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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 ULTIMA InfoSys Digital Zone: Jurnal Teknologi Informasi dan Komunikasi CSRID (Computer Science Research and Its Development Journal) Melek IT: Information Technology Journal MATHunesa: Jurnal Ilmiah Matematika Jurnal Sistem Informasi Bisnis (JUNSIBI) Jurnal Nasional Teknik Elektro dan Teknologi Informasi Sewagati Jurnal Ilmu Sosial, Humaniora dan Seni Jurnal INFOTEL Journal of Information Technology and Cyber Security

Daniel Oranova Siahaan

Departemen Teknik Informatika Institut Teknologi Sepuluh Nopember Surabaya

Author-ID : 379272

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 Law, Crime, Criminology & Criminal Justice Library & Information Science Mathematics Mechanical Engineering Physics Public Health Social Sciences Other

Published : 65 Documents Claim Missing Document

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Articles

Title

Generating Requirement Dependency Graph Based on Class Dependency Samosir, Hernawati; Siahaan, Daniel
IPTEK The Journal for Technology and Science Vol 29, No 2 (2018)
Publisher : IPTEK, LPPM, Institut Teknologi Sepuluh Nopember

A set of software requirements is an important element in software development. Engineers realize that requirements are interrelated. The interconnections between requirements indicate interdependences between requirements. This interdependence is crucial in decision-making processes of requirement engineering, such as a change management requirement, a version launch plan, and a requirement management. Researchers have been focused on visualizing dependency between requirements, analyzing the impact of changes in software by using changes to UML class diagrams, and predicting bug occurrences based on dependencies between requirements. Previous studies assumed that the requirements dependency information was pre-build by requirements engineer during the previous development process. This paper introduces a method that builds a requirements dependency model. The model was built based on realization associations between requirements and classes in the system design as well as dependencies between classes. The modeling process used semantic similarities between the requirements and the classes. A class is said to have a realization association with a requirement if and only if the semantic similarity is higher than a certain threshold. The output obtained from the dependent software development method was compared with the output produced by annotators. The method reliability was measured by the level of agreement between the method and the annotator using kappa statistical index. The preliminary result shows that the method was fair agreement (0.37) reliable as an annotator when generating requirements dependency graph.

Natural Language Processing for Detecting Forward Reference in a Document Siahaan, Daniel; Umami, Izzatul
IPTEK The Journal for Technology and Science Vol 23, No 4 (2012)
Publisher : IPTEK, LPPM, Institut Teknologi Sepuluh Nopember

Meyerâ€™s seven sins have been recognized as types of mistakes that a requirements specialist are often fallen to when specifying requirements. Such mistakes play a significant role in plunging a project into failure. Many researchers were focusing in ambiguity and contradiction type of mistakes. Other types of mistakes have been given less attentions. Those mistakes often happened in reality and may equally costly as the first two mistakes. This paper introduces an approach to detect forward reference. It traverses through a requirements document, extracts, and processes each statement. During the statement extraction, any terms that may reside in the statement is also extracted. Based on certain rules which utilize POS patterns, the statement is classified as a term definition or not. For each term definition, a term is added to a list of defined terms. At the same time, every time a new term is found in a statement, it is check against the list of defined terms. If it is not found, then the requirements statement is classified as statement with forward reference. The experimentation on 30 requirements documents from various domains of software project shows that the approach has considerably almost perfect agreement with domain expert in detecting forward reference, given 0.83 kappa index value.

User Story Extraction from Online News with FeatureBased and Maximum Entropy Method for Software Requirements Elicitation Nafingatun Ngaliah; Daniel Siahaan; Indra Kharisma Raharjana
IPTEK The Journal for Technology and Science Vol 32, No 3 (2021)
Publisher : IPTEK, LPPM, Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j20882033.v32i3.11625

Software requirements query is the frst stage in software requirements engineering. Elicitation is the process of identifying software requirements from various sources such as interviews with resource persons, questionnaires, document analysis, etc. The user story is easy to adapt according to changing system requirements. The user story is a semi-structured language because the compilation of user stories must follow the syntax as a standard for writing features in agile software development methods. In addition, user story also easily understood by end-users who do not have an information technology background because they contain descriptions of system requirements in natural language. In making user stories, there are three aspects, namely the who aspect (actor), what aspect (activity), and the why aspect (reason). This study proposes the extraction of user stories consisting of who and what aspects of online news sites using feature extraction and maximum entropy as a classifcation method. The systems analyst can use the actual information related to the lessons obtained in the online news to get the required software requirements. The expected result of the extraction method in this research is to produce user stories relevant to the software requirements to assist systems analysts in generating requirements. This proposed method shows that the average precision and recall are 98.21% and 95.16% for the who aspect; 87,14% and 87,50% for what aspects; 81.21% and 78.60% for user stories. Thus, this result suggests that the proposed method generates user stories relevant to functional software.

Weighted k Nearest Neighbor Using Grey Relational Analysis To Solve Missing Value Desepta Isna Ulumi; Daniel Siahaan
IPTEK The Journal for Technology and Science Vol 29, No 3 (2018)
Publisher : IPTEK, LPPM, Institut Teknologi Sepuluh Nopember

Software defect prediction model is an important role in detecting the most vulnerable component error software. Some research have been worked to improve the accuracy of the prediction defects of the software in order to manage human, costs and time. But previous research used specific dataset for software defect prediction model. However, there is no a generic dataset handling for software defect prediction model yet. This research proposed improvements to the results of the software defect prediction on the merged dataset, which is called generic dataset, with a number of different features. In order to balance the number of features, each dataset should be filled with a missing value. To fill the missing values, Weighted k Nearest Neighbor (WkNN) method was used. Then, after missing values were filled, Naïve Bayes was used to classify the selected features. This research needed to obtain a set of features which was relevant, then performed a feature selection method. The results showed that by using seven NASA public MDP datasets, Naïve Bayes with Information Gain (IG) or Symmetric Uncertainty (SU) feature selection presented the best balance value.Software defect, NASA public MDP, weighted KNN,Naive Bayes

Web-Based Tsunami Early Warning System Daniel Siahaan; Royke Wenas; Amien Widodo; Umi Yuhana
IPTEK The Journal for Technology and Science Vol 24, No 3 (2013)
Publisher : IPTEK, LPPM, Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j20882033.v24i3.552

Tsunami is a serious threat to the island nation such as Indonesia. The tsunami disasters that were occurred in some parts of Indonesia have immerged the need for tsunami early warning system that is reliable and can be applied to the Indonesian archipelago. North Sulawesi is one of the areas prone to tsunamis since this area lies in the path called the ring of fire's. This article describes a tsunami simulation application for the north coast of North Sulawesi. Web-based applications were built so that they can be monitored online from anywhere and at anytime. This system reads the real-time seismic data that affect the North Sulawesi region from a number of sources. Dynamic and static data that are received are processed using data mining method to predict the chances of a tsunami, while flood flooding algorithm is used to visualize the map of affected areas of North Sulawesi. The resulting information is available in detail in the form of web pages and also through short message to the relevant authorities handling of the tsunami disaster in order for them to act in accordance with applicable standard operating procedures. With this application, the public can obtain information that is more accurate. Relevant authorities can conduct tsunami disaster mitigation measures more effectively.

Algorithms Comparison for Non-Requirements Classification using the Semantic Feature of Software Requirement Statements Achmad An'im Fahmi; Daniel Siahaan
IPTEK The Journal for Technology and Science Vol 31, No 3 (2020)
Publisher : IPTEK, LPPM, Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j20882033.v31i3.7606

Noise in a Software Requirements Specification (SRS) is an irrelevant requirements statement or a non-requirements statement. This can be confusing to the reader and can have negative repercussions in later stages of software development. This study proposes a classification model to detect the second type of noise, the non-requirements statement. The classification model that is built is based on the semantic features of the non-requirements statement. This research also compares the five best-supervised machine learning methods to date, which are support vector machine (SVM), naïve Bayes (NB), random forest (RF), k-nearest neighbor (kNN), and Decision Tree. This comparison aimed to determine which method can produce the best non-requirements classification, model. The comparison shows that the best model is produced by the SVM method with an average accuracy of 0.96. The most significant features in this non-requirement classification model are the requirements statement or non-requirements, id statement, normalized mean value, standard deviation value, similarity variant value, standard deviation normalization value, maximum normalized value, similarity variant normalization value, value Bad NN, mean value, number of sentences, bad VB score, and project id.

Comparison Of KNN, Random Forest, And F-PSO Algorithms On Simple Feature Scaling for Agility Level Classification Nugroho, Tri Yulianto; Yuhana, Umi Laili; Siahaan, Daniel
IPTEK The Journal for Technology and Science Vol 35, No 3 (2024)
Publisher : IPTEK, DRPM, Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j20882033.v35i3.21992

Classifying agility levels presents challenges due to variations in team members’ personalities, roles, and undesirable behaviors. This study aims to enhance classification accuracy by comparing the performance of three algorithms: K-Nearest Neighbors (KNN), Random Forest, and Fuzzy-Particle Swarm Optimization (F-PSO) in classifying agility levels using simple feature scaling as part of the data preprocessing. Simple feature scaling is employed to ensure that all parameters are on the same scale, thereby improving the model’s effectiveness in learning classification patterns. F-PSO was selected for its ability to perform adaptive global search optimization within a fuzzy environment, while KNN and Random Forest serve as benchmarks. The study involved 160 participants from various Scrum teams to evaluate the effectiveness of these algorithms. The parameters considered included team members’ personalities (based on the Keirsey model), roles within the team, and the identification of negative behavior patterns (antipatterns). The results indicated that the F-PSO algorithm significantly outperformed KNN and Random Forest in terms of accuracy, improving from an average accuracy of 25% before optimization to 93.75% after applying F-PSO. This approach enables Scrum teams to identify and address obstacles affecting agility, facilitating earlier problem prediction and resolution, leading to more adaptive and effective teams.

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 Susanto Arif Wibisono Asyrofi, Raka Baskoro, Fajar Bawamenewi, Yuliaman Busro Umam Cahya Bagus Sanjaya Chastine Fatichah Dady Khairul Imam Damanik, Juli Yanti Darnoto, Brian Depandi Enda Desepta Isna Ulumi Divi Galih Prasetyo Putri Dzhalila, Dzhillan Eko Prasetyo Evi Triandini F.X. Arunanto Fachrul Pralienka Bani Muhamad Fachrul Pralienka Bani Muhamad Fajar Baskoro Fajar Baskoro Fatimatus Zulfa Ferdika Bagus Permana FX Arunanto Ghipari, Maulana Halawa, Enggi 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 Ramadhani, Nia 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 Siahaan, Gabriel Silaban, Monica Sinaga, Hasan Siti Rochimah Sitohang, Francisko Situmorang, Andreas Supriyanto, Ricky Tiurma Lumban Gaol Tony Dwi Susanto Toshihiro Kita Umam, Busro Umami, Izzatul Umi Yuhana Utomo Pujianto Vriza Wahyu Saputra Welly Purnomo Yuhana, Umi Laili Yuhana, Umi Laili Yunata Dede Pratiwi

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