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All Journal Jurnal Minfo Polgan (JMP) Horizon: Indonesian Journal of Multidisciplinary Review: Journal of Multidisciplinary in Social Sciences
Ira Windarti
Universitas Gunadarma
Agriculture, Biological Sciences & Forestry Humanities Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Languange, Linguistic, Communication & Media Library & Information Science Mathematics Social Sciences Other

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Perancangan Loker Cerdas untuk Penerimaan Paket dirumah menggunakan Sistem Pengenalan Wajah Andini Sintawati; Ira Windarti; Iqbal Baihaqi
Jurnal Minfo Polgan Vol. 12 No. 1 (2023): Artikel Penelitian 2023
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/jmp.v12i1.12726

Abstract

Mungkin sering kali saat kurir jasa pengiriman barang mengantarkan barang yang dibeli atau pemberian dari seseorang untuk diantarkan ke rumah waktu yang tidak tepat, sehingga pemilik rumah sedang tidak berada di lingkungan rumah, jadi terkadang kurir meletakkan barang tersebut di teras depan rumah, sehingga dapat menimbulkan barang hilang atau rusak, mungkin ini sering terjadi. Tujuan penelitian yang hendak dicapai adalah merancang dan membuat alat yang digunakan untuk meningkatkan cara kerja sistem terima barang paket dari kurir jasa pengiriman dengan memanfaatkan teknologi dari Internet of Things pada sistem Computer Vision dengan menggunakan metode pengenalan wajah (face recognition) menggunakan Local Binary Pattern Histogram (LBPH) dan akan mengirimkan notifikasi menggunakan Telegram Bot melalui aplikasi Telegram pemilik rumah supaya user dapat memonitoring siapa yang mengakses pintu pada smart locker.Penulis membuat sebuah rancangan yaitu, Perancangan Smart Locker Untuk Penerimaan Paket di Rumah Menggunakan Sistem Face Recognition. Pendeteksian dan pengenalan wajah dari sistem ini menggunakan metode Haar Cascade Classifier dan Local Binary Pattern Histogram (LBPH). Dari pengujian sistem ini bekerja sangat baik dalam pengujian pengenalan wajah berdasarkan jarak, tingkat pencahayaan, pengenalan wajah berdasarkan posisi wajah, pengenalan wajah user, bukan wajah user, dan pengujian keaamanan sistem pintu smart locker serta uji notifikasi Telegram Bot, sehingga mendapatkan hasil rata-rata keakuratan sebesar 93,73%.
Creating the Leyndell RPG Game Using the Godot Engine Ira Windarti; Sari Noorlima Yanti
Review: Journal of Multidisciplinary in Social Sciences Vol. 3 No. 01 (2026): January 2026
Publisher : Lentera Ilmu Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59422/rjmss.v3i01.1151

Abstract

  This research aims to design and build Leyndell-RPG games along with web-based game distribution websites that are able to support the process of digital game information, transactions, and distribution. The development method used is Waterfall which includes the stages of needs analysis, design, implementation, testing, and maintenance. Games are developed using the Godot Engine, while distribution websites are built with Next.js, TypeScript, and MySQL databases. System testing is carried out using the black box testing method on user, admin, and browser compatibility. The test results show that all the main features of the user and admin systems run according to the needs designed, and the website is well accessible on the Google Chrome and Microsoft Edge browsers. Thus, the Leyndell–RPG game distribution website was declared successful and was able to provide effective support for users and developers in the digital game distribution process.
Implementation of Coffee Bean Roasting Level Classification System Using CNN and Knn Models with Web-Based Real-Time Camera Andini Sintawati; Ira Windarti; Ari Rosemalatriasari; Muhammad Alan Darma Saputra
HORIZON: Indonesian Journal of Multidisciplinary Vol. 4 No. 3 (2026): HORIZON: Indonesian Journal of Multidisciplinary
Publisher : Lembaga Intelektual Muda (LIM) Maluku

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54373/hijm.v4i3.6545

Abstract

Manual coffee bean roasting assessment is still susceptible to operator subjectivity, variations in lighting conditions, and inconsistencies in results between assessors. This study aims to develop a real-time web-based coffee bean roasting classification system by integrating Convolutional Neural Network (CNN) and K-Nearest Neighbor (KNN) models. The study uses a quantitative experimental approach with a dataset of coffee bean images collected independently and expanded to 6,470 images, which are grouped into five classes: GreenRoasting, LightRoasting, MediumRoasting, DarkRoasting, and Unknown. All images are processed through a pre-processing stage including resizing to 160 × 160 pixels, normalization, data augmentation, and splitting training and test data with a ratio of 80:20 in stages. MobileNetV2 is used as a feature extractor in CNN, while KNN with a value of k = 7 and a cosine distance metric is applied for feature vector classification. The final prediction was obtained using a weighted ensemble method with a composition of 60% CNN and 40% KNN, then implemented in a Flask-based web application with support for real-time image upload and camera. Test results showed the ensemble model achieved an accuracy of 85.67% with an average response time of 1,247 ms. This system has the potential to support faster, more consistent, and objective coffee roasting level assessments, especially for small to medium-scale coffee businesses.
Co-Authors Andini Sintawati Ari Rosemalatriasari Iqbal Baihaqi Muhammad Alan Darma Saputra Sari Noorlima Yanti