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All Journal Madani: Multidisciplinary Scientific Journal Socius: Social Sciences Research Journal
Zuli Agustina Gultom
Fakultas Ilmu Komputer dan Teknologi Informasi, Universitas Muhammadiyah Sumatera Utara
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Interpretation of the Logistic Regression Model Using the SHAP Method in the Classification of Zakat Recipient Eligibility in Sei Rampah Village Anggi Soraya; Zuli Agustina Gultom
Socius: Jurnal Penelitian Ilmu-Ilmu Sosial Vol 3, No 11 (2026): June 2026
Publisher : Penerbit Yayasan Daarul Huda Kruengmane

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5281/zenodo.20487107

Abstract

Accurate zakat distribution requires a recipient determination process that is objective, consistent, and accountable. This study aims to build a Logistic Regression model for classifying zakat recipient eligibility in Sei Rampah Village and to interpret the prediction results using the SHAP (Shapley Additive Explanations) method. The data used in this study is secondary data of zakat recipient candidates obtained from the zakat management committee in Sei Rampah Village, consisting of 1,000 records, with variables including income, number of dependents, occupation, housing condition, and asnaf category. The research stages include data preprocessing, data structure standardization, data cleaning, categorical variable encoding, numerical feature normalization, splitting the dataset into training and testing sets with an 80:20 proportion, building the Logistic Regression model, and evaluating the model using accuracy, precision, recall, and F1-score. The evaluation results show that the model achieved 99% accuracy, 100% precision, 98.28% recall, and 99.13% F1-score.In addition to achieving excellent classification performance, this study also shows that the SHAP method is able to provide transparent interpretations of the model’s decisions, both globally and locally. In the global interpretation, the most influential features in predicting eligibility are income, number of dependents, housing condition, and occupation. In the local interpretation, SHAP is able to explain the contribution of each feature to the prediction for specific individuals. Therefore, the combination of Logistic Regression and SHAP can be used as an effective approach to support the classification of zakat recipient eligibility in an accurate, transparent, and accountable manner.
Design and Development of an IoT-Based System for Monitoring and Automatic Control of Nutrient Delivery During the Generative Phase of Chili Plants Using Firebase Irfan Sanjaya Sipayung; Zuli Agustina Gultom
Madani: Jurnal Ilmiah Multidisiplin Vol 4, No 9 (2026): October 2026
Publisher : Penerbit Yayasan Daarul Huda Kruengmane

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Abstract

This research aims to design and develop an Internet of Things (IoT) system based on the ESP32 microcontroller and the Firebase Realtime Database cloud platform to monitor and automatically control nutrient solution quality in real-time. The research and development (R&D) methodology was applied using a Three-Tier architecture approach. The system integrates a pH-4502C sensor and a KS0429 TDS sensor as the input layer, alongside a relay module and a DC pump as actuators for threshold-based automatic control (setpoint). Experimental results demonstrate high reading validity, with the pH sensor achieving an average accuracy rate of 99.29% (RMSE = 0.0486 pH; MAPE = 0.71%) and the TDS sensor reaching 99.38% (RMSE = 8.46 PPM; MAPE = 0.62%). A 24-hour reliability test proved consistent system performance without any instances of system crashes or data loss, maintaining a Relative Standard Deviation (RSD) below 1%. Data transmission latency to the ChiliNutri Pro web dashboard remained remarkably low, with a response time ranging between 1.1 and 1.9 seconds. Furthermore, when external disturbances were introduced, the control logic embedded in the ESP32 firmware responsively executed autonomous dosing of concentrated nutrients until the TDS parameter stabilized back to the target value of 800 PPM. The developed system successfully eliminates human error and is ready for implementation to support nutrient absorption optimization in modern chili cultivation.
Co-Authors Anggi Soraya Irfan Sanjaya Sipayung