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Pemanfaatan Teknologi QR Code Dalam Menunjang Kegiatan Pada RPTRA Mardani Asri Yoseph Tajul Arifin; Oky Irnawati; Sri Watmah; Yesni Malau
Jurnal Abdimas Komunikasi dan Bahasa Vol. 3 No. 2 (2023): Desember
Publisher : LPPM Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/abdikom.v3i2.2914

Abstract

Attendance is an activity that determines a person's level of discipline in an organization, school or company. Attendance levels can influence the productivity and sustainability of an organization's processes. Unclear attendance procedures or inconsistent attendance can result in errors in recording the attendance of participants in RPTRA activities, as well as reducing the motivation of administrators and participants in RPTRA activities. Mardani Asri Child-Friendly Integrated Public Space (RPTRA) is a public space concept in the form of an open space with human resources who are active as administrators at the Mardani Asri RPTRA consisting of PKK mothers, youth youth groups and local residents who have other jobs such as kindergarten teachers. It has activities including 10 PKK Programs and Sapa Posts, Disaster Services, Reading Gardens, Sports Places, Playgrounds. Problems related to absenteeism at RPTRAs (Child-Friendly Integrated Public Spaces) can be an important thing to pay attention to in RPTRA management because they can affect the effectiveness of programs and facility management. Utilizing QR Code technology for presence in RPTRA business management can help optimize operations and increase the effectiveness of existing work programs at the RPTRA. So training is needed on the use of QR Code technology to support the activities of the Mardani Asri RPTRA. The outputs to be achieved include articles in electronic media released on website media, as well as increasing knowledge and utilization of QR Code technology which can be implemented for activities at the Mardani Asri RPTRA. As for the results of this Community Service activity, RPTRA Mardani Asri participants were able to gain insight into QR Code technology and implement it for organizational needs. Kehadiran merupakan aktivitas yang menentukan tingkat kedisiplinan seseorang dalam suatu organisasi, sekolah maupun perusahaan. Tingkat kehadiran dapat berpengaruh pada produktifitas dan keberlangsungan proses suatu organisasi. Ketidakjelasan prosedur absensi atau absensi tidak konsisten dapat mengakibatkan kesalahan dalam mencatat kehadiran peserta dalam kegiatan-kegiatan di RPTRA, serta menurunkan motivasi pengurus maupun peserta kegiatan di RPTRA. Ruang Publik Terpadu Ramah Anak (RPTRA) Mardani Asri adalah konsep ruang publik berupa ruang terbuka dengan SDM yang aktif menjadi pengurus di RPTRA Mardani Asri terdiri dari Ibu-Ibu PKK, pemuda karang taruna serta warga sekitar yang memiliki pekerjaan lain seperti Guru TK. Memiliki kegiatan diantaranya 10 Program PKK dan Pos Sapa, Layanan Kebencanaan, Taman Bacaan, Tempat Berolahraga, Playground. Permasalahan terkait absensi di RPTRA (Ruang Publik Terpadu Ramah Anak) dapat menjadi hal yang penting untuk diperhatikan dalam manajemen RPTRA karena dapat mempengaruhi efektivitas program dan pengelolaan fasilitas. Pemanfaatan teknologi QR Code untuk presensi dalam manajemen usaha RPTRA dapat membantu mengoptimalkan operasional dan meningkatkan efektivitas program kerja yang ada di RPTRA tersebut. Maka dibutuhkan pelatihan pemanfaatan teknologi QR Code dalam menunjang kegiatan RPTRA Mardani Asri. Adapun luaran yang ingin dicapai antara lain artikel di media elektronik yang direlease dalam media website, serta peningkatan pengetahuan dan pemanfaatan teknologi  QR Code yang dapat diimplementasikan untuk kegiatan-kegiatan yang ada di RPTRA Mardani Asri.  Adapun hasil  dari kegiatan Pengabdian Masyarakat ini peserta RPTRA Mardani Asri dapat menambah wawasan mengenai teknologi QR Code dan mengimplementasikannya untuk kebutuhan organisasi.
Comparative Analysis of Transfer Learning-Based Deep Learning Models for Jatropha Leaf Disease Classification Sarifah Agustiani; Sulistiyah; Agus Junaidi; Cucu Ika Agustyaningrum; Yoseph Tajul Arifin
SMATIKA JURNAL : STIKI Informatika Jurnal Vol 16 No 02 (2026): SMATIKA Jurnal : STIKI Informatika Jurnal
Publisher : LPPM Universitas Bhinneka Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32664/smatika.v16i02.2325

Abstract

Plant disease identification is essential for enhancing agricultural productivity and promoting sustainable crop management practices. Jatropha curcas has considerable potential as a biofuel-producing plant; however, its growth and productivity can be significantly affected by various leaf diseases. Conventional disease diagnosis often requires substantial time and relies heavily on expert knowledge, creating a need for automated solutions based on deep learning techniques. Although deep learning has been widely applied in plant disease recognition, comparative studies focusing on transfer learning models for Jatropha leaf disease classification remain limited, particularly for datasets characterized by distinctive visual features and relatively small sample sizes. This research conducts a comparative assessment of several deep learning architectures to determine the most effective model for classifying Jatropha leaf diseases. The evaluated architectures include MobileNetV2, EfficientNetB0, ResNet50, DenseNet121, and VGG16. All models utilized ImageNet pre-trained weights and were adapted through fine-tuning of the final classification layers to accommodate a dataset containing healthy and diseased Jatropha leaf images. Experimental findings reveal that ResNet50 achieved the highest classification accuracy of 93.81%, followed by VGG16 at 93.58% and EfficientNetB0 at 90.49%. In comparison, DenseNet121 and MobileNetV2 attained accuracies of 85.40% and 74.56%, respectively. Model effectiveness was assessed using accuracy, training duration, confusion matrix analysis, and ROC curve evaluation to examine classification capability across categories. The results demonstrate that ResNet50 offers the most balanced combination of predictive accuracy and performance stability. Overall, the study confirms that transfer learning-based deep learning models are highly effective for Jatropha leaf disease classification, with ResNet50 emerging as the most suitable architecture among those investigated. These findings may serve as a valuable reference for the development of reliable and efficient plant disease detection systems in agricultural environments.
Analisis Kepuasan Pelanggan Terhadap Layanan Parking LTC Glodok Menggunakan Algoritma C 4.5 Muhammad Kharis Akbar; Yoseph Tajul Arifin; Rizky Ade Safitri; Cucu Ika Agustyaningrum
INFORMATICS FOR EDUCATORS AND PROFESSIONAL : Journal of Informatics Vol. 10 No. 1 (2025): INFORMATICS FOR EDUCATORS AND PROFESSIONAL : JOURNAL OF INFORMATICS (Juni 2025
Publisher : Lembaga Penelitian dan Pengabdian Masyarakat Universitas Bina Insani

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51211/itbi.v10i1.3491

Abstract

Dengan berkembangnya teknologi informasi dan transportasi, kebutuhan akan parkir yang aman dan nyaman menjadi semakin penting dalam kehidupan perkotaan modern. Penggunaan teknologi dalam mengelola layanan parkir telah menjadi semakin umum, dengan banyak perusahaan yang menawarkan layanan parkir aman dengan menggunakan sistem keamanan modern seperti Secure Parking. Penelitian ini bertujuan untuk menganalisis kepuasan pelanggan pada layanan Parkir LTC Glodok dengan menggunakan algoritma C4.5. Metode ini digunakan untuk mengidentifikasi faktor-faktor yang paling berpengaruh terhadap tingkat kepuasan pelanggan, dimana terdapat 2 komponen utama variabel penukuran yaitu Kualitas dan Jaminan, dan Informasi. Data diperoleh melalui penyebaran kuesioner sebanyak 100 responden menggunakan teknik pengambilan sampel Lemeshow. Proses uji penelitian ini menggunakan software RapidMinner untuk membuat pohon keputusan. Setelah dilakukan pengujian diperoleh performa yang cukup baik menggunakan algoritma C4.5 dimana diperoleh nilai akurasi sebesar 90.00% dan didapat pengetahuan berupa pohon keputusan bahwa terdapat beberapa faktor yang mempengaruhi kepuasan pelanggan diantaranya Kualitas, Informasi, dan tarif. Dari hasil analisis tersebut didapatkan hasil puas sebesar 91% dan tidak puas sebesar 9%.