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All Journal Applied Information System and Management Journal of Applied Data Sciences
Taher, Taslim
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Languange, Linguistic, Communication & Media

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Classification of Tweets Causing Deadlocks in Jakarta Streets with the Help of Algorithm C4.5 Aini, Qurrotul; Hammad, Jehad A H; Taher, Taslim; Ikhlayel, Mohammed
Journal of Applied Data Sciences Vol 2, No 4: DECEMBER 2021
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v2i4.43

Abstract

Congestion seems to be a daily occurrence in the Indonesian city of Jakarta. As a consequence, the rider has access to essential information regarding traffic conditions at all times, which is beneficial. Through social media platforms such as Twitter, this information is readily available to the public. On the other hand, the information offered on Twitter is still uncategorized text. DKI Jakarta, as a consequence, developed a congestion classification system that included data mining techniques, a classification approach based on the decision tree technique, and C4.5 as a component. This C4.5 method transforms a large amount of information into a decision tree that shows the rules. Geocoding will be utilized to illustrate the locations that have been gathered, and a data split with a confusion matrix will be used to assess how well the categorization process has worked. According to the study's results, the average accuracy rate is 99.08 percent, the average precision rate is 99.46 percent, and the average recall rate is 97.99 percent.
Underlying Structure of Online Risks and Harm among Bangladeshi Teenagers Taher, Taslim; Suhaimi, Mohd Adam; Molok, Nurul Nuha Abdul
Applied Information System and Management (AISM) Vol 3, No 1 (2020): Applied Information System and Management (AISM)
Publisher : Depart. of Information Systems, FST, UIN Syarif Hidayatullah Jakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15408/aism.v3i1.13251

Abstract

This paper gives an account of the consequences of the procedure of EFA (exploratory factor analysis) invoking online risks as well as harm information acquired by an overview of 443 adolescents in Bangladesh. The information was gathered utilizing a 42-item, adjusted Likert survey investigating the critical variables. PCA (Principal Component Analysis) with Varimax rotation was chosen by the researcher to accomplish it. Based on data, the procedure took out the crucial factors social, religiosity, psychological, online risks as well as harm. 65.594% of the variance was clarified by these nine dimensions together. The procedure of reliability analysis generated internal consistency estimates which may be considered acceptable. The range was found from 0.625 which belongs to Emotional Problems to 0.930 which belongs to Online Risks. These discoveries give comprehensive justification to build the legitimacy for the items. The presence of the components influencing altogether the young people online in Bangladesh has been identified as well.
Automated Glaucoma Detection and Classification from Large-Scale Fundus Image Dataset Using YOLOv8 and CNN Islam, Sheikh Aminul; Khan, Humana; Taher, Taslim
Applied Information System and Management (AISM) Vol. 8 No. 2 (2025): Applied Information System and Management (AISM)
Publisher : Depart. of Information Systems, FST, UIN Syarif Hidayatullah Jakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15408/aism.v8i2.46658

Abstract

Glaucoma is a major eye condition that slowly damages the optic nerve and remains one of the top causes of permanent blindness around the world. This study presents an automated framework for early detection and classification of glaucoma using artificial intelligence techniques applied to large-scale retinal fundus image dataset of over 17,000 images. The optic disc (OD) and optic cup (OC) were localized using YOLOv8. Following this, we conducted Region of Interest (ROI) extraction and contour masking to isolate the OD and highlight critical regions for further examination. We extracted essential features, such as the Cup-to-Disc Ratio (CDR), Vertical CDR (VCDR), neuroretinal rim (NRR) thinning, and compliance with the ISNT (Inferior > Superior > Nasal > Temporal) rule, resulting in a detailed tabular dataset. For classification, we applied ML and DL models. YOLOv8 demonstrated superior detection precision and CNN led the classification models with 87.13% accuracy. The proposed method offers a reliable, automated solution that can support large-scale glaucoma screening in clinical settings. This framework has the potential to assist ophthalmologists by improving the speed and accuracy of early glaucoma diagnosis, reducing the risk of vision impairment in affected patients.
Co-Authors Hammad, Jehad A H Ikhlayel, Mohammed Islam, Sheikh Aminul Khan, Humana Molok, Nurul Nuha Abdul Rony, Zahara Tussoleha Suhaimi, Mohd Adam