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All Journal Jurnal Teknologi Informasi dan Ilmu Komputer Scientific Journal of Informatics IJTB | INTERNATIONAL JOURNAL OF TECHNOLOGY AND BUSINESS SemanTIK : Teknik Informasi I-STATEMENT SEINASI-KESI Jurnal UNITEK Journal Computer Science and Informatic Systems : J-Cosys Jurnal Teknik Informatika dan Elektro (JURTIE) Internet of Things and Artificial Intelligence Journal Prosiding Seminar Nasional Rekayasa dan Teknologi (TAU SNAR- TEK)
Ahlijati Nuraminah, Ahlijati
STIMIK ESQ
Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Other

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Implementation of Data Access Object Pattern in Bookkeeping System Development Nuraminah, Ahlijati
Internet of Things and Artificial Intelligence Journal Vol. 4 No. 1 (2024): Volume 4 Issue 1, 2024 [February]
Publisher : Association for Scientific Computing, Electronics, and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/iota.v4i1.712

Abstract

The bookkeeping system is vital for managing a catering business, and incorporating design patterns in software development, specifically the Data Access Object (DAO) Pattern, can enhance structure, flexibility, and efficiency. This study focuses on implementing the DAO Pattern in the catering bookkeeping system to improve data management and overall system performance. Utilizing a software development framework, the DAO Pattern facilitates CRUD operations (Create, Read, Update, Delete) on entities, ensuring flexibility. Tests validate data integrity and proper DAO functions. The system test results through unit testing showed that 96% of the features that implemented DAO were successfully implemented. This demonstrates significant benefits, with improved data management structure and centralized changes. Implementing the DAO Pattern in catering's bookkeeping system ultimately enhances structured data management and overall system performance, providing insights for software developers working on similar business applications.
Pengukuran Focus Factor Tim Scrum pada Proyek Perangkat Lunak Skala Kecil Nuraminah, Ahlijati
JURNAL UNITEK Vol. 18 No. 1 (2025): Januari - Juni 2025
Publisher : Sekolah Tinggi Teknologi Dumai

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52072/unitek.v18i1.1387

Abstract

Scrum, an Agile methodology widely employed in software development, emphasizes collaboration, adaptability, and delivering high-value products. While Velocity is commonly used to measure team performance, it may not provide a comprehensive picture due to factors like the number of developers and working days considered. To address this, the Focus Factor is introduced, accounting for these factors and providing a more nuanced evaluation of team focus and concentration during each Sprint. This research analyzes a Scrum Team's performance using the Focus Factor in a small-scale software project, aiming to understand its advantages in enhancing software development efficiency. The method involves Scrum initiation, data collection, Focus Factor calculation, analysis, and evaluation. The results from seven Sprints indicate varying team performances, with some achieving high Focus Factor values, reflecting efficient focus, while others exhibit lower performance. In conclusion, the Focus Factor proves valuable in assessing team performance, offering insights into areas of improvement. Future research can explore its application in larger projects to evaluate teams' ability to maintain high focus and productivity amidst more extensive user stories
Penerapan Algoritma Fuzzy Mamdani untuk Memberikan Saran yang Optimal dalam Pengambilan Keputusan pada Permainan KArtu Monster Perdana, Muhammad Rizky; Sundawaijaya, Andika; Nuraminah, Ahlijati
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 9 No 7: Spesial Issue Seminar Nasional Teknologi dan Rekayasa Informasi (SENTRIN) 2022
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jtiik.2022976765

Abstract

Permainan kartu monster Yu-Gi-Oh merupakan permainan strategi yang dilakukan bergiliran antar pemain dengan mengeluarkan kartu yang tepat untuk menyerang atau bertahan dengan tujuan mengurangi poin kehidupan musuh dan melindungi poin kehidupan pemain. Dalam menentukan kartu yang tepat dalam permainan, diperlukan informasi yang akurat pada suatu kondisi permainan. Banyak pemain salah mengambil langkah dalam permainan, sehingga kesempatan kalah menjadi lebih besar. Untuk itu diperlukan sebuah sistem saran yang mampu membantu pemain dalam menentukan kartu yang sesuai untuk digunakan dalam permainan tersebut. Sistem saran akan memberikan masukan kepada pemain dalam menentukan kartu yang tepat dan optimal dalam suatu langkah. Salah satu algoritma yang mampu memetakan dan menentukan keputusan yang dapat menjadi sebuah sistem saran adalah fuzzy dengan inferensi Mamdani. Pada penelitian ini, pemilihan saran kartu menggunakan algoritma Fuzzy Mamdani dilakukan berulang kali sebanyak 15 kali menggunakan data kartu Yu-Gi-Oh yang sudah diolah. Hasil akurasi dari model pemilihan saran kartu yang dirancang sebesar 0,733 yang artinya cukup baik. Dari hasil tersebut rekomendasi pengembangan penelitian antara lain dengan menambah kartu pada dataset seperti beberapa tipe kartu yang berbeda dan menambahkan atau menggunakan metode yang berbeda serta analisis penambahan input pada variabel Fuzzy untuk menambah akurasi sistem dalam memilih saran kartu yang lebih optimal.AbstractThe Yu-Gi-Oh monster card game is a strategy game that takes turns between players by issuing the right cards to attack or defend with the aim of reducing enemy life points and protecting the player's own life points. In determining the right card to use in the game, the right information is needed in a game condition. Many players take the wrong steps in the game, so the chance of lose is greater. It need a suggestion system that is able to assist players in determining the appropriate cards to be used in the game. The suggestion system will provide input to players in determining the right and optimal card in a move. Algorithm that is able to map and determine decisions that can become a suggestion system is fuzzy with Mamdani inference. In this study, the selection of card suggestions using the Fuzzy Mamdani algorithm was repeated 15 times using processed Yu-Gi-Oh card data. The results of the accuracy of the proposed card selection model are 0.733, which means it is quite good. From these results, recommendations for further research include adding cards to the dataset such as several different card types and adding or using different methods and analysis of adding input to fuzzy variables to increase system accuracy in choosing more optimal card suggestions.
Penerapan Blok SE-NET Pada Deep Learning Inceptionv3 untuk Meningkatkan Deteksi Penyakit Mpox pada Manusia Rachman, M. Bakhara Alief; Kurniasih, Aliyah; Sundawijaya, Andika; Nuraminah, Ahlijati
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 11 No 5: Oktober 2024
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jtiik.2024117978

Abstract

Mpox atau cacar monyet adalah penyakit yang disebabkan oleh virus monkeypox. Penelitian terdahulu membuktikan sudah tersedia beberapa pre-trained model yang terbukti mampu mendeteksi penyakit mpox dengan menggunakan dataset MSLD (Monkeypox Skin Lesion Dataset) seperti VGG16, ResNet50, InceptionV3, dan penggabungan ketiga model tersebut. Dari penelitian tersebut didapatkan hasil model InceptionV3 memiliki tingkat akurasi paling rendah dengan nilai 74.07% berbanding jauh dengan ResNet50 yang mampu hingga 82.96% dan menjadikannya akurasi tertinggi. Namun, terdapat peluang akurasi model InceptionV3 mampu ditingkatkan. Oleh sebab itu, pada penelitian diimplementasikan arsitektur baru dan penambahan blok SE-Net (Squeeze and Excitation Networks) pada pre-trained model InceptionV3. Untuk training dan evaluasi model akan menggunakan dataset MSLD. Penelitian ini dilaksanakan dengan harapan mampu meningkatkan akurasi pre-trained model InceptionV3 dalam mendeteksi penyakit mpox. Dari hasil penelitian berdasarkan nilai confusion matrix penerapan arsitektur baru berhasil dilakukan terbukti dengan peningkatan akurasi dari 74.07% menjadi 82.22%. Selain itu, penambahan blok SE-Net terhadap arsitektur baru terbukti mampu meningkatkan akurasi menjadi 91.11% dan menjadikan performa InceptionV3 menjadi lebih baik dari akurasi ResNet50. Dari hasil penelitian tersebut memberikan rekomendasi untuk melakukan percobaan dengan mengganti pre-trained model, blok SE-Net, dan jumlah perbandingan dataset antara train, validation, dan test.   Abstract   Mpox or monkeypox is a disease caused by the monkeypox virus. Previous research has proven that there are several pre-trained models that are proven to be able to detect mpox disease using MSLD (Monkeypox Skin Lesion Dataset) datasets such as VGG16, ResNet50, InceptionV3, and a combination of these three models. From this research, it was found that the InceptionV3 model has the lowest level of accuracy with a value of 74.07% compared to ResNet50 which is capable of up to 82.96% and makes it the highest accuracy. However, there is a chance that accuracy can be improved. Therefore, this research will apply a new architecture and SE-Net blocks to the InceptionV3 pre-trained model using the MSLD dataset. From the results of research based on the value of the confusion matrix the application of the new architecture was successfully carried out as evidenced by an increase in accuracy from 74.07% to 82.22%. In addition, the addition of the SE-Net block to the new architecture is proven to be able to increase accuracy to 91.11% and make InceptionV3's performance better than ResNet50's accuracy. The results of this study provide recommendations for conducting experiments by changing the pre-trained model, the SE-Net block, and the number of dataset comparisons between train, validation, and test.
Damerau-Levenshtein Distance Algorithm Based on Abstract Syntax Tree to Detect Code Plagiarism Nuraminah, Ahlijati; Ammar, Abdullah
Scientific Journal of Informatics Vol 11, No 1 (2024): February 2024
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v11i1.48064

Abstract

Purpose: This research aimed to detect source code plagiarism based on Abstract Syntax Tree using Damerau-Levenshtein Distance algorithm, which is expected to streamline the inaccuracies and time-consumption associated with the manual process.Methods: Damerau-Levenshtein Distance algorithm was used to determine the similarity between source code files and calculate F-Measure. The dataset, which consisted of 178 source code files from 20 coursework assignments, was obtained from GitHub by Lawton Nichols in 2019. Damerau-Levenshtein Distance algorithm was used to compute the minimum cost required to transform one line of code into another. Furthermore, ANTLR detected AST, which was processed through preprocessing, including node pruning, function and variable sorting, and log output removal. Result: The result showed that the two methods took 5.704 seconds and 0.996 seconds to complete. The lowest and highest values obtained using F-Measure were 0.16 and 0.8, respectively. Therefore, the system performed detection processes quickly and effectively detected common forms of code plagiarism with difficulty in the more complex forms. Novelty: In conclusion, this research used AST and Damerau-Levenshtein Distance algorithm to calculate the 5 levels of similarity in Java programming language source code. For further development, preprocessing steps were needed to prune unnecessary nodes and detect equivalent but differently syntaxed code. 
Co-Authors Adji Sodikin, Damar Aliyah Kurniasih Ammar, Abdullah Arjunal Akmal, Refido Billah, Mu’tashim Hanafi, Faiz Mohammad, Gagas Ananta Musyaffa, TB Nawaf Novella, Trevy Jonatya Nur Hidaya, Ahmad Perdana, Muhammad Rizky Qowy, Bilal Abdul Rachman, M. Bakhara Alief Rasyid, Naufal Riandi, M.Akbar Risnawati Risnawati Sundawaijaya, Andika Sundawijaya, Andika Zaky Cakramusi Putra, Alif