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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 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
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REDESIGN UI/UX WEBSITE PT SERENA HARSA UTAMA MENGGUNAKAN METODE DESIGN THINKING Firmansyah, Rolan; Umbara, Fajri Rakhmat; Sabrina, Puspita Nurul
Jurnal Informatika Vol 9, No 4 (2025): JIKA (Jurnal Informatika)
Publisher : University of Muhammadiyah Tangerang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31000/jika.v9i4.14731

Abstract

Di tengah pesatnya perkembangan teknologi digital, PT. Serena Harsa Utama, sebuah perusahaan produsen makanan olahan beku, menyadari pentingnya kehadiran media daring yang optimal untuk mendukung bisnis. Website perusahaan yang berfungsi sebagai sarana informasi produk, peluang kemitraan, dan media branding masih memiliki kekurangan pada aspek antarmuka pengguna (UI) dan pengalaman pengguna (UX). Berdasarkan evaluasi awal melalui wawancara dengan manajer serta pengujian menggunakan User Experience Questionnaire (UEQ) dan evaluasi heuristik, ditemukan sejumlah permasalahan seperti tampilan visual kurang menarik, tata letak kurang efektif, dan inkonsistensi desain. Untuk mengatasi masalah tersebut, penelitian ini melakukan perancangan ulang (redesign) UI/UX website dengan menerapkan metode Design Thinking dengan 5 tahapan utama: Empathize, Define, Ideate, Prototype, dan Test. Hasil pengujian menggunakan A/B testing menunjukkan bahwa mayoritas besar pengguna lebih memilih desain baru pada semua komponen yang diuji, termasuk homepage dan halaman produk. Sedangkan pengujian ulang untuk UEQ menunjukkan peningkatan skor yang signifikan pada seluruh dimensi. Nilai rata-rata daya tarik (1.587), kejelasan (1.585), stimulasi (1.680) dan kebaruan (1.515) mencapai kategori “Good” dan “Above Average” dalam benchmark global. Ini menunjukkan penerapan metode Design Thinking berhasil menciptakan pengalaman yang lebih nyaman pada website PT. Serena Harsa Utama.Kata Kunci : UI/UX, Design Thinking, Redesign, UEQ,  A/B Testing
Analisis Sentimen Tweet Penanganan Covid-19 di Indonesia Menggunakan SVM dan Naïve Bayes dengan Operator Seleksi Fitur Information Gain Hasna, Aisyah Nur; Umbara, Fajri Rakhmat; Sabrina, Puspita Nurul
Jurnal Ilmiah Wahana Pendidikan Vol 10 No 5 (2024): Jurnal Ilmiah Wahana Pendidikan
Publisher : Peneliti.net

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5281/zenodo.10516379

Abstract

Opinion that is present from the public is one indicator of sentiment assessment that can be used to assess a matter. In 2020, the world is experiencing a COVID-19 pandemic so that Indonesia is also affected. On Twitter social media at that time there was a lot of discussion about the virus and the state of government policy at that time. Through these tweets, there are those who agree to provide a response to the policy, there are also those who oppose or disagree. Producing these responses is divided into two types of groups, namely positive and negative groups. In this study, tweets were analyzed using two algorithms, namely SVM and Naïve Bayes compared with and without feature selection by the information gain operator so that information is extracted that public opinion tends to be positive or negative. Comparing the algorithms in this study resulted in the highest level of accuracy using the SVM method plus information gain which resulted in an accuracy rate of 66.7% with a precision of 65.5%, a recall value of 66.9% and an f1-score of 66.2%.
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%.
Peningkatan Klasifikasi Serangan DDoS pada SDN Menggunakan XGBoost dan RAMOBoost Badar, Ahmad; Rakhmat Umbara, Fajri; Nurul Sabrina, Puspita
Jurnal Algoritma Vol 22 No 2 (2025): Jurnal Algoritma
Publisher : Institut Teknologi Garut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33364/algoritma/v.22-2.2460

Abstract

The aim of this study is to detect Distributed Denial of Service (DDoS) attacks in Software Defined Networking (SDN) environments using the XGBoost algorithm and the RAMOBoost balancing technique to address the issue of data imbalance. SDN offers flexibility in network management but remains vulnerable to DDoS attacks. The dataset used in this research consists of two classes (normal and attack) with an imbalanced distribution. XGBoost was chosen for its ability to deliver accurate predictions, while RAMOBoost was employed to enhance data representation for the minority class. The results show that before balancing, the model achieved 100% precision for the majority class and 96% precision for the minority class, with recall values of 97% and 100%, respectively. After applying RAMOBoost, precision and recall became more balanced, ranging between 97%–99%, while maintaining a high overall accuracy of 98%. Grouped Feature Importance analysis revealed that randomizing important features reduced accuracy from 97.88% to 49.78%, whereas randomizing unimportant features only slightly decreased accuracy to 97.82%. The main contribution of this study lies in the combined application of RAMOBoost and XGBoost, which proved effective in improving classification performance on imbalanced datasets, and in emphasizing the critical role of feature selection in maintaining model stability. These findings provide valuable insights for network administrators in developing effective attack detection systems for SDN environments.
Implementasi Yolo Untuk Menghitung Kepadatan Kendaraan Tempat Parkir Hidayat, Ferdian Afza; Umbara, Fajri Rakhmat; Ilyas, Ridwan
Jurnal Algoritma Vol 22 No 2 (2025): Jurnal Algoritma
Publisher : Institut Teknologi Garut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33364/algoritma/v.22-2.2919

Abstract

The significant increase in the number of vehicles entering the Universitas Jenderal Achmad Yani area—especially after the construction of the Faculty of Science and Informatics building—has caused congestion at several strategic points on campus, including the area in front of the campus mosque. This study aims to develop a real-time vehicle density monitoring system to support more efficient campus traffic management. The method used involves applying the YOLOv5 object detection algorithm to identify and count vehicles from video recordings in selected monitoring areas. The system is designed to deliver fast and accurate detection while providing real-time vehicle density information. Testing results show that the system achieved strong detection performance, with a maximum precision value of 1.00 at a confidence threshold of 0.983. The maximum recall value of 0.90 was obtained at a lower confidence threshold, reflecting the system’s ability to detect most objects present. These findings highlight the trade-off between model confidence in predictions and its ability to avoid missing relevant objects. The contribution of this study is the development of a prototype system capable of automatically and in real time monitoring vehicle density in campus areas. This system has the potential to become part of a smarter, data-driven campus traffic management solution to reduce congestion and improve the comfort and mobility of the academic community.
Klasifikasi Indeks Standar Pencemaran Udara Menggunakan Algoritma Catboost Dengan Teknik Balancing Data Random UnderSampling Aditya, Aldy; Umbara, Fajri Rakhmat; Sabrina, Puspita Nurul
Jurnal Algoritma Vol 22 No 2 (2025): Jurnal Algoritma
Publisher : Institut Teknologi Garut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33364/algoritma/v.22-2.2971

Abstract

Air quality is an important factor that affects public health and the environment. The Air Pollution Index is used as an indicator to measure the level of air pollution in a region. The main challenge in the air quality classification process is the imbalance of data that can affect the modeling results. This study aims to analyze the performance of the Categorical Boosting (CatBoost) algorithm in ISPU classification by applying the Random Under sampling technique to overcome class imbalance. The dataset used was obtained from air quality monitoring in DKI Jakarta for the period 2020–2024 with a total of 5,386 records and 12 attributes. The research stages included data collection, data cleaning, data transformation, data balancing, feature selection using Recursive Feature Elimination (RFE), modeling with CatBoost, and model evaluation using a confusion matrix. The feature selection results showed five main features that had the most influence, namely PM10, PM2.5, SO2, NO2, and max. The CatBoost model built with the best parameters produced an accuracy of 98 percent, precision of 100 percent, recall of 98.91 percent, and an F1-score of 99.44 percent. Thus, the application of CatBoost and Random Under sampling techniques proved to be effective in improving ISPU classification performance. The results of this study are expected to be used as a decision support system in efforts to mitigate the impact of air pollution in DKI Jakarta.
Peningkatan UI/UX Learning Management System Melalui Redesain Berbasis Design Thinking Harfin Aqbil Falah; Fajri Rakhmat Umbara; Herdi Ashaury
Jurnal Algoritma Vol 23 No 1 (2026): Jurnal Algoritma
Publisher : Institut Teknologi Garut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33364/algoritma/v.23-1.2454

Abstract

The Learning Management System (LMS) at SMKN 4 Padalarang faces various problems in terms of interface and user experience due to the use of default Moodle templates that are not designed according to user needs. An unintuitive interface, confusing navigation, and unattractive visual aesthetics are obstacles to the effectiveness of online learning. This study aims to evaluate the effectiveness of the Design Thinking method in improving the quality of the LMS UI/UX through a user-centered approach. The testing methods used include the System Usability Scale (SUS), User Experience Questionnaire (UEQ), and A/B Testing. Before the redesign, the average SUS score was only 45, which falls into the “Awful” (F) category, and the UEQ results showed an average value below -0.8, which is a negative evaluation. After implementing the Design Thinking method, the SUS score increased to 85, which falls into the “Excellent” (A) category, and the A/B Testing results showed that users preferred the new design, especially in terms of navigation, visuals, and ease of use. These findings indicate that the Design Thinking approach is not only effective in improving usability and functionality, but also capable of increasing user satisfaction and comfort in using the LMS.
Co-Authors -, 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 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 fery bayu aji FIQRI FAKHRUL GUNAWAN Firmansyah, Rolan Fitri Nurbaya Gestavito, Rio Ginanjar Rahayu Gita Mahesa Hadiana, Asep Id Harfin Aqbil Falah 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 Komarudin, Agus Krisdianto Sitanggang, Sari Levi Sabili, Naufal Lio Wilianto Mazid Hidayat Melina Melina Miftahul Falah 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