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All Journal Letters in Information Technology Education (LITE) Journal of Applied Data Sciences Proceeding of International Conference on Special Education in South East Asia Region Journal of Educational Technology and Learning Creativity
Mahamad, Abd Kadir
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Social Sciences

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Determining the Articles Acceptance Using Logic of Fuzzy Inference System Tsukamoto Fauzi, Juwita Annisa; Rahman, Nukleon Jefri Nur; Handayani, Anik Nur; Mahamad, Abd Kadir
Letters in Information Technology Education (LITE) Vol 3, No 1 (2020)
Publisher : Universitas Negeri Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (905.63 KB) | DOI: 10.17977/um010v3i12020p001

Abstract

Publication of scientific article is now getting bigger, but the process of the scientific article acceptance takes a long time. The longest stage in the process, especially, is in the review process. The process about article assessment consists of many criteria which cause a very high level of subjectivity. Computerized system on the assessment of the scientific article acceptance apply a reasoning scheme using Fuzzy Inference Tsukamoto Logic; therefore, by using the logic, the duration issue in the assessment process can be handled fast.
Android Application Prototype of Basic BISINDO Introduction and Practice for General People Fadlilah, Umi; Mahamad, Abd Kadir; Handaga, Bana; Saon, Sharifah; Ratih, Koesoemo; Rahmawati, Laili Etika; Thamrin, Husni
Proceeding of International Conference on Special Education in South East Asia Region Vol. 1 No. 1 (2022): Technology and Education for Student with Special Needs
Publisher : Angstrom Centre of Education

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57142/picsar.v1i1.30

Abstract

One of the sign languages used in Indonesia is BISINDO (Indonesian Sign Language) created by the deaf. BISINDO is like the mother tongue for the deaf in Indonesia, although it is not as popular as SIBI (Indonesian Sign System). Therefore, the researchers propose to develop an Android-based prototype to promote BISINDO to others and help the deaf in daily communication. This prototype was built using coding in React Native framework and code editor of Visual Studio Code, then it was compiled to an Android Smartphone. The features include BISINDO's basic dictionary, translation from hand gesture to text, voice to sign language, text to sign language, and voice to text. This prototype is expected to introduce basic BISINDO vocabulary and practice using BISINDO. This prototype ia expected to be developed further into a more useful application, especially to assist communication between deaf people and the general public, but not as a substitution of BISINDO teachers.
The Development of Stacking Techniques in Machine Learning for Breast Cancer Detection Van FC, Lucky Lhaura; Anam, M. Khairul; Bukhori, Saiful; Mahamad, Abd Kadir; Saon, Sharifah; Nyoto, Rebecca La Volla
Journal of Applied Data Sciences Vol 6, No 1: JANUARY 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v6i1.416

Abstract

This study addresses the challenges of accurately detecting breast cancer using machine learning (ML) models, particularly when handling imbalanced datasets that often cause model bias toward the majority class. To tackle this, the Synthetic Minority Over-sampling Technique (SMOTE) was applied not only to balance the class distribution but also to improve the model's sensitivity in detecting malignant tumors, which are underrepresented in the dataset. SMOTE was effective in generating synthetic samples for the minority class without introducing overfitting, enhancing the model's generalization on unseen data. Additionally, AdaBoost was employed as the meta model in the stacking framework, chosen for its ability to focus on misclassified instances during training, thereby boosting the overall performance of the combined base models. The study evaluates several models and combinations, with K-Nearest Neighbors (KNN) + SMOTE achieving an accuracy of 97%, precision, recall, and F1-score of 97%. Similarly, C4.5 + Hyperparameter Tuning + SMOTE reached 95% in all metrics. The stacking model with Logistic Regression (LR) as the meta model and SMOTE achieved a strong performance with 97% accuracy, precision, recall, and F1-score all at 97%. The best result was obtained using the combination of Stacking AdaBoost + Hyperparameter Tuning + SMOTE, reaching an accuracy of 98%. These findings highlight the effectiveness of combining SMOTE with stacking techniques to develop robust predictive models for medical applications. The novelty of this study lies in the integration of SMOTE and advanced stacking methods, particularly using AdaBoost and Logistic Regression, to address the issue of class imbalance in medical datasets. Future work will explore deploying this model in clinical settings for accurate and timely breast cancer detection.
IoT-VR Integrated Framework for Precision Prebiotic Dosing in Intensive Aquaculture: A Technology-Based Approach to Sustainable Fish Production Junfithrana, Anggy Pradiftha; Suryana, Anang; Saputri, Utamy Sukmayu; Hasbullah, Dasep; Langlangbuana, Muhammad Labib; Mahamad, Abd Kadir; Utomo, Wahyu Mulyo; Onn, Choo Wou; Korkmaz, Hatice Betul
Journal of Educational Technology and Learning Creativity Vol. 3 No. 2 (2025): December
Publisher : Cahaya Ilmu Cendekia Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37251/jetlc.v3i2.2497

Abstract

Purpose of the study: This study aimed to develop and validate an integrated precision aquaculture framework that combines IoT-controlled multi-prebiotic dosing, centralized environmental monitoring, and virtual reality-based operator training to improve water quality, fish growth performance, and operational competency in intensive multi-pond fish farming systems. Methodology: A closed-loop precision aquaculture system was implemented for 30 days in 40 homogeneous circular ponds. The system used centralized sensors (dissolved oxygen, pH, temperature), IoT-actuated solenoid valves with inline flow sensors for four prebiotic formulations, Water Quality Index computation, VR-based operator training, and statistical analysis using one-way ANOVA, multiple linear regression, and paired t-tests. Main Findings: IoT-based management significantly improved Water Quality Index, survival rate, and specific growth rate compared with manual management. Automated prebiotic dosing was volumetrically accurate and consistently on time. Higher water quality strongly correlated with better growth and survival. VR training substantially reduced operator task completion time and operational errors, enhancing overall system efficiency and reliability. Novelty/Originality of this study: This study presents a fully integrated multidisciplinary precision aquaculture framework that uniquely combines IoT-driven multi-prebiotic automated dosing, centralized environmental monitoring for homogeneous pond networks, and VR-based immersive training as an active human–system interaction layer. It advances current knowledge by demonstrating a scalable, technology-mediated model that unites automation, water quality management, and skill development in intensive aquaculture.
Co-Authors Anam, M Khairul Anang Suryana Anggy Pradiftha Junfithrana Anik Nur Handayani Bana Handaga Fauzi, Juwita Annisa Hasbullah, Dasep Husni Thamrin Koesoemo Ratih Korkmaz, Hatice Betul Laili Etika Rahmawati Langlangbuana, Muhammad Labib Lucky Lhaura Van FC, Lucky Lhaura Onn, Choo Wou Rahman, Nukleon Jefri Nur Rebecca La Volla Nyoto Saiful Bukhori Saon, Sharifah Saputri, Utamy Sukmayu Umi Fadlilah, Umi Wahyu Mulyo Utomo