Article Per Year (5 Year)
p-Index From 2021 - 2026
7.983
P-Index
This Author published in this journals
All Journal Jurnal Pendidikan Teknologi dan Kejuruan Prosiding Seminar Nasional Sains Dan Teknologi Fakultas Teknik Prosiding SNATIF Jurnal Edukasi dan Penelitian Informatika (JEPIN) Infotech Journal Jurnal Ilmiah Matrik IKRA-ITH Informatika : Jurnal Komputer dan Informatika JITTER (Jurnal Ilmiah Teknologi Informasi Terapan) 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 Jurnal Informatika dan Rekayasa Elektronik JATI (Jurnal Mahasiswa Teknik Informatika) JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) JIKA (Jurnal Informatika) JOINT (Journal of Information Technology jurnal syntax admiration Jurnal Teknologi Informatika dan Komputer Innovation in Research of Informatics (INNOVATICS) Informatics and Digital Expert (INDEX) Jurnal Ilmiah Wahana Pendidikan Journal of Informatics and Communication Technology (JICT) Jurnal Locus Penelitian dan Pengabdian Prosiding Seminar Nasional Sisfotek (Sistem Informasi dan Teknologi Informasi) Jurnal Algoritma Jurnal Sosial dan Sains Riwayat: Educational Journal of History and Humanities Jurnal Global Ilmiah Journal of Informatics and Communication Technology (JICT) Seminar Nasional Riset dan Teknologi (SEMNAS RISTEK)
Fajri Rakhmat Umbara
Fakultas Sains Dan Informatika Universitas Jenderal Achmad Yani, Cimahi, Indonesia
Religion Aerospace Engineering Humanities Astronomy Automotive Engineering Biochemistry, Genetics & Molecular Biology 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 Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Mathematics Mechanical Engineering Medicine & Pharmacology Physics Social Sciences Other

Published : 90 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 90 Documents
Search

 3   4   5   6   7 
ANALISIS CLUSTER PADA KELOMPOK MASYARAKAT YANG RENTAN TERHADAP PAPARAN COVID-19 MENGGUNAKAN METODE K-MEANS CLUSTERING DAN VISUALIASI DENGAN SIG Drl, Indra Raja; Chrisnanto, Yulison Herry; Umbara, Fajri Rakhmat
Informatics and Digital Expert (INDEX) Vol. 4 No. 2 (2022): INDEX, November 2022
Publisher : LPPM Universitas Perjuangan Tasikmalaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36423/index.v4i2.885

Abstract

Covid-19 adalah penyakit yang menular serta laju infeksi yang cepat,setelah mencapai 100 kasus yang dikonfirmasikan terinfeksi tingkat penyebarannya meluas, Dengan cepatnya penyebaran wabah Covid-19 masyarakat sangat prihatin dengan penyebaran dan dampaknya ,orang yang sebelumnya sudah memiliki gangguan kesehatan akan meningkatkan risiko terinfeksi Covid-19 gangguan kesehatan ini seperti,tuberkulosis,diabetes ,diare ,hipertensi.Ada pun Faktor lain yang mempengaruhi penyebaran Covid-19 sepert kepadatan penduduk yang tinggi di kota besar ,iklim,suhu dan daerah metropolitan merupakan faktor risiko utama untuk tertular virus. Dari adanya faktor yang mempengaruhi kasus covid-19 sehingga Satgas Penanganan Covid-19 menilai pentingnya bagi semua pihak termasuk masyarakat memahami faktor-faktor lonjakan kasus Covid-19 agar terhindar dari kasus itu.tujuan dari penelitian ini Menggunakan metode K-Means Clustering untuk analisis cluster pada wilayah yang memiliki karakteristik tingginya kasus covid-19 dan variable apa yang berpengaruh terhadap tingginya kasus covid-19 dan divisualisasi menggunakan Sistem informasi geografis sehingga diharapakan dapat menjadi informasi bagi masyarakat dan instansi kesehatan untuk memahami kelompok wilayah yang rentan. kesimpulannya wilayah kota bandung dikelompokan menjadi 3 cluster yang dimana cluster 1 itu wilayah dengan kasus covid-19 tertinggi dan faktor yang mempengaruhi covid-19 juga tinggi untuk cluster 2 memiliki tingkat kasus yang rendah dan cluster 3 memiliki tingkatan yang yang lebih rendah dari kedua cluster.
Deteksi Ujaran Kebencian dengan Metode Klasifikasi Naïve Bayes dan Metode N-Gram pada Dataset Multi-Label Twitter Berbahasa Indonesia Yazid, Rija Muhamad; Umbara, Fajri Rakhmat; Sabrina, Puspita Nurul
Informatics and Digital Expert (INDEX) Vol. 4 No. 2 (2022): INDEX, November 2022
Publisher : LPPM Universitas Perjuangan Tasikmalaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36423/index.v4i2.894

Abstract

Ujaran kebencian adalah ungkapan atau bahasa yang digunakan untuk mengekspresikan kebencian terhadap seseorang atau sekelompok orang. Ujaran kebencian juga memiliki tingkatan ancaman, semakin tinggi tingkat ancaman ujaran kebencian maka akan semakin luas dan cepat penyebarannya sehingga dapat menimbulkan konflik antar individu sampai konflik antar kelompok. Untuk dapat mendeteksi dan mengklasifikasikan ujaran kebencian sekaligus tingkat ancamannya dalam penelitian ini digunakan dataset multi-label dari penelitian sebelumnya dengan menggunakan label yang masuk kedalam topik ujaran kebencian dan tingkat ancaman dengan total sebanyak 4 label. Dalam menyelesaikan permasalahan multi-label tersebut digunakan metode Naïve Bayes sebagai metode klasifikasi dan metode Label Power-set sebagai metode transformasi data, dalam penelitian ini juga digunakan pembobotan TF-IDF sekaligus melakukan beberapa skenario penelitian berdasarkan metode ekstraksi fitur n-gram. Hasil terbaik yang didapatkan berdasarkan hasil evaluasi F-score adalah sebesar 64,957% ketika menggunakan kombinasi metode ekstraksi fitur word unigram, word bigram dan character quadgram. Dari penelitian ini juga didapatkan bahwa semakin banyak fitur yang digunakan maka semakin baik nilai hasil evaluasinya terhadap jenis dataset yang digunakan.
Prediksi Pengagguran Menggunakan Decision Tree Dengan Algoritma C5.0 Pada Data Penduduk Kecamatan Caringin Kabupaten Bogor Kahfi, Muhammad Dzatul; Umbara, Fajri Rakhmat; Ashaury, Herdi
Informatics and Digital Expert (INDEX) Vol. 4 No. 2 (2022): INDEX, November 2022
Publisher : LPPM Universitas Perjuangan Tasikmalaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36423/index.v4i2.913

Abstract

Tingkat kesejahteraan dalam kehidupan bermasyarakat dapat dilihat dari tingkat penganggurannya. Pemerintah daerah biasanya mengadakan sebuah program untuk membantu mengurangi jumlah pengangguran, entah itu dengan mengadakan sebuah pelatihan atau hal lain yang dapat mendorong kreativitas masyarakat dan meningkatkan kemampuan hardskill agar dapat bersaing di dunia kerja. Ada banyak penelitian yang memprediksi tingkat pengangguran dan juga ada penelitian yang menggunakan algoritma C5.0 untuk melakukan prediksi, namun belum ada penelitian yang menggabungkan subjek dan metode tersebut. penelitian ini bertujuan untuk membuat sebuah model prediksi menggunakan algoritma C5.0 terhadap data penduduk kecamatan caringin dan mencari skenario dengan hasil akurasi yang paling tinggi. namun terdapat beberapa permasalahan yang harus dihadapi seperti bagaimana tingkat akurasi Model klasifikasi Decision Tree dengan algoritma C5.0 terhadap dataset penduduk Kecamatan Caringin dan Bagaimana resio data latih data uji dan penggunaan pruning memengaruhi tingkat akurasi prediksi yang akan dilakukan. Penelitian ini dievaluasi menggunakan beberapa skenario rasio data latih dan data uji yang berbeda beda dan penggunaan pruning yang berbeda. Hasil dari penelitian ini adalah model prediksi pengangguran berhasil dibuat dengan tingkat akurasi paling tinggi yaitu pada skenario data latih 70% dan data uji 30% dengan menerapkan teknik post pruning.
Prediksi Jangka Pendek Harga Bahan Pokok Dki Jakarta Menggunakan Metode Weighted Exponential Moving Average Junior, Rifqi Pratama; Umbara, Fajri Rakhmat; Sabrina, Puspita Nurul
Jurnal Ilmiah Matrik Vol. 25 No. 3 (2023): Jurnal Ilmiah Matrik
Publisher : Direktorat Riset dan Pengabdian Pada Masyarakat (DRPM) Universitas Bina Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33557/jurnalmatrik.v25i3.2575

Abstract

Weighted Exponential Moving Average (WEMA) is a new method that combines WMA and EMA, predicting data based on the future and calculating the value of the data weighting factor over time. Commodities are goods that can be sold freely in the market, one of which is staple food to meet daily needs. This study implements the WEMA method in the short-term prediction of staple food prices, with pre-processing stages using data selection and imputation to overcome missing values. Then the data is divided into training data (75%), and test data (25%), on sugar attribute data, chicken eggs, cooking oil, chicken, and beef. A mean absolute percentage error (MAPE) evaluation was carried out on training data and test data to measure prediction accuracy. The experimental and evaluation results show that accuracy depends on the range and length of the data used. The use of span 2 for both data shows the best results on all evaluated attributes; the results of the MAPE evaluation are below 10%.
PERAMALAN GENRE FILM TERPOPULER BERDASARKAN DATASET MYMOVIE MENGGUNAKAN METODE AUTOREGRESSIVE INTEGRATED MOVING AVERAGE (ARIMA) Asrul; Witanti, Wina; Umbara, Fajri Rakhmat
INFOTECH journal Vol. 9 No. 2 (2023)
Publisher : Universitas Majalengka

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31949/infotech.v9i2.7358

Abstract

At this time the film industry is experiencing very rapid progress, this is because extraordinary technological developments have had a major influence on the film industry. Successful films tend to have a large audience. To find out why the audience likes a film, there are several variables that must be considered, one of which is the genre of the film. This research was conducted to predict what film genres the audience is most interested in. To predict the genre of this film using the autoregressive integrated moving average (arima) method. The autoregressive integrated moving average (arima) method or commonly known as the Box-Jenkins method is a method used to make precise and accurate short-term forecasts, compared to long-term forecasts which usually tend to be flat (flat/constant). From this research a prediction of the popularity or number of viewers of each film genre will be generated which can be used as a reference to find out what genre of film the audience is interested in. So that film production companies can adjust film releases according to their interests. audience, in order to gain greater profits.
KLASIFIKASI TINGKAT PENJUALAN VIDEO GAME DENGAN MENGGUNAKAN METODE K – NEAREST NEIGHBORS Adzani, Nadhif Nurul Fajri; Witanti, Wina; Umbara, Fajri Rakhmat
INFOTECH journal Vol. 9 No. 2 (2023)
Publisher : Universitas Majalengka

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31949/infotech.v9i2.7371

Abstract

Klasifikasi Tingkat Penjualan Video Game Dengan Menggunakan Metode K – Nearest Neighbors memiliki fungsi untuk mengklasifikasikan video game berdasarkan penjualannya, dan memerlukan variabel, seperti genre, platform, publisher, best seller. Permasalahan yang terjadi di Platform penjualan game seperti di Steam, Epic games, etc. Adalah dimana saat gamers membeli game tersebut dan ternyata game tersebut tidak sesuai dengan ekspetasi dari gamers yang membeli game tersebut alhasil game tidak lagi dimainkan. Oleh karena itu, solusi yang dibuat disini yaitu klasifikasi video game berdasarkan karakteristik yang menggunakan metode KNN, dimana nantinya video game akan dibagi berdasarkan karakteristiknya, dan akan ditampilkan beberapa game sesuai klasifikasi karakternya, sehingga diharapkan dapat meminimalisir kejadian pembeli game / gamers yang menyesal karena tidak sesuai dengan ekspetasi mereka
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.
Co-Authors -, Agus Komarudin -, Ridwan Ilyas Adam, Marcellino Ade Kania Ningsih Aditya Bahrul 'Alam, Moch Aditya, Aldy Adzani, Nadhif Nurul Fajri AGIEL FADILLAH HERMAWAN Agri Yodi Prayoga Ahsin Fauzi Aldi Sidik Permana Anwar Fauzi, Mochammad Ardiyansyah, Muhamad Salman Ashaury, Herdi Asrul Badar, Ahmad Cepi, Gan Dava Maulana, Muhammad Delfany Arcadia Valeska Destiyanti, Fitri Dewi Kartika Sari Dewi, Wulan Dian Nursantika Drl, Indra Raja Ella Wahyu Guntari Erna Sesarliana* Fadhilahsyah Ramadhan, Muhammad Diky Faiza Renaldi Fauzan, Ariq Febriansyah Istianto, Andrian Ferdiansyah Ferdian FERDIANSYAH, ALDOVA fery bayu aji FIQRI FAKHRUL GUNAWAN Firmansyah, Rolan Fitri Nurbaya Gestavito, Rio Ginanjar Rahayu Gita Mahesa Hadiana, Asep Id Hasna, Aisyah Nur Hendro, Tacbir Herdi Ashaury Hidayat, Ferdian Afza Hidayat, Mazid Hidayatulah Himawan Hovi Sohibul Wafa Hovi Hovi, Hovi Sohibul Wafa Ilham Danoppati Junior, Rifqi Pratama Kahfi, Muhammad Dzatul Kasyidi, Fatan Kharis Pratama, Adam Kharisma Jevi Shafira Sepyanto Krisdianto Sitanggang, Sari Levi Sabili, Naufal Lio Wilianto Mazid Hidayat Melina Melina Miftahul Falah Muhamad Ramdan, Muhamad Muhammad Ramdhani, Muhammad Nelsih Putriani Novi Hermansyah Nugroho, Akbar Satrio Nurul Sabrina, Puspita Nusantara, Madya Dharma Oktariansyah, Indro Abri Permana, Acep Handika Pujo Sulardi Puspita Nurul Sabrina Puspita Nurul Sabrina Puspita Nurul Sabrina, Puspita Nurul Putra, Dion Revaldy Putri, Ika Rahmah Rachadian Novansyah Rahandanu Rachmat Reno Setiawan Rezki Yuniarti Ridwan Ilyas Salsabila Fajriati Romli Salsabila Salsabila, Salsabila Fajriati Romli Sapari, Albi Mulyadi Sepyanto, Kharisma Jevi Shafira SETIAWAN, YOSEP Shisi Prayesti Sigit Pratama Siti Aisah Sulardi, Pujo Susanti, Adisti Dwi Susilowati, Merliana Tri Syarifudin Yoga Pinasty Syarifudin Yoga Pinasty Tacbir Hendro Tacbir Hendro Pudjiantoro Tacbir Hendro Pudjiantoro Tacbir Hendro Pudjiantoro Tacbir Hendro Pudjiantoro Tacbir Hendro Pudjiantoro Tiara Rahmawati Tri Wijaya Permana Sidik Wibowo, Ditto Ridhwan Wilianto, Lio Wina Witanti Wina Witanti Yanuar, Muhammad Rizki Yazid, Rija Muhamad Yoga, Yoga Yulison Herry Chrisnanto