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All Journal Jurnal Teknologi dan Manajemen Informatika REKAYASA Jurnas Nasional Teknologi dan Sistem Informasi Edu Komputika Journal Sistemasi: Jurnal Sistem Informasi JIKO (Jurnal Informatika dan Komputer) INOVTEK Polbeng - Seri Informatika JOURNAL OF APPLIED INFORMATICS AND COMPUTING Compiler KACANEGARA Jurnal Pengabdian pada Masyarakat MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer INTEK: Informatika dan Teknologi Informasi Jurnal Linguistik Komputasional JASIEK (Jurnal Aplikasi Sains, Informasi, Elektronika dan Komputer) Jurnal Tekinkom (Teknik Informasi dan Komputer) JOURNAL OF INFORMATION SYSTEM MANAGEMENT (JOISM) International Journal of Engineering, Technology and Natural Sciences (IJETS) JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) JUKI : Jurnal Komputer dan Informatika International Journal of Business, Humanities, Education and Social Sciences (IJBHES) Indonesian Journal of Data and Science TIN: TERAPAN INFORMATIKA NUSANTARA Infotek : Jurnal Informatika dan Teknologi Teknika Science in Information Technology Letters Bulletin of Computer Science Research Journal of Social Research Journal Of World Science Jurnal Informatika Teknologi dan Sains (Jinteks) NERO (Networking Engineering Research Operation) SmartComp Jurnal Informatika: Jurnal Pengembangan IT International Journal of Artificial Intelligence in Medical Issues
Adityo Permana Wibowo
Universitas Teknologi Yogyakarta
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Penerapan Laundry Management System Web-Mobile Menggunakan Metode Waterfall dengan Evaluasi TAM dan Blackbox Testing Pangesty, Shandika Sayyid Ammar; Wibowo, Adityo Permana
TIN: Terapan Informatika Nusantara Vol 6 No 7 (2025): December 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/tin.v6i7.8731

Abstract

The main problems faced by Mulia Laundry stem from business processes that are still carried out manually, including service recording, inventory management, and customer transactions. This situation reduces operational efficiency, increases the possibility of recording errors, and hinders service speed. Therefore, the purpose of this study is to develop and implement a web- mobile based Laundry Management System that can automate the entire laundry business process to be more efficient, accurate, and integrated. The system is designed using a client-server architecture through REST API, with a structured software engineering approach through Unified Modeling Language (UML) modeling and using the Waterfall model Software Development Life Cycle (SDLC) development method. The main features of this system include customer management, services, inventory, transactions, and real-time order status tracking. The system was evaluated using Blackbox Testing, which showed that all features operated according to specifications without any errors in the main application processes. In addition, user acceptance testing using the Technology Acceptance Model (TAM) obtained an average perceived usefulness (PU) score of 4.67 and a perceived ease of use (PEOU) score of 4.47, both of which are in the excellent category. The results of this study indicate that the system is well accepted by users and can prove its effectiveness in improving operational efficiency and supporting the digitization of business processes in laundry MSMEs.
Sistem Informasi Rental PlayStation Berbasis Client-Server: Evaluasi Penerimaan Pengguna Menggunakan Technology Acceptance Model Setyadi, Dian Febry; Wibowo, Adityo Permana
TIN: Terapan Informatika Nusantara Vol 6 No 7 (2025): December 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/tin.v6i7.8743

Abstract

Effective operational management is a critical aspect in the PlayStation rental business as it plays a role in ensuring service smoothness and supporting comprehensive business oversight. At Bossman PlayStation, operational processes are still managed manually using conventional recording methods. This condition results in slow customer service (average 4 minutes per transaction), vulnerability to transaction and inventory recording errors (3-5 errors per week) that potentially cause financial losses, and limited owner capability to monitor business development in real-time. This study aims to design an integrated information system with a client-server architecture whose solution includes two main platforms: Mobile Application to accelerate daily employee operations, and Web Application focused on monitoring and approval functions by the owner. The system design uses a UML-based approach to model requirements and workflows, utilizing WebSocket technology to provide real-time unit status updates and database triggers to automate business rules such as stock updates. The achieved result is a mature and detailed system design, where all main workflows including two rental models, sales transactions, to approval processes have been clearly defined and are ready for implementation. Functional testing showed 100% success in 10 main scenarios, and user acceptance testing (TAM) obtained a score of 4.525/5.0. The system successfully reduced transaction time to 1 minute and eliminated recording errors.
Comparative Study of Machine Learning Methods for Disease Classification Based on Natural Language Symptom Descriptions Jullev Atmadji, Ery Setiyawan; Wibowo, Adityo Permana; Faizal, Edi
International Journal of Artificial Intelligence in Medical Issues Vol. 3 No. 2 (2025): International Journal of Artificial Intelligence in Medical Issues
Publisher : Yocto Brain

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56705/ijaimi.v3i2.361

Abstract

The growing demand for remote healthcare solutions has increased the importance of efficient disease diagnosis based on textual symptom descriptions. This study explores the application of machine learning models Multinomial Naive Bayes, Random Forest, and Support Vector Machine (SVM) to classify 24 different diseases from natural language symptom inputs. Utilizing a dataset of 1,200 balanced samples and TF-IDF for feature extraction, we trained and evaluated the models using both accuracy and cross-validation metrics. Among the models, SVM achieved the highest test accuracy of 97.5% and demonstrated consistent performance across all disease categories. These findings underscore the potential of classical machine learning approaches in enhancing digital diagnostic tools, particularly for early screening in telemedicine applications. Future work could extend this study by integrating deep learning architectures and multilingual capabilities to accommodate broader and more diverse healthcare scenarios.
Kinerja Metode Fine-Tuning IndoBERT untuk Klasifikasi Emosi Multi-Kelas pada Teks Informal Bahasa Indonesia Haikal Fawwaz Karim; Adityo Permana Wibowo
Bulletin of Computer Science Research Vol. 6 No. 1 (2025): December 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v6i1.850

Abstract

Automatic emotion analysis on informal Indonesian texts is a challenging task due to high linguistic variation, the use of slang, and abbreviations. This research focuses on the development and evaluation of an accurate emotion classification model, which can serve as a core component various relevant Natural Language Processing (NLP) applications. The proposed method is the fine-tuning of the pre-trained language model IndoBERT to classify texts from the social media platform Twitter (X) into five emotion classes: anger, fear, happy, love, and sadness. A custom dataset consisting of 4,940 Twitter posts was built through a targeted scraping process and statistically validated labeling to ensure data relevance and balance. Experiments show that after undergoing a comprehensive text preprocessing stage, including normalization using a custom abbreviation dictionary and stemming, the fine-tuned model achieved very high performance. Evaluation results on the test data show the model successfully reached an accuracy of 94% and a weighted average F1-score of 0.94. Learning curve analysis also confirms that the model did not suffer from overfitting and possesses good generalization capabilities. These results demonstrate that the IndoBERT fine-tuning approach is a highly effective and reliable solution for emotion classification in the informal Indonesian text domain.
DETEKSI OTOMATIS PENYAKIT LAYU FUSARIUM PADA DAUN TOMAT MENGGUNAKAN CONVOLUTIONAL NEURAL NETWORK BERBASIS EKSTRAKSI CITRA RGB Nanda, Dwita Dhara; Wibowo, Adityo Permana
Jurnal Informatika Teknologi dan Sains (Jinteks) Vol 7 No 4 (2025): EDISI 26
Publisher : Program Studi Informatika Universitas Teknologi Sumbawa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51401/jinteks.v7i4.6918

Abstract

Tanaman tomat memainkan peranan krusial pada sektor pertanian, tetapi seringkali produksi tanaman tomat terancam oleh penyakit salah satunya adalah penyakit layu yang disebabkan oleh Fusarium. Deteksi yang cepat dan akurat sangat penting untuk mengurangi kerugian pada saat panen. Penelitian ini bertujuan untuk mengevaluasi kinerja model Convolutional Neural Network (CNN) dalam mendeteksi penyakit layu Fusarium pada daun tomat berbasis ekstraksi citra RGB. Dataset yang digunakan dalam penelitian ini terdiri dari gambar daun tomat yang sehat serta yang terinfeksi, yang dibagi dengan rasio 80% untuk pelatihan dan 20% untuk pengujian. Model CNN dilatih selama 20 epoch dengan memakai optimasi Adam pada learning rate sebesar 0.0001. Hasil pengujian menunjukkan performa model yang sangat memuaskan, dengan akurasi pada validasi mencapai 98,47% dan nilai loss serendah 0,0457. Temuan ini menunjukkan bahwa model CNN yang telah diuji sangat efektif dan dapat diandalkan dalam mendeteksi penyakit layu Fusarium pada daun tanaman tomat, sehingga dapat menjadi dasar yang berharga bagi petani dalam pengelolaan tanaman tomat dan pengambilan keputusan.
Co-Authors AA Sudharmawan, AA Achmad Safruddin Ahmad Fathan Hidayatullah, Ahmad Fathan Ahmad Naswin Akmal Setiawan Wijaya Albertus Carloson Fallo Allam, Sayyidan Fahd Arief Hermawan Arif Amrulloh Barus, M Akmal Shievo Bella Primin Donny Avianto Edi Faizal Fernanda, Niko Haikal Fawwaz Karim Hamzah Fachrurozi Ifannur, Muhammad Taufik Imantoko Imantoko Jullev Atmadji, Ery Setiyawan Kuncorojati, Fariz Wahyu Kustrianugraha, Afri Fajar Kusumastuti, Asriana Dyah Luh Putu Ratna Sundari M. Raihan Alghifari Muhammad Fachrie Muhammad Habibi Muhammad Insan Kamil, Muhammad Insan Nanda, Dwita Dhara Nararya Budiyono Nastiti, Kartika Rizqi Nugroho, Bagus Candra Nur Hidayati Pangesty, Shandika Sayyid Ammar Pratama, Khrisna Yudha Primin, Bella Putra, Elvanda Adi Putra, Muhammad Fakhri Despawida Aulia Qowiim, Asyhar Raihan Anmar Widyasmoro Rianto Ridwan Halim Setyadi, Dian Febry Sri Hartati Sri Wulandari Suryo Sudiro Sutarman Sutarman Tamam Huda, Muhammad Tarigan, ⁠⁠Thomas Edyson Taruk, Medi Tri Waluyo Wiwik Handayani