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All Journal Jurnal Teknik ITS IPTEK Journal of Proceedings Series IPTEK The Journal for Technology and Science TELKOMNIKA (Telecommunication Computing Electronics and Control) JUTI: Jurnal Ilmiah Teknologi Informasi Jurnal Ilmiah Kursor Scan : Jurnal Teknologi Informasi dan Komunikasi Jurnal EECCIS Jurnal Teknologi Informasi dan Ilmu Komputer Jurnal Edukasi dan Penelitian Informatika (JEPIN) REKAYASA Journal of Information Systems Engineering and Business Intelligence Jurnal IPTEK Register: Jurnal Ilmiah Teknologi Sistem Informasi PROCEEDING IC-ITECHS 2014 Jurnal Inspiration Proceeding of the Electrical Engineering Computer Science and Informatics E-Dimas: Jurnal Pengabdian kepada Masyarakat Jurnal ELTIKOM : Jurnal Teknik Elektro, Teknologi Informasi dan Komputer Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Jurnal ULTIMA InfoSys Digital Zone: Jurnal Teknologi Informasi dan Komunikasi CSRID (Computer Science Research and Its Development Journal) Melek IT: Information Technology Journal Jurnal Sistem Informasi Bisnis (JUNSIBI) Jurnal Nasional Teknik Elektro dan Teknologi Informasi Sewagati Jurnal INFOTEL Journal of Information Technology and Cyber Security
Daniel Oranova Siahaan
Departemen Teknik Informatika Institut Teknologi Sepuluh Nopember Surabaya
Aerospace Engineering Agriculture, Biological Sciences & Forestry Arts Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Education Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Library & Information Science Mechanical Engineering Physics Public Health Social Sciences Other

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NOISE DETECTION IN SOFTWARE REQUIREMENTS SPECIFICATION DOCUMENT USING SPECTRAL CLUSTERING Patricia Gertrudis Manek; Daniel Siahaan
JUTI: Jurnal Ilmiah Teknologi Informasi Vol 17, No. 1, Januari 2019
Publisher : Department of Informatics, Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j24068535.v17i1.a771

Abstract

Requirements engineering phase in software development resulting in a SRS (Software Requirements Specification) document. The use of natural language approach in generating such document has some drawbacks that caused 7 common mistakes among the engineer which had been formulated by Meyer as "The 7 sins of specifier". One of the 7 common mistakes is noise. This study attempted to detect noise in software requirements with spectral clustering. The clustering algorithm working on fewer dimensions compared to others. The resulting kappa coefficient is 0.4426. The result showed that the consistency between noise prediction and noise assessment made by three annotators is still low.
FRECOMTWEET: PRODUCT RECOMMENDATION APPLICATION USING FRIENDSHIP CLOSENESS ON TWITTER Ratih Nur Esti Anggraini; Ainatul Maulida; Daniel Oranova Siahaan
JUTI: Jurnal Ilmiah Teknologi Informasi Vol. 20, No. 1, January 2022
Publisher : Department of Informatics, Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j24068535.v19i3.a1104

Abstract

The information and communication technology development makes someone interact with each other easier. This convenience is used to exchange ideas, like using social media Twitter for product recommendations before buying it. It brings up a trend that consumers seek product recommendations through other people on social media. Social media, especially Twitter, has several features such as tweets, ReTweet and mentions to interact with other people. Users can describe the product, attach a link, and give a positive or negative rating in a tweet. These types of tweets can be used as an alternative to product recommendations. FrecomTweet is an Android-based product recommendation application that can detect close friendships based on the user’s ReTweet and mentions. This application also detects a product recommendation that appears in a conversation between users. This detection uses the keyword filtering method, which matches the conversation content with the markers in the database. If the conversation has a positive rating, it will recommend the user’s closest friends. This research uses a crawling method with the Twitter API streaming filter built using the CodeIgniter framework. The results of the black box test show that Twitter user conversations can be used as a product recommendation with a precision and recall value of 0.94 and 0.81, respectively.
Extraction of Class Candidates from Scenario in Software Requirements Specifications Rasi Aziizah Andrahsmara; Siahaan, Daniel Oranova
ULTIMA InfoSys Vol 16 No 2 (2025): Ultima InfoSys : Jurnal Ilmu Sistem Informasi
Publisher : Universitas Multimedia Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31937/si.v16i2.4325

Abstract

The development of software applications involves translating software requirement specifications (SRS) into structured models that guide system design. Among these, sequence diagrams are essential for visualizing dynamic interactions, but their manual construction from natural language descriptions is often error-prone and time-consuming. This study proposes an automated method for extracting sequence diagram elements namely classes, subclasses, and attributes from scenario sections of SRS documents. The approach leverages Natural Language Processing (NLP) techniques, combining Bidirectional Encoder Representations from Transformers (BERT) for contextual embeddings and Support Vector Machine (SVM) for classification. Noun phrases are identified and classified into UML-relevant entities using this hybrid model. To evaluate performance, two datasets SIData and SILo were used, each exhibiting distinct textual styles and domain characteristics. The system’s effectiveness was assessed using standard evaluation metrics such as precision, recall, and F1-score. Results indicate that the method is capable of capturing contextual relationships between extracted elements, although its performance varies across datasets, suggesting the need for further refinement. Overall, the study contributes toward automating early software design phases and reducing manual modeling effort.
An Applied Evaluation of Multi-Label Hate Speech Detection for Indonesian Digital Platforms Dwija Wisnu Brata; Arif Djunaidy; Daniel Oranova Siahaan; Edio da Costa
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 10 No 3 (2026): June 2026
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v10i3.7591

Abstract

The rapid growth of user-generated content on digital platforms has increased the difficulty of moderating hate-related expressions, particularly in linguistically diverse environments such as Indonesian social media. Automated hate speech detection systems are therefore expected to operate not only with reliable predictive behavior but also with practical efficiency under large-scale deployment conditions. This study reports an applied evaluation of Transformer-based models for multi-label hate speech detection on Indonesian digital platforms. Rather than introducing a new classification architecture, the work focuses on assessing multiple pretrained language models within a unified and reproducible evaluation framework. The analysis examines overall model behavior, per-label performance tendencies, inference efficiency, and common error patterns under realistic multi-label settings. The results indicate that IndoBERT-based models (indobenchmark/indobert-base-p1 and cahya/bert-base-indonesian) achieved the strongest predictive performance for multi-label hate speech detection, although performance differences across the evaluated Transformer models remained relatively incremental. Experimental results show that the best-performing model achieved a macro-F1 score of 0.9742 and a micro-F1 score of 0.9749, while other Transformer models demonstrated competitive performance with macro-F1 values ranging from 0.955 to 0.964. In terms of efficiency, distilled models provided faster inference (approximately 5–7 ms per sample) compared to full-size models (8–11 ms per sample), highlighting a practical trade-off between predictive performance and computational cost. These findings emphasize the importance of practical evaluation strategies and suggest that flexible model configurations are more suitable than reliance on a single high-capacity model in real-world moderation systems.
Enhancing Agile Defect Prediction with Optimized Machine Learning and Feature Selection Faiq Dhimas Wicaksono; Daniel Siahaan
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 9 No 4 (2025): August 2025
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v9i4.6713

Abstract

In Agile software development, efficient defect prediction is crucial because of the rapid and iterative nature of the delivery. Conventional methods that rely on source code or commit logs often fail to capture the critical contextual signals necessary for early bug detection. This study proposes a hybrid machine learning framework that leverages enriched contextual features from Jira issue tickets and combines them with optimized feature selection techniques. Various classification models, including Random Forest, XGBoost, CatBoost, SVM, and Transformer, are employed to predict defects. To further enhance model performance, metaheuristic-based feature selection methods such as the Bat Algorithm (BA) and Particle Swarm Optimization (PSO) are applied to reduce dimensionality and improve predictive relevance. Experimental results show that Random Forest with BA optimization achieves the highest performance, with an F1-score of 0.83 and an AUC-ROC of 0.86, outperforming other models. While the Transformer model does not surpass tree-based algorithms in all metrics, it shows high recall and competitive F1-scores, making it suitable for high-sensitivity applications. These findings highlight the importance of integrating optimized machine learning models and feature selection techniques to improve model robustness, reduce computational complexity, and meet the needs of Agile development. This approach supports software teams in prioritizing quality assurance tasks, reducing long-term maintenance costs, and optimizing defect management processes.
Co-Authors Aang Kisnu Darmawan Abd. Rasyid Syamsuri Achmad An'im Fahmi Achmad Fariz Adi Kurniawan Aditya Eka Bagaskara Ahmad Saikhu Ahmadiyah, Adhatus Solichah Ainatul Maulida Akbar, Rizky Januar Albert Bungaran Manik Amalia, Rosa Amien Widodo Andi Besse Firdausiah Andini Prastiwi Andrias Meisyal Yuwantoko Anggraini, Ratih Nur Esti Ansyah, Adi Surya Suwardi Anwari Anwari Anwari, Anwari Arif Djunaidy Arif Djunaidy Arif Susanto Arif Wibisono Asyrofi, Raka Baskoro, Fajar Busro Umam Cahya Bagus Sanjaya Chastine Fatichah Dady Khairul Imam Damanik, Juli Yanti Darnoto, Brian Depandi Enda Desepta Isna Ulumi Divi Galih Prasetyo Putri Dwija Wisnu Brata Dzhalila, Dzhillan Edio da Costa Eko Prasetyo Evi Triandini F.X. Arunanto Fachrul Pralienka Bani Muhamad Fachrul Pralienka Bani Muhamad Faiq Dhimas Wicaksono Fajar Baskoro Fajar Baskoro Fatimatus Zulfa Ferdika Bagus Permana FX Arunanto Hamidi, Mohammad Zaenuddin Hoiriyah Hoiriyah Hoiriyah, Hoiriyah I Gede Suardika I Made Mika Parwita Imam Kuswardayan Indra Kharisma Raharjana Irfandianto, Taqarra Rayhan Irsyad Arif Mashudi Istighfar, Muhammad Bagus Ivan Agung Pandapotan izqi Paradisiaca , Brian R Karimi, Muhammad Ihsan Karolita, Devi Kusuma, Selvia Ferdiana Luh Putu Ary Sri Tjahyanti Mauladani, Furqon Mirotus Solekhah Mohammad Nazir Arifin Muhamad, Fachrul Pralienka Bani Muhammad Dery Rahma Muhammad Ihsan Karimi Mutia Rahmi Dewi Nafi', Abdun Nafingatun Ngaliah Nanang Fakhrur Rozi Nugroho, Tri Yulianto Nuralamsyah, Bintang Nurul Fajrin Ariyani Nurul Jannah Pasaribu, Monalisa Patricia Gertrudis Manek Peter Gelu Pratama Wirya Atmaja Putra Kurniawan, Arya Putri, Rahmi Rizkiana Rahmi Rizkiana Putri Rakhmat Arianto Rasi Aziizah Andrahsmara Reza Fauzan Reza Fauzan Richard Alvin Sianturi Riduwan, Muhammad Risnauli Sumiati Sinaga Riyanarto Sarno Rizky Januar Akbar Royke Wenas Rully Soelaiman Rully Soelaiman Safitri, Winda Ayu Samosir, Hernawati Sari Sahadi, Fitria Vera Sarwosri Sarwosri Sarwosri Sarwosri Sarwosri Satrio Agung Wicaksono Shiddiqi, Ary Mazharuddin Silaban, Monica Sinaga, Hasan Siti Rochimah Supriyanto, Ricky Tiurma Lumban Gaol Tony Dwi Susanto Toshihiro Kita Umam, Busro Umami, Izzatul Umi Laili Yuhana Umi Yuhana Utomo Pujianto Vriza Wahyu Saputra Welly Purnomo Yuhana, Umi Laili Yunata Dede Pratiwi