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All Journal Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) Jurnal Informatika dan Teknik Elektro Terapan INTEGER: Journal of Information Technology JUTIM (Jurnal Teknik Informatika Musirawas) JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) bit-Tech Jurnal Teknologi Informasi, Komputer, dan Aplikasinya (JTIKA ) CICES (Cyberpreneurship Innovative and Creative Exact and Social Science) Journal Cerita: Creative Education of Research in Information Technology and Artificial Informatics Jurnal Teknik Informatika (JUTIF) Journal of Information System and Technology (JOINT) International Journal Of Computer, Network Security and Information System (IJCONSIST) COMSERVA: Jurnal Penelitian dan Pengabdian Masyarakat Algoritme Jurnal Mahasiswa Teknik Informatika Biner : Jurnal Ilmiah Informatika dan Komputer Ranah Research : Journal of Multidisciplinary Research and Development
Nurlaili, Afina Lina
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Humanities Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice Public Health Social Sciences Transportation Other

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Optimization of Earthquake B-Value Prediction in Java Using GRU and Particle Swarm Optimization Nursyahada, Kesya; Rahmat, Basuki; Nurlaili, Afina Lina
bit-Tech Vol. 8 No. 1 (2025): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i1.2521

Abstract

Accurate prediction of earthquake parameters is essential for seismic risk assessment and disaster mitigation, particularly in tectonically active regions such as Java Island, Indonesia. This study presents a novel predictive model for estimating the earthquake b-value a fundamental seismological parameter representing the logarithmic relationship between earthquake frequency and magnitude by integrating a Gated Recurrent Unit (GRU) neural network with Particle Swarm Optimization (PSO). The model is trained using earthquake catalog data from 1962 to 2024, sourced from the Indonesian Meteorological, Climatological, and Geophysical Agency (BMKG). The GRU architecture is selected for its effectiveness in modeling temporal dependencies in seismic time series data. PSO is employed to optimize essential hyperparameters, including the number of GRU units, learning rate, and dropout rate. The optimized model achieves notable improvements in predictive performance: Mean Squared Error (MSE) is reduced from 0.00435 to 0.00030, Root Mean Squared Error (RMSE) from 0.0509 to 0.0173, and Mean Absolute Percentage Error (MAPE) from 3.42% to 1.12%. Training time is also reduced from 57 seconds to 33 seconds, indicating greater computational efficiency. The optimal PSO settings include an inertia weight of 0.8, cognitive and social coefficients of 1.0, 40 particles, and 10 iterations. The primary novelty of this study lies in its targeted application of PSO-optimized GRU architecture for b-value prediction in a seismically complex region. These results demonstrate that evolutionary optimization significantly enhances deep learning performance, providing a robust and efficient framework to support earthquake forecasting and risk mitigation efforts in high-risk zones such as Java Island.
Random Forest – Deep Convolutional Neural Network Ensemble Model for Skin Disease Classification Kurniawan, Ananda Rheza; Via, Yisti Vita; Nurlaili, Afina Lina
bit-Tech Vol. 8 No. 1 (2025): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i1.2528

Abstract

Skin diseases such as psoriasis, atopic dermatitis, and tinea are chronic conditions that significantly affect quality of life and require rapid and accurate classification to support early treatment. However, limited medical personnel and inadequate classification tools in various regions remain major challenges in handling these cases. This study proposes an automatic skin disease classification system based on digital images using an ensemble method that combines Deep Convolutional Neural Network (DCNN) and Random Forest (RF). The dataset used comprises 4,246 images categorized into four classes (psoriasis, atopic dermatitis, tinea, and normal skin), sourced from Kaggle and DermNet. Preprocessing steps include image resizing, normalization, and data augmentation, while hyperparameter tuning is conducted using Bayesian Optimization. The ensemble model applies a soft voting mechanism to integrate predictions from both DCNN and RF. Experimental results show that the RF-DCNN model achieves an accuracy of up to 84.35% in the 80:10:10 data split scenario, surpassing the performance of the conventional CNN model. These results suggest that the hybrid DCNN-RF approach enhances accuracy, stability, and generalization in skin disease classification. The proposed model holds strong potential for implementation in artificial intelligence-based clinical decision support systems, especially in regions with limited access to dermatology specialists. Future work is encouraged to explore more advanced architectures such as EfficientNet and Swin Transformer for further performance improvements.
Design and Development of a Counseling Service System Using Extreme Programming Methodology Nobrian, Ikhsan; Nurlaili, Afina Lina; Aditiawan, Firza Prima
bit-Tech Vol. 8 No. 1 (2025): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i1.2928

Abstract

This study addresses the inefficiency and error-prone nature of manual counseling and student violation point recording processes in schools, which often result in delays and inaccuracies. To overcome these challenges, we propose the development of a digital guidance and counseling service system designed to improve data management and enhance service accessibility for school administrators and counselors. The innovation lies in the creation of an integrated, browser-accessible application built using the MERN (MongoDB, Express.js, React, Node.js) stack, which ensures robust functionality and scalability. By applying modern development and testing methodologies, the system is designed to be both reliable and user-friendly. The core objective of this system is to streamline processes such as counseling appointment scheduling, alumni tracking, certificate submission, and student behavior reporting. It was developed using the Extreme Programming (XP) methodology, which encourages flexibility and iterative planning through close collaboration with end users. White Box Testing techniques, including cyclomatic complexity analysis and independent path testing, were employed to validate the system's internal logic. The system’s usability was assessed using the System Usability Scale (SUS), achieving an excellent score of 93.25, indicating high user satisfaction. Furthermore, the Lighthouse performance test yielded a perfect score of 100, confirming the system's high responsiveness. These results demonstrate that the developed system significantly enhances the efficiency, accuracy, and accessibility of guidance services, reduces administrative burdens, and enables better monitoring of student development, making it ideal for deployment in real-world school environments.
Aplikasi Mobile Penjualan Makanan Sisa Dengan Geolocation Dan Metode Haversine Maulana, Dimas Octa; Akbar, Fawwaz Ali; Nurlaili, Afina Lina
CICES (Cyberpreneurship Innovative and Creative Exact and Social Science) Vol 11 No 2 (2025): CICES
Publisher : UNIVERSITAS RAHARJA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33050/cices.v11i2.3523

Abstract

Food waste is a critical global issue with significant economic and environmental implications. In 2021, approximately 40% of the total global food production, equivalent to 2.5 billion tons, was wasted. This figure marks a notable increase of 1.2 billion tons from the estimated food waste in 2011. Globally, Indonesia ranks second in terms of the highest levels of food waste, with 39.4% of the total waste generated in the country being food waste. This statistic makes food waste the predominant type of waste in Indonesia. This research aims to design and develop a mobile geolocation based application for the sale of surplus food, utilizing the haversine method to facilitate this process. The application is designed to allow users to access the locations of surplus food sellers within a maximum radius of 25 kilometers from their position. The results indicate that the developed application successfully displays the distance between consumers and surplus food sellers. Black box testing successfully ensures that all application functionality runs well. The level of user acceptance of the application is at the acceptable level, and the application grade is B.
PENGEMBANGAN SISTEM REKOMENDASI UNTUK SIMULASI RAKIT KOMPUTER MENGGUNAKAN ALGORITMA GENETIKA BERBASIS WEBSITE Maulana, Vieri Arief; Haromainy, Muhammad Muharrom Al; Nurlaili, Afina Lina
JTIKA (Jurnal Teknik Informatika, Komputer dan Aplikasinya) Vol 7 No 2 (2025): September 2025
Publisher : Program Studi Teknik Informatika, Fakultas Teknik, Universitas Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/jtika.v7i2.491

Abstract

This research develops a web-based recommendation system for computer assembly simulations using genetic algorithms. The system is designed to assist users in selecting optimal computer components based on their available budget and desired performance. Component data were collected from e-commerce platforms and online sources, then preprocessed using Min-Max normalization to ensure balanced data scaling. The system was developed using Laravel for the frontend interface and Flask API for computational processing of the genetic algorithm. System evaluation was conducted using the System Usability Scale (SUS) method involving 21 respondents, resulting in an average score of 86.67, which falls into the "Excellent" category and Grade B on the usability scale. Additionally, performance comparisons with prebuilt systems from online stores show that the recommendation system produced assemblies with lower costs and higher performance. The implementation of selection, crossover, and mutation in the genetic algorithm effectively evaluates component combinations to achieve optimal configurations. This research contributes to the development of intelligent optimization-based systems that simplify the computer assembly process, particularly for novice users with limited technical knowledge and constrained budgets.
Penerapan Teknik Basis Path pada Pengujian White Box Sistem Informasi Perencanaan dan Penganggaran Responsive Gender di Diskominfo Kabupaten Jombang Putri, Della Atika; Wahanani, Henni Endah; Nurlaili, Afina Lina
JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Vol 10, No 3 (2025)
Publisher : STKIP PGRI Tulungagung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29100/jipi.v10i3.6338

Abstract

Sistem informasi merupakan media penting yang digunakan untuk menyediakan informasi secara akurat dan tepat waktu. Mengingat signifikansi sistem ini bagi organisasi, pengujian kualitas dan keandalan sistem menjadi krusial. Penelitian ini mengkaji sistem informasi perencanaan dan penganggaran responsif gender yang dikelola oleh Diskominfo Jombang dengan menerapkan teknik basis path dalam metode pengujian white box. Pengujian melibatkan pembuatan flowgraph, perhitungan cyclomatic complexity (CC), penentuan jalur independen, dan pembuatan test case. Teknik basis path digunakan untuk memastikan bahwa setiap jalur dalam program dapat dilalui sekali tanpa adanya jalan pintas atau perulangan, melalui analisis kode program sistem. Hasil pengujian menunjukkan bahwa dari empat fungsi yang diuji, satu fungsi memiliki prosedur yang terstruktur dengan baik dan konsisten, sedangkan tiga fungsi lainnya sederhana dan memiliki risiko rendah. Secara keseluruhan, sistem ini dinilai memiliki risiko rendah. Namun, evaluasi usability menggunakan metode SUS menunjukkan bahwa, meskipun sistem berfungsi dengan baik dari segi logika internal, antarmuka yang rumit, serta navigasi yang membingungkan menyebabkan skor SUS yang rendah. Hal ini menunjukkan bahwa sistem belum sepenuhnya ramah pengguna dan memerlukan perbaikan.
Penerapan Algoritma K-Nearest Neighbor Dalam Klasifikasi Penyakit Daun Padi Menggunakan Ekstraksi HOG Yana, Baktiar Yudha; Via, Yisti Vita; Nurlaili, Afina Lina
Algoritme Jurnal Mahasiswa Teknik Informatika Vol 6 No 1 (2025): Oktober 2025 || Algoritme Jurnal Mahasiswa Teknik Informatika
Publisher : Program Studi Teknik Informatika Universitas Multi Data Palembang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35957/algoritme.v6i1.13306

Abstract

Rice (Oryza sativa) is a strategic Indonesian food commodity that is susceptible to leaf disease attacks, causing decreased productivity and even crop failure. Conventional detection methods based on visual observation have limited accuracy and consistency, so an automated approach based on computer vision technology is needed for more effective early detection. This study applies the K-Nearest Neighbors (KNN) algorithm in rice leaf disease classification using Histogram of Oriented Gradients (HOG) feature extraction. A secondary dataset from Kaggle of 1,400 images covers four categories: Bacterial Leaf Blight, Brown Spot, Leaf Blast, and Healthy. The methodology includes image preprocessing (resize, augmentation, grayscale conversion, normalization), HOG feature extraction, and KNN classification with evaluation on a training-test data ratio of 85:15. The results show that KNN with k=2 achieves optimal performance at a ratio of 85:15 with an accuracy of 90.24%, a precision of 90.27%, a recall of 90.24%, an F1-score of 90.23%, and an efficient computational time of 3.34 seconds. The combination of HOG and KNN is proven to be effective for the automatic classification of rice leaf diseases with high accuracy and good computational efficiency.
Penerapan Sentence-Bert dan Cosine Similarity untuk Pencarian Semantik Dokumen Skripsi dalam Format PDF Fathuddin, Muhammad Abdul Hafizh; Mandyartha, Eka Prakarsa; Nurlaili, Afina Lina
Ranah Research : Journal of Multidisciplinary Research and Development Vol. 8 No. 1 (2025): Ranah Research : Journal Of Multidisciplinary Research and Development
Publisher : Dinasti Research

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.38035/rrj.v8i1.1865

Abstract

Pencarian dokumen skripsi pada repositori digital umumnya masih terbatas pada pencocokan kata kunci sehingga sering menghasilkan temuan yang kurang relevan. Berdasarkan permasalahan tersebut, penelitian ini bertujuan untuk membangun sistem pencarian semantik dokumen skripsi dalam format PDF dengan memanfaatkan Sentence-BERT (SBERT) dan metode Cosine Similarity yang dipadukan dengan ontologi untuk memperkaya pemahaman makna query. Sistem ini dirancang agar mampu memahami maksud pengguna secara lebih mendalam, baik ketika query diberikan dalam bentuk kata, frasa, kalimat, maupun paragraf. Tahapan penelitian meliputi ekstraksi teks dari dokumen PDF, preprocessing, tokenisasi WordPiece, serta pembentukan vektor representasi kalimat menggunakan SBERT. Skor relevansi dihitung dengan kombinasi bobot cosine similarity (0,7) dan ontologi (0,3) sehingga sistem dapat menampilkan dokumen dengan makna paling mendekati query. Hasil pengujian menunjukkan bahwa sistem mampu memberikan hasil pencarian yang relevan dengan nilai Mean Reciprocal Rank (MRR) konsisten sebesar 1.0 pada semua jenis query. Nilai Precision rata-rata mencapai 0,80 dan Recall rata-rata sebesar 0,92. Perbandingan dengan metode Keyword Matching menunjukkan bahwa metode semantik lebih unggul dengan Precision rata-rata 0,88 dan Recall 0,65 dibandingkan keyword yang hanya mencapai Precision 0,24 dan Recall 0,12. Temuan ini membuktikan bahwa sistem semantik efektif dalam menempatkan dokumen paling relevan di peringkat teratas dan lebih unggul dibandingkan pencarian berbasis kata kunci, meskipun cakupan hasil masih perlu ditingkatkan melalui pengayaan ontologi dan perluasan dataset.
Dual Mode MIMO-Beamforming Four Elements Array Antenna for Mobile Robot Communications at 5.6 GHz Muhsin, Muhsin; Saharani, Aulia; Nurlaili, Afina Lina
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol. 10 No. 2 (2024): June
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v10i2.28797

Abstract

Mobile robot communications are essential for robot teamwork. To enable communication between robots, reliable wireless communications must be deployed. Higher performance and capacity of communication are required. Multiple-input multiple-output (MIMO) and beamforming are important wireless communication technologies that use multiple antennas to improve communications performance and capacity. However, these two technologies have different requirements. MIMO requires the antenna element to be independent. While beamforming needs antennas to be coupled and fed by the same source. This paper proposes a dual-mode antenna for mobile robot communications at 5.6 GHz that supports both beamforming and MIMO. A single antenna consists of a planar dipole antenna arranged in a circular configuration. This antenna is then expanded to a four-element array antenna. Both MIMO and beamforming evaluations are performed. In MIMO mode, the BER performance is very similar to a non-correlated MIMO antenna. It is supported by the very low correlation between antennas below 0.01. Low coupling is also achieved below -16.5 dB. In beamforming mode, the proposed antenna achieves more than 8.6 dBi gain and good beam steering capability. It is supported by beam suppression with a 90° phase difference between the front and back direction. The proposed antenna performs well in both the MIMO and beamforming modes.
BLACK BOX TESTING WITH THE EQUIVALENCE PARTITIONING AND CAUSE EFFECT GRAPH METHOD IN ARCHIVE INFORMATION SYSTEM Ismiati, Suci; Aditiawan, Firza Prima; Nurlaili, Afina Lina
Jurnal Teknik Informatika (Jutif) Vol. 5 No. 4 (2024): JUTIF Volume 5, Number 4, August 2024
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2024.5.4.1944

Abstract

The Wijaya Putra University Archives Information System is a website-based information system used by Wijaya Putra University teaching staff as a digital archive storage medium. Several users mentioned that there were errors in the system, such as login problems, data access problems, and no file delivery notifications, so testing was needed to find functional errors in the system so that repairs could be made. Testing was carried out using the Black Box method with Equivalence Partitioning and Cause Effect Graph techniques. The use of Equivalence Partitioning is used to divide data input into each form, and each form input will be tested and grouped based on its function, whether it is appropriate or not appropriate. Meanwhile, the Cause Effect Graph is used to find out whether the test results obtained from the Equivalence Partitioning process are in accordance with the relationship between cause (input) and effect (output) expected in the system. Based on the research conducted, the final results show that out of a total of 58 test cases, there were 50 appropriate test cases and 8 inappropriate test cases, resulting in an effectiveness value of 87.67%. With this value, the Wijaya Putra University Archives Information System is running according to its function, but still needs to be repaired and further developed for functions that still have errors.
Co-Authors Achmad Junaidi Akbar, Fawwaz Ali Andreas Nugroho Sihananto Ani Dijah Rahajoe Arfiqi, Fawwaz Azira, Volem Alvaro Azira Basuki Rahmat Masdi Siduppa Chaurina, Agfanadita Rezkia Dhian Satria Yudha Kartika Eka Prakarsa Mandyartha Fathoni, Fuad Mahrus Fathuddin, Muhammad Abdul Hafizh Firza Prima Aditiawan Gusti Eka Yuliastuti Henni Endah Wahanani Ismiati, Suci Kurniawan, Ananda Rheza Mardhavi, Arif Maulana, Dimas Octa Maulana, Hendra Maulana, Vieri Arief Muhammad Muharrom Al Haromainy Muhsin Muhsin Nobrian, Ikhsan Nursyahada, Kesya Pratama, Novandi Kevin Putra, Chrystia Aji Putri, Della Atika Retno Mumpuni Rifan, Triyono Riski, Agung Mustika Sadewa, Rahma Danu Saharani, Aulia Siregar, Talitha Aurora Nadenggan Waskito, Muhammad Rizal Yana, Baktiar Yudha Yisti Vita Via