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All Journal PIKSEL : Penelitian Ilmu Komputer Sistem Embedded and Logic JURNAL PENGABDIAN KEPADA MASYARAKAT Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Bina Insani ICT Journal Information System for Educators and Professionals : Journal of Information System Jurnal Mahasiswa Bina Insani SMARTICS Journal Jurnal Pengabdian Pada Masyarakat Jurnal SOLMA Antivirus : Jurnal Ilmiah Teknik Informatika Jurnal ICT : Information Communication & Technology JATI (Jurnal Mahasiswa Teknik Informatika) Tematik : Jurnal Teknologi Informasi Komunikasi Infotech: Journal of Technology Information Jurnal ABDIMAS (Pengabdian kepada Masyarakat) UBJ Media Jurnal Informatika Jurnal Buana Pengabdian J-ABDIPAMAS (Jurnal Pengabdian Kepada Masyarakat) CHAIN: Journal of Computer Technology, Computer Engineering and Informatics
Rully Pramudita
Universitas Bina Insani
Religion Agriculture, Biological Sciences & Forestry Arts Humanities Automotive Engineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Medicine & Pharmacology Public Health Social Sciences Other

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IMPLEMENTATION OF A VEHICLE MONITORING SYSTEM AT PT JAYA BUANA PERKASA Nawawi, Agus; Pramudita, Rully
Antivirus : Jurnal Ilmiah Teknik Informatika Vol 19 No 2 (2025): November 2025
Publisher : Universitas Islam Balitar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35457/p4504q72

Abstract

Operational vehicle security is a very important aspect in supporting the smooth running of company activities, especially for PT Jaya Buana Perkasa which is engaged in information technology services. This study aims to design and implement an Internet of Things (IoT)-based vehicle security system using Arduino Nano devices, SIM800L modules, and GPS Neo-6M. The system is designed to receive commands via SMS messages to remotely control the vehicle engine and provide location information directly. The system development uses a prototype method, while the testing process is carried out through a black box approach and user evaluation. The test results show that the system is able to respond to commands well, regulate relays for engine control, and send location information with adequate accuracy. This system provides a real solution in improving vehicle security and the efficiency of operational fleet management
Sistem Deteksi Penyakit Gigi Berbasis Deep Learning Menggunakan YOLOv8 dan ResNet-18 Bagas Aditya; Rully Pramudita
CHAIN: Journal of Computer Technology, Computer Engineering, and Informatics Vol. 4 No. 3 (2026): Volume 4 Number 3 July 2026
Publisher : PT. Tech Cart Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58602/chain.v4i3.280

Abstract

Penyakit gigi dan mulut menjadi salah satu masalah kesehatan yang paling umum di Indonesia, dengan prevalensi mencapai 57,6% populasi menurut Survei Kesehatan Indonesia. Proses diagnosis konvensional masih sangat bergantung pada pemeriksaan visual manual oleh dokter gigi, yang memiliki keterbatasan dari segi waktu, subjektivitas, dan aksesibilitas. Penelitian ini mengembangkan sistem deteksi penyakit gigi berbasis deep learning yang mengintegrasikan arsitektur YOLOv8 untuk object detection dan ResNet-18 untuk image classification dalam sebuah pipeline ensemble. Sistem dirancang untuk mendeteksi enam jenis kelainan gigi: karies, karang gigi, radang gusi, hipodontia, sariawan, dan diskolorasi gigi dari foto kamera ponsel. Dataset yang digunakan berjumlah 11.957 citra yang dibagi menjadi 70% data latih, 15% validasi, dan 15% pengujian. Teknik weighted sampling diimplementasikan untuk menangani ketimpangan kelas dengan rasio 7,96x. Pelatihan ResNet-18 menggunakan optimizer Adam (learning rate 0,001) dengan fungsi kerugian CrossEntropyLoss berbobot kelas dinamis. Hasil evaluasi menunjukkan YOLOv8 mencapai mAP@50 sebesar 88,17%, sementara ResNet-18 memperoleh akurasi klasifikasi 92,25% dengan F1-Score 92,37%. Validasi statistik 5-Fold Cross Validation mengonfirmasi stabilitas ResNet-18 (Standar Deviasi = ±0,45%) dan YOLOv8 (Standar Deviasi = ±1,95%). Sistem ini diimplementasikan dalam aplikasi web menggunakan FastAPI dan Next.js pada GPU NVIDIA T4, dengan latensi end-to-end 2-4 detik, serta dilengkapi modul Grad-CAM untuk interpretabilitas prediksi.
Sistem Monitoring dan Kontrol Larutan Nutrisi Hidroponik NFT Berbasis IoT Menggunakan EMA dengan Analisis Interferensi Sensor Rafif Zetta Rajendra Pragiwoko; Rully Pramudita
CHAIN: Journal of Computer Technology, Computer Engineering, and Informatics Vol. 4 No. 3 (2026): Volume 4 Number 3 July 2026
Publisher : PT. Tech Cart Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58602/chain.v4i3.291

Abstract

The Nutrient Film Technique (NFT) hydroponic system requires stable nutrient solution management, particularly pH and nutrient concentration parameters that affect plant nutrient absorption. Based on observations conducted at the Mega Regency greenhouse, pH and nutrient monitoring are still performed manually, causing changes in nutrient conditions to not be monitored continuously in real-time. This study aims to design and implement an Internet of Things (IoT)-based monitoring and control system for nutrient solutions in NFT hydroponics. The development method used is Rapid Application Development (RAD). The system was developed using an ESP32 microcontroller integrated with pH, TDS, DS18B20 temperature, and ultrasonic sensors, as well as actuators in the form of pH up, pH down, and AB Mix nutrient pumps. Sensor data were processed using the Exponential Moving Average (EMA) method to reduce reading noise, while the control process applied the hysteresis method. Monitoring data were sent to the ThingsBoard platform and displayed through a Cloud-based dashboard. The results showed that the system was capable of continuously monitoring pH, nutrient concentration, temperature, and water level, with sensor accuracy reaching 95.84% for pH, 98.66% for TDS, and 97.14% for ultrasonic sensors. The EMA method effectively reduced sensor reading fluctuations by up to 0.74 pH units, while the hysteresis method maintained parameters within the specified range through automatic pump activation. During integration, electrochemical interference was found between pH and TDS sensor probes in the same reservoir. Various solutions were attempted progressively, from voltage stabilization using capacitors, switching power mechanism via transistors, to probe relocation to different pipe flow points as the more effective final solution. The developed system supports hydroponic management more effectively and automatically
Timbangan Digital Berbasis AIOT Dengan Deteksi Otomatis Jenis Buah Menggunakan YOLOv8 Dan Infrastruktur VPS Tiana Ramdani; Rully Pramudita
CHAIN: Journal of Computer Technology, Computer Engineering, and Informatics Vol. 4 No. 3 (2026): Volume 4 Number 3 July 2026
Publisher : PT. Tech Cart Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58602/chain.v4i3.310

Abstract

PT Interskala Mandiri Indonesia relies on manual input of Price Look Up (PLU) codes on the keypad for digital weighing, which results in human errors and lower operational efficiency. This study presents the development of an AIoT-based digital scale that integrates YOLOv8 for automatic fruit classification and leverages a Virtual Private Server (VPS) as a centralized data management infrastructure. The ADDIE model is used as the research and development framework. The hardware is built using an ESP32 NodeMCU-32S microcontroller, an ESP32-S3 CAM for image capture, and a load cell with an HX711 module for precise weight measurement. The YOLOv8n model was trained on five fruit classes (fuji apple, orange, lemon, century pear, and dragon fruit) and deployed on a VPS backend via Flask API. Receipt printing is performed through a Bluetooth T3 thermal printer using RawBT software, while monitoring is conducted through a React.js dashboard. Test results show that YOLOv8n achieved mAP@50 of 99.5%, precision of 99.97%, recall of 100%, and F1-score of 100%. The load cell provided 99.74% accuracy with a 0.26% error tolerance. All 25 Black Box Testing scenarios returned a Successful status. Average end-to-end latency was 7.55 seconds. The system proved capable of eliminating manual PLU input, centralizing transaction management, and providing a digital scale modernization solution for the retail industry.
Penerapan Algoritma Machine Learning untuk Klasifikasi Pesan Media Sosial pada Perguruan Tinggi Dewi Ariani; Rully Pramudita
INFORMATION SYSTEM FOR EDUCATORS AND PROFESSIONALS : Journal of Information System Vol 11 No 1 (2026): INFORMATION SYSTEM FOR EDUCATORS AND PROFESSIONALS (Juni 2026)
Publisher : Lembaga Penelitian dan Pengabdian kepada Masyarakat Universitas Bina Insani

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51211/isbi.v11i1.3835

Abstract

Abstrak: Di era digital, media sosial seperti Direct Message (DM) Instagram dan WhatsApp seringkali menjadi sarana utama komunikasi antara perguruan tinggi dan calon mahasiswa. Universitas Bina Insani menerima banyak pesan setiap harinya yang berisi berbagai pertanyaan terkait pendaftaran, informasi_kampus, biaya, kelas_karyawan, jurusan, beasiswa dan lainnya. Namun, pesan-pesan tersebut masih dikelola secara manual tanpa pengelompokan topik yang terstruktur, sehingga menyulitkan admin dalam mengidentifikasi kebutuhan informasi yang paling sering diajukan. Penelitian ini bertujuan untuk mengembangkan sistem klasifikasi pesan media sosial menggunakan algoritma machine learning guna mengelompokkan pesan berdasarkan topik secara otomatis. Dataset yang digunakan terdiri dari 350 pesan yang dikumpulkan dari Instagram dan WhatsApp, yang telah diberi label ke dalam beberapa kategori. Tahapan pengolahan data meliputi pra-pemrosesan teks, ekstraksi fitur menggunakan Term Frequency–Inverse Document Frequency (TF-IDF), serta proses klasifikasi menggunakan algoritma Support Vector Machine (SVM). Hasil pengujian menunjukkan bahwa model SVM mampu melakukan klasifikasi pesan dengan tingkat akurasi sebesar 61%. Untuk meningkatkan performa pada kelas dengan jumlah data yang tidak seimbang, diterapkan pendekatan gabungan antara model machine learning dan rule-based berbasis kata kunci, yang menghasilkan peningkatan akurasi hingga 70%. Sistem yang dikembangkan kemudian diimplementasikan dalam bentuk dashboard interaktif berbasis Streamlit untuk memudahkan pengguna dalam mengunggah data, melihat hasil klasifikasi, dan memantau distribusi topik pesan. Abstract: In the digital era, social media platforms such as Instagram Direct Messages (DM) and WhatsApp have become the primary communication channels between higher education institutions and prospective students. Universitas Bina Insani receives a large number of messages daily containing various inquiries related to admission information, tuition fees, study programs, scholarships, and campus services. However, these messages are still managed manually without structured topic classification, making it difficult for administrators to identify the most frequently requested information. This study aims to develop a social media message classification system using machine learning algorithms to automatically group messages based on their topics. The dataset used in this study consists of 350 messages collected from Instagram and WhatsApp, which were manually labeled into several categories. The data processing stages include text preprocessing, feature extraction using Term Frequency–Inverse Document Frequency (TF-IDF), and message classification using the Support Vector Machine (SVM) algorithm. The experimental results show that the SVM model achieved a classification accuracy of 61%. To improve performance on imbalanced classes, a hybrid approach combining machine learning and keyword-based rule-based methods was applied, resulting in an accuracy improvement of up to 70%. The developed system was then implemented in the form of an interactive dashboard based on Streamlit, enabling users to upload data, view classification results, and monitor the distribution of message topics more effectively.
Rancang Bangun Sistem Keamanan dan Pemantauan Kapasitas Kotak Amal Masjid Berbasis IoT Samiyah Iftinan, Salsabila; Alfian, Ari Nurul; Shadiq, Jafar; Pramudita, Rully
Bahasa Indonesia Vol 12 No 2 (2025): Bina Insani ICT Journal (Desember) 2025
Publisher : Lembaga Penelitian dan Pengabdian Masyarakat Universitas Bina Insani

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51211/biict.v12i2.3750

Abstract

Pencurian kotak amal di masjid menjadi salah satu masalah yang sering terjadi, terutama ketika lingkungan sekitar dalam keadaan sepi. Masjid Ghoiru Jami Mambaul Istiqomah, keamanan kotak amal hanya mengandalkan kunci gembok dan CCTV, yang terbukti tidak efektif mencegah pencurian. Tujuan dari penelitian ini adalah merancang sistem keamanan kotak amal berbasis Internet of Things (IoT) untuk meningkatkan keamanan dan kenyamanan dalam pengelolaan dana amal. Metode yang digunakan adalah Research and Development (R&D) dengan model pengembangan ADDIE yang mencakup tahapan analisis, desain, pengembangan, implementasi, dan evaluasi. Penelitian ini menggabungkan berbagai komponen seperti sensor getar SW-420, RFID RC522, solenoid door lock, buzzer, dan sensor ultrasonik HC-SR04 yang diintegrasikan menggunakan mikrokontroler ESP32 serta dikendalikan melalui aplikasi Blynk. Hasil penelitian menunjukkan bahwa sistem ini mampu mendeteksi getaran mencurigakan dan memberikan peringatan melalui buzzer dan notifikasi Blynk. Sistem dapat memantau kapasitas kotak amal dan mengontrol akses buka/tutup menggunakan kartu RFID. Kesimpulan peneliitian ini menghasilkan sistem yang dapat meningkatkan keamanan kotak amal dan berpotensi mencegah serta mengurangi terjadinya pencurian kotak amal.
Comparative Study of Machine Learning Algorithms Using Bagging and XGBoost Techniques for Breast Cancer Classification Rully Pramudita; Dwi Ismiyana Putri; Bambang Kriswantara; Vina Zahrotun Nazah; Rahmat Budiarto
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 10 No 4 (2026): August 2026
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v10i4.7216

Abstract

Machine learning (ML) has become an important data-driven approach for classification and prediction, including applications in medical diagnosis. Ensemble methods can improve classifier performance by combining complementary learning mechanisms. However, systematic evidence on the sequential use of Bagging and XGBoost across different classifier architectures remains limited. This study develops a staged ensemble framework in which five classifiers—Support Vector Machine (SVM), Neural Network (NN), Logistic Regression (LR), Decision Tree (DT), and K-Nearest Neighbours (KNN)—are first optimized through Bagging and subsequently enhanced using XGBoost. The experiments were conducted on the Breast Cancer Wisconsin (Diagnostic) dataset under a consistent 70:30 train–test protocol. Performance was assessed using accuracy, confusion matrices, ROC curves, and Area Under the Curve (AUC), while repeated experiments were used to examine statistical significance. The results show that the staged Bagging–XGBoost approach improves both predictive accuracy and class discrimination across the evaluated classifier types. Neural Network achieved the largest improvement, with mean accuracy increasing from 93.1% to 97.0% across repeated experiments. The findings demonstrate that the sequential framework can improve non-tree-based as well as tree-based classifiers, providing empirical evidence for broader use of staged ensemble integration in breast cancer classification.
Co-Authors Alfian, Ari Nurul Anggoro, Muhammad Seno Ari Nurul Alfian Ayu Medina Prameswari Bagas Aditya Bambang Kriswantara Chusyairi, Ahmad Dewi Ariani Didik Setiyadi Dwi Ismiyana Putri Endang Retnoningsih Febriyanti Valentina Herwin Simanjuntak Ismail Marzuki Ismail Marzuki Kevin Setyawan Kristiana Widiawati Muhammad Dhedy Dwi Putra Muhammad Rizal Muis, Saludin Nada Aura Wansa Nadya Safitri Nadya Safitri Nadya Safitri Nadya Safitri, Nadya Nawawi, Agus Nazah, Vina Zahrotun Pela, Maria Febrianti Pramana Saoni Aprilian Rafif Zetta Rajendra Pragiwoko Rahmat Budiarto Restu Amalia Novita Rita Wahyuni Arifin Saludin Muis Saludin Saludin Samhana Suryati Subari Samiyah Iftinan, Salsabila Shadiq, Jafar Shafirawati, Fitri Shilka Dina Anwariya Solikin Solikin Subari, Samhana Suryati Tiana Ramdani Vina Zahrotun Nazah Wira Hadi Kusuma, Mochamad Wulan Nabilla, Dwi