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Thematic Analysis and Game-based Learning for the Development of Virtual Cultural Heritage Museums as Learning Agents Fanani, Ahmad Zainul; Syarif, Arry Maulana; Laksana, Deddy Award Widya; Himawan, Heribertus; Haryanto, Hanny
Techno.Com Vol. 24 No. 2 (2025): Mei 2025
Publisher : LPPM Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/tc.v24i2.12723

Abstract

This study aims to develop a virtual cultural heritage museum as a learning agent. Qualitative approach with thematic analysis method was applied to design a virtual museum based on the perspective of museum management as a provider of learning facilities. The opinions collected were in the form of challenges and obstacles in functioning the museum as a provider of learning facilities. Opinions were used to identify the theme of the virtual museum, and synthesized with six strategies in effective learning. The resulting synthesis was then used to develop a virtual museum model using a game-based learning approach. The Photogrammetry technique was used for 3D reconstruction of cultural heritage objects to achieve high precision results, both in terms of shape and texture. The evaluation conducted using the User Accaptence Test technique shows that the proposed model and method can actualize the characteristics of effective learning strategies.   Keywords - Thematic analysis, Game-based learning, Learning agent, Virtual museums, Photogrammetry
DEEP LEARNING JARINGAN SARAF TIRUAN UNTUK PEMECAHAN MASALAH DETEKSI PENYAKIT DAUN APEL Sutriawan, Sutriawan; Fanani, Ahmad Zainul; Alzami, Farrikh; Basuki, Ruri Suko
Jurnal Teknologi Informasi dan Komunikasi (TIKomSiN) Vol 11, No 1 (2023): Jurnal TIKomSiN, Vol. 11, No. 1, April 2023
Publisher : STMIK Sinar Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30646/tikomsin.v11i1.729

Abstract

Diseases on apple leaves are becoming a major issue for apple growers since they can cause the crop to fail. Due to the diversity of diseases that can affect apple leaves, it can be challenging for farmers to determine the cause of leaf damage. The purpose of this research is to evaluate a convolutional neural network (CNN) method for its potential use in solving the problem of apple leaf disease identification. Four types of illness are dealt with: normal, multi-illness, rusty, and scabby. Many methods, such as data preparation and a preset VGG-16 artificial neural network (CNN) architecture, are recommended for use in the deep artificial neural network processing method. The most precise outcomes occurred when the beta parameter value was set to 2 = 0.999 at Ephoch to 85/100 with an accuracy of 0.7582, and when the epsilon parameter value was set to 1e-07 at Ephoch to 32/100 with an accuracy of 0.7582 with the best accuracy.
PENDAMPINGAN PENGELOLAAN SAMPAH BERBASIS APLIKASI DIGITAL DI KELURAHAN GISIKDRONO SEMARANG BARAT Saraswati, Galuh; Gustina Alfa Trisnapradika; Abdul Syukur; Ahmad Zainul Fanani; Lakui Johary
Jurnal Pengabdian Informatika Vol. 2 No. 2 (2024): JUPITA Volume 2 Nomor 2, Februari 2024
Publisher : Jurusan Informatika, Fakultas Matematika dan Ilmu Pengetahuan Alam, Universitas Udayana

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Kelurahan Gisikdrono memiliki 1 bank sampah bernama “KARYA IBU” yang berada di RW 10 yang memiliki pengurus berjumlah 12 orang dan memiliki nasabah sebanyak 97 orang. Bank sampah tersebut merasa kesulitan dalam melakukan pendataan sampah dikarenakan semakin banyaknya jumlah nasabah dan pencatatan masih dilakukan secara manual. Terkadang pengelola dibidang adiministrasi harus mencari data dalam buku batik satu -persatu agar mendapatkan nama nasabah yang dicari selain itu pengelola kesulitan saat mencari record pengambilan tabungan sampah. Untuk menghadapi masalah terbut penulis memperkenalkan aplikasi SIKECIK yang digunakan untuk mengelola data bank sampah meliputi pemilihan sampah, penyetoran sampah, penimbangan sampah serta pencatatan dan hasil sampah yang dapat diakses melalui aplikasi, sehingga para pengelola dapat melakukan manajemen pendataan pengelolaan sampah secara mudah dan nasabah sampah/warga dapat melihat data tabungan sampah secara cepat tanpa harus datang ke bank sampah. Metode yang digunakan adalah ABCD (Asset Based For Community Development) terdiri dari Wawancara Apresiatif, pemetaan potensi masyarakat, Tautan dan Mobilisasi Aset, penyusunan Rencana Aksi dan prioritas kegiatan, Monitoring dan evaluasi. Hasilnya menunjukan bahwa Masyarakat Kelurahan Gisikdrono memahami pentingnya pemanfaatan teknologi untuk pengelolan sampah di wilayahnya. Sebagai kesimpulan, kegiatan ini berhasil memperkenalkan SIKECIK untuk meningkatkan kesadaran akan bijak mengelola sampah
Adab Menuntut Ilmu dalam Islam bagi Anak Asuh Lembaga Amil Zakat (LAZ) Universitas Dian Nuswantoro Astuti, Yani Parti; Aripin, Aripin; Izzhati, Dwi Nurul; Jazuli, Jazuli; Mulyanto, Edy; Faisal, Edi; Fanani, Ahmad Zainul
ABDIMASKU : JURNAL PENGABDIAN MASYARAKAT Vol 9, No 1 (2026): JANUARI 2026
Publisher : LPPM UNIVERSITAS DIAN NUSWANTORO

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/ja.v9i1.3190

Abstract

Lembaga Amil Zakat (LAZ) Udinus merupakan suatu lembaga yang berada di bawah organisasi Pusat Aktivitas Muslim (PAM) Udinus. LAZ mempunyai kegiatan yang sudah bertahun tahun dilaksanakan yaitu membina anak – anak asuh yang mana mereka mendapatkan bantuan untuk membayar sekolah setiap bulannya. Akan tetapi mereka harus mengikuti kegiatan yang wajib yang harus dilaksanakan yaitu mengikuti kajian setiap minggunya. Pada kegiatan tersebut mereka diberikan ilmu – ilmu yang berkaitan dengan ajaran Islam. Salah satunya adalah memberikan pendampingan dan pengarahan tentang adab menuntut ilmu dalam Islam. Hal ini dikarenakan semua anak – anak LAZ adalah peserta didik SD, SMP, SMA/K dan Pondok Pesantren. Namun untuk yang di Pondok Pesantren tidak dilakukan pendampingan. Dengan adanya pendampingan untuk anak SD, SMP, SMA/K ini, diharapkan mereka menjalankan adab menuntut ilmu yang sesuai dengan ajaran Islam. Selain harus sesuai dengan ajaran Islam, mereka juga diberikan pengertian tentang ilmu dan teknologi. Teknologi sangat berpengaruh terhadap ilmu yang dijalankan pada masa sekarang. Untuk itu mereka diberikan cara untuk menyikapi teknologi jaman sekarang dengan adab menuntut ilmu menurut ajaran Islam. Dengan pendampingan ini, diharapkan anak – anak Islam bisa menuntut ilmu menurut perkembangan jaman yang tidak menyimpang dengan ajaran Islam. Menuntut ilmu di sini harus didasari dengan rasa tanggung jawab yang bisa membentuk karakter positif bagi anak – anak LAZ.
Underwater image enhancement with fuzzy histogram equalization and adaptive color correction Suharyanto, Suharyanto; Andono, Pulung Nurtantio; Fanani, Ahmad Zainul; Pujiono, Pujiono
International Journal of Advances in Intelligent Informatics Vol 12, No 1 (2026): February 2026
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/ijain.v12i1.2174

Abstract

Marine exploration continues to increase as new technologies, such as computer vision implemented in underwater vehicles and robots, develop. Identifying underwater objects is challenging due to environmental conditions, including poor lighting and color absorption in the viewed image. Underwater image enhancement has been widely applied to overcome these obstacles. Therefore, this study presents a new workflow for improving the quality of underwater images. A combination of the fuzzy histogram equalization (FHE) and adaptive color correction (ACC) methods is used to increase contrast and restore absorbed colors. This study proposes combining FHE and ACC to improve underwater image quality, using the FHE method with the FHEACC method. The results of the UIQM and ENTROPY metrics obtained the highest values, while UCIQE ranked third. This shows that the image quality improved using the FHEACC combination method is objectively better than that achieved with the HE, AHE, CLAHE, FHE, IBLA, RCP, and UDCP methods, especially in maintaining color balance. This research can introduce a new workflow to improve the quality of underwater images by combining Fuzzy Histogram Equalization and Adaptive Color Correction methods, thereby supporting the optimization of underwater image identification systems in wild environments using computer vision technology.
Perbandingan Performa Algoritma Random Forest dan XGBoost dalam Memprediksi Hujan di Area Gunung Ungaran Arizal Irsyad Imanullah; Ahmad Zainul Fanani
JURNAL RISET KOMPUTER (JURIKOM) Vol. 13 No. 1 (2026): Februari 2026
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v13i1.9416

Abstract

Hiking activities in Mount Ungaran are frequently hindered by extreme and unpredictable weather changes, which potentially endanger the safety of hikers. One of the primary challenges in developing an automated rainfall prediction model for this region is the class imbalance in historical meteorological data, where the number of non-rainy days significantly dominates rainfall events. This condition often causes machine learning models to become biased toward the majority class, leading to a failure in detecting actual rainfall events (false negatives). This study aims to address this issue through a comparative analysis of the performance of two popular ensemble algorithms, namely Random Forest and Extreme Gradient Boosting (XGBoost). Specifically, this research investigates the impact of applying the Synthetic Minority Oversampling Technique (SMOTE) to balance the training data distribution in order to enhance minority class detection accuracy. Using the ERA5 reanalysis daily dataset for the 2019–2023 period with input variables including temperature, humidity, air pressure, and wind speed, the models were trained and validated using a time-based split method with an 80:20 ratio. Performance evaluation was conducted comprehensively using accuracy, precision, recall, and F1-score metrics. The results provide strong empirical evidence that the application of SMOTE yields the most optimal impact on the XGBoost algorithm. The combined XGBoost-SMOTE model successfully achieved the best performance with an accuracy of 80.50% and an F1-score of 83.23%, outperforming the Random Forest model which remained at an accuracy of 78.21%. In conclusion, the integration of boosting methods with data resampling techniques proves to be highly effective in improving rainfall prediction reliability in regions with complex topography.