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All Journal International Journal of Electrical and Computer Engineering Jurnal Buana Informatika Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Jurnal Ilmiah Kursor Telematika : Jurnal Informatika dan Teknologi Informasi Jurnal Sistem Informasi dan Bisnis Cerdas EMITTER International Journal of Engineering Technology KLIK (Kumpulan jurnaL Ilmu Komputer) (e-Journal) Sistemasi: Jurnal Sistem Informasi Jurnal Teknologi dan Sistem Komputer Jurnal ELTIKOM : Jurnal Teknik Elektro, Teknologi Informasi dan Komputer Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Abdimas: Jurnal Pengabdian Masyarakat Universitas Merdeka Malang Indonesian Journal of Electrical Engineering and Computer Science Jurnal Pengabdian Polinema Kepada Masyarakat Prosiding Seminar Nasional Pengabdian Kepada Masyarakat Journal Of Artificial Intelligence And Software Engineering Jurnal INFOTEL Jurnal Informatika Polinema (JIP) Jurnal Cendekia Ilmiah
Vivi Nur Wijayaningrum
Politeknik Negeri Malang
Aerospace Engineering Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Industrial & Manufacturing Engineering Library & Information Science Public Health Social Sciences Other

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An Improved Crow Search Algorithm for Data Clustering Vivi Nur Wijayaningrum; Novi Nur Putriwijaya
EMITTER International Journal of Engineering Technology Vol 8 No 1 (2020)
Publisher : Politeknik Elektronika Negeri Surabaya (PENS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24003/emitter.v8i1.498

Abstract

Metaheuristic algorithms are often trapped in local optimum solutions when searching for solutions. This problem often occurs in optimization cases involving high dimensions such as data clustering. Imbalance of the exploration and exploitation process is the cause of this condition because search agents are not able to reach the best solution in the search space. In this study, the problem is overcome by modifying the solution update mechanism so that a search agent not only follows another randomly chosen search agent, but also has the opportunity to follow the best search agent. In addition, the balance of exploration and exploitation is also enhanced by the mechanism of updating the awareness probability of each search agent in accordance with their respective abilities in searching for solutions. The improve mechanism makes the proposed algorithm obtain pretty good solutions with smaller computational time compared to Genetic Algorithm and Particle Swarm Optimization. In large datasets, it is proven that the proposed algorithm is able to provide the best solution among the other algorithms.
Interview Result Extraction For Functional Requirements Identification Using TextRank Algorithm Yunianto, Dika Rizky; Putri, Annisa Rahmania; Wijayaningrum, Vivi Nur
Journal of Artificial Intelligence and Software Engineering Vol 6, No 1 (2026): Maret
Publisher : Politeknik Negeri Lhokseumawe

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30811/jaise.v6i1.8868

Abstract

Functional requirement identification is a critical stage in information system development that determines system alignment with user needs. Interviewing is a dominant technique for eliciting requirements, but interview results in the form of long, unstructured text require significant analysis time and are prone to subjectivity if done manually. This research aims to apply the TextRank algorithm to automatically summarize interview results so that important information can be obtained more concisely. The research was conducted through text preprocessing stages, sentence similarity calculation (word overlap), graph construction, and sentence ranking using the PageRank algorithm. Important sentences were selected based on threshold variations (k = 3, 5, 7, 9). Three methods were compared: standard TextRank, TextRank with TF-IDF weighting, and TextRank with stemming preprocessing. Evaluation used precision, recall, and F1-score metrics against ground truth. Results show that standard TextRank with threshold 7 provided the most balanced performance (F1-score 71.05%), being more stable than TF-IDF based methods for interview data. This algorithm proved effective in improving the efficiency of the functional requirement identification process.
Automatic essay assessment in e-learning using winnowing algorithm Amalia, Eka Larasati; Lestari, Vivin Ayu; Wijayaningrum, Vivi Nur; Ridla, Ali Ar
Indonesian Journal of Electrical Engineering and Computer Science Vol 29, No 1: January 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v29.i1.pp572-582

Abstract

The pandemic has caused almost all educational institutions to use online learning media to support learning activities. E-learning is a technology that is widely used because it can accommodate all learning activities. However, in general, e-learning can only perform automatic assessments for multiple choice answers but not for essay answers, so that manual assessment by the teacher becomes difficult and takes a long time. In this study, the winnowing algorithm was applied to the automatic assessment process on students' essay answers by measuring their similarity to the teacher's answer key. The stages in the automatic assessment using the winnowing algorithm begin with forming a series of k-grams, calculating the hash value, forming a window from the hash value, calculating the fingerprint value, and calculating the Jaccard Coefficient to obtain the percentage of text similarity results. The test results show that the winnowing algorithm can provide good performance when the answers to questions are in the form of short entries with the number of hashes not smaller than the window value. Meanwhile, on questions with long answers, the winnowing algorithm can still work well with an average difference of 5.2% from the results of the assessment carried out by the teacher.
Co-Authors Abdillah, Muhammad Navis Alysha Ghea Arliana Ananta, Ahmadi Yuli Andi Maulidinnawati A. K. Parewe Anggi Mahadika Purnomo Angki Christiawan Rongre Anim Rofi’ah Annisa Puspa Kirana Annisa Puspa Kirana Astiningrum, Mungki Asyrofa Rahmi Augusta, San Sayidul Akdam Aziz, Hamim Fathul Berryl Radian Hamesha Budi Harijanto, Budi Chintya Puspa Dewi Davia Werdiastu Deatrisya Mirela Harahap Dika Rizky Yunianto Dimas Shella Charlinawati Dini, Robih Eka Larasati Amalia Ermi Pristiyaningrum Farida Ulfa Farida Ulfa Febri Ramadhani Febrianti, Yane Marita Ficry Agam Fathurrachman Gotami, Nurina Savanti Widya Haekal, Muhammad Hamim Fathul Aziz Heny Dwi Jayanti Iftitah Hidayati Ika Kusumaning Putri Ika Kusumaning Putri Ilham Sinatrio Gumelar Imam Fahrur Rozi Lia Agustina Lubis, Wahyuni M. Hasyim Ratsanjani Malakianno P.N., Valentino Mamluatul Hani’ah Maulidina, Hanif Prasetyo Moch Zawaruddin Abdullah Mochammad Hairullah Muhammad Dimas Setiawan Sanapiah Muhammad Haekal Muhammad Isa Ansori Muhammad Rizki Mubarok Mustika Mentari Nabilah Argyanti Ardyningrum Naufal Yukafi Ridlo Noprianto Noprianto Noprianto Noprianto Noprianto, Noprianto Noprianto, Noprianto Novi Nur Putriwijaya Nur Khozin Nurina Savanti Widya Gotami Pambudi, Rizki Agung Putri, Annisa Rahmania Putri, Ika Kusumaning Qoirul Kotimah Restu Fitriawanti Restu Widodo Ridla, Ali Ar Robih Dini Rokhimatul Wakhidah Rudy Ariyanto San Sayidul Akdam Augusta Saputra, Firhad Rinaldi Saragih, Triando Hamonangan Talitha Raissa Vipkas Al Hadid Firdaus Vivin Ayu Lestari Wayan Firdaus Mahmudy Widiareta Safitri Yane Marita Febrianti