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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 MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer JISKa (Jurnal Informatika Sunan Kalijaga) Infotekmesin Abdimasku : Jurnal Pengabdian Masyarakat 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 Selma Marsya Finda; 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.
Visual Analysis Based on CMY and RGB Image Cryptography Using Vigenere and Beaufort Cipher Atika Sari, Christy; Utomo, Danang Wahyu; Doheir, Mohamed A S
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Vol. 8, No. 2, May 2023
Publisher : Universitas Muhammadiyah Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22219/kinetik.v8i2.1664

Abstract

The achievement of visual aspects and image security often cannot meet visibility standards, for example the acquisition of PSNR and UACI NPCR values. To increase security, this research has implemented a combination of the Vigenere cipher and Beaufort and the use of Fibonacci as a randomizer. The combination of the Vigenere Cipher and Beaufort Cipher substitution algorithms with the Fibonacci technique can be applied to encrypt color images in RGB and CMY, with a size of 256x256 pixels and in .bmp format. The Fibonacci cut-off value used in this study is 10000. The highest entropy value of the cipher image peppers.bmp is 7,991. The lowest PSNR cipher image value is accordion.bmp where for RGB it is 5,439 dB and for CMY it is 5,403 dB. accordion.bmp's highest UACI value is 44.018% for RGB and 44.312% for CMY. The NPCR value in the airplane.bmp image has the highest value in RGB of 99.792% and for CMY the highest value is in splash.bmp with a value of 99.798%. Evaluation of the decryption results shows that the decryption process can run perfectly as indicated by the values of MSE=0, PSNR=inf, UACI and NPCR=0%. Therefore, encrypt and decrypt was proven that the results obtained in the visual aspect are very good.
Pembelajaran Ensemble untuk Klasifikasi Ulasan Pelanggan E-commerce Menggunakan Teknik Boosting Matius Rama Hadi Suryanto; 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.v15i2.2314

Abstract

Technological developments have developed rapidly and impact changing behavior in daily activities. Now, selling and buying activities are carried out in e-commerce services. The increase in e-commerce users is the main factor in improving the quality of e-commerce services. One of the factors to improve the quality of e-commerce services is customer reviews. Customer reviews are useful for shop owners to find out whether the product offered has positive or negative reviews. The large number of customer reviews is the main factor in the difficulty of shop owners in classifying customer reviews. This study proposes classifying customer reviews using ensemble learning with boosting techniques such as XGBoost, AdaBoost, Gradient Boosting, and LightGBM. The use of an ensemble with a boosting technique aims to improve the algorithm’s performance. In a test scenario apply majority voting to produce the best performance from each algorithm. The result shows that the XGBoost algorithm produces higher accuracy than other techniques are 92.30%. On the analysis of matric evaluation of precision, recall, and F1-Score, XGBoost produces higher true positive values than other techniques such as AdaBoost, Gradient Boosting, and Light GBM
Penyusunan Analisis Kebutuhan Perangkat Lunak untuk Web Profil SMP Negeri 7 Semarang Utomo, Danang Wahyu; Kurniawan, Defri; Zeniarja, Junta; Dewi, Ika Novita; Salam, Abu; Muljono, Muljono
ABDIMASKU : JURNAL PENGABDIAN MASYARAKAT Vol 8, No 1 (2025): JANUARI 2025
Publisher : LPPM UNIVERSITAS DIAN NUSWANTORO

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/ja.v8i1.2700

Abstract

Penggunaan web profil sebagai alat penyebaran informasi telah banyak digunakan pada institusi Pendidikan utamanya sekolah. SMP N 7 Semarang menggunakan web profil untuk menyampaikan informasi terkait identitas sekolah seperti visi dan misi sekolah, kurikulum serta kegiatan siswa dalam sekolah. Namun web tersebut masih terdapat kekurangan dan perlu diperbaiki menyesuaikan dengan perkembangan saat ini. Pemahaman tentang analisis kebutuhan perangkat lunak penting bagi para guru dan tenaga pendidik untuk mengetahui kebutuhan pengguna dan kebutuhan sistem yang harus disediakan dalam sistem. Program pengabdian Masyarakat dilaksanakan dalam bentuk pelatihan kepada para guru dan tenaga pendidik. Para peserta diberikan materi analisis kebutuhan termasuk kebutuhan pengguna, kebutuhan sistem, kebutuhan fungsional dan non-fungsional. Selain itu, para peserta juga menerima pelatihan tentang desain antarmuka pengguna dan tata letak konten situs web. Hasil dari program ini, para peserta dapat mengidentifikasi perbaikan yang diperlukan untuk situs web profil SMP N 7 Semarang. Fitur berita diidentifikasi sebagai kebutuhan fungsional yang perlu ditambahkan pada situs web profil. Untuk kebutuhan non-fungsional, para peserta menyarankan desain ulang tata letak konten web
Pekalongan Regency Tourism Recommendation System with Content based Filtering Salsabilla, Cinta; Utomo, Danang Wahyu
Sistemasi: Jurnal Sistem Informasi Vol 14, No 1 (2025): Sistemasi: Jurnal Sistem Informasi
Publisher : Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v14i1.4839

Abstract

This study implements content-based filtering for a tourist recommendation system in Pekalongan Regency. The method utilizes the TF-IDF algorithm to measure the weight of tourist attraction categories and cosine similarity to assess the similarity between attractions based on their categories. The dataset, provided by the Pekalongan Regency Tourism Office, includes eight categories: nature, water, artificial, culture, hills, beaches, camping, and adventure. The system is designed through the calculation of TF-IDF and cosine similarity to generate relevant recommendations. The findings show that the system effectively provides recommendations aligned with user preferences by presenting a list of tourist attractions with relevant categories. This system assists tourists in discovering destinations that match their interests while supporting the promotion of tourism in Pekalongan Regency.
Early Detection of Mental Health Disorders based on Sentiment using Stacking Method Maldini, Naufal; Utomo, Danang Wahyu; Tresyani, Rahmadika Putri
Sistemasi: Jurnal Sistem Informasi Vol 14, No 1 (2025): Sistemasi: Jurnal Sistem Informasi
Publisher : Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v14i1.4842

Abstract

Mental health disorders are a serious and growing global concern, including in Indonesia. This study aims to predict mental health disorders through sentiment analysis using the Stacking Classifier approach, which combines Random Forest, Gradient Boosting Classifier, and Logistic Regression algorithms. The dataset was sourced from various social media platforms, consisting of textual data classified into seven mental health categories, such as depression, anxiety, and personality disorders. The data underwent preprocessing steps, including cleaning, balancing, and dimensionality reduction using the TF-IDF algorithm. The study results indicate that the Stacking Classifier method achieved an accuracy of 95.66%, with a precision of 95.63%, recall of 95.66%, and F1-Score of 95.64%. These results outperform the individual algorithms tested in the research. The findings demonstrate the significant potential of sentiment analysis powered by machine learning for early detection of mental health disorders, making it a valuable tool to enhance diagnosis and intervention in mental health care more effectively.
Penerapan Deep Learning dengan Mekanisme Attention untuk Meningkatkan Performa Segmentasi Liver dan Tumor pada Citra CT Menggunakan ResUnet Eka Putra, Zaky Dafalas; Utomo, Danang Wahyu
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.
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 Aplikasi OncoDoc Untuk Mendeteksi Potensi Kanker Bagi Warga Kelurahan Tegalsari Semarang Defri Kurniawan; Ardytha Luthfiarta; Abu Salam; Catur Supriyanto; Danang Wahyu Utomo; Dhita Aulia Octaviani
Community : Jurnal Pengabdian Pada Masyarakat Vol. 4 No. 1 (2024): Maret : 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/community.v4i1.497

Abstract

Penerapan Pola Hidup Bersih dan Sehat (PHBS) diperlukan bagi warga kelurahan Tegalsari, kecamatan Candisari, Semarang khususnya bagi warga RW 002. Warga RW 002 yang didominasi oleh orang tua rentan terhadap penyakit kanker, apabila tidak menerapkan PHBS pada kehidupakan sehari-hari. Kegiatan Program Kemitraan Masyarakat (PKM) diselenggarakan dengan topik pentingnya PHBS dan pengenalan aplikasi OncoDoc untuk melakukan deteksi dini terhadap resiko penyakit kanker. Metode pelaksanaan kegiatan pengabdian meliputi Analisa Kebutuhan dan Identifikasi Masalah, Menetapkan Tujuan, Menyusun Materi Pengabdian, Pendampingan Aplikasi, dan Publikasi Kegiatan. Kegiatan PKM dilaksanakan di Balai RT 003 / RW 002 yang dihadiri oleh 18 warga yang terdiri dari pengurus RW, RT dan ibu-ibu PKK. Kegiatan PKM diawali dengan sambutan oleh Ketua Kegiatan PKM dan Ketua RW 002, dilanjutkan dengan penyampaian materi, diakhiri dengan diskusi, tanya jawab, kuis serta penyerahan plakat. Kegiatan PKM memberikan manfaat berupa pengetahuan pentingnya penerapan PHBS dan memberikan manfaat teknis berupa ketrampilan bagi warga untuk dapat melakukan deteksi dini melalui aplikasi OncoDoc.
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 Ika Novita Dewi Isinkaye, Folasade Olubusola 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 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