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All Journal Journal of Intelligent Decision Support System (IDSS) Innovative: Journal Of Social Science Research Horizon: Indonesian Journal of Multidisciplinary
Windarti, Ira
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Agriculture, Biological Sciences & Forestry Humanities Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Public Health Social Sciences Other

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Perancangan Aplikasi Pengisian Rapot Nilai Akhir Siswa (TPORT) Berbasis Web Windarti, Ira; Noorlimayanti , Sari; Triasari , Ari Rosemala; Riskania, Riskania
Innovative: Journal Of Social Science Research Vol. 4 No. 6 (2024): Innovative: Journal Of Social Science Research
Publisher : Universitas Pahlawan Tuanku Tambusai

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/innovative.v4i6.16491

Abstract

Kegiatan pengisian nilai rapot siswa dilakukan pada saat kenaikan semester atau kenaikan kelas. Pada kurikulum 2013 memiliki berbagai aspek penilaian. Saat ini pengisian nilai rapot siswa menggunakan macro Microsoft Excel 2010. Aplikasi ini hanya dapat beroperasi pada Microsoft Excel 2010 sehingga aplikasi tersebut tidak fleksibel. Data yang diisikan berupa, profile sekolah, biodata siswa, kriteria komulatif minimal, kompetensi dasar dan nilai siswa. Pengisian data tersebut dilakukan kembali pada saat kenaikan kelas. Pengisian data kembali menyebabkan sistem pengisian nilai dan data siswa menjadi tidak efektif. Oleh karena itu, agar pengisian data dapat dilakukan secara efektif dan dapat digunakan secara fleksibel dibuat suatu aplikasi pengisian nilai rapot siswa berbasis website dengan menggunakan sistem DBMS. Aplikasi ini dirancang dengan menggunakan bahasa program html,php dan css serta menggunakan sublimetext sebagai text editor yang digunakan untuk melakukan penulisan dan penyusunan koding program langsung. Selanjutya dilakukan uji coba webste dengan blackbox. Dengan adanya aplikasi pengisian niai rapot siswa berbasis website diharapkan user(guru) dapat melakukan pengolahan nilai rapot secara efektif dan lebih mudah karena dapat dilakukan dengan menggunakan perangkat apa pun.
Design of noise detection and warning in library reading room using Wemos D1 R2 Based on IoT Sintawati, Andini; Windarti, Ira; Arrafi, Muhamad Dzammar
Journal of Intelligent Decision Support System (IDSS) Vol 6 No 4 (2023): December: Intelligent Decision Support System (IDSS)
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/idss.v6i4.194

Abstract

The library is one of the places needed by the community to improve academic knowledge, get information and become a means for students to learn. Library visitors certainly need a comfortable and quiet atmosphere so they can focus on reading books in the library. But not all visitors understand about the rules that apply to the library. Some visitors come just to chat, thus disturbing other visitors who are reading. Therefore, a system that can monitor the room is needed. The reading room noise detector aims to provide comfort and tranquility to visitors who come to read. the components used in this design are the Wemos D1 R2 microcontroller, DFRobot Analog Sound Sensor V2, DHT11, LCD, and Buzzer. This tool will work if the Sound Sensor detects Sound Intensity of more than 55dB then the Buzzer will be active and the LCD displays "PLEASE LET YOUR VOICE THANK YOU" and sends a notification to the Blynk application. Furthermore, DHT11 will function as realtime temperature monitoring in the room and display it on the LCD and Blynk Application.
Implementation of Coffee Bean Roasting Level Classification System Using CNN and Knn Models with Web-Based Real-Time Camera Sintawati, Andini; Windarti, Ira; Rosemalatriasari, Ari; Saputra, Muhammad Alan Darma
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 Arrafi, Muhamad Dzammar Noorlimayanti , Sari Riskania, Riskania Rosemalatriasari, Ari Saputra, Muhammad Alan Darma Sintawati, Andini Triasari , Ari Rosemala