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All Journal Seminar Nasional Aplikasi Teknologi Informasi (SNATI) Techno.Com: Jurnal Teknologi Informasi Jurnal Simetris Jurnal Buana Informatika Jurnal Inovasi Teknologi Pendidikan Jurnas Nasional Teknologi dan Sistem Informasi JAIS (Journal of Applied Intelligent System) Proceeding SENDI_U Jurnal Ilmiah Dinamika Rekayasa (DINAREK) Sistemasi: Jurnal Sistem Informasi Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control JOURNAL OF APPLIED INFORMATICS AND COMPUTING JISKa (Jurnal Informatika Sunan Kalijaga) Infotekmesin Abdimasku : Jurnal Pengabdian Masyarakat Jurnal Sistem Komputer dan Informatika (JSON) Jurnal Nasional Teknik Elektro dan Teknologi Informasi Jurnal Komtekinfo Community: Jurnal Pengabdian Pada Masyarakat Jurnal Informatika: Jurnal Pengembangan IT Seminar Nasional Riset dan Teknologi (SEMNAS RISTEK) INOVTEK Polbeng - Seri Informatika
Utomo, Danang Wahyu
Universitas Dian Nuswantoro
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 Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Library & Information Science Mechanical Engineering Social Sciences Other

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Klasifikasi Stunting Balita menggunakan Metode Ensemble Learning dan Random Forest Finda, Selma Marsya; Danang Wahyu Utomo
Infotekmesin Vol 15 No 2 (2024): Infotekmesin, Juli 2024
Publisher : P3M Politeknik Negeri Cilacap

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35970/infotekmesin.v15i2.2326

Abstract

Stunting is a long-term condition that describes nutritional deficiencies that affect children's growth and development from an early age, especially linear growth. Examination of the stunting status of toddlers in Indonesia, especially at the Karanganyar Community Health Center, still uses book calculations so errors are still found in the use of formulas which result in inaccuracies in the classification of stunting. Efforts to improve research results were carried out using the Random Forest algorithm which was enhanced with ensemble methods such as the Bagging and Boosting methods to classify stunting data. The aim of this research is to find out which technique will produce the best and most accurate accuracy. The Ensemble Boosting techniques used are XGBoost and Gradient Boosting. This research uses a dataset from the Karanganyar Health Center, Semarang City with a total of 2000 data records. The test results produced the highest accuracy algorithm, namely the Random Forest + Bagging algorithm which obtained accuracy results of 98.25%. Based on the analysis results obtained, the Bagging and Boosting methods can accurately predict stunting data.
Prediksi Diabetes menggunakan Metode Ensemble Learning dengan Teknik Soft Voting Hilmi Hanif; Danang Wahyu Utomo
Infotekmesin Vol 16 No 1 (2025): Infotekmesin: Januari 2025
Publisher : P3M Politeknik Negeri Cilacap

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35970/infotekmesin.v16i1.2534

Abstract

Diabetes is a chronic disease characterized by high blood glucose levels due to the body's inability to produce or use insulin effectively. This disease is one of the serious global health problems, and it has a significant impact; therefore, early detection is very important. Efforts to overcome this challenge can be made by applying machine learning, which provides a new and effective approach. This study aims to predict diabetes with a higher accuracy level through the Ensemble Learning Soft Voting method. In addition, the data balancing technique using SMOTE is applied to overcome the problem of imbalance in the data set. This study also compares various classification models using Machine Learning algorithms, namely LightGBM, XGBoost, and Random Forest. The test results show that the Random Forest model achieves the highest level of accuracy at 97.20%. In comparison, the Ensemble Learning Soft Voting method that combines the three algorithms has increased the accuracy to 97.74%. This Ensemble Learning approach has proven effective in significantly improving predictions and performing better than a single model.
Deteksi Dini Gangguan Kesehatan Mental dengan Model Bert dan Algoritma Xgboost Rahmadika Putri Tresyani; Wahyu Utomo, Danang; Maldini, Naufal
Infotekmesin Vol 16 No 1 (2025): Infotekmesin: Januari 2025
Publisher : P3M Politeknik Negeri Cilacap

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35970/infotekmesin.v16i1.2535

Abstract

Mental health disorders are severe conditions that affect a person's thoughts, feelings, behavior, and well-being. Data from the World Health Organization (WHO) shows that more than 264 million people worldwide experience depression, one of the most common forms of mental health disorders. However, limited access to psychological services, such as lack of professionals and high costs, are major challenges in providing adequate support. Therefore, innovative technology-based solutions are needed for efficient and affordable psychological support. Efforts to improve research results to develop a mental health chatbot model by combining BERT (Bidirectional Encoder Representations from Transformers) and XGBoost (Extreme Gradient Boosting) models. The BERT model is used to understand the context of the conversation, while the XGBoost algorithm is used for text classification. The dataset used comes from Kaggle, which consists of 312 question patterns with several patterns or classes, namely 79 classes. The results of the program implementation test produced a percentage of 93.05% and output in the form of a program in the execution of the model on Google Colab..
Pendampingan Pembuatan Video Animasi untuk Siswa SMA At Thohiriyyah Semarang Astuti, Yani Parti; Utomo, Danang Wahyu; Sudibyo, Usman; Fahmi, Amiq; Kartikadarma, Etika; Dolphina, Erlin; Subhiyakto, Egia Rosi
Community : Jurnal Pengabdian Pada Masyarakat Vol. 4 No. 3 (2024): November : Jurnal Pengabdian Pada Masyarakat
Publisher : LPPM Sekolah Tinggi Ilmu Ekonomi - Studi Ekonomi Modern

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51903/3s4ejr82

Abstract

Information technology has developed and provided progress such as the increasing use of computers and the internet in the world, especially in the world of education. Through computers and the internet, all information can be disseminated and can be used as learning materials for students. The development of information technology and the internet, not all information is disseminated positively. Some information is disseminated negatively such as fake news (hoaxes), radicalism, and hate speech. There needs to be skills in using the development of information technology. Digital literacy trains users not only to be proficient in using information technology but also to have the ability to think critically, creatively, and innovatively to produce digital competence. SMA At Thohiriyyah is one of the high schools in Semarang that focuses on understanding and improving the abilities of its students in digital literacy. Insight is needed for SMA At Thohiriyyah students in understanding the importance of digital literacy. Animation video training is one way to increase student creativity in digital literacy in creating learning videos. With this training, it is hoped that students can develop learning videos that can be used on social media such as YouTube
Pendampingan Pembuatan Media Pembelajaran Berbasis Multimedia Bagi Guru SD Negeri Pedurungan Kidul 02 Semarang Utomo, Danang Wahyu; Kartikadarma, Etika; Dolphina, Erlin; Kurniawan, Defri; Purwanto, Purwanto
ABDIMASKU : JURNAL PENGABDIAN MASYARAKAT Vol 7, No 1 (2024): JANUARI 2024
Publisher : LPPM UNIVERSITAS DIAN NUSWANTORO

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/ja.v7i1.1802

Abstract

Media pembelajaran saat ini telah berkembang, salah satu contohnya adalah media pembelajaran digital atau biasa disebut literasi digital. Literasi digital dapat berupa teks, audio, atau video. Cara mendapatkannya dapat melalui berbagai sumber seperti media sosial dan halaman web. Keuntungan dari media pembelajaran digital adalah dapat meningkatkan kemampuan belajar siswa. Guru juga dapat menggunakan berbagai sumber seperti teks, gambar, audio dan video dalam materi pembelajaran. Maka program kemitraan Masyarakat (PKM) dari Udinus menawarkan pendampingan pembuatan media pembelajaran berbasis multimedia dengan Canva. Metode yang digunakan dalam program kemitraan Masyarakat adalah praktek dengan Canva. Dalam praktek tersebut, para guru diawali membuat slide presentasi kemudian diubah menjadi video pembelajaran dengan memanfaatkan asset yang disediakan oleh Canva.
Algoritma Principal Component Analysis (PCA) dan Metode Bounding Box pada Pengenalan Citra Wajah Riski, Habibu; Utomo, Danang Wahyu
Jurnal Informatika: Jurnal Pengembangan IT Vol 9, No 1 (2024)
Publisher : Politeknik Harapan Bersama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30591/jpit.v9i1.6165

Abstract

Current facial recognition system utilizes the Principal Component Algorithm (PCA) and the Bounding Box method to recognize facial locations based on brightness levels. The problem that was found in the experiment was that unclear or illuminated light factors could cause inaccuracies in facial area recognition. PCA is an algorithm capable of performing dimensional reduction to recognize the face area. The recognition process involves image pre-processing, PCA analysis to produce vectors, and application of a Bounding Box to focus on critical areas. This research contributes to the development of reliable and efficient facial recognition systems, potentially applied in security and access management. The experiment used the Grimace dataset using the .jpg format, with tests on normal brightness and -50 decreases in brightness level. At the decrease in -50, the result shows that the smallest distance value is 3540.1, and the greatest distance is 6849.4 with the average value being 5810.110. face recognition results can recognize face images with the original image
Deteksi Diabetes Mellitus dengan Menggunakan Teknik Ensemble XGBoost dan LightGBM Pratama, Naufal Adhi; Utomo, Danang Wahyu
JISKA (Jurnal Informatika Sunan Kalijaga) Early Access
Publisher : UIN Sunan Kalijaga Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14421/jiska.4908

Abstract

Diabetes mellitus is a metabolic disease characterized by elevated blood sugar levels due to impaired insulin secretion, insulin action, or both. The disease has a major impact on public health and contributes to high morbidity and mortality rates in many countries. Prevention and early detection are essential to reduce the adverse effects of this disease. This study aims to analyze and apply machine learning algorithms in detecting diabetes mellitus, focusing on the use of XGBoost and LightGBM algorithms. The dataset used in this study includes various features related to diabetes risk factors, such as age, gender, body mass index (BMI), hypertension, smoking history, and HbA1c and blood glucose levels. Preprocessing was performed to clean and balance the data using the SMOTE-Tomek technique. Next, the model was built and evaluated using the K-Fold cross-validation method to measure the accuracy and stability of the model. The results showed that the XGBoost model achieved 97.31% accuracy, while the LightGBM model produced 97.26% accuracy. Combining the two models through blending techniques resulted in an accuracy of 97.51%, indicating that the combination of models can improve prediction performance. This study shows the great potential of machine learning algorithms, especially XGBoost and LightGBM, in detecting diabetes mellitus accurately and efficiently. Hopefully, the results of this study can contribute to the development of decision support systems for more effective early diagnosis of diabetes.
Enhancing Interpretable Multiclass Lung Cancer Severity Classification using TabNet Norman, Maria Bernadette Chayeenee; Dewi, Ika Novita; Salam, Abu; Utomo, Danang Wahyu; Rakasiwi, Sindhu
Journal of Applied Informatics and Computing Vol. 9 No. 6 (2025): December 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i6.11417

Abstract

Lung cancer poses a significant global mortality challenge, with early clinical detection hindered by non-specific symptoms making accurate diagnosis dependent on extracting subtle patterns from often complex medical tabular data. Traditional machine learning approaches often fall short in capturing intricate patterns within such heterogeneous datasets, hindering effective clinical decision support. This research introduces TabNet, an interpretable deep learning architecture, for multiclass lung cancer severity prediction (low, medium, high). Utilizing the Kaggle Lung Cancer dataset, our methodology leverages TabNet's unique attention-based feature selection for end-to-end processing of tabular data, enabling adaptive identification of key predictors and crucial model interpretability. To effectively assess its predictive capabilities and ensure robust performance, the model was trained with default configurations and validated through stratified 5-fold cross-validation, achieving outstanding performance on the test set: 98.50% accuracy, a 0.98 F1-score, and a 0.9996 macro-AUC-ROC. Beyond its robustness, confirmed by stable learning curves, interpretability analysis highlighted 'Genetic Risk' and 'Shortness of Breath' as dominant factors. Our results underscore TabNet's efficacy as a reliable, robust, and inherently interpretable solution, offering significant potential to improve the precision and transparency of lung cancer severity assessment in clinical practice.
Penerapan Deep Learning dengan Mekanisme Attention untuk Meningkatkan Performa Segmentasi Liver dan Tumor pada Citra CT Menggunakan ResUnet Zaky Dafalas Eka Putra; Danang Wahyu Utomo
Jurnal Nasional Teknologi dan Sistem Informasi Vol 10 No 3 (2024): Desember 2024
Publisher : Departemen Sistem Informasi, Fakultas Teknologi Informasi, Universitas Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25077/TEKNOSI.v10i3.2024.231-239

Abstract

Kanker hati merupakan salah satu penyebab kematian paling tinggi di dunia. Dalam mendeteksi kelainan pada hati perlu dilakukan segmentasi untuk mengambil bagian dari hati yang mengalami gangguan. Namun, metode segmentasi manual memakan waktu dan rawan kesalahan. Selain itu, metode tradisional juga sering kali kesulitan menangani variasi bentuk, ukuran, dan tekstur tumor, serta kualitas citra yang heterogen, sehingga mengurangi akurasi segmentasi. Oleh karena itu, penelitian ini mengusulkan penerapan model segmentasi menggunakan mekanisme Attention ResUnet, yang menggabungkan arsitektur residual dan konvolusi berbasis skip connection, ditingkatkan dengan attention untuk meningkatkan akurasi deteksi tumor. ResUnet dirancang untuk meningkatkan akurasi dan stabilitas segmentasi tumor dengan mengatasi masalah vanishing gradient dan meningkatkan kemampuan deteksi fitur kompleks. Dataset citra CT yang digunakan dalam penelitian ini dipra-pemroses melalui windowing untuk fokus pada rentang intensitas organ hati dan menghilangkan organ yang tidak penting. Hasil penelitian menunjukkan bahwa model Residual Unet dengan mekanisme Attention mampu meningkatkan performa segmentasi gambar CT hati dan tumor secara signifikan, mencapai akurasi 99.54% dan nilai Dice sebesar 95% pada segmentasi liver, serta akurasi 99.5% dan nilai Dice sebesar 90% pada segmentasi tumor. Penambahan modul Residual dan Attention secara efektif membantu model menangkap fitur yang relevan, khususnya dalam menangani lesi kompleks dan batas kabur, yang sering menjadi tantangan dalam segmentasi citra medis.
Implementasi Algoritma Random Forest untuk Prediksi Kesehatan Mental Berdasarkan Faktor Gejala Depresi Jullita, Efandra Eka; Danang Wahyu Utomo
Jurnal Sistem Komputer dan Informatika (JSON) Vol. 7 No. 2 (2025): Desember 2025
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/json.v7i2.9258

Abstract

Kesehatan mental adalah fondasi esensial bagi kualitas hidup individu dan produktivitas bangsa, dengan depresi menjadi salah satu kondisi yang paling umum dan memprihatinkan, memengaruhi jutaan orang secara global dan menyebabkan disabilitas signifikan. Diagnosis tradisional depresi yang bergantung pada penilaian klinis subjektif seringkali memakan waktu, memerlukan ketersediaan ahli, dan dapat terhambat oleh stigma, yang memperlambat penanganan efektif. Kebutuhan akan alat bantu yang objektif, skalabel, dan efisien untuk deteksi dini gejala depresi menjadi sangat mendesak. Penelitian ini mengusulkan solusi inovatif melalui implementasi dan evaluasi algoritma Random Forest untuk prediksi kesehatan mental, khususnya gejala depresi. Algoritma Random Forest, sebagai metode pembelajaran ensemble, dipilih karena kemampuannya dalam mengintegrasikan berbagai faktor risiko kompleks, mengurangi overfitting, serta mencapai akurasi dan stabilitas prediksi yang tinggi, yang sangat relevan untuk data kesehatan mental yang beragam. Hasil pengujian menunjukkan performa model yang sangat tinggi dengan akurasi mencapai 99,97% serta nilai presisi, recall, dan F1-score masing-masing sebesar 1,00. Tujuan utama penelitian ini adalah untuk mengembangkan model prediksi depresi yang akurat dan robust, sekaligus memberikan wawasan tentang faktor-faktor risiko kunci. Diharapkan model ini dapat menjadi alat bantu praktis bagi profesional kesehatan mental, meningkatkan efisiensi skrining awal, mempercepat identifikasi individu berisiko tinggi, dan mendukung pengambilan keputusan klinis yang lebih informatif, serta berkontribusi pada upaya pencegahan dan pengelolaan depresi.
Co-Authors Abu Salam Abu Salam Aden Rahmat, Aden Rahmat Agustina, Feri Aldhi Ari Kurniawan Ardytha Luthfiarta Arifin, Muhammad Farhan Astuti, Yani Parti Astuti, Yani Parti Catur Supriyanto Christy Atika Sari Churniansyah, Faskal Defri Kurniawan Defri Kurniawan Defri Kurniawan Defri Kurniawan Dhita Aulia Octaviani Doheir, Mohamed A S Donny Saputro Donny Saputro Dzaki, Azmi Abiyyu Egia Rosi Subhiyakto Egia Rosi Subhiyakto Egia Rosi Subhiyakto Egia Rosi Subhiyakto Egia Rosi Subhiyakto Egia Rosi Subhiyakto, Egia Rosi Eka Putra, Zaky Dafalas Eko Hari Rachmawanto Erlin Dolphina Etika Kartikadarma Fahmi Amiq Faskal Churniansyah Finda, Selma Marsya Gusnita Darmawati Haresta, Alif Agsakli Hilmi Hanif Husin Sufi Ijayanti, Selvi Ika Novita Dewi Jullita, Efandra Eka Junta Zeniarja Kurniawan, Defri Kurniawan, Defri Liya Umaroh Maldini, Naufal Matius Rama Hadi Suryanto Muljono Muljono Ningrum, Novita Kurnia Norman, Maria Bernadette Chayeenee Novita Kurnia Ningrum Octaviani, Dhita Aulia parti astuti, yani Prajanto Wahyu Adi Prajanto Wahyu Adi, Prajanto Wahyu Pratama, Naufal Adhi Purwanto Purwanto Putri, Cindy Arlita Rahmadika Putri Tresyani Rama Hadi Suryanto, Matius Ramadhan Rakhmat Sani Riski, Habibu Rosi Subhiyakto, Egia Salsabilla, Cinta Selma Marsya Finda Sepbriant, Genta Dwigi Sindhu Rakasiwi Subhiyakto, Egia Rosi Sudibyo, Usman Tanjung, Reza Phina Tresyani, Rahmadika Putri Yani Parti Astuti Yani Parti Astuti Zaky Dafalas Eka Putra