Article Per Year (5 Year)
p-Index From 2021 - 2026
0.444
P-Index
This Author published in this journals
All Journal JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Jurnal Aksioma Al-Musaqoh
Latif, Ahmad
Unknown Affiliation
Religion Computer Science & IT Economics, Econometrics & Finance Education

Published : 2 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search

 1 
AT ANALISIS TEKNIK KEMITRAAN DALAM MENINGKATKAN KEPERCAYAAN MASYARKAT UNTUK ZAKAT, INFAK DAN SEDEKAH PADA LEMBAGA AMIL ZAKAT: ANALISIS TEKNIK KEMITRAAN DALAM MENINGKATKAN KEPERCAYAAN MASYARKAT UNTUK ZAKAT, INFAK DAN SEDEKAH PADA LEMBAGA AMIL ZAKAT Latif, Ahmad; Budiman; Hidayat, Deden; Daryono, Setyabudi
e-Jurnal Aksioma Al-Musaqoh : Journal of Islamic Economics and Business Studies Vol 7 No 1 (2024): JUNI 2024
Publisher : STAI La Tansa Mashiro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55171/jam.v7i1.1045

Abstract

The phenomenon that occurs in the institution is a symptom that can be observed, the establishment of an amil zakat institution in the midst of the community is a positive thing and helps the community in distributing zakat funds, infak and alms to the community around the hususnya mustahik (beneficiaries). By using techniques that have been prepared by the institution. technique is an effort or way to achieve an organizational goal by setting a goal long-term and allocate the necessary resources and pay attention to all possibilities that occur and prepare for all the potential that exists, therefore the author analyzes fundraising with partnership techniques that become the author's title in an effort to increase muzzaki trust in order to increase public interest in LAZ Harfa (amil zakat harapan dhuafa institution). In this study, the author has objectives, including to analyze the application of fundraising with the LAZ Harfa partnership technique and to find out the increase in public trust and interest in zakat, infak and alms in the amil zakat institution (LAZ Harfa). The research methodology used in this study is to use a qualitative approach to school action. Then from the data and data sources that have been obtained using observation techniques, observations, interviews and documentation are analyzed in depth by prioritizing the case material findings. Furthermore, from the findings material to find out the questions that exist in the background of the problem. The results of his research are, (1) the application of partnership technology applied based on ritail from personal, MSMEs, health, majlis ta'lim, schools / campuses and communities. (2) The increase in public trust has proven to increase from 2018 there were 728 partners who joined, in 2021 the partners who joined increased to as many as 3292 partners with a percentage of 85.4% which is also proven in the increase in ZIS funds collected in 2018 amounting to Rp 1,236,319,371. and experienced a significant increase in 2021 of 83.5% with a total set of Rp.2,269,504,006, it is proven that with the partnership technique applied by LAZ Harfa, it is very influential on increasing public trust for zakat, infak and alms in institutions. Keywords: Partnership Technique, Trust, Zakat, Amil Zakat Institution
ENHANCING HANDWRITTEN DIGIT RECOGNITION ACCURACY ON THE MNIST DATASET USING A HYBRID CNN-BILSTM MODEL WITH DATA AUGMENTATION Yugi, Muhtyas; Latif, Ahmad; Utomo, Fandy Setyo; Barkah, Azhari Shouni
JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Vol 11, No 1 (2026)
Publisher : STKIP PGRI Tulungagung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29100/jipi.v11i1.7758

Abstract

Handwritten digit recognition is a classic challenge in the field of computer vision and machine learning, and continues to be developed to achieve higher accuracy. This study proposes a hybrid method that combines Convolutional Neural Networks (CNN) and Bidirectional Long Short-Term Memory (BiLSTM) to enhance performance in handwritten digit classification using the MNIST dataset. CNNs are em-ployed to extract spatial features from digit images, while BiLSTMs are used to capture the temporal patterns and sequential context from the extracted features. To address limitations in data variation and improve the model’s generalization capabilities, the study also applies data augmentation techniques based on image transformations such as rota-tion, translation, scaling, and flipping. Experimental results demonstrate that the hybrid CNN-BiLSTM model with data augmentation signifi-cantly improves classification accuracy compared to baseline ap-proaches without augmentation or without BiLSTM. The model achieved the following accuracy on the MNIST test data: CNN Model Accuracy: Before Augmentation: 98.0%. After Augmentation: 98.5%; CNN-BiLSTM Model Accuracy: Before Augmentation: 98.0%. After Augmentation: 98.7%. These results highlight the effectiveness of the hybrid approach in enhancing handwritten digit recognition perfor-mance. This research contributes to the development of more accurate and robust deep learning models for handwritten image processing
Co-Authors Azhari Shouni Barkah Budiman Daryono, Setyabudi Fandy Setyo Utomo Hidayat, Deden Yugi, Muhtyas