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All Journal Publikasi Pendidikan Jurnal Simetris Prosiding SNATIF Jurnal Teknologi Jurnal Transformatika Scientific Journal of Informatics InfoTekJar : Jurnal Nasional Informatika dan Teknologi Jaringan Jurnal Informatika Upgris JOURNAL OF APPLIED INFORMATICS AND COMPUTING JURNAL MANAJEMEN INFORMATIKA (JUMIKA) J I M P - Jurnal Informatika Merdeka Pasuruan Martabe : Jurnal Pengabdian Kepada Masyarakat Jurnal Nasional Komputasi dan Teknologi Informasi IKRA-ITH ABDIMAS JURIKOM (Jurnal Riset Komputer) JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) EDUMATIC: Jurnal Pendidikan Informatika Progresif: Jurnal Ilmiah Komputer Jurnal Ekonomi Manajemen Sistem Informasi JATI (Jurnal Mahasiswa Teknik Informatika) JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) Jurnal Sistem Komputer dan Informatika (JSON) Jurnal Pengabdian kepada Masyarakat Nusantara Jurasik (Jurnal Riset Sistem Informasi dan Teknik Informatika) Jurnal Digit : Digital of Information Technology RESOLUSI : REKAYASA TEKNIK INFORMATIKA DAN INFORMASI Jurnal Pendidikan dan Teknologi Indonesia Jurnal Dialektika Informatika (Detika) Journal La Multiapp JAST Biner : Jurnal Ilmiah Informatika dan Komputer Jurnal Ilmiah Nusantara Journal of Blockchain, Nfts and Metaverse Technology INOVTEK Polbeng - Seri Informatika JuTISI (Jurnal Teknik Informatika dan Sistem Informasi) Jurnal Bina Informatika dan Komputer (BINER)
Aditya Akbar Riadi
Universitas Muria Kudus
Aerospace Engineering Agriculture, Biological Sciences & Forestry Arts Humanities 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 Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Library & Information Science Mechanical Engineering Neuroscience Social Sciences Transportation

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Journal : Progresif: Jurnal Ilmiah Komputer

 1 
Rancang Bangun Dashboard Monitoring Pengiriman Bermasalah pada E-Commerce marta, amelia vidora revita; riadi, aditya akbar; chamid, ahmad abdul
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
Sistem Monitoring dan Kontrol Suhu Kandang Ayam Kalkun Yudha, Raka Gifaris Anega; Evanita, Evanita; Riadi, Aditya Akbar
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.v22i1.3236

Abstract

Turkey farming requires stable temperature and humidity management to maintain optimal growth and health. Manual monitoring often causes delays in handling environmental changes, which can reduce productivity. This study designs and implements a temperature and humidity monitoring and control system for turkey cages based on the Internet of Things (IoT). The system utilizes a NodeMCU ESP8266 microcontroller, a DHT11 sensor, and a relay module to automatically control a heating lamp according to temperature conditions. Data are transmitted to a server and displayed on a web-based dashboard accessible remotely, with additional notifications sent via WhatsApp. The development process applied a prototyping method, including hardware and software design, experiment and black-box testing. The results show that the system successfully maintains cage temperature within the ideal range of 28–32°C, displays real-time temperature and humidity data, and achieved a user satisfaction level of 91.25%. This system is considered effective in assisting farmers to monitor turkey cages more efficiently and responsively.Keywords: Internet of Things; Turkey farming; Temperature monitoring; Humidity; NodeMCU ESP8266.AbstrakPeternakan kalkun membutuhkan pengelolaan suhu dan kelembaban kandang yang stabil agar pertumbuhan dan kesehatan ternak tetap optimal. Pemantauan manual sering menimbulkan keterlambatan dalam penanganan perubahan suhu sehingga berpotensi menurunkan produktivitas. Penelitian ini merancang dan mengimplementasikan sistem monitoring serta kontrol suhu kandang kalkun berbasis Internet of Things (IoT). Sistem memanfaatkan mikrokontroler NodeMCU ESP8266, sensor DHT11, dan modul relay untuk mengendalikan lampu pemanas secara otomatis sesuai kondisi suhu. Data dikirimkan ke server dan ditampilkan pada dashboard web yang dapat diakses jarak jauh, serta dilengkapi notifikasi melalui WhatsApp. Metode pengembangan menggunakan pendekatan prototyping, meliputi perancangan perangkat keras, perangkat lunak, pengujian eksperimen dan pengujian Black box. Hasil pengujian menunjukkan sistem mampu menjaga suhu kandang dalam rentang ideal 28–32°C, menampilkan data suhu dan kelembaban secara real-time, serta memperoleh tingkat kepuasan pengguna sebesar 91,25%. Sistem ini dinilai efektif membantu peternak dalam memantau kondisi kandang kalkun secara lebih efisien dan responsif.Kata kunci: Internet of Things ; Kalkun; Monitoring suhu; Kelembaban; NodeMCU ESP8266.
CNN-Based Automatic Detection of Corn Leaf Diseases Using Desktop GUI Application Salsabilah, Nisrina Rona; Riadi, Aditya Akbar; Chamid, Ahmad Abdul
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 fatmarini, dini; Riadi, Aditya Akbar; Chamid, Ahmad Abdul
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 A, Ifta Abdul Abdullah, Khabib Ahmad Abdul Chamid Ahmad Lutfi Hakim Akh Sokhibi Alif Catur Murti, Alif Catur Alvin Rainaldy Hakim Aminatus Syarifah Arief Susanto Azami, Farich Al Chamid, Ahmad Abdul Darsilowati Darsilowati Dicky Prasetiyo Durrun Nada Amarylis Dwimaelani, Riska Dwyan Akbar Putra Esti Wijayanti Esti Wijayanti, Esti Evanita Evanita Evanita, Evanita Evanita, E Fadhilah, Norma Farich Al Azami Farichah, Ainul fatmarini, dini Fera Anggraini Fistiana Fistiana, Fera Anggraini Gabriel Dwiki Ari Permadani Gunawan, Bagus Suseto Hanik Hidayati Hilma, Intan Nabila Ifta Abdul A Indra Lina Putra Ivan Bagus Prasetiyo Jannah, Zuliana Nurul Khabib Abdullah Khilmawan, Muhammad Rizqi Khoirun Nisa', Nining Khoirunnisaa, Ulayya Salmaa Khusna, Nor Milatul Mahendra, Vicki Yuda marta, amelia vidora revita Maulana Aditya Yusman Muhammad Imam Ghozali Murniawati, Mita Ningrum, Diah Ayu Cahya Oktavianus, Yohanes Pegianti, Nanda` Prasetiyo, Dicky Prasetyo, Herlambang Dwi Putra Aprilian Prastianing Huda Putra, Indralina Lina Ratih Nindyasari Riasti, Savira Rizal Ramli, Rizal Rizkysari Meimaharani Rizkysari Meimaharani Salsabila, Karima Salsabilah, Nisrina Rona Saputra, Andre Tri Sari, Fadia Karlika Shofa Allaisya Shofiana, Arista Sokhibi, Akh Sokhibi, Akh Sri Lestari Sugiarto, Elmalia Risma Putri Sugiharto, Wibowo Harry Sukmawati, Zesiy Risna Dewi Syirojuddin, Ahmad Idris Syukron, Muhammad Habib Tri Listyorini UMAM, MUHAMAD KHOTIBUL Vera Meirotun Hidayatika Vicki Yuda Mahendra Wulandari, Gilang Ayu Yehezkiel Febri Kurniawan Yudha, Raka Gifaris Anega Yuliana Fitriani