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All Journal Compiler Informasi Interaktif Emerging Information Science and Technology Jurnal Mandiri IT Jurnal Informatika Teknologi dan Sains (Jinteks) AnoaTIK: Jurnal Teknologi Informasi dan Komputer Fahma : Jurnal Informatika Komputer, Bisnis dan Manajemen Buletin Pos dan Telekomunikasi
Ryan Ari Setyawan
Program Studi Teknik Informatika Universitas Janabadra
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Engineering Industrial & Manufacturing Engineering Library & Information Science Mathematics Social Sciences

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Implementation of Finite State Automata to Optimize the Waste Collection Process in the Greenify Application Setyawan, Ryan Ari; Christianto, Devri Budi; Bening, Ridho Gilang
Emerging Information Science and Technology Vol. 6 No. 1 (2025)
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/eist.v6i1.27032

Abstract

Efficient waste management is a major challenge in urban life. One promising solution is the use of Finite State Automata (FSA) to optimize the waste containment process within the Greenify application. This study aims to explore the application of FSA in designing the logical flow for waste management, which includes identifying waste types, collection locations, and pick-up schedules. The methodology employed is a theoretical approach that implements the FSA model to regulate statuses and transitions between different steps in the waste management process. The results demonstrate that FSA can improve operational efficiency, reduce management errors, and enhance the user experience. The application of FSA in Greenify facilitates a more structured and automated waste management system, while also improving the accuracy of scheduling and waste collection. This conclusion highlights the significant potential of FSA as a technological solution for environmentally friendly waste management, with the goal of optimizing the performance of the Greenify application and advancing urban waste management practices.
PENERAPAN STRING MATCHING PADA INFORMATION RETRIEVAL DARI EKSTRAKSI METADATA DAN ANALISIS AKURASI VIDEO YOUTUBE Christianto, Devri Budi; Setyawan, Ryan Ari; Bororing, Jemmy Edwin
Jurnal Informatika Teknologi dan Sains (Jinteks) Vol 7 No 2 (2025): EDISI 24
Publisher : Program Studi Informatika Universitas Teknologi Sumbawa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51401/jinteks.v7i2.5647

Abstract

Meningkatnya jumlah konten di platform youtube shorts menghadirkan tantangan dalam menyaring informasi yang relevan serta menghindari konten yang mengandung kata-kata negatif. Penelitian ini mengembangkan sistem information retrieval dengan metode string matching untuk mencocokkan kata kunci yang dimasukkan pengguna dengan metadata video, seperti judul, hashtag, dan transkripsi audio. Sistem ini dirancang sebagai aplikasi berbasis web menggunakan PHP dan Python dengan framework Laravel serta PostgreSQL sebagai basis data. Pengujian dilakukan untuk mengevaluasi tingkat akurasi dan performa sistem. Hasil pengujian menunjukkan bahwa sistem mencapai akurasi hingga 82,5% saat menggunakan lebih dari 10 kata kunci, dengan 33 data valid. Pengujian performa juga menunjukkan bahwa MacBook Pro M1 memiliki efisiensi terbaik dibandingkan perangkat lainnya. Penerapan semantik dalam sistem memungkinkan peningkatan akurasi dengan mengurangi ambiguitas makna kata kunci.
IMPLEMENTASI DEEP LEARNING MENGGUNAKAN LONG SHORT-TERM MEMORY UNTUK PREDIKSI STABILITAS PONDASI TAHAN GEMPA Safitri; Fitriastuti, Fatsyahrina; Setyawan, Ryan Ari; Widodo, Teguh
Jurnal Informatika Teknologi dan Sains (Jinteks) Vol 7 No 2 (2025): EDISI 24
Publisher : Program Studi Informatika Universitas Teknologi Sumbawa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51401/jinteks.v7i2.5663

Abstract

Pondasi merupakan elemen struktural yang sangat krusial dalam menjamin kestabilan bangunan terhadap beban gempa. Ketidakstabilan pada sistem pondasi dapat memicu keruntuhan total struktur meskipun elemen atas telah dirancang tahan gempa. Penelitian ini bertujuan untuk mengembangkan model prediksi stabilitas pondasi menggunakan pendekatan deep learning berbasis Long Short-Term Memory (LSTM) dengan memanfaatkan data gaya dan momen sambungan (joint reactions) hasil analisis struktur dari perangkat lunak SAP2000. Dataset terdiri atas tiga jenis bangunan berbeda, yakni pondasi utama, gedung service, dan gedung klinik teduh, yang masing-masing memuat komponen gaya (F1, F2, F3) dan momen (M1, M2, M3). Data diproses melalui tahapan normalisasi, penyusunan time series lima langkah ke belakang, dan pelatihan model menggunakan arsitektur LSTM dua lapis dengan dropout 0,2. Evaluasi dilakukan dengan skema validasi silang (Time Series Split) dan pengujian data unseen. Hasil penelitian menunjukkan bahwa model mampu melakukan prediksi dengan akurasi, presisi, recall, dan F1-score sebesar 1,0000. Grafik perbandingan training loss dan validation loss memperlihatkan konvergensi yang stabil tanpa indikasi overfitting. Sistem ini juga berhasil mengintegrasikan deteksi noise berbasis threshold error, memungkinkan klasifikasi anomali struktural secara otomatis. Kesimpulan dari penelitian ini menyatakan bahwa model LSTM sangat efektif dalam memprediksi respons pondasi terhadap gempa serta mendeteksi ketidaknormalan pola gaya dan momen.
Implementation of a deep neural network model to predict critical joint loads based on SAP2000 structural data Ridwan, Ridwan; Setyawan, Ryan Ari; Fitriastuti, Fatsyahrina
Jurnal Mandiri IT Vol. 14 No. 1 (2025): July: Computer Science and Field.
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/mandiri.v14i1.425

Abstract

This study propose~s a De~e~p Ne~ural Ne~twork (DNN) frame~work to pre~dict joint re~action force~ ratios in structural analysis using datase~ts obtaine~d from SAP2000 simulations. The~ datase~ts cove~r various load case~s and ge~ome~trical parame~te~rs, e~nsuring the~ mode~l is e~xpose~d to dive~rse~ structural sce~narios. The~ DNN archite~cture~ comprise~s multiple~ fully conne~cte~d laye~rs, e~mploying Re~LU activation functions, dropout re~gularization, and batch normalization for stable~ training. Mode~l pe~rformance~ was e~valuate~d using Me~an Square~d E~rror (MSE~), Me~an Absolute~ E~rror (MAE~), R² score~, and pre~diction accuracy within a 5% e~rror margin critical for civil e~ngine~e~ring applications. The~ re~sults de~monstrate~ e~xce~lle~nt pre~dictive~ capabilitie~s, achie~ving accuracy le~ve~ls e~xce~e~ding 98% across all datase~ts. Notably, the~ third datase~t yie~lde~d the~ lowe~st accuracy at 98.97% and an R² score~ of 0.9915, with slightly e~le~vate~d e~rror me~trics (MSE~ of 5.11, RMSE~ of 2.26, and MAE~ of 1.51). De~spite~ the~se~ challe~nge~s, the~ DNN mode~l consiste~ntly de~live~rs robust pre~dictions, showcasing its pote~ntial for practical structural he~alth monitoring and de~sign optimization. Future~ work should conside~r incorporating more~ dive~rse~ and e~xpe~rime~ntal data to e~nhance~ mode~l robustne~ss furthe~r.
Implementation of vision transformer for offensive language detection on tiktok social media Rahmawaty, Zulekha; Fitriastuti, Fatsyarina; Setyawan, Ryan Ari
Jurnal Mandiri IT Vol. 14 No. 1 (2025): July: Computer Science and Field.
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/mandiri.v14i1.435

Abstract

The rise of social media platforms such as TikTok has introduced new challenges in content moderation, particularly concerning the spread of offensive language and hate speech. One promising approach to addressing this issue is through automatic detection using deep learning technology. This study implements the Vision Transformer (ViT) to detect offensive language on the TikTok platform based on visual data in the form of comment screenshots. The dataset used consists of 1,401 labeled images categorized into two classes: offensive and non-offensive. The training process was conducted over 50 epochs without a validation split, and the evaluation was carried out using accuracy, precision, recall, and F1-score metrics. Results showed high performance, with an accuracy of 99.93%, precision of 0.9979, recall of 1.000, and F1-score of 1.000 at the 40th epoch, maintaining stability through the end of training. These findings demonstrate that ViT is effective in extracting visual features from image-based comments, even without access to raw text. This approach is particularly relevant in the context of TikTok, where comments often appear in visual formats such as thumbnails, screenshots, or reaction videos. This research opens up opportunities for the implementation of image-based offensive language detection systems that can enhance content moderation by adapting to various visual formats. Further development is recommended using a larger dataset and more systematic data splitting to test the model’s generalization capability.
Implementation of role-based access control, multi tenancy and audit logging in a single sign-on system Aswintama, Putranta; Haryanto, Eri; Setyawan, Ryan Ari
Jurnal Mandiri IT Vol. 14 No. 1 (2025): July: Computer Science and Field.
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/mandiri.v14i1.441

Abstract

As enterprises increasingly require centralized, secure, and efficient authentication mechanisms, Single Sign-On (SSO) has emerged as a strategic approach to managing user access. This study discusses the implementation of an SSO system based on Laravel Livewire with support from JSON Web Token (JWT) and OAuth, developed for PT Radiator Springs Indonesia. The system integrates three main components: Role-Based Access Control (RBAC) for access rights management, a Multi-Tenancy architecture for separating users across organizational units, and Audit Logging to track user activities. The analysis shows significant improvements in security, with 87.5% fewer unauthorized access attempts and enhanced user management efficiency, evidenced by a 71.43% reduction in time to onboard new users. Additionally, the system generates over 300+ audit log entries per day, improving monitoring and compliance capabilities.
IMPLEMENTASI FINITE STATE AUTOMATA UNTUK OPTIMALISASI PROSES DISTRIBUSI DAUR ULANG SAMPAH Tyas Nur Taufiq; Sulthon Syahril Oku; Ryan Ari Setyawan
AnoaTIK: Jurnal Teknologi Informasi dan Komputer Vol 3 No 1 (2025): Juni 2025
Publisher : Program Studi Ilmu Komputer FMIPA-UHO

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33772/anoatik.v3i1.100

Abstract

Effective waste management is a significant challenge in efforts to create a sustainable environment. One crucial step in waste management is the recycling process, which requires an efficient and accurate sorting system. This study aims to implement Finite State Automata (FSA) as a modeling method to optimize the waste recycling process. FSA is used to model the waste sorting flow based on categories such as organic, inorganic, and hazardous materials. This model is designed to improve the speed and accuracy of identifying waste types suitable for recycling.
Convolutional Neural Network-Based Model for Indonesian Offensive Text Classification Mayndeta, Daniel; Setyawan, Ryan Ari; Haryanto, Eri
Emerging Information Science and Technology Vol. 6 No. 2 (2025)
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/eist.v6i2.29704

Abstract

This study presents a Convolutional Neural Network (CNN)-based model for classifying offensive and non-offensive Indonesian text using a dataset of 10,054 tweets collected from Twitter/X. The dataset was manually annotated into two classes and processed through a series of text-cleaning, tokenization, and padding steps before being used to train the model. Several training durations were tested to evaluate the effect of epoch variation on model performance. The results show that the model trained for 70 epochs achieved the best overall performance, with a testing accuracy of 86.73%, precision of 0.8793, recall of 0.8834, F1-score of 0.8814, and a ROC-AUC value of 92.08%. The confusion matrix analysis indicates strong classification capability for both classes, with the model performing slightly better in identifying offensive text due to distinctive lexical patterns. These findings demonstrate that the CNN architecture, supported by trainable word embeddings, is effective for Indonesian offensive-text classification. Future improvements may include integrating pretrained language models or expanding the dataset to enhance contextual understanding and robustness.
Co-Authors Aswintama, Putranta Atapukan, Walter F Bening, Ridho Gilang Bororing, Jemmy Edwin Christianto, Devri Budi Fatsyahrina Fitriastuti Fitriastuti, Fatsyarina Haryanto, Eri Helio Rofino Correia Mayndeta, Daniel Rahmawaty, Zulekha Ridwan, Ridwan Rizqi Mirza Fadilla Safitri Sulthon Syahril Oku Taofiq Krisdiyanto Teguh Widodo Tyas Nur Taufiq Walter F Atapukan Yumarlin MZ