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All Journal Techno.Com: Jurnal Teknologi Informasi Scan : Jurnal Teknologi Informasi dan Komunikasi Proceeding of the Electrical Engineering Computer Science and Informatics Journal of Information System JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Jurnal Mantik bit-Tech JATI (Jurnal Mahasiswa Teknik Informatika) Jurnal Abdi Insani Indonesian Journal of Electrical Engineering and Computer Science International Journal of Advances in Data and Information Systems Jurnal Teknologi dan Manajemen Reputasi: Jurnal Rekayasa Perangkat Lunak International Journal Of Computer, Network Security and Information System (IJCONSIST) COMSERVA: Jurnal Penelitian dan Pengabdian Masyarakat Jurnal Informatika Dan Tekonologi Komputer (JITEK) Jurnal Informatika Teknologi dan Sains (Jinteks) ILTEK : Jurnal Teknologi Jurnal Informatika Dan Tekonologi Komputer
Nurlaili, Afina Lina
Program Studi Informatika, UPN Veteran Jawa Timur
Agriculture, Biological Sciences & Forestry Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mathematics Mechanical Engineering Social Sciences Other

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Comparison of Batch Size Values in MobileNetV2 for Stroke Classification Using CT Scan Images Ajeng Listya Devani; Anggraini Puspita Sari; Afina Lina Nurlaili; Nurul Hidajati
bit-Tech Vol. 8 No. 2 (2025): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i2.3301

Abstract

Stroke is still one of the world's leading causes of death and permanent disability, necessitating a quick and precise diagnosis in order to choose the best course of treatment.  The purpose of this study is to examine how different batch size configurations affect the MobileNetV2 architecture's ability to classify stroke types from CT-scan brain pictures. The dataset comprises three categories Normal, Ischemic, and Bleeding sourced from Kaggle and RSUD Haji, East Java Province. The strategy to transfer learning was used utilizing pretrained ImageNet weights, with the network fine-tuned for stroke classification tasks. Experimental testing was conducted using three batch size configurations: 16, 32, and 64, while maintaining consistent hyperparameters for other training components. Among the assessment measures were accuracy, macro F1-score, and AUC (macro) to measure performance comprehensively. The results revealed that a batch size of 16 achieved the highest overall performance, with an accuracy of 96.14%, a macro F1-score of 96.15%, and an AUC of 99.62%, outperforming larger batch configurations. These findings indicate that smaller batch sizes enhance model generalisation and improve gradient update dynamics, enabling the CNN to better capture subtle patterns within CT-scan images. Thus, our study finds that the best trade-off between convergence speed and batch size is 16., model generalisation, and diagnostic accuracy, demonstrating the effectiveness of the MobileNetV2 architecture for automated stroke detection based on CT-scan imaging
Application of Transfer Learning for Breast Tumor Classification Adinda Putri Budi Saraswati; Anggraini Puspita Sari; Afina Lina Nurlaili
bit-Tech Vol. 8 No. 2 (2025): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i2.3343

Abstract

Breast tumor classification from mammogram images plays an essential role in supporting clinical decision-making, particularly because manual interpretation is often challenged by variations in breast tissue density and suboptimal image quality. This study develops a three-class classification model for normal, benign and malignant categories using the ResNet50 architecture with a transfer learning strategy on the mini-MIAS dataset, which contains 322 images with an imbalanced class distribution. Three optimizers are compared, namely Adam, RMSProp and SGD. Adam represents an adaptive moment-based optimization approach. RMSProp emphasizes stable updates under fluctuating gradients. SGD with momentum serves as a conventional baseline relying on direct gradient updates. The model is trained using a 60 percent training and 40 percent validation split with class weighting and evaluated through accuracy, AUC and F1-score metrics. Experimental results show that Adam achieves the highest performance with 68.27 percent accuracy, 88.58 percent AUC and an F1-score of 0.68. RMSProp attains 58.63 percent accuracy, 76.05 percent AUC and an F1-score of 0.59. SGD yields the lowest performance with 44.18 percent accuracy, 61.33 percent AUC and an F1-score of 0.44. Confusion matrix analysis for the Adam configuration indicates reasonably consistent recognition across all classes, although misclassification remains present. The findings demonstrate that adaptive optimizers are more effective for training ResNet50 on small and imbalanced mammogram datasets. This study provides a foundation for developing more reliable computer-aided diagnostic systems for early breast cancer detection.
Optimizing Plantation Production Prediction Using Category Boosting with Random Search and Walk-Forward Validation Faishal Fernando Hutama; Eva Yulia Puspaningrum; Afina Lina Nurlaili
bit-Tech Vol. 8 No. 2 (2025): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i2.3354

Abstract

The plantation subsector is a cornerstone of the national economy, yet its productivity is increasingly volatile due to climate change. Predicting production yields remains challenging as traditional models often fail to capture complex nonlinear temporal dependencies and seasonal cycles. This study aims to improve the prediction accuracy of five major plantation commodities, namely palm oil, rubber, coffee, tea, and sugarcane, by optimizing the Category Boosting (CatBoost) algorithm. The analysis uses monthly data from 2009 to 2024, combining official production and land statistics from the Central Bureau of Statistics (BPS) with national temperature and rainfall records from the Meteorology, Climatology, and Geophysics Agency (BMKG) to ensure transparency. Unlike standard approaches that rely on default parameters and random data splitting, this research applies a rigorous optimization pipeline. Random Search is used for hyperparameter tuning, supported by lag features to capture short term dynamics and sinusoidal transformations to represent seasonal cycles. A Walk Forward Validation technique with an expanding window is employed to prevent look ahead bias and ensure realistic evaluation. The optimized model significantly outperforms the baseline. Sugarcane (R² 0.95) and Coffee (R² 0.97) show excellent accuracy, while Palm Oil improves markedly (R² 0.80) as more historical patterns are learned. Rubber and Tea remain difficult to predict, indicating insufficient explanatory features rather than model limitations. The study concludes that combining hyperparameter optimization with temporal feature engineering enables CatBoost to effectively model agricultural time series data and provides a solid foundation for strategic production planning.
ARAS Method for Ranking Vocational High School Students Achmad Andrian Maulana; Muhammad Muharrom Al Haromainy; Afina Lina Nurlaili
bit-Tech Vol. 8 No. 2 (2025): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i2.3369

Abstract

Student performance assessment is a crucial component in strengthening the quality of vocational education. At SMK Muhammadiyah 2 Jogoroto, Kab. Jombang, the evaluation process is still conducted manually and relies primarily on report card scores, leading to subjectivity, inconsistency, and a limited representation of students’ competencies. This study develops a Decision Support System (DSS) by integrating the Rank Order Centroid (ROC) weighting technique and the Additive Ratio Assessment (ARAS) method to provide a clearer and more systematic multicriteria evaluation framework. The analysis involves ten student alternatives evaluated using six criteria: average report card score, attitude, absenteeism, extracurricular activities, achievements, and industrial internship performance. ROC is applied to generate proportional criterion weights based on ranked priority, while ARAS is used to execute the core computational stages, including normalization of each criterion, application of weighted values, calculation of the optimal function score (Si), and determination of utility values (Ui) to rank student performance. The results indicate that the system yields consistent outcomes, with Nikmatuz achieving the highest utility value of 2.65224526 and identified as the top-performing student. These findings show that combining ROC and ARAS enhances assessment accuracy, reduces evaluator bias, and improves transparency in the ranking process. Beyond this case study, the proposed model demonstrates potential for broader application in vocational institutions seeking structured, data-driven mechanisms to evaluate academic and non-academic competencies more comprehensively.
Implementation of the WASPAS Method for Selecting an Optimal Project Leader Erwin Erdiyanto; M. Muharrom Al Haromainy; Afina Lina Nurlaili
bit-Tech Vol. 8 No. 2 (2025): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i2.3373

Abstract

Selecting an optimal project leader is a critical organizational process that strongly influences project performance, coordination efficiency, and overall operational outcomes. Poor selection decisions may increase delays, inefficiencies, and reduced team productivity. To address these challenges, this study applies the Weighted Aggregated Sum Product Assessment (WASPAS) method to evaluate eight project leader candidates using five leadership-related criteria: leadership ability, communication skills, professional experience, technical expertise, and problem-solving capability. All candidate scores were compiled into a decision matrix and normalized to ensure comparability across criteria. WASPAS was implemented through its dual-component structure, combining the additive Weighted Sum Model (WSM) and the multiplicative Weighted Product Model (WPM) to generate comprehensive preference values (Qi). This hybrid mechanism enables the method to capture both absolute and proportional differences in candidate competencies. The results show that WASPAS successfully ranked all candidates and identified the strongest performer, with the highest Qi value recorded at 3.00 and the lowest at 2.09, demonstrating a clear distinction in overall competency levels. The top-ranked candidate, Sintya Dwi Rachmawati, consistently scored high across all criteria, confirming the method’s capability to differentiate performance profiles effectively. These findings highlight the methodological precision of WASPAS in supporting structured leadership selection and underscore its potential to enhance fairness and analytical rigor in organizational decision-making. Overall, the study concludes that WASPAS is a reliable and practical multi-criteria decision-making technique suitable for leadership-oriented evaluations within diverse organizational contexts.
Implementation of PHP Unit-Based Test Driven Development in Pharmacy ERP System Development Silvia Dwi Cahyani; Fetty Tri Anggraeny; Afina Lina Nurlaili
Reputasi: Jurnal Rekayasa Perangkat Lunak Vol. 7 No. 1 (2026): Mei 2026
Publisher : LPPM Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/reputasi.v7i1.12583

Abstract

This research applies the Test-Driven Development (TDD) methodology using PHPUnit to develop an ERP system for Apotek Pasyha using the Laravel 12 framework. Through a case study design and Red-GreenRefactor cycles, the development of eight core modules yielded 751 test cases and 2,146 assertions. System success was measured using the Test Pass Rate, achieving an "Excellent" category. Most modules reached a perfect pass rate, while technical constraints were only found in external libraries and rendering sequences rather than business logic. The results demonstrate that TDD is effective for SME-scale ERP development by enabling early defect detection, ensuring data accuracy, and producing functionally verified code to maintain the quality and reliability of complex systems. 
PERBANDINGAN STRATEGI FINE-TUNED DAN PRE-TRAINED EFFICIENTNET-B0 UNTUK KLASIFIKASI PENYAKIT DAUN PADI Egar Firmansyah; Faisal Muttaqin; Afina Lina Nurlaili
Jurnal Informatika Teknologi dan Sains (Jinteks) Vol 8 No 2 (2026): EDISI 28
Publisher : Program Studi Informatika Universitas Teknologi Sumbawa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51401/jinteks.v8i2.7409

Abstract

Penyakit daun padi menjadi salah satu penyebab utama menurunnya produktivitas padi di Indonesia sehingga diperlukan pendekatan deteksi yang cepat dan akurat. Penelitian ini membandingkan performa dua strategi pelatihan pada arsitektur EfficientNet-B0, yaitu pre-trained yang membekukan seluruh lapisan base model dan fine-tuned yang melatih ulang keseluruhan parameter, untuk mengklasifikasikan enam kelas citra daun padi meliputi Bacterial Leaf Blight, Brown Spot, Healthy, Leaf Blast, Rice Hispa, dan Sheath Blight. Sebanyak 4.770 citra digunakan dengan pembagian 3.333 data training, 958 data validasi, dan 479 data testing. Konfigurasi hyperparameter terbaik diperoleh melalui empat tahap pengujian bertahap yang mencakup variasi batch size, epoch, learning rate, dan optimizer. Hasil evaluasi menunjukkan bahwa model pre-trained gagal total akibat fenomena class collapse dengan akurasi hanya 19,62% karena seluruh citra diprediksi pada satu kelas yang sama. Di sisi lain, model fine-tuned berhasil meraih akurasi 99,79% dengan f1-score sebesar 0,9981 pada data testing, di mana 478 dari 479 citra berhasil diklasifikasikan secara tepat. Hasil ini menegaskan bahwa proses fine-tuning memegang peranan kritis dalam mengadaptasi fitur EfficientNet-B0 terhadap domain penyakit daun padi.
Traffic Sign Detection Using Region And Corner Feature Extraction Method Hendra Maulana; Dhian Satria Yudha Kartika; Agung Mustika Riski; Afina Lina Nurlaili
IJCONSIST JOURNALS Vol 3 No 1 (2021): September
Publisher : International Journal of Computer, Network Security and Information System

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (3139.835 KB) | DOI: 10.33005/ijconsist.v3i1.54

Abstract

Traffic signs are an important feature in providing safety information for drivers about road conditions. Recognition of traffic signs can reduce the burden on drivers remembering signs and improve safety. One solution that can reduce these violations is by building a system that can recognize traffic signs as reminders to motorists. The process applied to traffic sign detection is image processing. Image processing is an image processing and analysis process that involves a lot of visual perception. Traffic signs can be detected and recognized visually by using a camera as a medium for retrieving information from a traffic sign. The layout of different traffic signs can affect the identification process. Several studies related to the detection and recognition of traffic signs have been carried out before, one of the problems that arises is the difficulty in knowing the kinds of traffic signs. This study proposes a combination of region and corner point feature extraction methods. Based on the test results obtained an accuracy value of 76.2%, a precision of 67.3 and a recall value of 78.6.
Pengembangan Sistem Manajemen Inventaris dengan Integrasi OCR dan QR Code untuk Validasi Identitas Mahasiswa Muhammad Faizul Ulum; Retno Mumpuni; Afina Lina Nurlaili
JOINS (Journal of Information System) Vol 11 No 1 (2026): Edisi (Desember 2025 - Mei 2026)
Publisher : Fakultas Ilmu Komputer, Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33633/joins.v11i1.15972

Abstract

Inventory management at the Islamic Spiritual Activity Unit (UKKI) of UPN "Veteran" Jawa Timur still relies on manual methods, which are prone to human error and borrower identity manipulation. This study aims to develop a web-based inventory management information system integrating Optical Character Recognition (OCR) technology for identity validation and Quick Response (QR) Code for transaction security. Software development utilized the Rapid Application Development (RAD) method through an iterative approach with end-users. The results demonstrated that the OCR module, supported by pre-processing algorithms and Levenshtein Distance, successfully extracted and validated Student Identity Card (KTM) data automatically, achieving an acceptable similarity score (R) of over 0.87. Furthermore, implementing QR Codes as digital tokens proved effective in minimizing recording errors and ensuring officer accountability during handovers. Black-Box testing confirmed that 100% of the features, including dynamic stock management, operate precisely. In conclusion, this system successfully replaces conventional methods while enhancing time efficiency, data transparency, and organizational asset security.
VERIFIKASI WAJAH HIBRID DAN EVALUASI JAWABAN BERBASIS TOPIK PADA APLIKASI TA’ARUF ONLINE SEKUFU Muhamad Fihris Aldama; Muhammad Muharrom Al Haromainy; Afina Lina Nurlaili
ILTEK : Jurnal Teknologi Vol. 21 No. 01 (2026): ILTEK : Jurnal Teknologi
Publisher : Fakultas Teknik Universitas Islam Makassar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47398/iltek.v21i01.270

Abstract

Digitalisasi proses perkenalan secara Islami (ta’aruf) saat ini masih menghadapi tantangan utama berupa ketidakpastian identitas (gharar) dan kurangnya parameter kecocokan yang objektif. Penelitian ini bertujuan membangun aplikasi web ta’aruf bernama "Sekufu" yang mengintegrasikan fitur keamanan biometrik dan evaluasi terstruktur guna meningkatkan keamanan serta kualitas interaksi. Penelitian dilaksanakan di lingkungan UPN "Veteran" Jawa Timur menggunakan metode Rapid Application Development (RAD) dengan sampel pengujian usability sebanyak 30 responden, serta melibatkan pengembangan sistem berarsitektur client-server yang mengolah variabel citra wajah dan skor evaluasi jawaban. Hasil penelitian menunjukkan bahwa fitur verifikasi wajah hibrid (Haar Cascade & FaceNet) mencapai akurasi 95% dengan threshold 0.3, Unit Testing mencapai tingkat keberhasilan lebih dari 80%, dan pengujian SUS memperoleh skor rata-rata 93.06 yang masuk kategori Excellent. Kesimpulannya, integrasi teknologi verifikasi wajah dan evaluasi terstruktur terbukti efektif dalam menciptakan ekosistem ta’aruf digital yang aman dari akun palsu dan terukur secara objektif.
Co-Authors Achmad Andrian Maulana Adinda Putri Budi Saraswati Agung Mustika Riski Ajeng Listya Devani Alya Izzah Zalfa Rihadah Ramadhani Nirwana Putri Ananda Rheza Kurniawan Anggraini Puspita Sari Anggraini Puspita Sari Ani Dijah Rahajoe Anya Ningrum Nur'afifah Aulia Saharani Basuki Rahmat Budi Mukhamad Mulyo Dhian Satria Yudha Kartika Egar Firmansyah Enryco Hidayat Erlin Widyastuti Erwin Erdiyanto Eva Yulia Puspaningrum Faisal Muttaqin Faishal Fernando Hutama Fetty Tri Anggraeny Fidela Carissa Aramintha Firlie Aurellia Az-zahra Firyal Wishal Nabili Firza Prima Aditiawan Firza Prima Aditiawan Henni Endah Wahanani I Dewa Gde Satria Pramana Erlangga Ikhsan Nobrian Indah Rahmawti Utami Isfa Fadil Muhammad Kartika Sari Kesya Nursyahada Kevin Iansyah Luthfiyana Mahrurin Abadi M. Muharrom Al Haromainy M. Ryan Nurdiansyah N.A Made Hanindia Prami Swari Maulana, Hendra Moh. Mario Subagio Mohammad Habim Hazidan Rifqi Mohammad Idhom Mohammad Idhom Muhamad Fihris Aldama Muhammad Albert Nur Agathon Muhammad Ariq Hawari Adiputra Muhammad Faizul Ulum Muhammad Muharrom Al Haromainy Muhammad Muharrom Al Haromainy Muhammad Muharrom Al Haromainy Muhammad Rohman Irsyadi Muhsin Mulyani Satya Bhakti Nadia Dita Salsabila Nanda Syarla Hariyanti Nurul Hidajati Raissa Atha Febrianti Retno Mumpuni Retno Mumpuni Riyanarto Sarno Rizki, Agung Mustika Rizky Amelia Ryan Eka Wiratna Ryan Purnomo Safitri, Eristya Maya Silvia Dwi Cahyani Sugiarto, Sugiarto Theressa Marry Christianty Volem Alvaro Azira Azira yisti vita via Yisti Vita Via