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All Journal IJCCS (Indonesian Journal of Computing and Cybernetics Systems) JURNAL SISTEM INFORMASI BISNIS Jurnal Simetris TEKMAPRO Journal of Industrial Engineering and Management Prosiding SNATIF Jurnal Teknologi Jurnal Teknologi Informasi dan Ilmu Komputer Jurnal Transformatika Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Jurnal Mantik Penusa SISFO Jurnal Riset Informatika Ekonomi dan Bisnis Jurnal SITECH : Sistem Informasi dan Teknologi Progresif: Jurnal Ilmiah Komputer Aisyah Journal of Informatics and Electrical Engineering bit-Tech JATI (Jurnal Mahasiswa Teknik Informatika) JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) Jurnal Teknologi Informatika dan Komputer Indonesian Journal of Technology, Informatics and Science (IJTIS) Jurnal Dialektika Informatika (Detika) Journal La Multiapp Bulletin of Computer Science Research Jurnal Sistem Informasi Triguna Dharma (JURSI TGD) Jurnal Pengabdian Masyarakat - Teknologi Digital Indonesia Malcom: Indonesian Journal of Machine Learning and Computer Science Jurnal Bina Informatika dan Komputer (BINER)
Ahmad Abdul Chamid
Program Studi Teknik Informatika, Fakultas Teknik, Universitas Muria Kudus
Aerospace Engineering Automotive Engineering 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 Energy Engineering Environmental Science Industrial & Manufacturing Engineering Mechanical Engineering Social Sciences Other

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Journal : Progresif: Jurnal Ilmiah Komputer

 1 
Rancang Bangun Dashboard Monitoring Pengiriman Bermasalah pada E-Commerce amelia vidora revita marta; aditya akbar riadi; ahmad abdul chamid
Progresif: Jurnal Ilmiah Komputer Vol 22, No 1 (2026): Januari
Publisher : STMIK Banjarbaru

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35889/progresif.v22i1.3516

Abstract

The rapid growth of e-commerce has increased the volume of orders that must be monitored by sellers, making the handling of delivery delays a major challenge. This study designs and develops a dashboard to monitor problematic shipments on an e-commerce platform by applying a Service Level Agreement (SLA) approach as the basis for evaluating delivery delays. The research adopts a Research and Development (R&D) method with the Waterfall system development model, which includes the stages of requirement analysis, system design, implementation, and functional testing. The developed dashboard is able to automatically classify shipment statuses based on SLA calculations, display indicators of orders at risk of delay, and present delivery data through graphical visualizations. The testing results indicate that the system successfully presents real-time delivery data, proactively detects potential delays, and supports sellers in making operational decisions. This system provides a more effective and measurable SLA-based approach for monitoring e-commerce deliveries.Keywords: Monitoring dashboard; Problematic delivery; Service Level Agreement; E-commerce; Automatic detection.AbstrakPerkembangan e-commerce telah meningkatkan volume pesanan yang harus dipantau oleh seller, sehingga penanganan keterlambatan pengiriman menjadi tantangan utama. Penelitian ini merancang dan membangun sebuah dashboard untuk memonitor pengiriman bermasalah pada platform e-commerce dengan pendekatan Service Level Agreement (SLA) sebagai dasar penilaian keterlambatan pengiriman. Metode yang digunakan adalah Research and Development (R&D) dengan model pengembangan sistem Waterfall, yang meliputi tahapan analisis kebutuhan, perancangan sistem, implementasi, dan pengujian fungsionalitas. Dashboard yang dikembangkan mampu mengklasifikasikan status pengiriman secara otomatis berdasarkan perhitungan SLA, menampilkan indikator pesanan berisiko terlambat, serta menyajikan grafik visualisasi pengiriman. Hasil pengujian menunjukkan bahwa sistem berhasil menampilkan data pengiriman secara real-time, mendeteksi potensi keterlambatan secara proaktif, dan membantu seller dalam pengambilan keputusan operasional. Sistem ini memberikan pendekatan monitoring pengiriman yang lebih efektif dan terukur berbasis SLA.Kata kunci: Dashboard monitoring; Pengiriman bermasalah; Service Level Agreement; E-commerce; Deteksi otomatis
CNN-Based Automatic Detection of Corn Leaf Diseases Using Desktop GUI Application Nisrina Rona Salsabilah; Aditya Akbar Riadi; Ahmad Abdul Chamid
Progresif: Jurnal Ilmiah Komputer Vol 22, No 1 (2026): Januari
Publisher : STMIK Banjarbaru

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35889/progresif.v22i1.3501

Abstract

Early detection of corn leaf diseases plays a crucial role in improving agricultural productivity. This study develops an automatic detection system for corn leaf diseases using a Convolutional Neural Network (CNN) implemented in a desktop-based GUI application. The model was developed using a transfer learning approach with a dataset of 2,400 corn leaf images divided into four classes: Blight, Common Rust, Gray Leaf Spot, and Healthy. The research stages included image preprocessing, model training and evaluation, and implementation of the trained model into an offline desktop application. Experimental results show that the CNN model achieved an accuracy of 90.00%, with precision of 90.02%, recall of 90.00%, and an F1-score of 90.00%. The Healthy class demonstrated the best performance, while the Blight class showed the lowest but remained in a good category. The developed system enables fast, practical, and efficient disease detection and has the potential to support objective early diagnosis in agriculture.Keywords: CNN; Deep Learning; Disease Detection; Corn Leaf; Transfer Learning  AbstrakDeteksi dini penyakit daun jagung berperan penting dalam meningkatkan produktivitas pertanian. Penelitian ini mengembangkan sistem deteksi otomatis penyakit daun jagung menggunakan metode Convolutional Neural Network (CNN) berbasis aplikasi GUI desktop. Model dikembangkan dengan pendekatan transfer learning menggunakan 2.400 citra daun jagung yang terbagi dalam empat kelas, yaitu Blight, Common Rust, Gray Leaf Spot, dan Healthy. Tahapan penelitian meliputi preprocessing citra, pelatihan dan evaluasi model, serta implementasi model ke dalam aplikasi desktop yang dapat digunakan tanpa koneksi internet. Hasil pengujian menunjukkan akurasi sebesar 90,00%, precision 90,02%, recall 90,00%, dan F1-score 90,00%. Kelas Healthy memiliki performa terbaik, sedangkan kelas Blight terendah namun tetap dalam kategori baik. Implementasi sistem memungkinkan proses deteksi dilakukan secara cepat, praktis, dan efisien. Sistem yang dikembangkan berpotensi mendukung deteksi penyakit secara objektif serta membantu meningkatkan produktivitas pertanian.Kata kunci: CNN; Deep Learning; Deteksi Penyakit; Daun Jagung; Transfer Learning
Klasifikasi Penulisan Huruf Hijaiyah Menggunakan Algoritma Convolutional Neural Network Pada TPQ I’anatut Tholibin dini fatmarini; Aditya Akbar Riadi; Ahmad Abdul Chamid
Progresif: Jurnal Ilmiah Komputer Vol 22, No 1 (2026): Januari
Publisher : STMIK Banjarbaru

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35889/progresif.v22i1.3531

Abstract

This research was conducted due to difficulties in recognizing handwritten Hijaiyah letters at TPQ I’anatut Tholibin caused by variations in writing styles and similarities among letters. The study aims to develop a handwritten Hijaiyah letter classification system based on a Convolutional Neural Network (CNN) using the MobileNetV2 architecture. The research employed a Research and Development (R&D) approach, including real-time data collection from students’ handwritten samples, image preprocessing (resizing to 224×224, pixel normalization, and augmentation), model design using transfer learning, training, and testing. Model evaluation was performed using test data that were not involved in the training process, with performance assessed through a confusion matrix and metrics such as accuracy, precision, recall, and F1-score. The experimental results show that the model achieved an accuracy of 78.46% with a macro F1-score of 77.29%, indicating a reasonably good and balanced classification performance across classes. The system was implemented as a web-based application supporting real-time testing through direct writing on a digital canvas, enabling interactive classification. These findings demonstrate that MobileNetV2 is effective for handwritten Hijaiyah letter classification and has potential as an intelligent learning support tool.Keywords: Hijaiyah letters; Convolutional neural network; MobileNetV2; Image classification; Real-time systemAbstrakPenelitian ini dilakukan karena pengenalan tulisan tangan huruf hijaiyah di TPQ I’anatut Tholibin masih terkendala variasi bentuk tulisan dan kemiripan antar huruf. Penelitian ini bertujuan mengembangkan sistem klasifikasi tulisan tangan huruf hijaiyah berbasis Convolutional Neural Network (CNN) dengan arsitektur MobileNetV2. Metode yang digunakan adalah Research and Development (R&D) dengan tahapan pengumpulan data tulisan tangan murid secara real-time, pra-pengolahan citra (resizing 224×224, normalisasi piksel, dan augmentasi), perancangan model dengan pendekatan transfer learning, pelatihan, dan pengujian. Pengujian dilakukan menggunakan data uji yang tidak dilibatkan dalam proses pelatihan, dengan evaluasi performa menggunakan confusion matrix dan metrik akurasi, precision, recall, dan F1-score. Hasil pengujian menunjukkan bahwa model mencapai akurasi sebesar 78,46% dengan nilai macro F1-score 77,29%, yang menandakan performa klasifikasi yang cukup baik dan relatif seimbang antar kelas. Sistem diimplementasikan dalam aplikasi web dengan pengujian real-time melalui penulisan langsung pada kanvas digital sehingga klasifikasi dapat dilakukan secara interaktif. Temuan ini menunjukkan MobileNetV2 efektif untuk klasifikasi huruf hijaiyah tulisan tangan dan berpotensi sebagai alat bantu pembelajaran.Kata Kunci: Huruf hijaiyah; Convolutional neural network; MobileNetV2; Klasifikasi citra; Sistem realtime
Co-Authors Aditya Akbar Riadi Ahdi Riyono Ahmad Hariyadi, Ahmad Ahmad Jazuli Ahmad Jazuli Akbar, Fadhil Akh Sokhibi Alif Catur Murti Alif Catur Murti, Alif Catur amelia vidora revita marta Anteng Widodo Aprilianto, Mohammad Andrean Jaya Ariyanto, Iqbal Haqiqi Bayu Surarso Daelami, Fahri Muhammad Dimas Satrio Oktavianto dini fatmarini Endang Supriyati Endang Supriyati Esti Wijayanti Esti Wijayanti Esti Wijayanti, Esti Evita Noor Sofiana Dewi Hakim, Alvin Rainaldy Hendra Dwi Kurniawan, Hendra Dwi Hendrawan, Andra Putra Ida Nur Aeni Ida Nur Aeni, Ida Nur Ivan Bagus Prasetiyo Juwanda, Farikhin Mahendra, Bagus Rosa Maula Yudhananta, Naufal Maulana, Sahidin Achmad Noor Mohammad Kanzunnudin, Mohammad Muhammad Imam Ghozali Muhammad Rieza Maulana Muhammad Rizki Naila Rizki Salisa Nisrina Rona Salsabilah Noor Azizah Nurainin, Enno Siti Prayogo, Sandi Ratih Nindyasari Riasti, Savira Rijal, Syaifur Rina Fiati Rizky Sari Mei Maharani, Rizky Sari Rizkysari Meimaharani Safitri, Sabrina Alya Samsudin, Syafri Sentanu, Arga Seva Amartya Shafian Syah, Aliffaza Sholihul Ibad Sokhibi, Akh Sugiharto, Wibowo Harry Teguh, Aris Wicakosno Umam, Arfiyan Khusnul Wibowo, Yuniar Satrio Wicaksono, Abraham Wahyu