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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Jurnal Teknik ITS IPTEK Journal of Proceedings Series IPTEK The Journal for Technology and Science Semantik Jurnal Buana Informatika 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) Infotech Journal Register: Jurnal Ilmiah Teknologi Sistem Informasi Jurnal Inspiration KLIK (Kumpulan jurnaL Ilmu Komputer) (e-Journal) JOIV : International Journal on Informatics Visualization Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control JURNAL TEKNIK INFORMATIKA DAN SISTEM INFORMASI Jurnal ULTIMA InfoSys SISFO Jurnal Nasional Pendidikan Teknik Informatika (JANAPATI) PINISI Discretion Review Jurnal Rekayasa Industri (JRI) Teknika Journal La Multiapp JEEE-U (Journal of Electrical and Electronic Engineering-UMSIDA) Melek IT: Information Technology Journal Jurnal Nasional Teknik Elektro dan Teknologi Informasi Sewagati Bulletin of Social Informatics Theory and Application Scientific Journal of Informatics
Yuhana, Umi Laili
Departemen Teknik Informatika, Fakultas Teknologi Elektro Dan Informatika Cerdas, Institut Teknologi Sepuluh Nopember Surabaya
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APLIKASI PENGAMBILAN KEPUTUSAN ALTERNATIF PEMILIHAN OBAT DENGAN MEMANFAATKAN XPERTRULE Umi Laili Yuhana; Joko Lianto Buliali; Dwi Sunaryono
JUTI: Jurnal Ilmiah Teknologi Informasi Vol 3, No 1 Januari 2004
Publisher : Department of Informatics, Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (210.008 KB) | DOI: 10.12962/j24068535.v3i1.a127

Abstract

Kemudahan masyarakat mendapatkan obat-obatan bebas membawa dampak yang baik maupun yang kurang baik. Dampak yang baik bahwa masyarakat tidak kesulitan dalam menemukan obat untuk penyakitnya. Dampak yang kurang baik adalah ketika masyarakat tidak mendapatkan informasi yang cukup tentang efektifitas obat tersebut, sehingga terkesan bahwa masyarakat melakukan coba-coba dalam menyembuhkan gejala sakit yang dideritanya. Untuk satu jenis penyakit saja tersedia berbagai pilihan obat dengan berbagai merek dan bentuk. Berawal dari masalah diatas dan dari beberapa informasi yang didapat dari indikasi dan faktor penyembuhan obat dibuat suatu sistem berbasis komputer dengan memanfaatkan XpertRule yang dapat memberikan informasi alternatif obat untuk membantu pemakai mengambil keputusan dalam hal pemilihan obat. Sistem ini menggabungkan dua sistem yaitu sistem pakar dan sistem pendukung keputusan. Pemakai dapat menggunakan dua jalur konsultasi yaitu jalur pakar dan jalur pengalaman. Dasar dari jalur pakar adalah informasi dari buku ISO yang berupa indikasi-indikasi sakit beserta obatnya. Sedangkan dasar dari jalur pengalaman adalah pengalaman sejumlah responden terhadap sakit dan kesembuhannya dari obat yang pernah diminum. Untuk jalur pengalaman disertai juga informasi prosentase kesembuhan untuk tiap obat berdasarkan data dari responden. Adapun cara kerja sistem adalah bahwa sistem memberikan pertanyaan-pertanyaan kepada pemakai berupa gejala-gejala sakit yang dideritanya, selanjutnya sistem akan memberikan pilihan alternatif obat yang tepat untuk gejala sakit tersebut. Berdasarkan hasil uji coba terutama untuk kasus-kasus gejala penyakit dengan gejala yang sama serta jalur yang berbeda, maupun untuk kasus yang berbeda-beda untuk jalur yang sama, sistem mampu memberikan solusi berupa alternatif beberapa obat sesuai yang diharapkan. Untuk kasus yang sama dengan jalur yang berbeda bisa saja menghasilkan alternatif obat yang berbeda. Dengan beberapa alternatif obat dan data histori (tingkat kesembuhan obat) untuk tiap-tiap obat tersebut pemakai dapat mengambil keputusan dengan memilih salah satu obat diantaranya. Kata kunci : sistem pakar, sistem pendukung keputusan
SELENIUM FRAMEWORK FOR WEB AUTOMATION TESTING: A SYSTEMATIC LITERATURE REVIEW Hazna At Thooriqoh; Tiara Nur Annisa; Umi Laili Yuhana
JUTI: Jurnal Ilmiah Teknologi Informasi Vol. 19, No. 2, Juli 2021
Publisher : Department of Informatics, Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j24068535.v19i2.a1021

Abstract

Software Testing plays a crucial role in making high-quality products. The process of manual testing is often inaccurate, unreliable, and needed more than automation testing. One of these tools, Selenium, is an open-source framework that used along with different programming languages: (python, ruby, java, PHP, c#, etc.) to automate the test cases of web applications. The purpose of this study is to summarize the research in the area of selenium automation testing to benefit the readers in designing and delivering automated software testing with Selenium. We conducted the standard systematic literature review method employing a manual search of 2408 papers, and applying a set of inclusion/exclusion criteria the final literature included 16 papers published between 2009 and 2020. The result is using Selenium as a UI for web automation, not only all of the app functionality that has been tested, But also it can be applied with added some method or other algorithms like data mining, artificial intelligence, and machine learning. Furthermore, it can be implemented for security testing. In the future research for selenium framework automation testing, the implementation should more focus on finding effective and maintainability on the application of Selenium in other methodologies and is applied with the better improvement that can be matched for web automation testing.
SOFTWARE DEFECT PREDICTION USING PCA BASED RECURRENT NEURAL NETWORK Eka Alifia Kusnanti; Lauretha Devi Fajar Vantie; Umi Laili Yuhana
JUTI: Jurnal Ilmiah Teknologi Informasi Vol. 22, No. 1, January 2024
Publisher : Department of Informatics, Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j24068535.v22i1.a1199

Abstract

Software quality is one of the important phases in software development. Software quality assesses the usability and quality of the software developed. Defect prediction early in software development helps in software quality assurance by reducing software defects that may occur. With good predictions, it will provide additional benefits in terms of resource and cost efficiency. The researchers in this study have proposed a software defect prediction method that utilizes a Recurrent Neural Network (RNN) based on Principal Component Analysis (PCA). The dataset used is the PROMISE dataset, namely JM1, CM1, PC1, KC1, and KC2. The test results showed that the PCA-RNN method was successfully applied. For the highest accuracy on the PC1 dataset, with an accuracy of 93.99% with the division of training data by testing data (70:30).
DEVELOPMENT OF A MODEL TO EVALUATE USERS' TECHNOLOGY READINESS AND ACCEPTANCE IN USING THE SELF-CHECK-IN KIOSK SERVICE AT SOEKARNO-HATTA INTERNATIONAL AIRPORT Muhammad Faisal Fanani; Umi Laili Yuhana; Ary Mazharuddin Shiddiqi
JUTI: Jurnal Ilmiah Teknologi Informasi Vol. 22, No. 2, July 2024
Publisher : Department of Informatics, Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j24068535.v22i2.a1238

Abstract

The self-check-in kiosk is one of the digital technologies used by the aviation industry to help passengers check in on passenger flights independently and efficiently without the need for a conventional check-in counter at the airport. However, the phenomenon on the ground indicates that many users have not yet used the service. As a result, the check-in area in some of the flight masks often has a long wait. Studies conducted by several airports in campsites such as Malaysia, South Africa, and Switzerland show that self-check-in kiosks do not meet the echoes of users. The same thing happened at Indonesian airports, where the use of self-check-in kiosks was still below 20% of total passenger traffic in 2022–2023. The study introduces the User Experience Technology Readiness and Acceptance Model (UX TRAM), which is used to evaluate user readiness and acceptance of the application of new technologies in the airport environment. The Partial Least Squares Structural Equation Modeling (PLS-SEM) method is used to analyze the research model and the proposed hypothesis. Based on the results of the test of significance and relevance of the relationship in this study, the structural model proposed by the majority is of significant value, except for the variables Innovativeness and Insecurity versus Perceived Ease of Use. Based on the results of the test of the hypothesis carried out, out of 15 hypotheses tested, there are 13 accepted and 2 rejected hypotheses related to the readiness and acceptance of users in the use of new technology on the Self-Check-in Kiosk service at Soekarno-Hatta International Airport. The results of this study show that the proposed research model has varying explanatory strengths (near moderate to substantial/high) as well as predictive strengths that offer better predictable performance. 
Design of Antasena: an AI-powered maritime surveillance and anomaly detection system for security decision support Badrudin, Arif; Sumantri, Siswo Hadi; Gemilang Gultom, Rudy Agus; Apriyanto, I Nengah Putra; Yuhana, Umi Laili; Ratnasari, Fitria Dwi
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 1: February 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i1.pp269-288

Abstract

Indonesia’s vast maritime territory faces serious challenges from illegal fishing, smuggling, and habitat destruction. To address these, the Indonesian Navy (TNI-AL) developed Antasena, an artificial intelligence (AI)-powered smart dashboard integrating automatic identification system (AIS) data, satellite imagery, and conservation metrics. Antasena leverages advanced anomaly detection algorithms, achieving 95.3% accuracy, 94.7% precision, 94.2% recall, and a 96.8% receiver operating characteristic-area under the curve (ROC-AUC) score in identifying vessel anomalies, including unauthorized fishing and smuggling activities. Using the analyze, design, develop, implement, and evaluate (ADDIE) framework, the system supports real-time maritime surveillance and biodiversity monitoring in conservation zones. The main contributions of this study include the development of a user-centric AI-based dashboard for maritime anomaly detection, the integration of multi-source data with machine learning models, and validation through operational field tests with maritime authorities. Antasena offers a scalable and effective solution to strengthen maritime security and protect Indonesia’s marine resources.
Pemanfaatan Pembelajaran Mesin untuk Klasifikasi Kebutuhan Perangkat Lunak Setiawan, Wahyu Fajar; Ariatama, Ilham Putra; Yuhana, Umi Laili; Alfian, Muhammad
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 13 No 1: Februari 2026
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jtiik.2026131

Abstract

Klasifikasi kebutuhan perangkat lunak merupakan salah satu langkah terpenting dalam rekayasa perangkat lunak. Klasifikasi ini membantu pengembang untuk mengategorikan kebutuhan fungsional atau functional requirement (FR) dan kebutuhan non-fungsional atau non-functional requirement (NFR). Klasifikasi ini sangat penting untuk memastikan bahwa setiap aspek kebutuhan perangkat lunak terpenuhi dengan efisien sehingga perangkat lunak yang dikembangkan akhirnya dapat memenuhi harapan penggunanya. Namun, klasifikasi manual memerlukan waktu lama dan rentan terhadap kesalahan manusia, terutama pada proyek skala besar. Sehingga pada penelitian ini kami bertujuan mengotomatisasi proses klasifikasi kebutuhan perangkat lunak menggunakan beberapa algoritma pembelajaran mesin seperti Logistic regression, SVM, Multinomial Naive Bayes, KNN, Random Forest, dan Decision Tree dengan ekstraksi fitur seperti TF-IDF, BoW, dan BERT menggunakan dataset PROMISE_exp yang berisi 969 kebutuhan perangkat lunak (444 FR dan 525 NFR), untuk mengetahui kombinasi terbaik antara metode ekstraksi fitur dengan algoritma klasifikasi. Hasil eksperimen menunjukkan bahwa Logistic regression dengan fitur TF-IDF menghasilkan akurasi tertinggi sebesar 97% di antara semua pendekatan. Model ini juga cukup seimbang dalam hal precision, recall, dan F1-Score. Model tersebut terbukti menjadi pilihan yang sangat andal untuk mengklasifikasikan kebutuhan perangkat lunak. Decision Tree yang dikombinasikan dengan BERT ternyata memiliki kinerja yang lebih buruk, yang menyatakan bahwa model ini kurang mampu menangani fitur-fitur yang kompleks dari BERT. Kontribusi utama penelitian ini adalah pembuktian empiris bahwa model klasifikasi sederhana (Logistic Regression + TF-IDF) dapat mengungguli pendekatan kompleks berbasis transformer (BERT) untuk tugas klasifikasi kebutuhan perangkat lunak, memberikan panduan praktis bagi pengembang dalam memilih pendekatan otomatisasi yang efektif dan efisien.   Abstract Software requirement classification is one of the most important steps in software engineering. This classification helps developers categorise functional requirements (FR) and non-functional requirements (NFR). This classification is very important to ensure that every aspect of software requirements is met efficiently so that the developed software can ultimately meet user expectations. However, manual classification is time-consuming and prone to human error, especially in large-scale projects. Therefore, in this study, we aim to automate the software requirement classification process using several machine learning algorithms such as Logistic regression, SVM, Multinomial Naive Bayes, KNN, Random Forest, and Decision Tree with feature extraction such as TF-IDF, BoW, and BERT using the PROMISE_exp dataset containing 969 software requirements (444 FR and 525 NFR), to determine the best combination of feature extraction methods with classification algorithms. The experimental results show that Logistic regression with TF-IDF features produces the highest accuracy of 97% among all approaches. This model is also quite balanced in terms of precision, recall, and F1-Score. The model proved to be a very reliable choice for classifying software requirements. Decision Tree combined with BERT turned out to have poorer performance, indicating that this model is less capable of handling the complex features of BERT. The main contribution of this research is the empirical proof that a simple classification model (Logistic Regression + TF-IDF) can outperform complex transformer-based approaches (BERT) for software requirement classification tasks, providing practical guidance for developers in choosing effective and efficient automation approaches.
BLACK BOX TESTING IN THE ACCESS BY KAI APPLICATION USING BOUNDARY VALUE ANALYSIS AND GRAPH-BASED TESTING Irnayanti Dwi Kusuma; I Gusti Lanang Agung Oka Cahyadi Pradipta; Umi Laili Yuhana
INFOTECH journal Vol. 10 No. 1 (2024)
Publisher : Universitas Majalengka

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31949/infotech.v10i1.9577

Abstract

The Access by KAI application is an application that focuses on the process of ordering train tickets via internet technology. The aim of making this application is to help make it easier for people to order train tickets. By creating this application, it is hoped that it can increase train ticket sales and make it easier for people to order train tickets online. Each feature of this application was subject to testing to ensure that it was running according to the expected functionality. The testing of a Black Box Testing using Boundaries Value Analysis and Graph-Based Test on the Access by KAI has success percentage 78%.
Co-Authors Achmad Affandi Agung Prasetya Ahmad Budi Kurniawan Ahmad Nur Hidayat Akbar Noto Ponco Bimantoro Akbar, Rizky Januar Alfan, Muhammad Bahauddin Ali Sofyan Kholimi Amelia Devi Putri Ariyanto Amirullah, Afif Andhik Ampuh Yunanto Andi Besse Firdausiah Anisah Herdiyanti Apriyanto, I Nengah Putra Ariatama, Ilham Putra Arief Rahman Ary Mazharuddin Shiddiqi As'ad Arismadhani Ayu Purwarianti Azzmi. H., M. Naufal ‘Azizah, Lutfiyatul Badrudin, Arif Bambang Setiawan Buliali, Joko Lianto Chastine Fatichah Daniel Oranova Daniel Oranova Siahaan Daniel Siahaan Darlis Herumurti Denni Aldi Ramadhani Denni Aldi Ramadhani Denni Aldi Ramadhani Denni Aldi Ramadhani Diana Purwitasari Diana Purwitasari Donny Fitrado Dwi Sunaryono Dwi Sunaryono Eka Alifia Kusnanti Eko M. Yuniarno Eko Mulyanto Yuniarno Esti Yuniar Fadilla Sukma Alfiani Faizah Alkaff Fanji Hastomo Febri Fernanda Gultom, Rudy Agus Gemilang Habibi, Ahmad Faqih Hadziq Fabroyir Hanim Maria Astuti Hazna At Thooriqoh Hersyaputra, Mohamad Syazimmi Hervit Ananta Vidada Hidayat, Taufik I Gusti Lanang Agung Oka Cahyadi Pradipta Ilham, Karina Fitriwulandari Imam Kuswardayan Imamah Imamah Irnayanti Dwi Kusuma Jaya, Muhammad Triyanda Taruna Kadek Anggrian Mahendra Putra Kurniawan, Adi Lailatul Hidayah Lailatul Husniah Lauretha Devi Fajar Vantie Lesmideyarti, Dwi Mamluatul Hani’ah Mauridhi Hery Purnomo Muhamad Fauzi Muhammad Alfian Muhammad Alfian, Muhammad Muhammad Faisal Fanani Muhammad Najib Muhammad Zain Fawwaz Nuruddin Siswantoro Nawang Sulistyani Nisa, Maidina Choirun Nugroho, Supeno Mardi S. Nuralamsyah, Bintang Oranova, Daniel Puspitaningrum, Ari Cahaya Putu Yuwono Kusmawan Ratnasari, Fitria Dwi Rizal Setya Perdana Rizky Januar Akbar Rizqa Raaiqa Bintana Rully Agus Hendrawan Rully Soelaiman Sally Indah Khansa Sally Indah Khansa Santi Tiodora Sianturi Santoso, Bagus Jati Saptarini, Istiningdyah Sari Sahadi, Fitria Vera Sartana, Bruri Trya Sarwosri Sarwosri Sarwosri Sarwosri, - Setiawan, Wahyu Fajar Siska Arifiani Siti Rochimah Sjahrunnisa, Anita Sumantri, Siswo Hadi Supeno Mardi S. Nugroho Supeno Mardi Susiki Nugroho, Supeno Mardi Suyadi Suyadi Talasari, Resky Ayu Dewi Tiara Nur Annisa Toshihiro Kita Wighneswara, Alifiannisa Alyahasna Yasinta Romadhona Yogi Kurniaawan Yogi Kurniaawan, Yogi Yuniarno, Eko M.