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All Journal Jurnal Pendidikan Teknologi dan Kejuruan Prosiding SNATIF Jurnal Edukasi dan Penelitian Informatika (JEPIN) Infotech Journal Jurnal Ilmiah Matrik IKRA-ITH Informatika : Jurnal Komputer dan Informatika Jiko (Jurnal Informatika dan komputer) JURIKOM (Jurnal Riset Komputer) JUMANJI (Jurnal Masyarakat Informatika Unjani) MIND (Multimedia Artificial Intelligent Networking Database) Journal Jurnal Tekno Kompak METIK JURNAL Building of Informatics, Technology and Science JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) JIKA (Jurnal Informatika) Innovation in Research of Informatics (INNOVATICS) Informatics and Digital Expert (INDEX) Jurnal Ilmiah Wahana Pendidikan Jurnal Locus Penelitian dan Pengabdian Jurnal Algoritma Jurnal Sosial dan Sains Jurnal Global Ilmiah Seminar Nasional Riset dan Teknologi (SEMNAS RISTEK)
Umbara, Fajri Rakhmat
Universitas Jenderal Achmad Yani
Religion Aerospace Engineering Astronomy Automotive Engineering Chemical Engineering, Chemistry & Bioengineering 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 Engineering Environmental Science Law, Crime, Criminology & Criminal Justice Library & Information Science Mathematics Medicine & Pharmacology Physics Social Sciences Other

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Klasifikasi Penyakit Jantung Tipe Kardiovaskular Menggunakan Adaptive Synthetic Sampling dan Algoritma Extreme Gradient Boosting Permana, Acep Handika; Umbara, Fajri Rakhmat; Kasyidi, Fatan
Building of Informatics, Technology and Science (BITS) Vol 6 No 1 (2024): June 2024
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v6i1.5421

Abstract

Cardiovascular diseases are conditions that commonly affect the cardiovascular system, such as heart disease and stroke. According to data from the World Health Organization (WHO), 17.9 million deaths worldwide in 2019 were attributable to cardiovascular disease. Early detection is crucial, but diagnosing heart disease is complex in developing countries due to the limited availability of diagnostic tools and medical personnel. This study uses the Heart Disease Dataset from Kaggle, consisting of 15 attributes and 4238 records, to develop a heart disease classification model using XGBoost. The research stages include data imputation, data transformation using LabelEncoder, data balancing using ADASYN, data splitting (80% training data, 20% testing data), and hyperparameter tuning with Bayesian Optimization. The results show that the XGBoost model with ADASYN performs better, with a ROC-AUC of 0.971 and an accuracy of 0.916, compared to the model without ADASYN, which has a ROC-AUC of 0.698 and an accuracy of 0.841. Based on the research results, ADASYN has proven effective in improving model performance on imbalanced datasets. Additionally, Bayesian Optimization plays an important role in finding the optimal parameter combination, which can further enhance model performance. With this research, the impact is quite significant in the development of early detection methods for cardiovascular heart disease, particularly through the application of the XGBoost classification algorithm
Klasifikasi Sentimen Untuk Mengetahui Kecenderungan Politik Pengguna X Pada Calon Presiden Indonesia 2024 Menggunakan Metode IndoBert Oktariansyah, Indro Abri; Umbara, Fajri Rakhmat; Kasyidi, Fatan
Building of Informatics, Technology and Science (BITS) Vol 6 No 2 (2024): September 2024
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v6i2.5435

Abstract

X has evolved into one of the most popular social media platforms in the world. In Indonesia, the use of X is quite widespread, especially in discussions about the presidential election, which is currently a hot topic. Everyone has different views on the candidates, both positive and negative. With a large amount of tweet data from users, this information can serve as a data source for processing and analysis. Various methods can be used to analyze and classify sentiment from this data, one of which is using BERT. This research conducts sentiment classification using BERT with the IndoBert model. The research aims to classify sentiments towards tweets related to the 2024 Indonesian presidential election to understand the political inclinations of X users, evaluate the performance of the IndoBert model in sentiment classification, and assess the extent to which back translation augmentation and synonym augmentation techniques can enhance the model's performance. Data was collected using crawling techniques for seven days leading up to the election and manually labeled by annotators. Synonym augmentation and back translation techniques were used to balance data in minority classes. The data was divided into 80% training data, 10% test data, and 10% validation data. The classification process was conducted using the IndoBert model that had been fine-tuned. The research results show that IndoBert with synonym augmentation achieved the highest accuracy, which was 82% in the first experiment and 81% in the second experiment. On the other hand, back translation only reached an accuracy of 78% in the first experiment and 74% in the second experiment. This indicates that synonym augmentation proved to be more effective in increasing data variation and model performance on the dataset used in this research.
Hybrid Cryptosystem Using RC5 and SHA-3 with LSB Steganography for Image Protection Susanti, Adisti Dwi; Hadiana, Asep Id; Umbara, Fajri Rakhmat; Himawan, Hidayatulah
Innovation in Research of Informatics (Innovatics) Vol 6, No 2 (2024): September 2024
Publisher : Department of Informatics, Siliwangi University, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37058/innovatics.v6i2.11515

Abstract

The rapid development of internet technology has been accompanied by a significant increase in information security threats. Ensuring the security of confidential information transmission is crucial. Cryptography and steganography are among the most efficient techniques for safeguarding data. Both fields focus on information concealment. This paper proposes a hybrid approach to protect confidential multimedia data, specifically image media, by using LSB steganography techniques in combination with the RC5 encryption algorithm and the SHA3 hashing algorithm to provide dual-layer protection for information. In the proposed method, image data is first encrypted using the RC5 encryption algorithm with a specified key. Subsequently, a hashing function using SHA3 is applied for dual protection, ensuring data authenticity and integrity. Finally, steganography is performed using the LSB technique to embed the hashed information into the image media. This study aims to enhance the security of information in digital image media, providing a reliable solution to address security challenges. The results indicate that data confidentiality was successfully achieved, with an average PSNR of 52.509 dB and an MSE of 0.3829. Tests were conducted using a dataset of images with various dimensions.
Air Quality Classification Using Extreme Gradient Boosting (XGBOOST) Algorithm Sapari, Albi Mulyadi; Hadiana, Asep Id; Umbara, Fajri Rakhmat
INNOVATICS: International Journal on Innovation in Research of Informatics Vol 5, No 2 (2023): September 2023
Publisher : Department of Informatics, Siliwangi University, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37058/innovatics.v5i2.8444

Abstract

Air pollution is a serious issue caused by vehicle exhaust, industrial factories, and piles of garbage. The impact is detrimental to human health and the environment. To quickly and accurately monitor classification, techniques are used. One efficient and accurate classification algorithm is XGBoost, a development of the Gradient Decision Tree (GDBT) with several advantages, such as high scalability and prevention of overfitting. The parameters used in the classification include (PM10), (PM2,5),(SO2),(CO),(O3) and (NO2). This study aims to classify air quality into three labels or categories: good, moderate, and unhealthy. In the dataset used to experience an imbalance class, to overcome the imbalance class, techniques will be carried out, namely SMOTE, Random UnderSampling, and Random OverSampling, by producing an accuracy of up to 98,61% with the SMOTE technique for class imbalance. Testing the level of accuracy is done by using the Confusion Matrix.
Talk show segmentation system based on Twitter using K-medoids clustering algorithm Sepyanto, Kharisma Jevi Shafira; Chrisnanto, Yulison Herry; Umbara, Fajri Rakhmat
Jurnal Pendidikan Teknologi Kejuruan Vol 3 No 3 (2020): Regular Issue
Publisher : Universitas Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24036/jptk.v3i3.15123

Abstract

Innovations on a talk show on television can be a threat. Audience will be divided into groups so that it can make a downgrade rating program. Program ratings affect companies that will use advertising services. Television companies will go bankrupt. The biggest source of income is sales of advertising services. One way to overcome them can be analyzed in public opinion. The results of the analysis can provide information about the attractiveness of the community towards the program. But the analysis process takes a long time and can be done only by a competent person so another process is needed to get the results of the analysis that is fast and can be done by anyone. In this study using K-Medoids Clustering in the process of identifying public opinion. The clustering process known as unsupervised learning will be combined with the labeling process. The previous episode's tweet data will be labeled and then used to obtain the predicted labels from other cluster members. Before going through the clustering stage, the tweet data will go through the text preprocessing stage then transformed into a numeric form based on the appearance of the word. Transformation data will be clustered by calculating proximity using Cosine Similarity. Labels from the Medoids cluster will be used on unlabeled tweet data. The cluster results were tested using the Silhouette Coefficient method to get 0.19 results. However, this method successfully predicted public opinion and achieved an accuracy of 80%.
PENGUKURAN KUALITAS PERANGKAT LUNAK PADA SIMRS KHANZA MENGGUNAKAN FUNCTION POINT DAN SYSTEM USABILITY SCALE Nugroho, Akbar Satrio; Umbara, Fajri Rakhmat; Yuniarti, Rezki
Jurnal Tekno Kompak Vol 19, No 1 (2025): FEBRUARI
Publisher : Universitas Teknokrat Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33365/jtk.v19i1.4672

Abstract

Pesatnya pertumbuhan perangkat lunak pada masa ini membuat setiap sektor memiliki kebutuhannya masing-masing dalam menggunakan suatu produk perangkat lunak, tidak terkecuali di sektor layanan kesehatan yang menjadi salah satu sektor paling penting dimasa pandemi COVID-19 sebelumnya. Setelah melewati masa pandemi COVID-19 masyarakat menjadi sadar akan pentingnya sektor E-health dalam membantu akses layanan kesehatan. Sehingga hal ini perlu diukur terkait kesiapan untuk situasi mendatang dimana perangkat lunak SIMRS Khanza menjadi salah satu objek pengukuran pada penelitian kali ini. Proses pengukuran ini dapat menggunakan metode Function Point yang bertujuan untuk mengukur tingkat kompleksitas disisi fungsionalitas dan penggunaan metode System Usability Scale dalam mengukur kegunaan menurut pengguna disisi Usabilitas. Dengan adanya dukungan data hasil pengukuran ini maka, penentuan kualitas dalam suatu produk perangkat lunak dapat ditentukan dengan mudah. Dari permasalahan tersebut menemukan bahwa dalam penelitian ini dalam menghitung nilai Function Point perangkat lunak SIMRS Khanza memperoleh nilai sebesar 6011,28 dengan nilai Crude Function Point (CFP) sebesar 4968 dan Relative Complexity Adjustment Factor (RCAF) sebesar 1.21. Pada pengukuran skala nilai System Usability Scale mendapatkan nilai Skor sebesar 62,14 dimana sistem dapat dikategorikan yang berada pada tingkat Grade Good dari jumlah responden sebanyak 55 orang yang telah dilakukan uji reliabilitas dengan skor Cronbach Alpha sebesar 0,634. Berdasarkan dari hasil tersebut maka, dapat disimpulkan bahwa kualitas perangkat lunak SIMRS Khanza dari sisi fungsionalitas memiliki tingkat kompleksitas yang sangat tinggi namun dari hasil evaluasi pengukuran metode System Usability Scale dimana penggunanya masih bisa menggunakannya secara efektif dan masih dapat merasa puas dalam menggunakannya.
CUSTOMER CHURN PREDICTION USING THE RANDOM FOREST ALGORITHM Setiawan, Yosep; Hadiana, Asep Id; Umbara, Fajri Rakhmat
JIKO (Jurnal Informatika dan Komputer) Vol 7, No 3 (2024)
Publisher : Universitas Khairun

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33387/jiko.v7i3.8711

Abstract

Customer churn prediction plays a vital role in modern business, accurately influencing strategic and operational decisions that influence customer loyalty to a service. Customer churn focuses on customer retention being more profitable than attracting new customers because long-term customers provide lower profits and costs while losing customers increases the costs and need to attract new customers. However, customer churn still occurs frequently and cannot be predicted. If customer churn is left unchecked, it will endanger the company or banking industry because it can cause loss of income, damage reputation, and decrease market share. Random Forest, a data mining technique, was used in this research because of its ability to predict and handle many variables. This research aims to predict customer churn using the Random Forest method with datasets from Europe, especially France, Spain, and Germany, hoping to benefit the banking industry by identifying customers at high risk of abandoning services. This research is expected to benefit business people from customer churn predictions. Especially in the banking industry, it can help identify customers at high risk of abandoning service. Thus, companies can take appropriate steps to retain these customers, increase customer retention, strengthen customer loyalty and optimize their business performance. The results of this research are an accurate system for predicting customer churn in the future. The research obtained accuracy results of 87% in predicting customer churn using accuracy testing in the form of a confusion matrix.
Prediksi Pola Keuangan pada Pasar Saham Bursa Efek Indonesia Menggunakan Algoritma Support Vector Machine For Regression (SVR) Adam, Marcellino; Chrisnanto, Yulison Herry; Umbara, Fajri Rakhmat
JEPIN (Jurnal Edukasi dan Penelitian Informatika) Vol 10, No 3 (2024): Volume 10 No 3
Publisher : Program Studi Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26418/jp.v10i3.86833

Abstract

Di era modern perdagangan saham yang dinamis, penggunaan algoritma Support Vector Machine Regression menjadi perhatian utama bagi para investor dan trader. Penelitian ini bertujuan untuk menganalisis cara kerja algoritma dalam memprediksi pola pasar saham menggunakan Support Vector Machine Regression. Metode penelitian yang digunakan adalah Support Vector Regression dengan kernel Radial Basis Function. Hasil penelitian menunjukkan tingkat akurasi yang baik, dengan Mean Absolute Percentage Error (MAPE) sebesar 6,04%. Hal ini menunjukkan bahwa model ini efektif dalam memprediksi pergerakan harga saham di Bursa Efek Indonesia. Sebaliknya, penggunaan kernel Linear dan Polynomial tidak memberikan hasil yang memuaskan, dengan tingkat kesalahan yang cukup signifikan, masing-masing sebesar 16,32% dan 22,47%. Bahkan, kernel Sigmoid menunjukkan tingkat kesalahan yang sangat tinggi, yaitu MAPE sebesar 808,46%, yang mengindikasikan bahwa model ini tidak cocok untuk prediksi harga saham. Penelitian ini berkontribusi dengan menunjukkan bahwa penggunaan Support Vector Regression dengan kernel Radial Basis Function dapat memberikan hasil prediksi yang akurat dalam konteks pergerakan harga saham. Kontribusi utama terletak pada pemahaman lebih lanjut mengenai efektivitas model Support Vector Regression dalam prediksi di pasar saham Indonesia, yang memberikan manfaat signifikan bagi investor, perusahaan keuangan, pemerintah, dan masyarakat.
Klasifikasi Kanker Payudara Berbasis Deep Learning Menggunakan Vision Transformer dengan Teknik Augmentasi Data Citra Ardiyansyah, Muhamad Salman; Umbara, Fajri Rakhmat; Melina, Melina
JURIKOM (Jurnal Riset Komputer) Vol 12, No 3 (2025): Juni 2025
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v12i3.8619

Abstract

Breast cancer ranks among the leading causes of death in women worldwide. Early detection through mammographic image analysis plays a crucial role in increasing survival rates. However, manual interpretation of mammograms requires expert knowledge and is prone to errors. This study aims to develop a breast cancer classification model using mammography images based on the Vision Transformer (ViT) architecture without employing transfer learning. The dataset used is the Digital Database for Screening Mammography (DDSM), consisting of two categories: benign and malignant. To address class imbalance, undersampling and data augmentation techniques (flipping, rotation, cropping, and noise injection) were applied. All images were normalized and resized to 224×224 pixels to match the ViT input requirements. The model was trained for five epochs with a batch size of 16. Evaluation on the test data was conducted using seven metrics: accuracy, precision, recall, F1-score, Matthews Correlation Coefficient (MCC), Cohen’s Kappa Score, and Area Under the Curve (AUC). The results show that the model achieved an accuracy of 92.50%, precision of 90.48%, recall of 95.00%, F1-score of 92.68%, MCC of 85.11%, Kappa Score of 85.00%, and AUC of 95.75%. These findings indicate that the Vision Transformer is highly effective for mammographic image classification and holds potential as a reliable tool for automated breast cancer diagnosis support.
Klasifikasi Sentimen Kebohongan Berita Menggunakan Metode Indobert Fadhilahsyah Ramadhan, Muhammad Diky; Umbara, Fajri Rakhmat; Ilyas , Ridwan
Jurnal sosial dan sains Vol. 5 No. 8 (2025): Jurnal Sosial dan Sains
Publisher : Green Publisher Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59188/jurnalsosains.v5i8.32425

Abstract

Dalam era digital, penyebaran informasi melalui berita daring berkembang pesat, tetapi ancaman disinformasi atau berita palsu menjadi tantangan signifikan. Penelitian ini menggunakan dataset yang mencakup berita hoaks dan fakta dari sumber-sumber terpercaya seperti Turnbackhoax dan Cek Fakta, untuk mengembangkan sistem klasifikasi sentimen kebohongan berita menggunakan algoritma Bidirectional Encoder Representations from Transformers (BERT) yang disesuaikan untuk bahasa Indonesia, yaitu IndoBERT. Tahapan penelitian meliputi imputasi data, pengolahan data atau pre-processing, yang meliputi pembersihan data untuk menangani masalah data yang tidak bersih, penyeimbangan data menggunakan random oversampler dan random undersampler, pembagian data (80% data latih, 20% data uji). Hasil menunjukan bahwa model IndoBERT dengan random oversampler dan random undersampler menunjukan akurasi yang cukup tinggi dalam klasifikasi berita palsu yaitu sebesar 99.35% berdasarkan atribut yang digunakan pada data. Penelitian ini diharapkan dapat memberikan kontribusi pada pengembangan sistem deteksi hoaks yang efektif, mendukung validasi informasi, dan mencegah dampak negatif dari penyebaran berita palsu.
Co-Authors -, Agus Komarudin -, Ridwan Ilyas Adam, Marcellino Aditya, Aldy Adzani, Nadhif Nurul Fajri Ardiyansyah, Muhamad Salman Ashaury, Herdi Asrul Cepi, Gan Destiyanti, Fitri Dewi, Wulan Dian Nursantika Drl, Indra Raja Fadhilahsyah Ramadhan, Muhammad Diky FERDIANSYAH, ALDOVA Firmansyah, Rolan Gestavito, Rio Hadiana, Asep Id Hasna, Aisyah Nur Hendro, Tacbir Hidayat, Ferdian Afza Hidayat, Mazid Hidayatulah Himawan Junior, Rifqi Pratama Kahfi, Muhammad Dzatul Kasyidi, Fatan Melina Melina, Melina Muhamad Ramdan, Muhamad Muhammad Ramdhani, Muhammad Nugroho, Akbar Satrio Nusantara, Madya Dharma Oktariansyah, Indro Abri Permana, Acep Handika Puspita Nurul Sabrina, Puspita Nurul Putra, Dion Revaldy Putri, Ika Rahmah Rezki Yuniarti Ridwan Ilyas Sapari, Albi Mulyadi Sepyanto, Kharisma Jevi Shafira SETIAWAN, YOSEP Sulardi, Pujo Susanti, Adisti Dwi Susilowati, Merliana Tri Tacbir Hendro Pudjiantoro Wibowo, Ditto Ridhwan Wilianto, Lio Wina Witanti Yanuar, Muhammad Rizki Yazid, Rija Muhamad Yoga, Yoga Yulison Herry Chrisnanto