Article Per Year (5 Year)
p-Index From 2020 - 2025
4.266
P-Index
This Author published in this journals
All Journal Syntax Jurnal Informatika TELKOMNIKA (Telecommunication Computing Electronics and Control) Jurnal Sistem dan Informatika JIKO (Jurnal Informatika dan Komputer) Informatika Pertanian Jurnal Sains dan Informatika JOURNAL OF APPLIED INFORMATICS AND COMPUTING SELAPARANG: Jurnal Pengabdian Masyarakat Berkemajuan Jurnal Informatika Universitas Pamulang Jurnal Sisfokom (Sistem Informasi dan Komputer) J-SAKTI (Jurnal Sains Komputer dan Informatika) EDUMATIC: Jurnal Pendidikan Informatika MULTINETICS JISKa (Jurnal Informatika Sunan Kalijaga) JATI (Jurnal Mahasiswa Teknik Informatika) Abdimasku : Jurnal Pengabdian Masyarakat Jurnal Teknologi Informatika dan Komputer Jurnal Ilmiah Wahana Pendidikan International Journal of Basic and Applied Science J-SAKTI (Jurnal Sains Komputer dan Informatika) CEBONG Journal Jurnal Informatika Polinema (JIP) Jurnal Sistem Informasi dan Aplikasi
Budi Arif Dermawan
Universitas Singaperbangsa Karawang
Religion Agriculture, Biological Sciences & Forestry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Mathematics Medicine & Pharmacology Nursing Physics Public Health Social Sciences Other

Published : 33 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 33 Documents
Search

 1   2   3   4 
SPEECH RECOGNITION UNTUK KLASIFIKASI PENGUCAPAN NAMA HEWAN DALAM BAHASA SUNDA MENGGUNAKAN METODE LONG-SHORT TERM MEMORY Aini Lailla Asri, Nur; Ibnu Adam, Riza; Arif Dermawan, Budi
JATI (Jurnal Mahasiswa Teknik Informatika) Vol. 7 No. 2 (2023): JATI Vol. 7 No. 2
Publisher : Institut Teknologi Nasional Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36040/jati.v7i2.6744

Abstract

Keberagaman suku, ras, agama, bahasa, dan adat istiadat membuat Indonesia memiliki kebudayaan yang dijalin erat dengan bahasa. Menurut data Badan Pusat Statistik (BPS) persentase penutur bahasa daerah semakin menurun pada generasi muda. Selain menurunnya penutur bahasa daerah juga terjadi kesalahan dalam pelafalan bahasa daerah. Kesalahan pelafalan ini dapat diatasi dengan meningkatkan latihan berbahasa, terutama membaca dan mendengarkan kosa kata bahasa daerah. Kosa kata yang dapat dilatih misalnya nama-nama hewan dalam bahasa daerah. Selain menambah kosa kata, dengan mengenal nama hewan dapat meningkatkan kecerdasan natural anak. Metode deep learning dapat digunakan untuk mengatasi pergeseran bahasa daerah salah satunya adalah menggunakan speech recognition. Metode Long-Short Term Memory dapat digunakan untuk klasifikasi suara pelafalan nama hewan dalam bahasa Sunda. Dataset yang digunakan adalah dataset baru dengan 16 cara yang berbeda dalam pengambilan data suara. Data terdiri dari 100 nama hewan yang digunakan sebagai kelas dalam proses klasifikasi. Hasil akurasi terbaik mencapai 97,50% didapat dengan menggunakan epoch 150 dan batch size 30. Pada proses testing mengguanakan data dari dataset, dapat memprediksi semua nama-nama hewan yang digunakan sebagai data kelas. Namun program belum dapat memprediksi dengan tepat apabila menggunakan data dari luar dataset dikarenakan jumlah data suara yang digunakan sebagai dataset belum cukup banyak.
PENERAPAN ALGORITMA K-MEANS CLUSTERING DALAM MENENTUKAN DAERAH PRIORITAS PENANGANAN KEMISKINAN DI WILAYAH JAWA TIMUR Sukarno Wijaya, Nanda; Jajuli, Mohamad; Arif Dermawan, Budi
JATI (Jurnal Mahasiswa Teknik Informatika) Vol. 8 No. 4 (2024): JATI Vol. 8 No. 4
Publisher : Institut Teknologi Nasional Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36040/jati.v8i4.10248

Abstract

Kemiskinan menjadi persoalan yang serius dengan membutuhkan penanganan baik di tingkat nasional maupun global. Di Indonesia terjadi peningkatan serupa sejak september 2022 dengan peningkatan 0,03% dan jumlah masyarakat dinyatakan miskin sebesar 26,36 juta jiwa. Jawa Timur merupakan salah satu wilayah dengan kemiskinan tertinggi di Indonesia. Untuk mendukung upaya penyelesaian kasus yang lebih terstruktur, diperlukan pengendalian dan penekanan kasus kemiskinan dengan menentukan daerah prioritas. Penelitian menggunakan data dari periode tahun 2021-2023. Penelitian ini menggunakan algoritma K-Means dengan metodologi Knowledge Discovery in Database (KDD) yaitu data selection, data preprocessing, transformation, data mining, dan interpretation/evaluation. Algoritma K-Means merupakan sistem untuk pengelompokkan data non hierarki yang dapat mempartisi data terdiri dari dua kelompok atau lebih. Penelitian ini bertujuan melakukan clustering menggunakan K-Means dengan bantuan Silhouette Coefficient, serta dilakukan visualisasi menggunakan tools Quantum Geograpic Information System (QGIS) dalam menentukan segementasi wilayah kemiskinan di Jawa Timur. Berdasarkan data persentase jumlah penduduk miskin dan tingkat pengangguran terbuka. Penelitian ini menghasilkan 2 cluster dengan hasil clustering menggunakan Davies Bouldin Index sebesar 0.600 serta score Silhouette sebesar 0.550. Hasil visualisasi melalui QGIS menunjukkan cluster 0 masuk ke kategori rendah, dan cluster 1 masuk kategori tinggi.
Classification of COVID-19 Aid Recipients in Kasomalang District Using the K-Nearest Neighbor Method Permatasari, Ismi Aprilianti; Dermawan, Budi Arif; Maulana, Iqbal; Kurniawan, Dwi Ely
Journal of Applied Informatics and Computing Vol. 8 No. 1 (2024): July 2024
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v8i1.3279

Abstract

The impact of the Coronavirus, also known as COVID-19, which emerged in 2019, has not only threatened public health but also affected the global economy, including Indonesia. The government has initiated various aid programs to assist the community during the COVID-19 pandemic. These aids are expected to alleviate the economic burden on the affected population. One such aid program is the Direct Cash Assistance (Bantuan Langsung Tunai/BLT) from the Village Fund, which has been distributed since the onset of COVID-19 in Indonesia. However, the distribution of BLT has encountered several issues, including misidentification of recipients and double or excessive distribution beyond the established criteria. To address these issues, data mining for the classification of aid recipients can be employed. This study uses the K-Nearest Neighbor (KNN) method for data mining classification to classify residents' data with new patterns, ensuring aid distribution aligns with the criteria and eliminating double recipients. The application of K-Nearest Neighbor to the population data in Kasomalang District yields optimal performance, with evaluation results showing an accuracy of 96%, precision of 0.98, recall of 0.96, and F1 score of 0.97 using the confusion matrix method.
Implementasi Modified Enhanced Confix Stripping Stemmer pada Klasifikasi Fake News Covid-19 Putri, Dyas Rahma; Dermawan, Budi Arif; Purnamasari, Intan
J-SAKTI (Jurnal Sains Komputer dan Informatika) Vol 5, No 2 (2021): EDISI SEPTEMBER
Publisher : STIKOM Tunas Bangsa Pematangsiantar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/j-sakti.v5i2.359

Abstract

Today's advances in technology and information make communication easier, so that the flow of information can quickly spread. The ease also allows anyone to upload anything on online platforms such as blogs, comments to news articles, social media, etc. that could lead to ambiguity of information or even lead to misleading information. Fake news is information that contains things that are uncertain or not a fact that actually happened. One of the popular news topics nowadays is about the covid-19 virus. This research evaluates the performance of Multinomial Naïve Bayes and Bernoulli Naïve Bayes in conducting fake news classifications related to covid-19. Beside that, we used Modified Enhanced Confix Stripping Stemmer in performing indonesian word standardization that has a variety of shapes and structures. The evaluation showed that Bernoulli Naïve Bayes model had the best performance than Multinomial Naïve Bayes, with the accuracy value of 91%, precision 0.93, recall 0.92, and f-1 score 0.92. In addition, the performance of Modified Enhanced Confix Stripping Stemmer (Modified ECS) algorithm is also perform very well in standardizing words (stemming) Indonesian language.
Implementasi Modified Enhanced Confix Stripping Stemmer pada Klasifikasi Fake News Covid-19 Putri, Dyas Rahma; Dermawan, Budi Arif; Purnamasari, Intan
J-SAKTI (Jurnal Sains Komputer dan Informatika) Vol 5, No 2 (2021): EDISI SEPTEMBER
Publisher : STIKOM Tunas Bangsa Pematangsiantar

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (731.051 KB) | DOI: 10.30645/j-sakti.v5i2.359

Abstract

Today's advances in technology and information make communication easier, so that the flow of information can quickly spread. The ease also allows anyone to upload anything on online platforms such as blogs, comments to news articles, social media, etc. that could lead to ambiguity of information or even lead to misleading information. Fake news is information that contains things that are uncertain or not a fact that actually happened. One of the popular news topics nowadays is about the covid-19 virus. This research evaluates the performance of Multinomial Naïve Bayes and Bernoulli Naïve Bayes in conducting fake news classifications related to covid-19. Beside that, we used Modified Enhanced Confix Stripping Stemmer in performing indonesian word standardization that has a variety of shapes and structures. The evaluation showed that Bernoulli Naïve Bayes model had the best performance than Multinomial Naïve Bayes, with the accuracy value of 91%, precision 0.93, recall 0.92, and f-1 score 0.92. In addition, the performance of Modified Enhanced Confix Stripping Stemmer (Modified ECS) algorithm is also perform very well in standardizing words (stemming) Indonesian language.
Peningkatan Deteksi Kecelakaan di Jalan Raya Menggunakan Real-ESRGAN pada Citra CCTV Persimpangan Jalan Ikhsal, Muhammad Fachry; Dermawan, Budi Arif; Adam, Riza Ibnu
Journal of Applied Informatics and Computing Vol. 7 No. 1 (2023): July 2023
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v7i1.5562

Abstract

The failure of the accident detection system on CCTV cameras can affect the increase in the death rate on the highway. The use of the CNN method in the construction of CCTV accident detection systems has been widely used before. However, common problems that are often encountered are dirty lenses and varifocal zooms that don't automatically focus, causing the quality of the resulting CCTV images to decrease, thus affecting system performance. In this research, a model was developed to detect accidents on CCTV images using the MobileNetV2 pre-trained model which was optimized by upscaling the dataset using the Real-ESRGAN model to produce more optimal performance. This study uses a CCTV image dataset totaling 989 and consisting of 2 types of prediction classes including accident and non-accident. The results showed that the MobileNetV2 model succeeded in producing 94% testing accuracy and an average inference time of 3.33 seconds in the GT test scenario. During the testing process, it was found that the model was not optimal if it identified new data with clustered objects. In addition, based on the test scenarios X2, X4, X8 it was found that the image quality calculated based on PSNR and SSIM values greatly influences classification performance such as accuracy, precision, recall, and AUC score.
IMPLEMENTASI OBJECT DETECTION DALAM KLASIFIKASI SAMPAH UNTUK MENINGKATKAN EFISIENSI PENGELOLAAN LIMBAH Anggara, Jerry; Ryansyah, Eddy; Arif Dermawan, Budi
JATI (Jurnal Mahasiswa Teknik Informatika) Vol. 9 No. 3 (2025): JATI Vol. 9 No. 3
Publisher : Institut Teknologi Nasional Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36040/jati.v9i3.13813

Abstract

Pengelolaan sampah di Indonesia masih menghadapi tantangan besar dengan produksi mencapai 68.5 juta ton per tahun, sementara tingkat daur ulang masih rendah. Salah satu kendala utama adalah proses pemilahan yang masih dilakukan secara manual menyebabkan inefisiensi, tingginya biaya operasional, serta meningkatnya pencemaran lingkungan akibat pembuangan sampah yang tidak terkelola dengan baik. Minimnya kesadaran masyarakat dalam memilah sampah semakin memperburuk kondisi ini. Untuk mengatasi permasalahan tersebut, penelitian ini mengembangkan sistem deteksi dan klasifikasi sampah berbasis algoritma YOLOv8 yang dikenal dengan kecepatan dan akurasi tinggi dalam mendeteksi objek. Model dilatih menggunakan dataset yang terdiri dari 1.822 gambar dalam enam kategori sampah yaitu kertas, plastik, kardus, metal, gelas, dan sampah organik yang diperoleh dari berbagai sumber termasuk Roboflow dan TrashNet. Hasil evaluasi menunjukkan bahwa model memiliki tingkat keakuratan yang cukup tinggi dengan nilai mAP (mean Average Precision) sebesar 0.905. Sistem ini di-deploy dalam bentuk web menggunakan Flask yang dilengkapi dengan fitur unggah gambar/video serta menampilkan hasil deteksi dengan tampilan yang informatif dan mudah digunakan. Penelitian ini menunjukkan bahwa object detection berbasis YOLOv8 dapat mendukung pengelolaan sampah secara lebih efisien.
Reconfigurable Metasurface Panels for Active Electromagnetic Shielding of Protective Domes Sihotang, Hengki Tamando; Dermawan, Budi Arif; Rasenda, Rasenda; Rizky A, Galih Prakoso
Cebong Journal Vol. 4 No. 3 (2025): July: Green dan Blue Economy
Publisher : IHSA Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

The increasing complexity of electromagnetic (EM) environments in defense and communication systems necessitates shielding solutions that are both adaptive and efficient. Conventional static shielding domes, while effective in blocking electromagnetic interference (EMI), are inherently limited by their fixed frequency response, high structural weight, and lack of real-time adaptability. This research investigates the design and performance of reconfigurable metasurface panels for active electromagnetic shielding of protective domes, with the aim of enhancing shielding effectiveness, tunability, and structural efficiency. The study explores the integration of reconfigurable metasurfaces into dome architectures, enabling dynamic control of electromagnetic wave propagation through electronically tunable elements. Performance metrics including shielding effectiveness (in dB), tunable frequency ranges, angular stability, and real-time adaptability were evaluated and benchmarked against conventional static shielding designs. Results indicate that reconfigurable metasurface domes achieve superior shielding performance across wide frequency bands while offering significant weight reduction and improved adaptability. These characteristics make them well-suited for critical applications such as military radomes, satellite communication shelters, aerospace systems, and secure civilian infrastructures. However, challenges remain regarding large-scale fabrication, integration complexity, power requirements for active tuning, and environmental durability. Despite these limitations, the findings highlight the transformative potential of reconfigurable metasurfaces as the foundation of next-generation adaptive shielding technologies. This research demonstrates that reconfigurable shielding domes not only address the shortcomings of static designs but also pave the way for resilient, flexible, and future-proof electromagnetic protection systems.
A bayesian dynamic latent state model for predicting infant sleep-wake patterns under daily massage intervention A , Galih Prakoso Rizky; Rasenda, Rasenda; Dermawan, Budi Arif; Arifuddin, Nurul Afifah; Alrasyid , Wildan
International Journal of Basic and Applied Science Vol. 14 No. 1 (2025): Computer Science, Engineering, Basic and Applied mathematics Science
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/ijobas.v14i1.699

Abstract

Sleep disturbances in infants present a persistent challenge for caregivers and healthcare providers. This study proposes a Bayesian Dynamic Latent State Model to predict infant sleep-wake patterns in response to daily massage, a non-pharmacological intervention. The model captures latent sleep propensity as a dynamic hidden process influenced by current and previous massages, individual random effects, and autoregressive components. Observed outcomes include nocturnal sleep duration and nighttime awakenings, modeled using Gaussian and Poisson distributions respectively. Through numerical simulations and a real-world case study, the model demonstrates clear benefits: average nocturnal sleep duration increased by approximately 1.2–1.5 hours, while nighttime awakenings decreased by about 35–40% on intervention days, with residual improvements on subsequent days. Compared to traditional static and time-series models, the proposed Bayesian approach provides greater flexibility in handling uncertainty, explicitly models carry-over effects, and integrates individual heterogeneity in sleep responses contributions that have not been fully addressed in prior infant sleep studies. This research thus advances the scientific understanding of dynamic, intervention-driven sleep processes, while also offering practical implications for evidence-based pediatric nursing and personalized infant care strategies. While promising, validation is currently limited to a small dataset and simplified assumptions. Future work will involve larger-scale testing, incorporation of additional external factors, and benchmarking against alternative machine learning models.
Game Edukasi Anak Menggunakan Metode Finite State Machine Berbasis Android Ashari, Aliffia Novsiyanti; Jajuli, Mohamad; Dermawan, Budi Arif
MULTINETICS Vol. 6 No. 2 (2020): MULTINETICS Nopember (2020)
Publisher : POLITEKNIK NEGERI JAKARTA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32722/multinetics.v6i2.2817

Abstract

Media belajar yang diterapkan saat ini masih bersifat konvensional. Maka dari itu, penelitian ini merancang game edukasi anak menggunakan metode finite state machine berbasis android, dimana metode finite state machine ini merupakan sistem kontrol alur aplikasi. Metode pengembangan aplikasi yang digunakan adalah Multimedia Development Life Cycle (MDLC) yang memiliki enam tahapan yaitu concept, design, material collecting, assembly, testing, dan distribution. Tujuan adanya aplikasi ini, untuk mengevaluasi pengetahuan anak pada pelajaran, dengan memanfaatkan media interaktif seperti gambar, animasi 2D transisi dan musik. Berdasarkan hasil uji aplikasi dengan alpha testing, fungsi aplikasi berjalan dengan baik dan hasil dari beta testing dengan kuisioner delapan pertanyaan yang diisi oleh 32 siswa kelas 1 (satu) Sekolah Dasar Negeri Sukamandi II diperoleh presentase 98,6%, sehingga aplikasi layak digunakan. Dari pengujian manual didapatkan hasil pengetahuan anak pada pelajaran Matematika sebesar 86,2%, Bahasa Indonesia 85,3% dan Bahasa Inggris 73,8% dan hasil evaluasi menggunakan aplikasi game pada pelajaran Matematika sebesar 97,8%, Bahasa Indonesia 95,6% dan Bahasa Inggris 92,5%, sehingga dapat disimpulkan bahwa pengetahuan anak dalam pelajaran mengalami peningkatan setelah menggunakan aplikasi game edukasi yaitu 11,6% pada pelajaran Matematika, 12,5% pelajaran Bahasa Indonesia dan 18,7% pelajaran Bahasa Inggris, sehingga aplikasi ini efektif untuk meningkatkan kemampuan belajar anak.
Co-Authors Adhi Rizal Agung Siswoyo Aini Lailla Asri, Nur Alfafisabil, Wahyu Alfanda Novebrian Maharadja Aliffia Kulsumarwati Alrasyid, Wildan Anggara, Jerry Anis Fitri Nur Masruriyah Apriade Voutama Ashari, Aliffia Novsiyanti Aurelly Claudia Budianto, Theresia Ayu Syaharani, Maesha Betha Nurina Sari Carudin Cika Oktavia Daffa Rafif Agustian Dwi Ely Kurniawan Eddy Ryansyah Erliza Hambali Fuza Putra Arwanda Haerudin Hannie, Hannie Hengki Tamando Sihotang Ibnu Adam, Riza Ikhsal, Muhammad Fachry Intan Purnamasari Iqbal Maulana Iqbal Maulana Isy Karima Fauzia LILIK BUDIPRASETYO Loemongga Oktaria Sihombing Muhammad Hilmi Syarif Nana Mulyana Maghfur Natcha Kwintarini Suparman Nur Padilah, Tesa Nurul Afifah Arifuddin Permatasari, Ismi Aprilianti Putri Dwi Rahayu Putri, Dyas Rahma Rasenda, Rasenda Renaldy Permana Sidiq Rijal Abdulhakim Riza Ibnu Adam, Riza Ibnu Rizky A, Galih Prakoso Rohanah, Aan Sofi Defiyanti Sukarno Wijaya, Nanda SUSANTO Susilawati Susilawati Syafi'i, Muhammad Tesa Nur Padilah Tesa Nur Padilah Wahyu Alfafisabil Yuyun Umaidah