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All Journal CESS (Journal of Computer Engineering, System and Science) EXPLORE International Journal of Informatics and Computation Jurnal Pengembangan Sistem Informasi dan Informatika Jurnal Teknologi Kharisma Tech EXPLORE Computing and Education Technology Journal JURNAL FLOBAMORATA MENGABDI International Journal of Informatics Engineering and Computing
M. Hizbul Wathan
Politeknik Manufaktur Negeri Bangka Belitung, Indonesia
Agriculture, Biological Sciences & Forestry Humanities Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering Environmental Science Health Professions Languange, Linguistic, Communication & Media Mechanical Engineering Social Sciences Other

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Klasifikasi Mata Katarak dan Mata Normal Menggunakan Algoritma Dasar Convolutional Neural Network (CNN) Swengky, Better; Wathan, M Hizbul; Irawan, Indra; Aulia, Rosaura
Jurnal Pengembangan Sistem Informasi dan Informatika Vol. 6 No. 3 (2025): Jurnal Pengembangan Sistem Informasi dan Informatika
Publisher : Training & Research Institute - Jeramba Ilmu Sukses

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47747/jpsii.v6i3.2758

Abstract

Eye diseases encompass a wide range of conditions, from mild visual impairments to complete blindness, with cataracts being one of the leading causes. Despite advances in medical imaging, automated classification of cataract versus normal eye images remains a challenging task. This study proposes a classification method using a Convolutional Neural Network (CNN) to distinguish between cataract-affected eyes and normal eyes accurately. The approach involves collecting and preprocessing a labeled dataset, extracting features such as color and vein patterns (including average RGB values), and training the CNN model with optimized parameters. Experimental results demonstrate that the proposed model achieves a high classification accuracy of 95.1%. These findings indicate that CNN-based image classification is a promising tool for supporting automated cataract detection and early diagnosis
InfusCare: Smart Infusion Monitoring System with Real-Time Notifications via ESP32 and Blynk Wathan, M Hizbul; Irawan, Indra; Swengky, Better; Cahyadi, Irsan
Jurnal Pengembangan Sistem Informasi dan Informatika Vol. 6 No. 3 (2025): Jurnal Pengembangan Sistem Informasi dan Informatika
Publisher : Training & Research Institute - Jeramba Ilmu Sukses

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47747/jpsii.v6i3.2759

Abstract

Manual infusion monitoring in medical settings can lead to errors and delays in care steps that can put patients at risk. Internet of Things (IoT) technology provides a solution that enhances the accuracy and efficiency of real-time infusion monitoring. This study develops an IoT-based infusion monitoring system with the HX711 module and ESP32 microcontroller, using a connected load cell sensor as a monitoring interface through the Blynk application. This system can accurately measure the volume of infusion fluid and provide automatic notifications when the fluid volume approaches the minimum limit. Tests were conducted with infusion fluid simulation, load cell sensor calibration, and system calibration integration testing. The test results indicate that the system can display data on fluid weight in real time with an accuracy level of 98.5%, and when the fluid volume reaches 1 second in average response time, you can send notifications at the right time. Therefore, this system is expected to be implemented in various medical facilities as a solution for patient safety and the effectiveness of infusion care, as well as for automatic and reliable infusion monitoring.
Tomato Ripeness Identification Using Recurrent Neural Network Algorithm Hamdani, Dede; Wathan, M.Hizbul
International Journal of Informatics Engineering and Computing Vol. 1 No. 2 (2024): International Journal of Informatics Engineering and Computing
Publisher : ASTEEC

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70687/ijimatic.v1.i2.43

Abstract

Tomatoes undergo distinct ripeness stages, typically categorized into ripe, semi-ripe, and unripe phases. Traditional methods for assessing ripeness often face challenges in accuracy due to difficulties in comparing variables and subjective interpretations. This study proposes an innovative approach to classify tomato ripeness using a dataset of 200 tomato images and employs a Recurrent Neural Network (RNN) for precise classification. The experimental results demonstrate that the RNN-based model achieves a 95.0% accuracy rate in identifying ripeness stages, significantly outperforming conventional methods. This high level of accuracy highlights the model's potential to minimize errors and provide reliable assessments of tomato maturity. The proposed method offers a robust and efficient solution for agricultural applications, enabling improved quality control and harvest timing. Future research could explore the integration of additional data sources or advanced machine learning techniques to further enhance the model's performance and applicability across diverse agricultural contexts.
EFEKTIVITAS PENERAPAN SISTEM ABSENSI GURU BERBASIS GEOLOKASI MENGGUNAKAN METODE FEATURE DRIVEN DEVELOPMENT (FDD) DI SMP NEGERI 2 RIAU SILIP deswita, deswita; Irwan; Wathan, M Hizbul
KHARISMA Tech Vol 20 No 2 (2025): KHARISMATech Journal
Publisher : STMIK KHARISMA Makassar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55645/kharismatech.v20i2.645

Abstract

The supervision of teacher attendance plays a crucial role in ensuring the smooth implementation of the learning process. However, the manual attendance system used at SMP Negeri 2 Riau Silip often leads to recording errors, time inefficiency, and data manipulation. This study aims to implement and evaluate the effectiveness of a teacher attendance system based on geolocation using the Feature Driven Development (FDD) method. The system was developed with the Flutter framework for the mobile application and Laravel for the backend, integrated through an API and supported by a MySQL database. The main features include GPS-based attendance validation, photo-based activity verification, attendance history, and real-time monitoring by administrators. The development process followed five stages of FDD, ensuring a structured and feature-oriented approach. System testing using black box testing and User Acceptance Testing (UAT) resulted in an average feasibility score of 89.73%, categorized as highly feasible. The results indicate that the implementation of the geolocation-based attendance system effectively improves efficiency, accuracy, and transparency in managing teacher attendance at SMP Negeri 2 Riau Silip.
Design and Development of a QR Code-Based Ordering System and Mobile Cashier Application at Rumah Seduh Coffee Shop Fadhilah, Indirokan; Rindri, Yang Agita; Wathan, M. Hizbul
KHARISMA Tech Vol 20 No 2 (2025): KHARISMATech Journal
Publisher : STMIK KHARISMA Makassar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55645/kharismatech.v20i2.636

Abstract

This research was carried out with the aim of designing and developing a QR Code–based ordering system integrated with a mobile cashier application at Warung Kopi Rumah Seduh. The previously manual process created several issues, including long queues, delayed service, and frequent transaction recording errors that disrupted operational efficiency. To address these challenges, a digital system was introduced that allows customers to place orders independently by scanning a QR Code, with the orders being automatically recorded in the cashier system. The mobile cashier application supports real-time order processing, transaction management, and structured sales data storage. The system was developed using a prototyping approach, beginning with requirements analysis through field observations and user interviews. The implementation results demonstrate that this solution improves service efficiency, accelerates the ordering process, reduces input errors, and enhances the organization of sales data. Ultimately, this study is expected to provide practical contributions for MSMEs in adapting to the challenges of service digitalization.
Sistem Inoklamasi Awards sebagai Media Penilaian Inovasi Daerah pada Innovative Government Awards (IGA) Kabupaten Bangka Oktaviani, Ajeng; Fujiyanti, Linda; Wathan, M. Hizbul
Jurnal Teknologi Vol 25, No 3 (2025): Desember 2025
Publisher : Politeknik Negeri Lhokseumawe

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30811/teknologi.v25i3.8368

Abstract

Regional innovation plays an important role in improving government performance efficiency and the quality of public services. As a form of appreciation, the Ministry of Home Affairs organizes the Innovative Government Awards (IGA) for regional governments that have successfully implemented innovations in governance, development, and public services. However, the process of collecting and managing innovation data is still carried out manually, making it prone to delays, data loss, and assessment inaccuracies. This study aims to design and develop the Innovation and Proclamation (Inoklamasi) Awards System, a web-based information system that supports the digitalization of innovation data collection, verification, and evaluation processes. The system was developed using an iterative prototyping method, consisting of requirement analysis, design, implementation, and evaluation stages. Its main features include innovation submission by local government agencies (OPD), both automatic and manual data validation, and online assessment by evaluators. Based on the User Acceptance Test (UAT) results, the system achieved a satisfaction rate of 84.75% from 240 respondents, indicating that it is feasible for implementation. The application of the Inoklamasi Awards System is expected to improve administrative efficiency, accelerate workflow, and strengthen coordination among OPD, evaluators, and Bappeda, thereby supporting a transparent and sustainable digital government transformation.
Penanaman Karakter Peduli Lingkungan Pada Siswa Kelas 4 SD Melalui Kegiatan Penanaman Sukulen dalam Implementasi Pembelajaran P5 Lesta, Lesta; Ningsih, Riski Meliya; Ali, Hendrik; Wathan, Muhammad Hizbul; Zain, Muhammad Syafrizal
Jurnal Flobamorata Mengabdi Vol. 3 No. 2 (2025): JURNAL FLOBAMORATA MENGABDI
Publisher : Program Studi Pendidikan Guru Sekolah Dasar, Fakultas Keguruan dan Ilmu Pendidikan, Universitas Muhammadiyah Kupang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51494/jfm.v3i2.2788

Abstract

Permasalahan lingkungan seperti berkurangnya kepedulian terhadap lingkungan, polusi udara, penumpukan sampah plastik, dan berkurangnya ruang hijau mendorong pentingnya pendidikan lingkungan sejak dini pada siswa SD. Pengabdian ini bertujuan menganalisis efektivitas kegiatan penanaman sukulen sebagai media pembelajaran berbasis proyek untuk menanamkan karakter peduli lingkungan pada 25 siswa kelas 4 SDN 3 Sungailiat melalui implementasi Proyek Penguatan Profil Pelajar Pancasila (P5). Metode partisipatif mencakup edukasi jenis dan manfaat sukulen, persiapan alat-bahan, simulasi penanaman, pelatihan perawatan, serta observasi perilaku dan keterampilan siswa. Hasil menunjukkan 85% siswa antusias mengikuti seluruh tahapan dengan banyak pertanyaan yang mencerminkan rasa ingin tahu terhadap alam, 100% membawa tanaman pulang, dan 68% berkomitmen merawat secara mandiri, sementara 44% sangat terampil serta 56% terampil dalam teknik penanaman. Tantangan utama adalah konsistensi perawatan yang memerlukan kolaborasi pengabdi, guru, dan orang tua. Model hands-on dengan sukulen ini dapat direplikasi di sekolah lain untuk memperluas penghijauan rumah tangga dan membentuk kebiasaan peduli lingkungan berkelanjutan, sekaligus memperkuat kurikulum P5 melalui program pengabdian rutin yang melibatkan komunitas.
Detection of DDoS Attacks Using Hybrid LSTM and SVM Algorithm Nahak, Ivansius; M. Hizbul Wathan
International Journal of Informatics Engineering and Computing Vol. 2 No. 2 (2025): International Journal of Informatics Engineering and Computing
Publisher : ASTEEC

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70687/vd7kk061

Abstract

Distributed Denial of Service (DDoS) attacks pose serious threats to network infrastructures by disrupting services through massive malicious traffic. This study proposes a hybrid detection model that integrates Long Short-Term Memory (LSTM) with a Support Vector Machine (SVM) classifier to improve the accuracy of DDoS detection in network traffic. The LSTM model captures temporal patterns within sequential traffic data, while the SVM performs the final classification to distinguish between normal and anomalous traffic. The experiment uses a dataset containing 104,345 records with 23 features that undergo preprocessing, encoding, scaling, and class balancing before model training. Experimental results demonstrate that the proposed hybrid model achieves stable learning performance with training accuracy reaching approximately 93% and validation accuracy around 94%. The loss curves show consistent decreases across 50 training epochs, indicating effective convergence and minimal overfitting. Confusion matrix analysis shows that the model correctly classifies the majority of normal and anomalous traffic samples, with relatively low false positive and false negative rates. Overall evaluation results show that the hybrid LSTM–SVM model achieves 95% accuracy with balanced classification performance. The model records strong precision, recall, and F1-score values for both normal and anomalous traffic classes.
RiceGuard: A Lightweight PSO-Optimized MobileNetV2 Framework for Stable Rice Leaf Disease Classification Wathan, M. Hizbul; Swengky, Better; Irawan, Indra; Atthariq Zami, Fatir
Computing and Education Technology Journal Vol 6, No 1 (2026): APRIL
Publisher : Pendidikan Komputer FKIP Universitas Lambung Mangkurat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20527/cetj.v6i1.18663

Abstract

Rice leaf diseases such as blast, brown spot, bacterial blight, and tungro pose a serious threat to agricultural productivity, with potential yield losses exceeding 50% under epidemic conditions. Therefore, rapid and accurate early detection is essential to support sustainable food security and precision agriculture. This study proposes a rice leaf disease classification system based on the MobileNetV2 architecture optimized using Particle Swarm Optimization (PSO). A dataset consisting of four rice leaf disease classes, collected from public repositories and sources representing real field conditions, is used to evaluate the proposed approach. Transfer learning is employed to improve training efficiency, while PSO is applied to optimize key hyperparameters to enhance model stability and convergence.Experimental results show that MobileNetV2 optimized with PSO consistently achieves superior classification performance and improved training stability compared to the standard MobileNetV2 baseline. The baseline MobileNetV2 achieves 92% accuracy, with the highest F1-score of 0.99 on the tungro class and the lowest performance of 0.88 on the blast class. In contrast, MobileNetV2–PSO demonstrates a significant improvement, reaching 99% accuracy, with F1-scores of 0.98 for bacterial blight, 0.98 for blast, 0.99 for brown spot, and 1.00 for tungro. The largest improvement occurs in the blast class, with a 10-point increase in F1-score, indicating that PSO optimization provides greater sensitivity to complex disease patterns that were previously difficult to classify.These findings indicate that the proposed framework provides an accurate and lightweight solution with strong potential for deployment on mobile and resource-constrained platforms to support intelligent rice disease diagnosis.
Co-Authors Ali, Hendrik Atthariq Zami, Fatir Aulia, Rosaura Better Swengky Bradika Almandin Wisesa Cahyadi, Irsan DEDE HAMDANI, DEDE Deswita Deswita Efendi, Muhamad Masjun Evvin Faristasari Fadhilah, Indirokan Fadli, Sofiansyah Fadli Hizbul Wathan Indra Irawan Indra Irawan Irwan Lesta, Lesta LINDA FUJIYANTI Moh. Aziz Nahak, Ivansius Oktaviani, Ajeng Prayogi, M. Bagus Ramadani, Ardi Rani Laple Satria Rani Laple Satria Putra Riski Meliya Ningsih Silvia Agustin Sirlus Andreanto Jasman Duli Sofiansyah Fadli Swengky, Better Wardianto, Wardianto Yang Agita Rindri Zain, M Syafrizal