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All Journal Techno.Com: Jurnal Teknologi Informasi TELKOMNIKA (Telecommunication Computing Electronics and Control) Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) International Journal of Artificial Intelligence Research JURNAL MEDIA INFORMATIKA BUDIDARMA JITK (Jurnal Ilmu Pengetahuan dan Komputer) J-SAKTI (Jurnal Sains Komputer dan Informatika) JURIKOM (Jurnal Riset Komputer) CSRID (Computer Science Research and Its Development Journal) EDUMATIC: Jurnal Pendidikan Informatika Journal of Information Systems and Informatics Abdimasku : Jurnal Pengabdian Masyarakat Jurnal TIKOMSIN (Teknologi Informasi dan Komunikasi Sinar Nusantara) J-SAKTI (Jurnal Sains Komputer dan Informatika) Jurnal Teknologi Informasi Cyberku Jurnal Pengabdian Informatika (JUPITA) Journal of Computing Theories and Applications
Ahmad Zainul Fanani
Universitas Dian Nuswantoro
Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Library & Information Science Social Sciences Other

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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
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.
Brain Tumor Detection and Classification from MRI Images Using a Convolutional Neural Network Approach Andiharsa Sih Setiarto, Rahardian; Ahmad Zainul Fanani
JURNAL RISET KOMPUTER (JURIKOM) Vol. 13 No. 2 (2026): April 2026
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v13i2.9610

Abstract

Brain tumors are a serious neurological disease that require rapid and accurate diagnosis to improve treatment success. However, conventional interpretation of brain MRI images is often time-consuming and highly dependent on radiologists’ expertise, which may lead to diagnostic inconsistency. This study aims to develop a brain tumor detection and classification model from MRI images using a Convolutional Neural Network (CNN) approach. The dataset consists of four classes, namely glioma, meningioma, pituitary, and no tumor. The research stages include data collection, image preprocessing, model training, and evaluation using accuracy, loss, precision, recall, and F1-score. The results show that the CNN model achieved a training accuracy of 1.0000 at the final epoch, while the testing phase produced an accuracy of 58.75% with a loss value of 1.9600. These findings indicate that the model was able to learn important patterns from MRI images, although the gap between training and testing performance suggests overfitting. This study contributes to the development of AI-based medical image classification for brain tumor identification and shows that CNN has potential as a supportive tool for assisting medical personnel in brain tumor diagnosis. Further improvements can be achieved through data augmentation, hyperparameter tuning, and optimization of model architecture.
COMPARATIVE STUDY OF RESAMPLING TECHNIQUES FOR STUDENT PERFORMANCE PREDICTION USING SMOTE-ENN AND ENSEMBLE LEARNING Eni Heni Hermaliani; Ahmad Zainul Fanani; Heru Agus Santoso; Affandy
JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) Vol. 11 No. 3 (2026): JITK Issue February 2026
Publisher : LPPM Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/jitk.v11i3.8214

Abstract

This study analyzes the effectiveness of resampling techniques and ensemble learning in addressing class imbalance problems in student performance prediction using the xAPI-Edu-Data dataset from the Kalboard 360 LMS. The class imbalance ratio of 1:1.66 leads to bias in traditional classification models toward the majority class. The study evaluates six resampling methods, including hybrid SMOTE-ENN, combined with nine individual classifiers and three ensemble models (bagging, voting, and stacking). Evaluation was conducted using accuracy, precision, recall, and F1-score with stratified 5-fold cross-validation and hyperparameter optimization through GridSearchCV. The results indicate that the combination of SMOTE-ENN with voting and stacking achieved the best performance of 98.18% across all evaluation metrics and significantly improved minority-class recall, demonstrating its effectiveness for developing early warning systems to identify at-risk students.
Attention-Augmented GRU for Stock Forecasting: A Trade-Off Between Directional Accuracy and Price Prediction Error R. Daniel Hartanto; Guruh Fajar Shidik; Farrikh Alzami; Ahmad Zainul Fanani; Aris Marjuni; Abdul Syukur
Journal of Computing Theories and Applications Vol. 3 No. 4 (2026): JCTA 3(4) 2026
Publisher : Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/jcta.15863

Abstract

Attention mechanisms have been widely incorporated into recurrent neural network architectures for financial time series forecasting, with most prior work reporting improvements in price-level error metrics. This study revisits that claim through a controlled empirical comparison of four deep learning architectures on nearly two decades of Telkom Indonesia (TLKM) closing price data from the Indonesia Stock Exchange (IDX). The models evaluated are a three-layer Gated Recurrent Unit (GRU) baseline, a comparable Long Short-Term Memory (LSTM) network, a Bahdanau end-attention GRU (Attn-GRU-V2), and a multi-head self-attention GRU hybrid (Attn-GRU-V3). Each architecture is trained over 30 independent runs with distinct random seeds, and performance is reported as 95% confidence intervals derived from the t-distribution. Statistical comparisons employ the Wilcoxon signed-rank test, a nonparametric paired test appropriate given the confirmed non-normality of residuals. The main finding is a consistent trade-off: the plain GRU achieves the lowest RMSE (94.02 ± 1.22 IDR) across all 30 runs, while Attn-GRU-V2 achieves the highest directional accuracy (45.91 ± 0.09%), surpassing GRU in every independent run. Bahdanau attention weights are nearly uniform across the 30-day lookback window (coefficient of variation: 3.21%), indicating that the mechanism cannot identify selectively informative timesteps in this univariate price series. This finding is consistent with the weak-form Efficient Market Hypothesis for the Indonesian market. An ablation study reveals that a 20-day lookback window maximizes directional accuracy (47.72 ± 0.21%) for the Attn-GRU-V2 model. These results suggest that Bahdanau end-attention consistently and significantly improves directional accuracy relative to a plain GRU baseline, providing an architecturally attributable advantage for direction-based applications, even when absolute price-level error is not reduced. The directional accuracy values remaining below 50% across all models are consistent with a weak-form efficiency characterization of the Indonesian market.
Optimizing Stroke Prediction Using Backward Elimination and SMOTE with C4.5 and K-Nearest Neighbors Imam Bagus Pratama; Ahmad Zainul Fanani; M. Arief Soeleman; Via Indriani Kumalasari
Journal of Information System and Informatics Vol 8 No 2 (2026): April
Publisher : Asosiasi Doktor Sistem Informasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63158/journalisi.v8i2.1521

Abstract

Early prediction of stroke risk is crucial for reducing mortality and the burden on the healthcare system, but class imbalance and irrelevant features often compromise model reliability. This study analyzes the impact of Backward Elimination and SMOTE on the performance of the C4.5 and K-NN algorithms in stroke prediction. The study used a fixed working subset of 1,239 data points and evaluated four modeling scenarios using Stratified 10-Fold Cross Validation. Model performance was measured using accuracy, precision, recall, F1-score, and AUC. The results showed that Backward Elimination improved model performance on the analyzed subsets. For C4.5, accuracy increased from 70.94% to 73.05%, stroke recall from 83.94% to 85.14%, and AUC from 0.776 to 0.806. For K-NN, accuracy increased from 72.31% to 74.82% and precision from 39.91% to 42.73%, while stroke recall remained relatively stable at 74.30%. These findings indicate that although the improvements are small numerically, the results remain practically relevant as they enhance the balance between sensitivity and class discrimination capability. In the context of stroke screening, reducing false negatives is more important because it helps minimize undetected high-risk cases, although false positives still need to be considered as a consequence of further testing. Overall, C4.5 with Backward Elimination demonstrates more balanced performance, although the results are still limited to the analyzed subset.
Adab Menuntut Ilmu dalam Islam bagi Anak Asuh Lembaga Amil Zakat (LAZ) Universitas Dian Nuswantoro Yani Parti Astuti; Aripin Aripin; Dwi Nurul Izzhati; Jazuli Jazuli; Edy Mulyanto; Edi Faisal; Ahmad Zainul Fanani
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.
Co-Authors Abdul Syukur Abdul Syukur Affandy Affandy Affandy Affandy Affandy Alzami, Farrikh Ananda, Imanuel Khrisna Andiharsa Sih Setiarto, Rahardian Arif, Muhammad Husnul Aripin Aripin Aris Marjuni Arizal Irsyad Imanullah Arry Maulana Syarif Arry Maulana Syarif, Arry Maulana Arwahyu Sugito Aton Rustandi Mulyana Dedi Nurcipto, Dedi Dwi Nurul Izzhati Edi Faisal Edy Mulyanto Eni Heni Hermaliani Etika Kartikadarma Evi Rokhayati Fikri Budiman Guruh Fajar Shidik Gustina Alfa Trisnapradika Hanny Haryanto Heribertus Himawan Heru Agus Santoso Imam Bagus Pratama Indra Gamayanto Indrawan Setiaji Jazuli Jazuli Khafiizh Hastuti Khairul Fahmi Laksana, Deddy Award Widya Lakui Johary Linda Marlinda Moch Arief Soeleman Moch Hariadi Mochamad Hariadi Mochammad Ilham Aziz Muhammad Syaifur Rohman Nanang Lestio Wibowo Pujiono Pujiono R. Daniel Hartanto Rohman, Muhammad Syaifur Rudi Nurcahyo Ruri Suko Basuki Saraswati, Galuh Saraswati, Galuh Wilujeng Sari Ayu Wulandari Sendi Novianto Setiawan, Dicky Stefanus Santosa Sutriawan Tri Daryatni Usman Sudibyo Via Indriani Kumalasari Wicaksono , Duta Firdaus Winarsih, Nurul Anisa Sri Yani Parti Astuti Yulianto, Satria Pradana Rizki Yuliman Purwanto