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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
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Klasifikasi Kanker Payudara Berbasis Deep Learning Menggunakan Vision Transformer dengan Teknik Augmentasi Data Citra Ardiyansyah, Muhamad Salman; Umbara, Fajri Rakhmat; Melina, Melina
JURNAL RISET KOMPUTER (JURIKOM) 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 Kesehatan Mental Mahasiswa Menggunakan Light Gradient Boosting Machine Dan Analisa Fitur Menggunakan SHAP Wibowo, Ditto Ridhwan; Umbara, Fajri Rakhmat; -, Ridwan Ilyas
JURNAL RISET KOMPUTER (JURIKOM) Vol. 12 No. 4 (2025): Agustus 2025
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v12i4.8771

Abstract

The mental health of college students is an important issue as many do not receive treatment despite needing it. According to the Association of University and College Counseling Center Directors 95% of college students experience an increase in psychopathology. This study uses the Light Gradient Boosting Machine algorithm to classify the mental health of college students based on a dataset that has a total of 61.794 rows and 16 columns. Light Gradient Boosting Machine is an implementation of Gradient Boosting Decision Tree which has two strategies namely gradient-base one-side sampling (GOSS) and leaf-wise growth. The accuracy results obtained using LightGBM reached 67% where the data used had been balanced using the class_weight parameter and the ADASYN technique. In addition, the research was analyzed to find the most contributing features using the SHAP (SHapley Additive exPlanations) method with the results obtained there are 6 features that have the highest contribution value including Country, treatment, mental_health_interview, family_history, Gender, dan self_employed.
Sistem Rekomendasi Film Menggunakan Data User-End dan Knowledge Graph Convolutional Network pada Dataset MovieLens 1 M Yanuar, Muhammad Rizki; Umbara, Fajri Rakhmat; -, Agus Komarudin
JURNAL RISET KOMPUTER (JURIKOM) Vol. 12 No. 4 (2025): Agustus 2025
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v12i4.8772

Abstract

Traditional recommendation systems such as Collaborative Filtering and Content-Based Filtering often fail to provide relevant recommendations due to their limitations in handling sparsity and cold-start problems. This study proposes a Knowledge Graph Convolutional Network (KGCN) model enriched with user demographic data from the MovieLens 1M dataset to address these issues. The primary focus of the research is to demonstrate that the Importance Sampling technique is significantly superior to Uniform Sampling in effectively training the model. After hyperparameter tuning, the optimal model configuration achieved peak performance with an AUC score of 0.8798 and NDCG@10 of 0.9719. These results demonstrate that the proposed approach is effective in building an accurate, personalised recommendation system capable of addressing sparsity and cold-start issues.
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.
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%.
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