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All Journal JUITA : Jurnal Informatika Semesta Teknika Jurnal Pengabdian Masyarakat IPTEKS DedikasiMU: Journal of Community Service Emerging Information Science and Technology Prosiding Seminar Nasional Program Pengabdian Masyarakat Prosiding University Research Colloquium
Haris Setyawan
Universitas Muhammadiyah Yogyakarta
Religion Agriculture, Biological Sciences & Forestry Humanities Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice Mathematics Medicine & Pharmacology Social Sciences

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Web-Based Data Information System for Students and Teachers at Al-Qur’an Education Parks in Kasihan, Bantul Teuku Syauqi Maulana; Haris Setyawan; Asroni Asroni
Emerging Information Science and Technology Vol 2, No 1: May 2021
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/eist.v2i1.16870

Abstract

Al-Qur’an Kindergarten (TKA) and Al-Qur’an Education Park (TPA) are non-formal religious education institutions emphasizing studying Islamic values. The development of society necessitates institutions that hold TKA/TPA-related information promptly and correctly. Currently, technology is required to aid an institution in maintaining data and providing TKA/TPA-related information. The data information system aims to provide data for the TKA/TPA coordination agency and ensure the implementation of the administrative order to assure the operational and development sustainability of the IT-based TKA/TPA operating regions. This research intends to develop a web-based information system that can handle the data of students and teachers to reduce the number of errors due to manual data management. A web-based application system for student and teacher data information in TPA in Kasihan was developed utilizing the CodeIgniter framework, the Hypertext Preprocessor (PHP) programming language, the MySQL database, the SDLC method, and the Prototype model. Due to its superiority, the website was selected as the application’s foundation since it is lightweight and could be accessed rapidly using a web browser and an internet or intranet connection to the server. This study has produced a web-based application constructed successfully and could be utilized as a data information system for TPA students and teachers in Bantul.
Sentiment Analysis of Public Responses on Indonesia Government Using Naïve Bayes and Support Vector Machine Haris Setyawan; Laila Ma’rifatul Azizah; Alvira Yusnia Pradani
Emerging Information Science and Technology Vol 4, No 1 (2023): May
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/eist.v4i1.18681

Abstract

Many people are interested in knowing how the public views President Joko Widodo's administration. Text Mining analysis can be one way to collect and analyze text data about Joko Widodo's administration and extract relevant information from the data. Data was obtained by collecting tweet data about Joko Widodo's government in 2022 on Twitter using Netlyitic. Then the Text Mining analysis of Joko Widodo's government was carried out using the Navie Bayes (NVB) classification and Support Vector Machine (SVM). This classification can be used to predict sentiment or public views of the government based on the tweets collected.  Based on a case study of the classification results of President Joko Widodo using Naive Bayesian classification, we obtained a precision value of 79%, a recall value of 91% and a precision value of 82%. And by using SVM, we get 85% precision, 95% recall, and 83% precision. Due to the high accuracy, recall, and precision, it can be said that SVM classification is more accurate than NVB.
Leafy AI: Integrating MobileNetV2 and TensorFlow Lite into a Flutter-Based Application for Real-Time Ornamental Plant Recognition Haris Setyawan; Nur Zareen Zulkarnain; Abian Ayatullah Fikri
JUITA: Jurnal Informatika JUITA Vol. 14 Issue 1, March 2026
Publisher : Department of Informatics Engineering, Universitas Muhammadiyah Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30595/juita.v14i1.28141

Abstract

Operating artificial intelligence on smartphones attracted interest in various applications, but in practice, device capacity limited AI capabilities. Limited processing power, restricted memory capacity, and unstable network connectivity could make AI models difficult to use outside lab environments. In this work, we describe Leafy AI, a mobile application that identifies ornamental plants designed to work fully on the device. The classifier is based on MobileNetV2 and trained with transfer learning using 67,200 images from 112 plant categories. Images were resized to 224 × 224 pixels and normalized before training. After training, the model was converted into TensorFlow Lite format and integrated within a Flutter application. A lightweight service layer manages preprocessing and inference so that the interface remains simple for the user. Evaluation using 13,440 test images achieved a top-one accuracy of 0.89. A smaller field experiment involving 226 photos captured under real-world conditions resulted in lower accuracy, primarily due to variations in lighting and background. Nevertheless, the system remained reliable in offline mode. The findings show that recognition of ornamental plants can be carried out on ordinary smartphones and that further improvements are possible through augmentation, domain adaptation, quantization, and hardware acceleration.
Co-Authors Abian Ayatullah Fikri Adiatsa Putra Santika Alvira Yusnia Pradani Arditya Nur Rokhim Asroni Asroni Asroni Asroni Bhaskara Satrya Priwandhana Deka Desilawati Elvan Diano Fahmi Azhar Iskandar Fidelia Farahi Irya Wisnubhadra Kurnianti, Apriliya Laila Ma’rifatul Azizah Muhammad Fadlun Subarkah Nur Zareen Zulkarnain Pascal Pahlevi Pasha Radityo Anindito Arighi Reza Giga Isnanda Risang Prakosa Teuku Syauqi Maulana