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All Journal Jurnal Informatika Global Jurnal Teknik Informatika dan Teknologi Informasi
Mustafa Ramadhan
Universitas Indo Global Mandiri
Computer Science & IT

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SISTEM INFORMASI PENGOLAHAN ZAKAT DAN INFAQ PADA MASJID AGUNG PALEMBANG Andrian Novansyah; Hastha Sunardi; Mustafa Ramadhan
Jurnal Informatika Global Vol 6, No 2
Publisher : UNIVERSITAS INDO GLOBAL MANDIRI

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (466.808 KB) | DOI: 10.36982/jiig.v6i1.7

Abstract

Zakat is a certain amount of assets that must be issued by a person who is Muslim and given to groups who deserve it (the poor, ibn sabil, fisabilillah and so on). Processing zakat includes implementation, collection and distribution of zakat. Processing Zakat and Infaq in Palembang Grand Mosque has a system for recording and distribution of zakat funds and infaq which still has the disadvantage that the recording system is still using the manual method that allows the loss or corruption of data, the registration process is slow to make the queue is getting and the process of distributing zakat increasingly delayed and slow report generation. Therefore, researchers propose Processing Information Systems Zakat and Infaq that can overcome the problems found in the previous system, enabling the recording, processing and printing of data is done quickly, precisely. Development of systems using the Waterfall method as the flow of system development. By creating a Data Flow Diagrams (DFD), Flowchart, Data Dictionary, Entity Relationship Diagram (ERD), Data Normalization, File Specifications, Process Specifications, Chart Structured as a tool in the analysis and design. The programming language used Visual Basic 6.0 and Microsoft Access 2007 as the database. These systems process data muzakki, mustahiq, infaq, operating expenses, zakat fitrah, zakat maal, zakat income, the distribution of zakat, reports. With applicable Zakat Processing Information Systems and Infaq expected to facilitate the processing of zakat and infaq.
Pengaruh TIngkat Skala Keabuan Terhadap Akurasi Klasifikasi Jenis Ikan Melalui Citra Sisik Ikan Menggunakan Jaringan Syaraf Tiruan Gilang Hadi Ramadhan; Gasim Gasim; Mustafa Ramadhan
Jurnal Teknik Informatika dan Teknologi Informasi Vol. 5 No. 2 (2025): Agustus: Jurnal Teknik Informatika dan Teknologi Informasi
Publisher : Lembaga Pengembangan Kinerja Dosen

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55606/jutiti.v5i2.5796

Abstract

This study was conducted to examine the effect of grayscale image variations on the accuracy of fish species recognition by utilizing fish scale images through the Artificial Neural Network (ANN) method. Automatic fish species identification plays a crucial role in the fisheries sector, both for research purposes, marine resource monitoring, and trade processes. One factor that can influence recognition accuracy is the quality of image representation, including the grayscale level used. Therefore, this study aims to analyze how much grayscale level variations affect fish species classification results. This research method uses a dataset consisting of 180 scale images for each fish species. Of these, 150 images are used as training data and 30 images as test data. The feature extraction process is carried out using the Gray Level Co-occurrence Matrix (GLCM) method, which utilizes contrast, energy, homogeneity, correlation, and entropy parameters. These features are then used as input to the ANN for the classification process. The analysis was conducted by comparing the accuracy results of various grayscale levels, namely 16, 32, 64, 128, and 256 levels. The results showed that variations in grayscale significantly influenced the accuracy level of fish species recognition. The highest accuracy was obtained at a scale of 256 levels with a value of 96%, followed by a scale of 128 levels at 95%, 64 levels at 92.5%, 32 levels at 84.2%, and the lowest at 16 levels with an accuracy of only 82.5%. In conclusion, the higher the variation in grayscale levels used, the better the recognition accuracy obtained. Thus, the use of images with 256 grayscale levels is recommended for research on fish scale image classification using the ANN method because it is able to provide the most optimal results.
Pengembangan Aplikasi Augmented Reality Berbasis Android dalam Pembelajaran Geometri Bangun Ruang di Sekolah Dasar Putri Shabira Pratiwi; Shinta Puspasari; Mustafa Ramadhan
Jurnal Teknik Informatika dan Teknologi Informasi Vol. 5 No. 2 (2025): Agustus: Jurnal Teknik Informatika dan Teknologi Informasi
Publisher : Lembaga Pengembangan Kinerja Dosen

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55606/jutiti.v5i2.5853

Abstract

This research aims to develop an Augmented Reality (AR) application for Android devices to support the teaching of three-dimensional geometry in primary education. The development process employed the Multimedia Development Life Cycle (MDLC) model, which consists of six stages: concept, design, material gathering, assembly, testing, and distribution. The application is designed to display three-dimensional representations of basic geometric figures such as cubes, cuboids, prisms, pyramids, cylinders, cones, and spheres. In addition to visual models, the application also provides related mathematical equations and explanatory commentary to strengthen students’ conceptual understanding. The testing phase demonstrated that the AR application is capable of presenting 3D objects with clarity and stability when viewed from a distance of 10–30 cm and within an angle range of 45° to 135°. These conditions ensure that the objects remain easily recognizable and interactive in classroom learning environments. User feedback from both teachers and students highlighted the engaging nature of the application, particularly in fostering motivation, improving visualization skills, and encouraging interactive learning experiences. Overall, the findings suggest that this AR-based application can serve as an effective educational tool for primary school students, bridging the gap between abstract geometry concepts and practical visualization. By integrating modern technology into mathematics instruction, the application has the potential to enhance both comprehension and interest in learning geometry while supporting innovative digital learning practices in the classroom.
Pengaruh Tingkat Pencahayaan Pemotretan Urat Daun terhadap Tingkat Akurasi Pengenalan Jenis Bibit Mangga Menggunakan Metode Pengenalan JST-PB dan Fitur LBP Suci Aulia Ramadhani; Gasim Gasim; Mustafa Ramadhan
Jurnal Teknik Informatika dan Teknologi Informasi Vol. 5 No. 1 (2025): April: Jurnal Teknik Informatika dan Teknologi Informasi
Publisher : Lembaga Pengembangan Kinerja Dosen

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55606/jutiti.v5i1.5854

Abstract

Mango (Mangifera indica L.) is one of the most important tropical fruits with high nutritional value and significant economic potential. However, manual identification of mango seedlings remains less accurate due to the similarities in leaf shape and size among different varieties, which often leads to misclassification. This study aims to develop an automated system to recognize five types of mango seedlings—Harum Manis, Indramayu, Golek, Madu, and Gedong Gincu by utilizing leaf vein textures as the main distinguishing features. The methodology employed the Local Binary Pattern (LBP) technique for feature extraction and a Backpropagation Neural Network (BPNN) as the classification model. The dataset consisted of 250 training images and 125 testing images with a resolution of 100×100 pixels, captured under varying lighting conditions ranging from one to five lamps. The experimental results indicate that lighting conditions significantly affect classification accuracy. The highest accuracy was achieved under four-lamp lighting conditions, reaching 91.20%, followed by two lamps (89.60%), three lamps (87.20%), five lamps (76.80%), and one lamp (67.20%). Furthermore, a BPNN configuration with 12 hidden neurons consistently demonstrated reliable recognition performance. These findings suggest that the combination of LBP and BPNN is effective for automatic classification of mango seedlings. The implementation of this system has the potential to assist farmers and seedling institutions by improving efficiency, accuracy, and reliability in seedling identification, thereby supporting the advancement of technology-based agriculture.
Co-Authors Andrian Novansyah Gasim Gasim Gilang Hadi Ramadhan Putri Shabira Pratiwi Shinta Puspasari Suci Aulia Ramadhani Sunardi, Hastha