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All Journal Transmisi: Jurnal Ilmiah Teknik Elektro Jurnal Teliska Jurnal technoscientia ELKOMIKA: Jurnal Teknik Energi Elektrik, Teknik Telekomunikasi, & Teknik Elektronika PROtek : Jurnal Ilmiah Teknik Elektro ITEj (Information Technology Engineering Journals) Indonesian Journal of Artificial Intelligence and Data Mining Jurnal RESISTOR (Rekayasa Sistem Komputer) Patria Artha Technological Journal Jurnal Teknik Industri Terintegrasi (JUTIN) JURNAL TEKNOLOGI TECHNOSCIENTIA Jurnal Teknik Informatika (JUTIF) Aptekmas : Jurnal Pengabdian Kepada Masyarakat INOVTEK Polbeng - Seri Informatika
Fadhli, Mohammad
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Journal : Indonesian Journal of Artificial Intelligence and Data Mining

 1 
Design and Development of An Intelligent Automatic Tilapia Fish Farming Device in A Bucket Based on Internet of Things Suni, Gina Amanah; Fadhli, Mohammad; Rose, Martinus Mujur
Indonesian Journal of Artificial Intelligence and Data Mining Vol 8, No 1 (2025): March 2025
Publisher : Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/ijaidm.v8i1.31809

Abstract

The cultivation of various freshwater fish species, such as catfish, tilapia, carp, and sepat, can be effectively managed through the budikdamber technique, where fish and vegetables are grown together in a single container. This research introduces an alternative method designed to control water temperature, automate fish feeding, and cover the container automatically when it rains. By integrating monitoring and control devices, budikdamber owners can manage automated feeding, monitor water temperature, measure pH levels, control water depth, and automatically activate rain covers. This smart device is expected to enhance budikdamber management efficiency, contributing to the improved welfare of the fish and overall system sustainability.
Development of a CNN-Based Mental Health Consultation Application Integrating Facial Expressions and DASS-42 Questionnaire Salsabila, Meidita; Lindawati, Lindawati; Fadhli, Mohammad
Indonesian Journal of Artificial Intelligence and Data Mining Vol 8, No 2 (2025): July 2025
Publisher : Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/ijaidm.v8i2.37525

Abstract

Early detection of psychological disorders such as Depression, stress, and anxiety is still limited due to a lack of awareness and inadequate access to mental health consultation services. This study aims to develop a mental health consultation application that utilizes facial expressions and the Depression, Anxiety, and Stress Scale (DASS-42) questionnaire, employing a Convolutional Neural Network (CNN) algorithm. The CNN algorithm is used to detect and classify facial expressions into emotional categories, such as anger, sadness, disgust, and fear,  as early indicators of mental conditions. In addition, the DASS-42 questionnaire provides a structured psychological assessment to determine the severity of Depression, anxiety, and stress. This combination offers a more comprehensive and accurate evaluation, thus bridging the gap in early detection methods for mental health. Based on the development and testing results, a mental health consultation app utilizing facial expressions and the DASS-42 questionnaire was successfully created by using the CNN algorithm as a facial expression detector. The system can identify facial expressions such as sadness, anger, disgust, and fear with an accuracy of 81%, showing excellent performance in detecting early signs of mental disorders.
Evaluating Entropy-Based Feature Selection for Sales Demand Forecasting Using K-Means Clustering and Naive Bayes Classification Wulandari, Fadhilah Dwi; Lindawati, Lindawati; Fadhli, Mohammad
Indonesian Journal of Artificial Intelligence and Data Mining Vol 8, No 2 (2025): July 2025
Publisher : Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/ijaidm.v8i2.37046

Abstract

Sales demand forecasting is crucial for inventory optimization in retail, especially for Micro, Small, And Medium Enterprises (MSMEs). This study examines the effect of entropy-based feature selection on the performance of a two-stage machine learning framework comprising K-Means clustering and Naive Bayes classification. The research was conducted on transactional data collected from a footwear MSME in Palembang, Indonesia, covering January to December 2024. Shannon Entropy and Information Gain were applied to identify and retain the most informative features before clustering and classification tasks. Two experimental scenarios were investigated: (1) using all features without selection and (2) applying entropy-based feature selection with Information Gain thresholds of 0.4 and 0.5 for category-based and quantity-based targets, respectively. The first scenario yielded moderate performance, with a Silhouette Score of 0.5747 and a classification accuracy of 96.97%. In contrast, the second scenario demonstrated superior results, achieving a Silhouette Score of 0.6261 and a classification accuracy of 99.49% when quantity sold was used as the target variable. These findings indicate that entropy-based feature selection reduces data dimensionality, enhances clustering compactness, and improves classification accuracy. This research contributes to the field by presenting a practical framework for sales demand forecasting in retail environments. Future work will focus on integrating additional contextual variables, such as seasonal trends and promotions, and validating the system in real-world retail settings
Co-Authors Ade Rosta Paramitha, Nadila Affrylia, Gita Aliffiyah, Muhammad Bayu Arsella, Shendy Cahya Iman, Tri Rizki Deta Mediana, Salwa Irma Salamah LINDAWATI Lindawati Lindawati Maharani, Fistania Ade Putri Martinus Mujur Rose Mujur Rose, Martinus Respati, Rayhan Dhafir Saifuddin Z.S, Joumil Aidil Saifuddin, Joumil Aidil Salsabila, Meidita Savitri, Yulivia Rhadita Septiani, Dinda Sopian Soim, Sopian Suni, Gina Amanah Suroso SURYANI, TRIA ANANDA Tarnita Rizky Prihandhita Ulima Achva, RA. Jihan Utomo, Tri Binarko Winursito, Yekti Condro Wulandari, Fadhilah Dwi