by heart disease and those not affected by heart disease based on various categories of heart attack causes. This study aims to urge people to take better care of their health and to serve as a reference for doctors to educate patients about the dangers of heart attacks. Methods: The model will be constructed via a machine learning methodology. The algorithms utilized in its development encompass the Support Vector Machine (SVM) algorithm, the K-Nearest Neighbor (k-NN) algorithm, and the Random Forest (RF) algorithm.  This study utilizes principal component analysis (PCA) as a means of extracting optimized features from the dataset, employing techniques for dimension reduction prior to modeling the data. Result: Cumulative explication of the concept of variance constitutes a foundational aspect of PCA (principal component analysis) within the scope of the current research, namely a dimensionality reduction technique employed in multivariate data analysis to facilitate model development, thereby enabling the creation of more optimal and comprehensive models. In this research, the dimensions of training data are incorporated during the process of model creation.   The results show KNN model exhibits the highest performance, with an accuracy of 86%, precision of 86%, recall of 91%, and F1-score of 88%. Furthermore, evaluation using the ROC metric also provides a relatively favorable value, 0.85. Novelty: Researchers used 1190 patient data sourced from Kaggle. Before modeling the algorithm, researchers conducted EDA & Preprocessing which includes missing values to find data that does not have information, then duplicate data to find duplicated data, there are 270 duplicated data, then the duplicated data is deleted so that the data becomes 737, then PCA implementation is carried out.  PCA is reducing features automatically without changing the data.
Comparison of the Accuracy of Drug User Classification Models Using Machine Learning Methods Basuni, Nursela; Amril Mutoi Siregar
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 7 No 6 (2023): December 2023
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v7i6.5401

Abstract

Drug abuse are on the rise, with many users enter the addiction phase, often resulting in overdose and death. Drugs are chemical compounds that are capable of affecting biological functions, and they can induce feelings of happiness and reduce pain. To address this growing problem, a proactive measure is needed. Therefore, this study aims to classify drug users and non-users, so that health workers and therapists can educate about the dangers of drugs to non-users and rehabilitate drug users. This study uses drug consumption data taken from the UCI Irvine Machine Learning Repository. The data consist of 1885 rows with 32 attributes and 2 classes, where there are 18 types of legal and illegal drugs. This research utilizes machine learning methods, specifically Artificial Neural Networks (ANN), Decision Tree (DT), K-Nearest Neighbor (KNN), Support Vector Machine (SVM) and Random Forest (RF), in addition to evaluation methods such as Confusion Matrix and Area Under Curve (AUC). The results showed that RF outperformed the other methods, with accuracy, precision, and recall of 93%, and an f1 score of 89%, while the AUC value was still suboptimal at 0.66. DT had the worst results, with 82% precision, 87% precision, 82% recall, 84% f1 score, and an AUC value of 0.56. With these results, this research can be continued into an application that can classify drug users and nonusers.
Executive Movement Mangrove Planting 2026 Tiawan; Jessika Welliana BR. Siahaan; Eliza Ariesta; Amril Mutoi Siregar; Surjandy; Merios Gusan Putra; Timotius Victory; Nilam Atsirina Krisnaputri; Ade Kurniawan; Dani Lukman Hakim
BERNAS: Jurnal Pengabdian Kepada Masyarakat Vol. 7 No. 2 (2026)
Publisher : Universitas Majalengka

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31949/jb.v7i2.17979

Abstract

Institut Teknologi Sains Bandung (ITSB) berkontribusi dalam kegiatan penanaman pohon mangrove yang dilaksanakan di kawasan Pantai Indah Kapuk (PIK) Tzu Chi sebagai bagian dari upaya pelestarian lingkungan pesisir, melalui undangan dan kolaborasi dengan Techconnect sebagai inisiator kegiatan. Kegiatan ini merupakan bentuk kolaborasi multipihak dalam mendukung restorasi ekosistem mangrove yang memiliki peran penting dalam menjaga keseimbangan lingkungan, seperti mencegah abrasi, menyerap karbon, serta menyediakan habitat bagi keanekaragaman hayati. Secara nasional, Indonesia memiliki ekosistem mangrove terbesar di dunia, namun mengalami degradasi signifikan akibat alih fungsi lahan dan pembangunan yang tidak terkendali. Melalui partisipasi aktif civitas akademika, ITSB tidak hanya berperan dalam aksi nyata penghijauan, tetapi juga menanamkan kesadaran lingkungan dan nilai keberlanjutan kepada mahasiswa. Kegiatan ini sejalan dengan tujuan pembangunan berkelanjutan (Sustainable Development Goals/SDGs), khususnya SDG 13 (Penanganan Perubahan Iklim), SDG 14 (Ekosistem Laut), SDG 15 (Ekosistem Daratan), serta SDG 17 (Kemitraan untuk Mencapai Tujuan). Dengan demikian, keterlibatan ITSB dalam penanaman mangrove menjadi kontribusi strategis dalam mendukung keberlanjutan lingkungan dan pembangunan berwawasan ekologis.
Co-Authors Abda Abda Abdul Mufti Achmad Indra Aulia Ade Kurniawan Ahmad Fauzi Ahmad Fauzi Albert Jofrandi Hutapea Alma Hidayanti Andri Juliyanto Angga Jovansyah Anton Romadoni Junior Ariesta, Eliza ARIF, SITI NOVIANTI NURAINI Baihaqi, Kiki Ahmad Basuni, Nursela Bunga Tiara, Vira Deden Wahiddin Dwi Sulistya Kusumaningrum Dwi Sulistya Kusumaningrum Dwi Vina Wijaya Faisal, Sutan Fariz Duta Nugraha Fariz Umam Farkhina Dwi Utari Fauzi Ahmad Muda Fitri Nur Masruriyah, Anis Gunawan Witjaksono Hanny Hikmayanti Handayani Hilda Yulia Novita Indi Nurul Hassanah Indra Maulana` Indra Maulana Indra, Jamaludin Jaman, Jajam Haerul Jayidan, Zirji Jessika Welliana BR. Siahaan Juwita, Ayu Ratna Kusumaningrum, Dwi Sulistya Kusumaningrum, Dwi Sulistya Kusumaningrum Lestari, Santi Arum Puspita Lilis Kartika Lutfiah Adeliana Maulana Abdur Rofik Maulana, Ikhsan Muhamad Ikbal Ramdani Mulya Cahya Ramadanty Murniasih nabila, putri Nahrowi Nahrowi Nahrowi Nilam Atsirina Krisnaputri Nofita Sari Nur Davi Kurniawan Permana, Tedi Pratama, Adi Rizky Priyatna, Bayu Rahmad Nahar Siregar Rahmat Rahmat Ramadhan, Naufal Cahya Rizqi Fahrozi Rohana, Tatang Romadoni, Nurul Romlah Salsa Desmalia 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 Surjandy Sutan Faisal Sutan Faisal Sutan Faisal Tatang Rohana Tia Astiyah Hasan Tiawan Timotius Victory Tjong Wan Sen Tjong Wan Sen Tohirin Al Mudzakir Tohirin Al Mudzakir Tria Pratiwi Sutriyani Tukino Tukino Wilda Amalia Y Aris Purwanto Yana Cahyana Yana Cahyana Yana Cahyana Cahyana Yholanda Maldini Yogi Firman Alfiansyah Yusuf Khoiruddin