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All Journal Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) JITK (Jurnal Ilmu Pengetahuan dan Komputer) KOMIK (Konferensi Nasional Teknologi Informasi dan Komputer) TIN: TERAPAN INFORMATIKA NUSANTARA MEANS (Media Informasi Analisa dan Sistem) Bulletin of Computer Science Research Sosial Simbiosis: Jurnal Integrasi Ilmu Sosial dan Politik
Abdullah Ahmad
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Aerospace Engineering Arts Humanities Automotive Engineering Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Earth & Planetary Sciences Economics, Econometrics & Finance Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Immunology & microbiology Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice Materials Science & Nanotechnology Mathematics Mechanical Engineering Medicine & Pharmacology Neuroscience Nursing Physics Social Sciences Veterinary Other

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SISTEM PENDUKUNG KEPUTUSAN PEMILIHAN KEPALA BAGIAN DISTRIBUSI PADA PDAM TIRTAULI PEMATANGSIANTAR DENGAN MENGGUNAKAN METODE SAW Ahmad, Abdullah; Solikhun, Solikhun; Andani, Sundari Retno
KOMIK (Konferensi Nasional Teknologi Informasi dan Komputer) Vol 2, No 1 (2018): Peranan Teknologi dan Informasi Terhadap Peningkatan Sumber Daya Manusia di Era
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (23.348 KB) | DOI: 10.30865/komik.v2i1.936

Abstract

The study, entitled "Supporting System for the Decision of the Head of Distribution Section at PDAM Tirtauli Pematangsiantar Using the SAW Method" aims to produce a Supportive System that is objective and systematic in determining the distribution heads who have the best qualifications. The research data is sourced from the relevant agencies in 2018. The data that is used as a reference for the requirements of the system requirements that are built is obtained from interviews with relevant agencies. From the interview, it was found that the problem of lack of objectivity and transparency of information in the process of determining the section head in any sector including the determination of the head of the distribution section is the absence of a standard or system used in determining the section head and only using manual selection method so that the determination of the section head is determined subjectively. In addition, the Candidates / alternatives recommended by PDAM Tirtauli themselves consist of 5 employees from a total employee population of 362 with the highest criteria and 5 criteria as the requirements for the assessment of the candidates for the head of the distribution department itself covering the criteria of responsibility (C1), achievement motivation (C2) , discipline (C3), communicative (C4), and working period (C5). This study uses the Simple Additive Weighting (SAW) method. With this system, agency leaders can produce a decision to choose the best distribution head more objectively.Keywords: Election of Distribution Section Head of PDAM Tirtauli Pematangsiantar, Decision Support System (SPK), Simple Additive Weighting (SAW)
Zakat Produktif Dalam Upaya Mengentaskan Kemiskinan (Studi Kasus: Baznas DKI Jakarta) Achmad Diponegoro; Abdullah Ahmad; Meylan Cahya Ningrum
Sosial Simbiosis : Jurnal Integrasi Ilmu Sosial dan Politik Vol. 1 No. 3 (2024): Agustus : Sosial Simbiosis : Jurnal Integrasi Ilmu Sosial dan Politik
Publisher : Lembaga Pengembangan Kinerja Dosen

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62383/sosial.v1i3.552

Abstract

Islam as a religion of rahmatan lil alamin, its teachings not only contain ritual worship, but also social worship. Zakat is a form of worship which is an obligation that must be fulfilled by every Muslim who has excess wealth. The potential figure for zakat in Indonesia is 327 trillion per year. The large potential zakat figures are not balanced with the welfare of the people, especially Muslims because the poverty rate in Indonesia is still high. The distribution of zakat in Indonesia is considered to be ineffective in overcoming the problem of poverty. BAZNAS as a national zakat management institution is now following in the footsteps of implementing productive zakat, although there are not many, but in Jakarta there are already BAZNAS that accept productive zakat, one of which is BAZNAS DKI Jakarta. This research was conducted to see the efforts and strategies of BAZNAS DKI Jakarta in managing productive zakat to reduce poverty. This research uses a qualitative descriptive method with case study techniques. Data was obtained from interviews with BAZNAS sources as primary informants and the community as secondary informants. The results of the research show that the role of productive zakat in alleviating poverty is just helping the mustahik in running a selling business and has not provided any significant changes to the mustahik, and BAZNAS DKI Jakarta has three strategies in managing productive zakat, namely direct assistance (cash), indirect assistance. directly (products), and empowerment of disabled people.
Bird and Drone Image Classification Using ResNet CNN: A Deep Learning Approach for Aerial Surveillance Ahmad, Abdullah; Anjar Wanto; Adnan, Syed Muhammad
Bulletin of Computer Science Research Vol. 5 No. 4 (2025): June 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v5i4.545

Abstract

Accurate classification of bird and drone images is crucial in supporting aerial surveillance and security systems, particularly to distinguish between natural objects such as birds and man-made objects such as drones. Manual classification methods have limitations in terms of speed and accuracy, thus necessitating a more efficient and reliable technology-based approach. This study aims to implement a ResNet-50 based Convolutional Neural Network (CNN) architecture to automatically classify bird and drone images. The dataset used was obtained from the Kaggle platform and consists of two classes: Bird and Drone, with a total of 22,407 images. The data was split into training (17,323 images), testing (844 images), and validation (1,740 images). All images underwent preprocessing and augmentation steps to enhance data quality and model training performance. The model was developed using the ResNet-50 architecture, which is well-regarded for handling complex image classification tasks. Evaluation results show that the model achieved an accuracy of 92%. For the Bird class, a precision of 0.83 and a recall of 0.99 were obtained, while for the Drone class, precision reached 0.99 and recall was 0.86. The average F1-score of 0.92 indicates that the model delivers balanced and reliable performance in the binary image classification task.
Implementasi Data Mining Menggunakan Algoritma Apriori Pada Penjualan Sepeda Motor Jenis Honda (Studi Kasus : Showroom Honda Arista Pematangsiantar) Sinaga, Lestari; Ahmad, Abdullah; Safii, Muhammad
MEANS (Media Informasi Analisa dan Sistem) Volume 5 Nomor 1
Publisher : LPPM UNIKA Santo Thomas Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (964.229 KB) | DOI: 10.54367/means.v5i1.518

Abstract

Showroom Honda Arista merupakan salah satu showroom atau Dealer yang menyediakan jual beli kendaraan sepeda motor Honda dengan berbagai type dan harga. Data mining ialah serangkaian proses untuk menggali nilai tambah dari suatu kumpulan data berupa pengetahuan yang selama ini tidak diketahui secara manual. Salah satu metode yang data mining yang digunakan dalam penelitian ini adalah Metode Apriori. Metode Apriori merupakan salah satu teknik data mining yang berfungsi untuk menemukan aturan asosiatif antara suatu kombinasi item. Barang yang dianalisis yaitu barang yang dipesan bersamaan dengan barang lainnya atau dengan kata lain pemesanan lebih dari satu barang namun tetap melibatkan data pemesanan keseluruhan. Adapun item-item yang masuk kedalam data penjualan ialah Revo, Supra, Beat, Vario, Sonic, PCX, CB, Scoopy, Megapro, Verza, CBR. Hasil dari association yang berupa informasi mengenai Honda merk apa saja yang dibeli secara bersamaan oleh konsumen, dapat digunakan sebagai bahan pertimbangan untuk menetapkan Strategi Pemasaran dan pada data transaksi. Kata kunci: Showroom Honda, Data Mining, Apriori
Optimization of Accuracy Improvement through Modified ShuffleNet Architecture in Rice Classification Ahmad, Abdullah; Hartama, Dedy; Windarto, Agus Perdana; Wanto, Anjar
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 9 No 5 (2025): October 2025
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v9i5.6411

Abstract

Accurate rice classification is essential to determine the quality and market value of rice. Traditional methods of rice classification are often time-consuming and error-prone, so a more efficient and accurate solution is needed. This study aims to optimize rice classification using Convolutional Neural Networks (CNN) combined with the ShuffleNet architecture, which offers high computational efficiency without sacrificing accuracy. The dataset used comes from Kaggle, containing 8750 rice grain images divided into five classes: Arborio, Basmati, Ipsala, Jasmine, and Karacadag. The uniqueness of this study is the application of ShuffleNet Proposed in rice classification, which provides improved performance compared to basic CNN models such as MobileNet, ShuffleNet, and RestNet. The results showed that the MobileNet model achieved 80% accuracy, RestNet 94%, and ShuffleNet achieved 100% accuracy with precision, recall, and F1 values also 100%. However, the ShuffleNet model experienced overfitting when tested with new data, resulting in an accuracy of only 20%. To overcome this, further optimization was carried out on the model. The results of statistical tests (paired t-test and Wilcoxon test) show significant differences between ShuffleNet Proposed and other models, which proves that the improvements applied to this model provide significant improvements. The implications of this study can improve the efficiency and accuracy of rice classification, which has the potential to improve the quality and market value of rice in the agricultural industry.
Comparative Study of Mobilenet and Resnet for Watermelon Leaf Disease Classification Using Deep Learning Ahmad, Abdullah; Wanto, Anjar; Windarto, Agus Perdana; Poningsih, Poningsih
TIN: Terapan Informatika Nusantara Vol 6 No 1 (2025): June 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/tin.v6i1.7543

Abstract

Watermelon leaf diseases, caused by various factors such as fungi, viruses, and bacteria, can have a significant impact on agricultural yields. To increase the amount and quality of watermelon produced, early diagnosis of this disease is essential. This study aims to compare the performance of two Convolutional Neural Networks (CNN) architectures included in Deep Learning, namely MobileNet and ResNet, in classifying watermelon leaf diseases using a dataset taken from Kaggle. This dataset consists of 1000 watermelon leaf images with three conditions, namely Downy Mildew (380 images), Healthy (205 images), and Mosaic Virus (415 images). ). 95% accuracy, 96% precision, 94% recall, and 95% f1-score are the results of the MobileNet model. In contrast, the ResNet model performs better, with 97% accuracy, 96% precision, 97% recall, and 97% f1-score. The study's findings show that ResNet outperforms MobileNet in the classification of watermelon leaf illnesses, despite both models' excellent and effective performance for automatic plant disease detection applications.
OPTIMIZING SHUFFLENET WITH GRIDSEARCHCV FOR GEOSPATIAL DISASTER MAPPING IN INDONESIA Ahmad, Abdullah; Hartama, Dedy; Solikhun, Solikhun; Poningsih, Poningsih
JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) Vol. 11 No. 2 (2025): JITK Issue November 2025
Publisher : LPPM Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/jitk.v11i2.6747

Abstract

Accurate classification of natural disasters is crucial for timely response and effective mitigation. However, conventional approaches often suffer from inefficiency and limited reliability, highlighting the need for automated deep learning solutions. This study proposes an optimized Convolutional Neural Network (CNN) based on the lightweight ShuffleNet architecture, enhanced through GridSearchCV for systematic hyperparameter tuning. Using a geospatial dataset of 3,667 images representing earthquake, flood, and wind-related disasters in Indonesia, the optimized ShuffleNet model achieved a peak accuracy of 99.97%, outperforming baseline CNNs such as MobileNet, GoogleNet, ResNet, DenseNet, and standard ShuffleNet. While these results demonstrate the potential of combining lightweight architectures with automated optimization, the exceptionally high performance also indicates possible risks of overfitting and dataset bias due to limited variability. Therefore, future research should validate this approach using larger, multi-source datasets to ensure robustness and real-world applicability
Co-Authors Achmad Diponegoro Adnan, Syed Muhammad Agus Perdana Windarto Hartama, Dedy Meylan Cahya Ningrum Muhammad Safii Poningsih Poningsih, Poningsih Sinaga, Lestari Solikhun Solikhun, Solikhun Sundari Retno Andani Wanto, Anjar