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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 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
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Prediksi Penyakit Diabetes Menggunakan Algoritma Support Vector Machine (SVM) Hovi, Hovi Sohibul Wafa; Id Hadiana, Asep; Rakhmat Umbara, Fajri
Informatics and Digital Expert (INDEX) Vol. 4 No. 1 (2022): INDEX, Mei 2022
Publisher : LPPM Universitas Perjuangan Tasikmalaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36423/index.v4i1.895

Abstract

Diabetes Mellitus (DM) atau lebih dikenal dengan sebutan penyakit kencing manis adalah penyakit kronis yang disebabkan oleh gagalnya organ pankreas memproduksi jumlah hormon insulin secara memadai sehingga menyebabkan peningkatan kadar glukosa dalam darah. Diabetes Mellitus merupakan penyakit yang berbahaya, banyak diberbagai negara terkena penyakit diabetes termasuk di Indonesia. Penyebab utama diabetes masih belum diketahui, namun banyak yang percaya bahwa faktor genetika dan gaya hidup dapat memainkan peran utama pada diabetes. Para peneliti di bidang bioinformatika telah berusaha untuk mengatasi penyakit ini dan membuat sistem untuk membantu dalam prediksi diabetes. Dari berbagai penelitian yang ada, banyak menggunakan metode seperti C4.5, KNN, Naïve Bayes, serta SVM Linier dalam membangun sistem, tapi metode SVM Radial Basis Function (RBF) jarang digunakan dikarenakan hasil akurasi yang didapat tidak cukup untuk digunakan pada sistem prediksi diabetes. Pada penelitian ini menjawab gap tersebut bahwa dengan menggunakan metode algoritma SVM Radial Basis Function (RBF) dapat menghasilkan akurasi yang tinggi dengan mencapai sebesar 91%. Pengujian akurasi yang dilakukan menggunakan Confusion Matrix dan peramalan Mean Square Error dengan kfold kelipatan 10. Penelitian ini bertujuan untuk menentukan apakah penderita/pasien dapat terkena penyakit diabetes atau tidak dengan menerapkan teknik data mining dan klasifikasi menggunakan algoritma SVM Radial Basis Function berbasis Forward Selection.
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.
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%.
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.
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.
Prediksi Penyakit Diabetes menggunakan Teknik Imputasi Missforest dan Klasifikasi LightGBM FERDIANSYAH, ALDOVA; UMBARA, FAJRI RAKHMAT; KASYIDI, FATAN
MIND (Multimedia Artificial Intelligent Networking Database) Journal Vol 10, No 2 (2025): MIND Journal
Publisher : Institut Teknologi Nasional Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26760/mindjournal.v10i2.221-234

Abstract

AbstrakDiabetes adalah salah satu penyakit kronis dengan grafik prevalensinya meningkat secara global. Penyakit ini disebabkan oleh gangguan metabolisme tubuh yang memengaruhi kadar gula darah, dan jika tidak ditangani sejak dini dapat menimbulkan komplikasi serius seperti stroke, gagal ginjal, kebutaan, hingga kematian. Penelitian ini mengembangkan model prediksi risiko diabetes berbasis klasifikasi biner menggunakan algoritma LightGBM yang dikombinasikan dengan teknik imputasi Missforest untuk menangani data yang hilang. Dataset yang digunakan berasal dari Pima Indian, tersedia secara publik di Kaggle. Tahapan pre-processing mencakup imputasi data hilang, penanganan outlier dengan Isolution Forest, pembagian data menjadi 80:20. Evaluasi model menunjukkan hasil akurasi sebesar 91,84% dan ROC AUC 0.9614. BMI menjadi faktor paling berpengaruh dalam prediksi yang diikuti oleh DiabetesPedigreeFunction dan Glucose.Kata kunci: diabetes melitus, data mining, klasifikasi, LightGBM, missforestAbstractDiabetes mellitus is one of the most common chronic diseases, with a globally increasing prevalence. It is caused by metabolic disorders that affect blood glucose levels and, if not treated early, can lead to serious complications such as stroke, kidney failure, blindness, and even death. This research develops a diabetes risk prediction model based on binary classification using the LightGBM algorithm combined with the Missforest imputation technique to handle missing data. The dataset used is the publicly available Pima Indian dataset from Kaggle. The pre-processing stages include missing value imputation, outlier handling using Isolution Forest, an 80:20 data split. Model evaluation shows an accuracy of 91.84% and a ROC AUC 0.9614. BMI was found to be the most influential factor in the prediction, followed by DiabetesPedigreeFunction and Glucose.Keywords: diabetes mellitus, data mining, classification, LightGBM, missforest
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 Melina 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