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All Journal JUITA : Jurnal Informatika Jurnal Informatika dan Teknik Elektro Terapan Sinkron : Jurnal dan Penelitian Teknik Informatika Bina Insani ICT Journal JURNAL MEDIA INFORMATIKA BUDIDARMA Jurnal Pilar Nusa Mandiri Information System for Educators and Professionals : Journal of Information System JITK (Jurnal Ilmu Pengetahuan dan Komputer) Jurnal Teknik Informatika UNIKA Santo Thomas Jurnal Riset Informatika Journal of Information System, Applied, Management, Accounting and Research JASIEK (Jurnal Aplikasi Sains, Informasi, Elektronika dan Komputer) JURSIMA (Jurnal Sistem Informasi dan Manajemen) Jurnal Sistem Informasi Jurnal AbdiMas Nusa Mandiri Jurnal ABDIMAS (Pengabdian kepada Masyarakat) UBJ HOAQ (High Education of Organization Archive Quality) : Jurnal Teknologi Informasi Computer Science (CO-SCIENCE) Indonesian Journal of Networking and Security - IJNS Jurnal Teknik Informatika Unika Santo Thomas (JTIUST) Paradigma Jurnal Insan INFORMATION SYSTEM FOR EDUCATORS AND PROFESSIONALS : Journal of Information System JURSIMA Jurnal Sistem Informasi dan Manajemen
Herlambang Brawijaya
Universitas Bina Sarana Informatika
Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Law, Crime, Criminology & Criminal Justice Library & Information Science Mathematics Physics Social Sciences Other

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Rancang Bangun Sistem Pendaftaran Webinar dan E-Certificate Otomatis Rahmawati, Eva; Brawijaya, Herlambang; Hertyana, Hylenarti
Jurnal Teknik Informatika UNIKA Santo Thomas Vol 9 No. 1 : Tahun 2024
Publisher : LPPM UNIKA Santo Thomas

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

In a world increasingly connected digitally, webinars have become a crucial tool for education, training, and information exchange. Along with the rapid growth in the use of webinars, there are often shortcomings in the registration process and distribution of e-certificates, which are inefficient and unsatisfactory. The main issue is the time and human resources required in this process, increasing the risk of errors. Thus, there arises a need for an effective system to manage registration and e-certificate distribution. The automated webinar registration and e-certificate system offer an efficient solution to meet these needs, with the primary goal of maximizing ease of use and operational effectiveness. This system is designed for the automation of the registration process, participant validation, and the creation and distribution of e-certificates, all aimed at enhancing user experience and reducing administrative workload. The development of this system is carried out using the Extreme Programming (XP) method. XP was chosen for its ability to handle changes in needs quickly and efficiently, and its focus on technical quality and customer satisfaction. With a focus on user needs and rapid adaptation to feedback, this system is expected to improve operational efficiency, reduce manual errors, and enhance user satisfaction.
Optimalisasi Presensi Sekolah Berbasis QR Code dengan Metode Rapid Application Development Rahmawati, Eva; Brawijaya, Herlambang; Andriansyah, Doni; Mufida, Elly
Computer Science (CO-SCIENCE) Vol. 5 No. 2 (2025): Juli 2025
Publisher : LPPM Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/coscience.v5i2.8505

Abstract

High School attendance systems play an important role in monitoring student attendance and enforcing discipline in the academic environment. However, many schools still use manual methods such as written attendance lists or teacher name calling, which are inefficient, time-consuming, and prone to manipulation and fraud. These methods present challenges for teachers and administrative staff, leading to inaccurate recording, data loss, and falsification of attendance. To address these issues, this study proposes the development of a QR Code-based school attendance system using the Rapid Application Development (RAD) methodology. RAD was chosen because of its ability to produce prototypes quickly and allow for iterative system improvements according to user needs. The proposed system allows students to scan a unique QR Code to automatically record their attendance, thereby reducing human intervention and minimizing errors. The expected outcomes of this study include increased accuracy, efficiency, and security in recording student attendance. The RAD approach is predicted to accelerate the development process without sacrificing ease of use and system reliability. In addition, this system is expected to be able to prevent fraud in attendance, because QR Code-based authentication provides a more secure validation mechanism. Through a series of trials and evaluations, this study aims to prove that the integration of RAD with QR Code technology can improve the effectiveness of attendance recording compared to conventional methods. Based on the results of the trials and evaluations, it can be concluded that the QR Code-based attendance system with the RAD approach has been proven to improve the efficiency, accuracy, and security of the attendance system in schools.
Building a Predictive Model for Chronic Kidney Disease: Integrating KNN and PSO Widodo, Slamet; Brawijaya, Herlambang; Samudi, Samudi
Paradigma - Jurnal Komputer dan Informatika Vol. 26 No. 1 (2024): March 2024 Period
Publisher : LPPM Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/p.v26i1.3282

Abstract

This study examines the improvement of prediction accuracy for Chronic Kidney Disease (CKD) through the integration of the K-Nearest Neighbors (KNN) method with Particle Swarm Optimization (PSO). Amidst the rising prevalence of CKD, closely related to diabetes and hypertension, early detection of CKD becomes a significant challenge, especially in Indonesia where access to healthcare facilities and public awareness remain limited. This study utilizes the Chronic Kidney Disease dataset from the UCI Machine Learning repository, encompassing 400 patient records with 24 clinical, laboratory, and demographic variables. With the KNN method, this approach classifies data based on feature proximity, while PSO is used for feature selection and parameter optimization, enhancing the model's accuracy and efficiency in identifying CKD at early stages. The findings indicate a significant improvement in prediction accuracy, from 80.00% using KNN to 97.75% after integration with PSO. These results affirm that the combined approach of KNN and PSO holds great potential in improving early detection and management of CKD, paving the way for further research into practical applications in the healthcare field.
Pengembangan Sistem Informasi Keuangan Sekolah Berbasis Web dengan Model Rapid Application Development Brawijaya, Herlambang; Rahmawati, Eva
Jurnal INSAN Journal of Information System Management Innovation Vol. 5 No. 2 (2025): Desember 2025
Publisher : LPPM Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/j-insan.v5i2.9731

Abstract

Pengelolaan keuangan sekolah yang efektif penting untuk menjaga transparansi dan akuntabilitas penggunaan dana. Banyak sekolah, khususnya tingkat SMP, masih menggunakan pencatatan manual sehingga rentan terhadap kesalahan, keterlambatan laporan, dan sulit memantau arus kas secara real-time. Penelitian ini bertujuan merancang Sistem Informasi Pengelolaan Keuangan Berbasis Web guna membantu pencatatan pemasukan, pengeluaran, dan pembuatan laporan secara cepat dan akurat. Metode pengembangan yang digunakan adalah Rapid Application Development (RAD) melalui tahapan requirements planning, user design, constructuon, dan cutover. Hasil pengujian menunjukkan sistem mampu mengelola data keuangan dengan baik dan menghasilkan laporan otomatis, serta memudahkan akses informasi bagi bendahara dan kepala sekolah. Kesimpulannya, metode RAD dapat mempercepat pengembangan dan menghasilkan aplikasi yang sesuai kebutuhan pengguna.
PENGEMBANGAN APLIKASI ESTIMASI KALORI MAKANAN BERBASIS CITRA DENGAN PENDEKATAN DETEKSI OBJEK MENGGUNAKAN YOLO Supriyadi, Rizqy; Irfan, Muhamad; Hapijar, Rizki Dwi; Abubakar, Fadil; Saputra, Rendy; Supriyanto, Kus; Dwiantara, Raihan Putra; Nainggolan, Esron Rikardo; Brawijaya, Herlambang
Jurnal Informatika dan Teknik Elektro Terapan Vol. 14 No. 1 (2026)
Publisher : Universitas Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23960/jitet.v14i1.8545

Abstract

Penelitian ini mengembangkan aplikasi estimasi kalori makanan berbasis citra untuk membantu pengguna memantau asupan energi secara praktis melalui foto ponsel. Sistem menggunakan deteksi objek YOLOv8n untuk mengenali makanan Indonesia dan memetakan tiap deteksi ke parameter nutrisi guna menghitung massa dan kalori. Dataset pelatihan berisi 3.772 citra pada 9 kelas makanan (dibagi 80% latih, 10% validasi, 10% uji). Model dilatih selama 100 epoch pada resolusi 640 piksel menggunakan optimizer AdamW dan early stopping. Backend FastAPI dalam lingkungan Docker menjalankan inferensi dan perhitungan kalori berdasarkan data nutrisi tiap kelas. Aplikasi mobile Flutter mengirim citra ke endpoint /predict dan menampilkan makanan terdeteksi beserta confidence, estimasi massa, dan total kalori. Hasil uji menunjukkan performa deteksi tinggi dengan mAP@0.5 0,975, sementara kesalahan terbesar terjadi pada kelas yang mirip secara visual atau minim data. Temuan ini menegaskan bahwa sistem end-to-end mampu mengestimasi kalori otomatis dari satu foto dan layak dikembangkan lebih lanjut dengan menambah kelas dan menyeimbangkan dataset.
Hybrid Sampling untuk Meningkatkan Akurasi Deteksi Kanker Serviks pada Data Tidak Seimbang: Kajian Komparatif Widodo, Slamet; Samudi; Brawijaya, Herlambang
JASIEK (Jurnal Aplikasi Sains, Informasi, Elektronika dan Komputer) Vol. 7 No. 2 (2025): Desember 2025
Publisher : Universitas Merdeka Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26905/jasiek.v7i2.16134

Abstract

Cervical Cancer has a high mortality rate among women, driving the adoption of early detection systems based on machine learning. However, their implementation is hindered by class imbalance issues, as seen in the UCI Cervical Cancer Behavior Risk Dataset, where positive cases constitute only 5.8–7.3% of the data. This study proposes an evaluation of resampling techniques—including SMOTE, ADASYN, Random Undersampling, and Borderline-SMOTE—combined with classification algorithms such as RF, XGBoost, LR, GNB, and k-NN. Using Stratified K-Fold Cross Validation to preserve the original class distribution in each fold and ensuring resampling is applied only to the training data in each iteration, the results demonstrate that Borderline-SMOTE significantly improved model performance. Specifically, the Random Forest model achieved a Recall of 0.87 and an AUC-ROC of 0.94. These findings are expected to provide a foundation for future research focused on optimizing adaptive sampling methods
PENERAPAN KECERDASAN BUATAN DALAM SISTEM PENGENALAN GERAK TANGAN UNTUK MENDUKUNG KOMUNIKASI PADA PENYANDANG TUNAWICARA: APPLICATION OF ARTIFICIAL INTELLIGENCE IN A HAND GESTURE RECOGNITION SYSTEM TO SUPPORT COMMUNICATION FOR PEOPLE WITH SPEECH IMPAIRMENTS Ardiansyah, Rija; Zamroni, Rio; Azhar, Muhammad; Mahesa, Kevin Indra; Reza, Syaiful Fan; Arkananta, Yudhistira; Nainggolan, Esron Richardo; Brawijaya, Herlambang
HOAQ (High Education of Organization Archive Quality) : Jurnal Teknologi Informasi Vol. 17 No. 1 (2026): Jurnal HOAQ - Teknologi Informasi
Publisher : STIKOM Uyelindo Kupang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52972/hoaq.vol17no1.p53-61

Abstract

Perkembangan teknologi kecerdasan buatan (AI) telah mendorong peningkatan signifikan dalam interaksi antara manusia dan mesin, khususnya dalam menyediakan akses komunikasi yang lebih baik bagi individu dengan gangguan bicara. Penelitian ini mengusulkan sebuah sistem penerjemah Bahasa Isyarat Indonesia (BISINDO) berbasis computer vision yang mengintegrasikan deteksi landmark tangan secara real-time menggunakan MediaPipe dengan proses klasifikasi gestur berbasis Convolutional Neural Network (CNN). Sistem ini dirancang untuk mengenali pola gerakan tangan secara dinamis dan mengubahnya menjadi teks atau suara sintetis melalui modul Google Text-to-Speech (gTTS). Pendekatan yang digunakan menggabungkan analisis spasial dan temporal untuk menghasilkan interpretasi gestur yang akurat dan responsif terhadap konteks. Penelitian ini juga mengidentifikasi tantangan implementasi terkait kemampuan generalisasi model terhadap perbedaan pengguna, kondisi pencahayaan, dan lingkungan, serta menawarkan solusi melalui teknik augmentasi data dan optimalisasi arsitektur jaringan saraf. Dengan desain yang fleksibel dan adaptif, sistem ini memiliki potensi besar untuk menjadi dasar pengembangan teknologi bantuan komunikasi inklusif berbasis AI di Indonesia serta mendorong kolaborasi antara bidang visi komputer, bahasa isyarat, dan teknologi Internet of Things (IoT).   Advances in artificial intelligence (AI) technology have driven significant improvements in human–machine interaction, particularly in enhancing communication accessibility for individuals with speech impairments. This study proposes an Indonesian Sign Language (BISINDO) interpreter system based on computer vision, integrating real-time hand landmark detection using the MediaPipe framework with gesture classification powered by a Convolutional Neural Network (CNN). The system is designed to dynamically recognize hand gesture patterns and convert them into text or synthetic speech through the Google Text-to-Speech (gTTS) module. This approach combines spatial and temporal analysis to produce accurate and contextually responsive gesture interpretations. The study also identifies implementation challenges related to the model’s generalizability across different users, lighting conditions, and environments, while offering solutions through data augmentation techniques and neural network architecture optimization. With its flexible and adaptive design, the proposed system has strong potential to serve as a foundation for the development of AI-based inclusive communication technologies in Indonesia and to foster collaboration across the fields of computer vision, sign language, and Internet of Things (IoT) applications.
Co-Authors - UBSI, Slamet Abubakar, Fadil Ardiansyah, Rija Arkananta, Yudhistira Doni Andriansyah Dwiantara, Raihan Putra Elly Mufida, Elly Endang Retnoningsih Eni Pudjiarti Eva Rahmawati Hapijar, Rizki Dwi Hikmah, Noer Hylenarti Hertyana Indah Ariyati Irfan, Muhamad Lala Nilawati Mahesa, Kevin Indra Muhammad Azhar Nainggolan, Esron Richardo Nainggolan, Esron Rikardo Noer Hikmah Rendy Saputra Reza, Syaiful Fan Rudianto, Biktra samudi Samudi Siti Faizah Slamet Widodo Slamet Widodo Slamet Widodo Slamet Widodo Slamet Widodo Supriyadi, Rizqy Supriyanto, Kus Toto Haryanto Verry Riyanto Zamroni, Rio