Article Per Year (5 Year)
p-Index From 2021 - 2026
1.037
P-Index
This Author published in this journals
All Journal JURNAL MEDIA INFORMATIKA BUDIDARMA Jurnal Sisfokom (Sistem Informasi dan Komputer) TEKNOLOGIA Building of Informatics, Technology and Science Journal of Data Science and Its Applications Jurnal Sistem Komputer dan Informatika (JSON) eProceedings of Engineering
Dody Qori Utama
Telkom University
Automotive Engineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Mathematics

Published : 15 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 15 Documents
Search

 1   2 
Aplikasi Pengenalan Gejala Penyakit Dengan Pemrosesan Bahasa Alami Jendral Muhamad Yusuf Zia Ul Haq; Dody Qori Utama; Adiwijaya Adiwijaya
eProceedings of Engineering Vol 8, No 2 (2021): April 2021
Publisher : eProceedings of Engineering

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Abstrak Banyak gejala yang akan dirasakan oleh manusia jika mengalami penyakit, dari gejala-gejala yang ada bisa dimiliki penyakit yang sama. Untuk memastikan kebenaran sebuah kesimpulan yang rumit dimiliki banyak penyakit tetap harus menggunakan pengetahuan dari dokter untuk pengambilan keputusan. Namun, tidak semua orang memiliki waktu dan kesempatan untuk menjumpai dokter. Hal ini dapat diatasi dengan kemajuan teknologi sekarang, dengan bermodalkan ponsel pintar, semua orang dapat mengakses apapun dan dimanapun. Dari permasalahan yang ada, kami memberikan solusi yaitu menyediakan aplikasi yang dapat mendeteksi penyakit berdasarkan gejala yang diberi nama “SiHelti”. Aplikasi ini dapat diakses menggunakan android. Model pengembangan aplikasi untuk aplikasi ini menggunakan metode Waterfall yang dimulai dari tahapan perancangan, implementasi, pengujian, dan deployment. Hasil dari pembuatan aplikasi ini diuji dalam pengujian acceptance testing dari pengguna untuk mengecek kelayakan dari aplikasi yang dibuat. Aplikasi diharapkan dapat membantu dalam mencegah masalah keterlambatan pengecekan penyakit pada masyarakat. Kata kunci : penyakit, aplikasi, android, waterfall, acceptance Abstract Many symptoms will be felt by humans if they experience a disease, from the symptoms that there can be the same disease. To ensure the correctness of a complex conclusion many diseases have to use the knowledge of doctors for decision making. However, not everyone has the time and opportunity to see a doctor. This can be overcome with advances in technology now, with smart phones, everyone can access anything, anywhere. From the existing problems, we provide a solution, namely providing an application that can detect diseases based on symptoms, which is named "SiHelti". This application can be accessed using android. The application development model for this application uses the Waterfall method which starts from the design, implementation, testing, and deployment stages. The results of making this application are tested in acceptance testing from the user to check the appropriateness of the application made. The application is expected to help prevent the problem of delays in checking disease in the community. Keywords: disease, application, android, waterfall, acceptance
Marketplace Pemesanan Katering Terstandarisasi “ketringan” Berbasis Website Muhammad Dafa Prima Aji; Dody Qori Utama; Aji Gautama Putrada
eProceedings of Engineering Vol 8, No 5 (2021): Oktober 2021
Publisher : eProceedings of Engineering

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Pengguna internet di Indonesia pada tahun 2020 tercatat mengalami peningkatan sebanyak 17% dari tahun sebelumnya. Peningkatan tersebut membuka peluang baru di bidang perdagangan barang dan jasa di Indonesia. Penyediaan Akomodasi dan Makan Minum (Kategori I) memiliki peringkat kedua terbanyak dengan jumlah 4.431.154 usaha atau sekitar 17% dari total UMK di Indonesia. Data tersebut menunjukkan bahwa banyaknya UMK di bidang Penyedia makan minum yang bisa dimaksimalkan potensinya untuk mendongkrak perekonomian Indonesia. Permasalahan pemesanan katering juga ditemukan pada sejumlah mahasiswa Telkom. Mereka melakukan survei langsung ke toko fisik pada saat akan memesan katering sehingga 28 dari 30 orang (93%) merasa jasa katering perlu dibuat dalam bentuk platform digital. Survey lanjutan terhadap 10 orang responden juga menunjukkan bahwa 8 orang diantara 10 orang membeli katering dengan cara mendatangi toko fisiknya. Berdasarkan permasalahan dan fenomena tersebut, kami memiliki sebuah solusi yaitu Ketringan, sebuah marketplace katering yang memungkinkan orang bertransaksi dan mencari menu yang sesuai secara instan. Ketringan memiliki Unique Value Proposition (UVP) yaitu standarisasi, sistem pembayaran yang aman, dan harga yang sangat terjangkau. Kata kunci : Marketplace, Katering, UMK, Online, Aplikasi.
Predicting Cryptocurrency Price Using RNN and LSTM Method Gunarto, Dzaki Mahadika; Sa'adah, Siti; Utama, Dody Qori
Jurnal Sisfokom (Sistem Informasi dan Komputer) Vol 12, No 1 (2023): MARET
Publisher : ISB Atma Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32736/sisfokom.v12i1.1554

Abstract

Cryptocurrency price prediction is a crucial task for financial investors as it helps determine appropriate investment strategies and mitigate risk. In recent years, deep learning methods have shown promise in predicting time-series data, making them a viable approach for cryptocurrency price prediction. In this study, we compare the effectiveness of two deep learning techniques, the Recurrent Neural Network (RNN) and Long-Short Term Memory (LSTM), in predicting the prices of Bitcoin and Ethereum. Results of this research show that the LSTM method outperformed the RNN method, obtaining lower Root Mean Squared Error (RMSE) and Mean Absolute Percentage Error (MAPE) values for predicting both cryptocurrencies. Bitcoin and Ethereum. Specifically, the LSTM model had a RMSE of 0.061 and MAPE of 5.66% for predicting Bitcoin, and a RMSE of 0.036 and MAPE of 4.58% for predicting Ethereum. In this research, we found that the LSTM model is a more effective method for predicting cryptocurrency prices than the RNN model.
Sentiment Classification and Interpretation of Tokopedia Reviews: A Machine Learning, IndoBERT, and LIME Approach Mbake Woka, Adrian Yoris; Purbolaksono, Mahendra Dwifebri; Utama, Dody Qori
Building of Informatics, Technology and Science (BITS) Vol 7 No 2 (2025): September 2025
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v7i2.8072

Abstract

Sentiment classification of user reviews plays a vital role in business decision-making, especially on e-commerce platforms like Tokopedia. This study evaluates the performance of various sentiment classification models such as Logistic Regression LinearSVC, and BERT models, both baseline and fine-tuned. Evaluation metrics used include accuracy, precision, recall, and F1-score, applied to Tokopedia review data labelled based on user ratings. The result is fine-tuned BERT model has the best and consistent result, with 92% accuracy and 0.92 f1-score for each class. This shows that fine-tuned BERT can effectively capture the semantic context of user reviews. Its consistent performance across classes makes it suitable for reliable sentiment classification in real-world applications. Furthermore, fine-tune BERT model is visualized by Local Interpretable Model-agnostic Explanation to identify features – in this case is word – that indicates sentiment as positive or negative. It will show as color, orange for positive and blue as negative. This method will make the model more transparent and more reliable.
Klasifikasi Gambar Gigitan Ular Menggunakan Regionprops dan Algoritma Decision Tree Yoga Widi Pamungkas; Adiwijaya Adiwijaya; Dody Qori Utama
Jurnal Sistem Komputer dan Informatika (JSON) Vol. 1 No. 2 (2020): Januari 2020
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/json.v1i2.1789

Abstract

Indonesia has a high biodiversity of snakes. Snake species that exist throughout Indonesia, consisting of venomous and non-venomous snakes. One of the dangers that can be posed by snakes is the bite of several types of deadly snakes. Snake bite cases recorded in Indonesia are quite high with not a few fatalities. Most of the deaths caused by snakebite occur due to errors in the handling procedure for the bite wound. This problem can be overcome one of them if we know how to classify snake bite wounds, whether venomous or non-venomous. In this study, a classification system for snake bite wound image was built using Regionprops feature extraction and Decision Tree algorithm. Snake bite images are classified as either venomous or non-venomous without knowing the kind of the snake. In Regionprops several features are used to help the process of feature extraction, including the number of centroids, area, distance, and eccentricity. Evaluation of the model that was built was found that the parameters of the number of centroids and the distance between centroids had the most significant influence in helping the classification of images of snakebite wounds with an accuracy of 97.14%, precision 92.85%, recall 91.42%, and F1 score 92.06%.
Co-Authors Adiwijaya Aji Gautama Putrada Astrima Manik Bayu Rahmat Setiaji Chiara Janetra Cakravania Chiara Janetra Cakravania Dewangga, Dhiya Ulhaq Dinda Karlia Destiani Fathur Rohman Gilang Titah Ramadhani Gunarto, Dzaki Mahadika Jendral Muhamad Yusuf Zia Ul Haq Mahendra Dwifebri Purbolaksono Mbake Woka, Adrian Yoris Muhammad Dafa Prima Aji sa'adah, siti Yoga Widi Pamungkas Zakia Firdha Razak