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PERANCANGAN DAN PEMBUATAN SISTEM INFORMASI TUGAS AKHIR (SIMTAK) Studi Kasus : Program Studi Teknik Informatika, Fakultas Teknik Universitas Trunojoyo Madura Ach. Khozaimi; Firdaus Solihin; Achmad Jauhari
Jurnal Simantec Vol 1, No 3 (2010): Desember
Publisher : Universitas Trunojoyo Madura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21107/simantec.v1i3.13389

PERANCANGAN DAN PEMBUATAN APLIKASI PENCARIAN INFORMASI BEASISWA DENGAN MENGGUNAKAN COSINE SIMILARITY Andry Kurniawan; Firdaus Solihin; Fika Hastarita
Jurnal Simantec Vol 4, No 2 (2014)
Publisher : Universitas Trunojoyo Madura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21107/simantec.v4i2.1392

ABSTRAKBanyak informasi beasiswa yang ada di internet, di satu sisi memunculkan kesulitan bagi para pencari beasiswa untuk mendapatkan informasi beasiswa. Konsep sistem pencarian berbasis information retrieval (IR), dapat digunakan untuk proses mencari informasi beasiswa melalui media internet. Pembangunan IR pada penelitian ini menggunakan konsep vector space model (VSM). Untuk pengumpulkan data informasi beasiswa menggunakan Web Crawler, hasil dari proses crawling akan disimpan ke dalam basis data. Aplikasi Web Crawler yang digunakan adalah Web Crawler Vietspider. Kesamaan data beasiswa yang akan diintegrasikan oleh cosine similarity. Dengan menggunakan cosine similarity untuk membentuk data beasiswa yang relevan satu sama lain yang dibutuhkan oleh user berdasarkan query yang dimasukkan. Berdasarkan hasil dari pengujian sistem, didapatkan nilai minimal cosine similarity paling baik adalah threshold 0,1. Dengan rata-rata presentase nilai f-measure atau tingkat efisiensi kinerja sistem ini mencapai 19,9 %.Kata kunci : Beasiswa, Information Retrieval, Vector Space Model, Web Crawler, Cosine Similarity.ABSTRACTMany a scholarship information available in the internet, on the other hand that makes difficult for seeker to find scholarship information. The concept of search system based information retrieval (IR) can be used for the process of searching for scholarship information via internet. The development of IR in this study uses the concept of vector space model (VSM). For the collecting data of scholarship information is using Web Crawler, the results of crawling process are stored in the basis data. The applications that use a web crawler is a web crawler vietspider. The similarity of scholarship data is to be integrated by the cosine similarity to form a scholarship of data relevant to each other is needed by user based on the query entered. Based on the results of the testing system, the score of at least the most well cosine similarity threshold is 0.1. The average percentage value of the f-measure system performance or the level of efficiency reached 19.9%.Keyword: Scholarship, Information Retrieval, Vector Space model, Web Crawler, Cosine Similarity.

PENERAPAN METODE SEO ON PAGE DAN OFF PAGE PADA WEB PENJUALAN ONLINE UNTUK MENINGKATKAN RANKING SERP Yoga Dwitya Pramudita; Huzaini Huzaini; Firdaus Solihin
Network Engineering Research Operation Vol 4, No 2 (2019): NERO
Publisher : Universitas Trunojoyo Madura

DESIGN AND DEVELOPMENT OF BACKEND APPLICATION FOR THESIS MANAGEMENT SYSTEM USING MICROSERVICE ARCHITECTURE AND RESTFUL API Ach. Khozaimi; Yoga Dwitya Pramudita; Firdaus Solihin
Jurnal Ilmiah Kursor Vol 11 No 4 (2022)
Publisher : Universitas Trunojoyo Madura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21107/kursor.v11i4.313

A thesis is a scientific work completed by students with the aim of developing the knowledge gained during the lecture period. Students at Universitas Trunojoyo Madura (UTM), Faculty of Engineering, particularly Informatics Engineering, carry out their theses manually and on paper. Thesis Management System (TMS) is software designed to help with the thesis execution process by reducing paper usage and increasing time efficiency. Monolithic system development can disrupt the service process if improvements are being made to the system. Therefore, in this research, a Thesis Management System (TMS) will be built using a microservice approach to make it easier to maintain and develop the system, for example, system scalability. As a means of communication between services, TMS is designed and developed using the REST API. TMS has undergone system performance testing to verify that it performs well under certain conditions. The results show that the number of requests increases the performance response time, CPU usage, and memory consumption, with an average resource usage of each service based on a response time of 61.64 ms, CPU usage of 8.64%, and memory usage of 89.47 Mb. As the number of requests on the service increases, so does resource usage in each service, but this has no effect on device performance because the increase is so low.

Imbalanced Text Classification on Tourism Reviews using Ada-boost Naïve Bayes Suzanti, Ika Oktavia; Kamil, Fajrul Ihsan; Rochman, Eka Mala Sari; Azis, Huzain; Suni, Alfa Faridh; Rachman, Fika Hastarita; Solihin, Firdaus
Jurnal ELTIKOM : Jurnal Teknik Elektro, Teknologi Informasi dan Komputer Vol. 9 No. 1 (2025)
Publisher : P3M Politeknik Negeri Banjarmasin

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31961/eltikom.v9i1.1496

Hidden paradise is a term that aptly describes the island of Madura, which offers diverse tourism potential. Through the Google Maps application, tourists can access sentiment-based information about various attractions in Madura, serving both as a reference before visiting and as evaluation material for the local government. The Multinomial Naïve Bayes method is used for text classification due to its simplicity and effectiveness in handling text mining tasks. The sentiment classification is divided into three categories: positive, negative, and mixed. Initial analysis revealed an imbalance in sentiment data, with most reviews being positive. To address this, sampling techniques—both oversampling and undersampling—were applied to achieve a more balanced data distribution. Additionally, the Adaptive Boosting ensemble method was used to enhance the accuracy of the Multinomial Naïve Bayes model. The dataset was split into training and testing sets using ratios of 60:40, 70:30, and 80:20 to evaluate the model’s stability and reliability. The results showed that the highest F1-score, 84.1%, was achieved using the Multinomial Naïve Bayes method with Adaptive Boosting, which outperformed the model without boosting, which had an accuracy of 76%.

Outcome-Based Education Scoring System Utilizing Modular Object-Oriented Dynamic Learning Environment Ni'mah, Ana Tsalitsatun; Solihin, Firdaus; Sari, Ita Uliyah
Jurnal Pamator : Jurnal Ilmiah Universitas Trunojoyo Vol 16, No 4: October - Desember 2023
Publisher : LPPM Universitas Trunojoyo Madura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21107/pamator.v16i4.23726

The dynamic nature of education necessitates inventive approaches to assessment and evaluation. This study concentrates on formulating a Scoring System aligned with Outcome-Based Education (OBE) principles, utilizing the Modular Object-Oriented Dynamic Learning Environment (MOODLE) platform. OBE prioritizes showcasing specific learning outcomes, cultivating a student-centric approach. The proposed system seeks to improve assessments through a flexible framework accommodating diverse learning objectives, employing a modular and object-oriented design. Integration with MOODLE, a widely-used e-learning platform, explores seamless implementation and user-friendly interaction. The Scoring System aids educators in efficiently evaluating student performance against predefined outcomes, fostering transparency and accountability.Key features include customizable assessments, progress tracking, and timely feedback. The study also examines the system's impact on student engagement, motivation, and overall learning outcomes, contributing valuable insights to innovative assessment methodologies in contemporary education. In conclusion, the research introduces a Scoring System harmonizing OBE principles with MOODLE's flexibility, benefiting educators, students, and institutions. The study's outcomes provide valuable implications for educators and technologists aiming to enhance assessments in the evolving education landscape.

Comparison of Support Vector Machine (SVM), K-Nearest Neighbor (K-NN), and Stochastic Gradient Descent (SGD) for Classifying Corn Leaf Disease based on Histogram of Oriented Gradients (HOG) Feature Extraction Solihin, Firdaus; Syarief, Muhammad; Rochman, Eka Mala Sari; Rachmad, Aeri
Elinvo (Electronics, Informatics, and Vocational Education) Vol. 8 No. 1 (2023): Mei 2023
Publisher : Department of Electronic and Informatic Engineering Education, Faculty of Engineering, UNY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21831/elinvo.v8i1.55759

Image classification involves categorizing an image's pixels into specific classes based on their unique characteristics. It has diverse applications in everyday life. One such application is the classification of diseases on corn leaves. Corn is a widely consumed staple food in Indonesia, and healthy corn plants are crucial for meeting market demands. Currently, disease identification in corn plants relies on manual checks, which are time-consuming and less effective. This research aims to automate disease identification on corn leaves using the Support Vector Machine (SVM), K-Nearest Neighbor (K-NN) with K=2, and Stochastic Gradient Descent (SGD) algorithms. The classification process utilizes the Histogram of Oriented Gradients (HOG) feature extraction method with a dataset of corn leaf images. The classification results achieved an accuracy of 71.44%, AUC of 79.16%, precision of 70.08%, recall of 71.44%, and f1 score of 67.11%. The highest accuracy was obtained by combining HOG feature extraction with the SGD algorithm.

Comparison of Word2Vec and GloVe Performance in Bi-LSTM Models for Indonesian News Classification Muhammad Faris Wafda; Husni; Ika Oktavia Suzanti; Firdaus Solihin; Mula'ab; Army Justitia
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Vol. 11, No. 3, August 2026
Publisher : Universitas Muhammadiyah Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22219/kinetik.v11i3.2608

The explosion in the volume of textual data from digital news presents challenges in classifying content automatically and efficiently. For the task of classifying Indonesian-language news, this study aims to compare the performance of several word embeddings specifically Word2Vec using CBOW and Skip-Gram architectures and GloVe when applied to a Bidirectional Long Short-Term Memory (Bi-LSTM) model. This study uses a dataset consisting of 6,715 news articles from the Indonesian news portal that have undergone pre-processing, divided into five categories. The model was trained using 80% of the training data with K-Fold Cross Validation (K=5), while the remaining 20% of the data was used for testing. The experimental findings indicate that the Bi-LSTM model, when combined with CBOW embedding, yielded the best performance, achieving 95.16% accuracy and a 95.15% F1-Score. The Skip-Gram model followed with solid performance, achieving an accuracy of 93.30% and the fastest computation time. Conversely, the model that used pre-trained GloVe embedding delivered the poorest performance, achieving 88.98% accuracy. This result suggests that training embeddings on a specific domain is more effective at capturing local context. The conclusion of this study confirms that selecting a word embedding method specifically trained on local datasets is also an important step in achieving optimal accuracy in Indonesian news text classification.

Outcome-Based Education Scoring System Utilizing Modular Object-Oriented Dynamic Learning Environment Ana Tsalitsatun Ni'mah; Firdaus Solihin; Ita Uliyah Sari
Jurnal Pamator : Jurnal Ilmiah Universitas Trunojoyo Vol 16, No 4: 2023 (Supplementary Issue)
Publisher : Universitas Trunodjoyo Madura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21107/pamator.v16i4.23726

The dynamic nature of education necessitates inventive approaches to assessment and evaluation. This study concentrates on formulating a Scoring System aligned with Outcome-Based Education (OBE) principles, utilizing the Modular Object-Oriented Dynamic Learning Environment (MOODLE) platform. OBE prioritizes showcasing specific learning outcomes, cultivating a student-centric approach. The proposed system seeks to improve assessments through a flexible framework accommodating diverse learning objectives, employing a modular and object-oriented design. Integration with MOODLE, a widely-used e-learning platform, explores seamless implementation and user-friendly interaction. The Scoring System aids educators in efficiently evaluating student performance against predefined outcomes, fostering transparency and accountability.Key features include customizable assessments, progress tracking, and timely feedback. The study also examines the system's impact on student engagement, motivation, and overall learning outcomes, contributing valuable insights to innovative assessment methodologies in contemporary education. In conclusion, the research introduces a Scoring System harmonizing OBE principles with MOODLE's flexibility, benefiting educators, students, and institutions. The study's outcomes provide valuable implications for educators and technologists aiming to enhance assessments in the evolving education landscape.

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