Jurnal CoSciTech (Computer Science and Information Technology)

Implementasi Asset Management Berbasis Android PT. PLN ICON PLUS

2026-01-22T08:15:01+07:00

Digital transformation urges companies to optimize asset management to improve operational efficiency. PT. PLN ICON PLUS, a subsidiary of PT PLN (Persero) specializing in information technology and network infrastructure, still uses manual asset management systems prone to errors and inefficiencies. This study aims to develop an Android-based asset management system integrated with a Laravel backend to enable real-time tracking and asset data updates. The methods used include Fishbone analysis, system design with UML, Android and Laravel-based application development, and black box testing. The implementation results show that the system effectively handles asset loan transactions, category and asset management, and user interactions through an interactive and user-friendly interface. This system improves data accuracy, reduces administrative burden, and facilitates data access for administrators and clients. In conclusion, the developed application successfully replaces manual systems with a more efficient and integrated digital solution.

Clickbait Text Classification with Deep Learning Hybrid LSTM-CNN Method

2025-05-01T12:04:37+07:00

This study aims to determine the category of news titles by dividing them into two groups, namely clickbait and non-clickbait using the LSTM-CNN hybrid method. The dataset used consists of 14,878 data in two categories with 6,285 clickbait news data and 8,693 non clickbait news data obtained from the kaggle page. The research stages include data preprocessing through cleaning, tokenizing, stopword removal, stemming, and text representation using the Word2Vec algorithm. The dataset will then be separated into training and test data using a ratio of 80:20. The LSTM-CNN hybrid model is used because of CNN's advantage in extracting local features as well as LSTM's ability to understand sequential relationships between words. The model performance evaluation was conducted using confusion matrix, with the results of 77.07% accuracy, 70% recall, 73% precision, and 71% F1-score. The LSTM-CNN hybrid model showed better performance than the separate models with an increase in accuracy from 77% to 77.07%. This research shows that the LSTM-CNN model combination is effective in handling clickbait and non-clickbait news text classification, providing quite good results in improving the performance of the previous model.

Optimization of Backpropagation Algorithms for Enhancing Market Prediction Accuracy in Emerging Industries

2026-04-10T15:09:54+07:00

This research aims to systematically review and analyze the application of backpropagation algorithms in data-driven business market prediction, focusing on emerging industries. Using the Systematic Literature Review (SLR) method, this study examined research articles from the Dimensions and Scopus databases published over the last 10 years. The analysis synthesizes findings related to the effectiveness, challenges, and potential advancements of Backpropagation in improving market prediction accuracy. The results reveal that backpropagation models, particularly LSTM and MLP have shown significant promise in various sectors, including financial forecasting, customer behavior analysis, and sales prediction. However, challenges such as overfitting, high computational costs, and the integration of real-world market complexities remain. The study emphasizes the need for continuous optimization of these models, as well as improvements in data quality and computational efficiency. This research contributes valuable insights for enhancing predictive models in business market forecasting and offers directions for future studies to further refine the use of backpropagation in addressing market prediction challenges.

Impact of Retinal Image Preprocessing on Diabetic Retinopathy Classification Using Prototypical Networks

2026-04-10T15:08:24+07:00

Diabetic retinopathy is a diabetes complication that can lead to progressive retinal damage and permanent blindness. Early detection through automated fundus image classification is essential but challenged by varying image quality, background noise, and color dominance that reduces lesion visibility. Prototypical networks have demonstrated good performance in few-shot learning settings, yet specialized preprocessing is rarely explored. This study proposes a prototypical network enhanced with modified circle crop to remove irrelevant regions and enhanced green channel to improve microvascular lesion contrast. Experiments were conducted on the APTOS 2019 dataset consisting of 3,662 images, split into 2,929 training and 733 testing samples, using a 5-way 5-shot configuration. The proposed preprocessing increases accuracy from 64.53 percent to 71.35 percent and improves quadratic weighted kappa from 0.5712 to 0.6990. These results indicate that preprocessing enhances feature representation and classification performance under limited data conditions.

Convolutional Neural Network with InceptionV3 Architecture for Food Image Classification Based on Origin of Java and Sumatra Regions

2026-04-25T08:56:05+07:00

This study aims to improve the accuracy of classifying traditional food images based on the regions of Java and Sumatra using the Convolutional Neural Network (CNN) algorithm with the InceptionV3 architecture. Traditional foods from these two regions are often difficult to distinguish due to visual similarities. The dataset consists of 472 food images processed through segmentation, augmentation, and rescaling. The InceptionV3 model was selected for its ability to capture complex visual patterns with high efficiency. The training process employed the Adam optimizer, a learning rate of 0.001, and a 50% dropout regularization technique to prevent overfitting. The model was evaluated using accuracy, precision, recall, and F1-score metrics. The results show that the model achieved an accuracy of 90.42%.precision of 91.07%, recall of 92.72%, and F1-score of 90%, significantly improving compared to previous research, which only achieved an accuracy of 64% using CNN without a specific architecture. This study is expected to contribute to the preservation of local culinary culture and support the promotion of tourism and technology-based culinary industries in Indonesia.

Comparison of YOLOv8n and YOLOv8s Model Performance in Object Detection Using Evaluation Metrics and Confidence Scores

2026-04-25T10:52:05+07:00

Deep learning-based object detection has developed rapidly and is widely used in various computer vision applications. One of the most widely used methods is the You Only Look Once (YOLO) algorithm, which is capable of real-time object detection with a high degree of accuracy. This study aims to analyze the performance comparison of two variants of the YOLOv8 model, namely YOLOv8n and YOLOv8s, in detecting objects using evaluation metrics and confidence scores. The dataset used consists of 5000 images, which are divided into training data (70%), validation data (20%), and testing data (10%). Model performance evaluation is carried out using several object detection metrics, namely precision, recall, and mean Average Precision (mAP), as well as additional analysis in the form of computation time and confidence scores to assess the stability of the model's predictions. The results show that the YOLOv8n model achieved a precision value of 0.9313, while the YOLOv8s model achieved a recall value of 0.8415 and a mean Average Precision (mAP0.5) of 0.9055, which is slightly higher than YOLOv8n with a mAP0.5 value of 0.9009. In terms of computational efficiency, YOLOv8n has a faster training time of around 2670 seconds, compared to YOLOv8s which takes around 4477 seconds. In addition, the YOLOv8s model shows a higher confidence score, which indicates a better level of prediction confidence in detecting objects. Based on these results, it can be concluded that YOLOv8n is superior in computational efficiency, while YOLOv8s provides more stable and accurate detection performance. The results of this study are expected to serve as a reference in selecting the optimal object detection model for various computer vision-based applications

Klasifikasi Kendaraan Bermotor Berdasarkan Jumlah Gandar Menggunakan Adaptive Minimal Ensemble

2026-04-25T09:45:00+07:00

The increasing volume of motor vehicles requires automated monitoring for the classification of heavy vehicle categories (Category I–V) based on the number of axles using side-view cameras. This process represents a complex fine-grained visual classification challenge due to the similar body shapes of trucks. To address the dilemma between the need for high accuracy and computational efficiency, this study implements an Adaptive Minimal Ensemble (AME) architecture that adaptively combines small-scale models. The model is evaluated using a confusion matrix along with accuracy, precision, recall, and F1-score metrics. The testing results demonstrate that a single EfficientNetV2-S model is only able to achieve a maximum accuracy of 83% and exhibits significant limitations in extracting crucial distinguishing features, leading to misclassification of Category 4 and 5 vehicles. In contrast, the AME architecture, which utilizes the two best-performing EfficientNetV2-S base models, successfully achieves a substantial performance improvement with 95% accuracy, 95.21% precision, 95% recall, and a 94.99% F1-score. In conclusion, the adaptive layer mechanism in AME is proven to be highly effective in compensating for the individual prediction weaknesses of its base models, resulting in a significantly more precise vehicle classification monitoring system.

Implementasi LoRa pada Monitoring Tempat Pembakaran Sampah berbasis Website

2026-04-25T09:46:15+07:00

Waste accumulation is a common problem caused by low public awareness of waste management and suboptimal waste handling by sanitation workers. Waste incineration can serve as a solution, but it has the potential to cause undesirable consequences if not properly supervised; therefore, a monitoring system capable of tracking the waste incineration process in real-time is necessary. With the rapid advancement of technology, wireless technology can be utilized to address this issue. However, most wireless technologies still rely on internet networks, making them less effective in areas with limited internet connectivity. Therefore, this study proposes the use of LoRa technology as a solution for data transmission without reliance on an internet network. The developed system can monitor waste incineration sites using MQ-2 sensors and flame sensors in real-time via a website, utilizing the LoRa SX1278 data transmission system. Implementation results show that the monitoring system can detect residual smoke and fire from waste incineration sites and monitor them up to a distance of 150 meters. Signal quality in LoRa SX1278 transmission is expressed in RSSI (Received Signal Strength Indicator) units, with an “excellent” signal category achieved up to a distance of 40 meters in the tests conducted.

Implementation of Random Forest Algorithm in a Web Application as an Early Detection Tool for Diabetes

2026-04-25T09:45:50+07:00

Diabetes is a chronic metabolic disease and one of the leading causes of death worldwide, with the number of sufferers projected to reach 1.3 billion by 2050. Delayed diagnosis remains a primary challenge, as nearly half of those affected are unaware of their condition in the early stages, thereby increasing the risk of fatal complications. Data mining approaches using classification algorithms have been widely utilized for early screening. However, the development of medical record models is often hindered by imbalanced data, which causes models to be biased toward the majority class and reduces detection sensitivity for the minority class (patients with diabetes). Furthermore, there is a lack of research integrating these predictive models into responsive application interfaces for end-users. Consequently, this study implements Random Forest optimized with the SMOTE (Synthetic Minority Over-sampling Technique) into a web-based application to serve as a practical early detection tool. Random Forest was selected for its ability to handle complex data and reduce the risk of overfitting. The research stages include data preprocessing, balancing training data using SMOTE, model parameter adjustment through hyperparameter tuning with Grid Search, and the development of a client-server architecture using AstroJS and Flask. The evaluation results demonstrate that the use of SMOTE significantly improves the model's ability to identify the minority class. The model achieved a Recall of 75.0% and an overall accuracy of 95.8%, effectively minimizing False Negative errors. The developed application was verified through Black Box Testing and was declared successful as a responsive and accessible early detection tool for both healthcare professionals and the general public.

Efficiency Analysis of the U-Net Architecture with a MobileNetV2 Encoder for Coffee Leaf Rust Segmentation.

2026-04-25T10:53:10+07:00

Coffee Leaf Rust (Hemileia vastatrix) poses a serious threat to Robusta coffee productivity. Manual identification is often slow and subjective, while standard Deep Learning segmentation methods like U-Net with VGG16 encoder bear heavy computational loads (~24.89 million parameters), hindering deployment on resource-constrained devices. This study aims to optimize computational efficiency by proposing a Lightweight U-Net architecture based on the MobileNetV2 encoder. The model's performance was comparatively evaluated against the VGG16 baseline using the PlantSeg public dataset. Experimental results show that MobileNetV2 integration successfully reduced model size massively by 96% (to ~0.95 million parameters) and accelerated inference time by ~20% (76.28 ms). Although there was a slight F1-Score decrease of 0.3% compared to the baseline, the proposed architecture offers the best trade-off between efficiency and accuracy, making it a viable solution for mobile implementation

Development Of Mobile Library Applications In The Lancang Kuning University Environment

2026-04-25T09:36:44+07:00

Libraries play an essential role in supporting academic activities in higher education. However, the limitations of web-based systems have hindered students of Universitas Lancang Kuning from optimally accessing digital collections via mobile devices. This study aims to develop a mobile library application for Android integrated with SLiMS as a data management API. The research employed the prototype method, involving requirements analysis, system design using UML, interface design, prototype development, and user feedback testing. The results show that the application runs properly on the Android platform, integrates with SLiMS for data communication, and enables users to access book collections and read digital books directly. The application performance depends on internet stability, but its features support efficiency and accessibility of library services. Therefore, this mobile application is expected to be a solution for the digitalization of library services at Universitas Lancang Kuning.

Application of Classification Algorithms in Data Mining in Various Case Studies: Literature Review

2026-04-10T15:11:02+07:00

Data mining is one of the most widely used approaches in the field of Information Technology to extract knowledge from large data sets. One of the main techniques in data mining is the classification method, which aims to predict a particular class or category based on available attributes. Various classification algorithms such as Naïve Bayes, Decision Tree (C4.5), Random Forest, Support Vector Machine, and Artificial Neural Network have been applied in various research domains, including health, education, government, agriculture, and cybersecurity. Differences in data characteristics and methods used cause variations in performance in each study. Therefore, this study aims to conduct a literature review on the application of data mining with classification methods in various data prediction and classification cases. The research method used is a literature review by examining eleven scientific articles from accredited national journals. The results of the study show that the Naïve Bayes and Decision Tree algorithms are the most frequently used methods due to their ease of implementation and interpretation, while Random Forest and Support Vector Machine tend to provide more stable performance on data with high complexity.

Implementing Victoriametrics as A Time Series Database For Kubernetes Cluster Monitoring

2026-04-04T21:04:57+07:00

Infrastructure monitoring is a critical component in managing Kubernetes clusters, particularly for ensuring service availability and analyzing system performance. As the complexity and scale of infrastructure increase, monitoring systems are required to efficiently handle large volumes of metric data. This study aims to analyze the performance of VictoriaMetrics as a time-series database within Kubernetes monitoring systems and compare it with Prometheus based on resource usage. The research employs a quantitative approach with benchmark experiments conducted under three load scenarios: 500, 750, and 1000 target hosts. The analyzed parameters include CPU usage, memory consumption, and storage capacity. The results indicate significant differences in resource efficiency, where VictoriaMetrics maintains CPU usage between 2–10% across all scenarios, substantially lower than Prometheus, which reaches 12–24%. In terms of memory consumption, VictoriaMetrics requires only 21–27%, whereas Prometheus increases to 41–67%. For storage usage, VictoriaMetrics consumes 5–13 GB, while Prometheus requires 13–45 GB. These findings are expected to serve as a reference for organizations in selecting an appropriate monitoring solution that aligns with their Kubernetes infrastructure scale and requirements.

Implementation of Blockchain in Medical Data Security: a Systematic Review of Publish or Perish

2026-04-04T21:02:47+07:00

The digitalization of healthcare services offers substantial opportunities to improve efficiency and quality of care; however, it also introduces significant challenges related to the security, privacy, and integrity of medical data. The literature indicates that conventional Electronic Health Record (EHR) systems remain vulnerable to data breaches, information manipulation, and system failures due to their centralized architecture. This review examines the development of blockchain technology as a potential solution for modern healthcare data management. The study employs a systematic literature review using the Publish or Perish approach, with Google Scholar as the data source to identify relevant scientific articles, which were subsequently screened based on predefined inclusion and exclusion criteria and analyzed qualitatively. Owing to its characteristics of decentralization, immutability, strong cryptography, and the use of smart contracts for access control, blockchain offers significant improvements in medical data security, transparency, and interoperability. Its applications have been reported in EHR systems, telemedicine, pharmaceutical supply chains, medical imaging, and clinical trial data management. Nevertheless, several limitations continue to hinder widespread adoption, including scalability issues, computational overhead, integration complexity with legacy systems, the transparency–privacy trade-off, and regulatory challenges such as compliance with data protection laws and international standards. Future research trends point toward the integration of blockchain with artificial intelligence, the Internet of Medical Things (IoMT), and federated learning, as well as the development of lightweight blockchain solutions for resource-constrained environments. Overall, blockchain demonstrates considerable potential to strengthen the security and reliability of healthcare information systems; however, its implementation requires a gradual, standardized, and regulation-compliant approach.

Tinjauan Penerapan CNN dan YOLO Pada Pengolahan Citra Agraria Medis Insdutri Cerdas

2026-04-04T20:49:14+07:00

The Rapid growth of Artificial Intelligence (AI), particularly Deep Learning, is driving significant transformations in digital image processing in the argricultural, medical, and smart industrial sectors. Two approaches are most dominant in this research Convolutional Neural Network (CNN) for image classification and You Only Look Once (YOLO) for real-time object detection. The purpose of this reasearch is to systematically review the application, performance, and defense of CNN and YOLO in various domains with different data characteristics. The method used is a Systematic Literatur Review (SLR) of the latest relevant scientific publications, focusing on evaluation matrics such as accuracy, pression, recall, F1-score, and Mean Average Precision (mAP). The review results show that CNN excels in image classification tasks with a high level of accuracy, especially on data with relatively stabel visual patterns, while YOLO is more effective in applications that demand inference speed and direct object detection. However, several major limitations were found, including decreased performance in extreme lighting conditions, complex backgrounds, small objects, and visual similarity between classes. It is concluded that the choice of architecture must be adjusted to the characteristics of the data and application needs,

Pemodelan dan Prediksi Tingkat Kemiskinan Provinsi Sumatera Barat Menggunakan Support Vector Machine

2026-04-25T10:52:40+07:00

This research is motivated by the problem of poverty distribution in West Sumatra Province, which still varies between regions. The objectives of this study are to build a prediction model using the Support Vector Machine (SVM) algorithm, evaluate the model's performance, and implement the prediction results in the form of an interactive dashboard to support local government decision-making. The study uses secondary data from the Central Statistics Agency (BPS) of West Sumatra Province for the period 2015–2024, covering 19 districts/cities. The dependent variable is the percentage of poor people (P0), while the independent variables consist of seven socio-economic indicators. The method used refers to the CRISP-DM stages. In the data preparation stage, missing values are handled using median imputation, outliers are handled using winsorizing, standardization is carried out using Z-Score, and the addition of a one-period lag variable (P0_lag1). The data is divided into training data (2015–2022) and test data (2023–2024), with parameter optimization using GridSearchCV and TimeSeriesSplit. The results showed that the Support Vector Regression (SVR) model with a radial basis function (RBF) kernel provided the best performance with parameters C=1000, epsilon=0.05, and gamma=0.001. This model produced an MAE value of 0.32, RMSE of 0.36, and R² of 0.98. The implementation of the prediction results in the Streamlit dashboard for the 2025–2030 period showed a downward trend in poverty levels in most regions. This model is considered effective as a basis for planning and evaluating data-based poverty alleviation policies.

Implementation of The Internet of Things for LPG Gas Leak Detection System

2025-12-13T18:03:35+07:00

LPG gas has become a primary household necessity due to its cost-effectiveness and ease of use. However, the risk of gas leaks, which can lead to fires, remains a significant drawback, primarily due to a lack of public awareness of how to mitigate them. This research aims to design and build a prototype of an integrated Internet of Things (IoT)-based security system for early detection and mitigation of gas leaks. The system uses two MQ-5 gas sensors to monitor gas concentrations in two different areas and two infrared (IR) sensors to control automatic door access. Data from the sensors is acquired by an ESP8266 microcontroller, which then sends it wirelessly to a server for processing. The system provides a multi-layered response: early warning via buzzer, real-time data visualization on a web dashboard, and instant notifications to users via Telegram. Test results show the system has a reasonable response, where the MQ-5 sensor is able to detect gas and process it on the server in approximately 20 seconds, and the infrared sensor accurately detects objects with 95% accuracy. With the implementation of IoT technology, the system is not only able to continuously monitor gas concentrations in the air, but also provide automatic response actions, thereby increasing alertness and significantly reducing the risk of fires due to gas leaks.

Pengembangan Web Scraper Menggunakan Algoritma Dfs untuk Analisis Penyusupan Situs Judi Online

2026-04-04T20:59:06+07:00

The infiltration of online gambling into official domains such as ac.id, go.id, my.id, .id, and sch.id has become a serious issue because it can damage institutional reputation, disrupt information security, and reduce public trust. One of the common objectives of this infiltration practice is to boost the SEO ranking of the gambling sites by exploiting the authority of official domains. This research aims to develop a web scraper using the Depth First Search DFS algorithm capable of performing deep traversal on website structures and automatically analyzing the infiltration of online gambling links. The system conducts crawling, extraction of html elements containing gambling-related links, text analysis, and keyword pattern matching. Testing was carried out in several stages, starting with an initial evaluation on 40 domains followed by a formal accuracy test on 50 domains. The results show that the system performs stably and consistently when conducting traversal using DFS. The achieved accuracy reached 98%, with 49 correct analyses and 1 detection error. Further evaluation on an additional 60 domains increased the accuracy to 98,33% after improvements were applied to the analysis module.

Implementation of the Mamdani Fuzzy Method for Daily Total Calorie Intake Recommendation for Patients with Type 2 Diabetes

2026-04-04T20:45:36+07:00

Type 2 Diabetes Mellitus is a metabolic disease that requires precise regulation of daily calorie intake to maintain stable
blood sugar levels. Determining calorie requirements is not simple because it must take into account factors such as age, gender,
body mass index (BMI), and physical activity level. This study aims to develop a Mamdani fuzzy logic-based expert system to
provide recommendations for daily calorie requirements for people with type 2 diabetes. The system process is carried out
through the stages of fuzzification, inference, aggregation, and defuzzification using the centroid method. Testing was conducted
using 20 type 2 diabetes patient data with input variables of age, height, weight, gender, and physical activity. The testing
methods used were accuracy and black-box. Accuracy testing was performed by comparing the system's results with manual
calculations based on medical standards, while black-box testing ensured that the system functioned as designed. The results
showed that the system had an accuracy rate of 80%, making it sufficiently valid and usable as a tool for recommending daily
calorie intake to support diet management for type 2 diabetes patients.

Usability Testing Analysis of Digital Transportation Applications Using the System Usability Scale (SUS)

2026-04-27T10:06:46+07:00

The development of information technology in the transportation sector has transformed the way people carry out their mobility activities, particularly through the use of digital transportation applications. Although these applications offer various conveniences, several issues are still encountered in their practical use, which may affect user comfort and satisfaction. Therefore, usability becomes a crucial aspect in evaluating the quality and performance of digital transportation applications. This study applies the System Usability Scale (SUS) method using a Likert scale in the research questionnaire to analyze usability. Primary data were collected through observation, interviews, literature review, and the distribution of SUS questionnaires to users of digital transportation applications in the DKI Jakarta area aged between 17 and 45 years. The results of the analysis show an average score of 73.4, which falls into the acceptable category and indicates that the application is well accepted by users. This research contributes an evaluation of the usability level of digital transportation applications, which can serve as a basis for system improvement recommendations, particularly in terms of usage flow, user interface, and user feedback, as well as a reference for future studies related to user experience development

Sistem Pakar Analisis Love Language untuk Menilai Kualitas Komunikasi Pasangan Milenial Menggunakan Forward Chaining dan Certainty Factor

2025-12-20T12:01:13+07:00

This research aims to analyze the influence of love language on communication quality among millennial couples in Indonesia. Millennials, who are highly immersed in digital interaction, tend to communicate quickly and text-based, making romantic interactions dynamic yet prone to misunderstanding. A quantitative survey was conducted involving 180 respondents aged 22–35 who had been in a relationship for at least one year. A Likert-scale questionnaire measured preferences across the five love language categories—Words of Affirmation, Quality Time, Acts of Service, Physical Touch, and Receiving Gifts, as well as communication quality indicators including openness, clarity, trust, and emotional intimacy.

Findings indicate that all five love languages significantly affect communication quality, each with varying levels of influence. Quality Time emerged as the most dominant category with an estimated tendency of 92–96%, followed by Words of Affirmation with a dominance level above 85%. These results suggest that understanding and adapting to one’s partner’s love language can serve as an effective communication strategy to increase emotional closeness, reduce conflict, and strengthen relationship harmony among millennial couples.

Klasifikasi Penyakit Daun Singkong Menggunakan Arsitektur Efficientnet Berbasis Transfer Learning

2026-05-12T18:00:18+07:00

Cassava (Manihot esculenta) is a strategic agricultural commodity whose productivity is frequently threatened by leaf diseases such as bacterial blight, brown streak, green mottle, and mosaic disease. Manual identification by humans tends to be subjective, time-consuming, and prone to error. This study aims to develop an automatic and intelligent Cassava leaf disease classification system based on Deep Learning that is both accurate and efficient. To overcome the computational burden of conventional models and address real-world data challenges, such as class imbalance and lighting variations, this research proposes the use of the EfficientNet architecture combined with the Transfer Learning method. The model utilizes pre-trained weights from ImageNet to accelerate convergence and optimize visual feature extraction. Experimental results on the Cassava leaf image dataset show that the proposed model successfully achieved an accuracy rate of 81%. These findings demonstrate that the EfficientNet approach provides objective predictions with high computational efficiency. This research has significant potential for implementation in portable devices as an early detection tool for farmers, supporting rapid mitigation actions and maintaining global food security stability

Recommendation for Determining Mustahik Priority at BAZNAS Pontianak Using the AHP–TOPSIS Method

2026-04-04T20:42:16+07:00

Prioritas mustahik menimbulkan tantangan signifikan dalam administrasi zakat, karena prosesnya sering dipengaruhi oleh faktor subjektif dan kurangnya efisiensi. Studi ini bertujuan untuk merancang Sistem Pendukung Keputusan (SPK) yang dapat memfasilitasi BAZNAS Pontianak untuk memprioritaskan mustahik dengan pendekatan objektif, sistematis, dan transparan, sehingga alokasi zakat menjadi lebih tepat sasaran. Metodologi yang diterapkan meliputi AHP untuk menentukan bobot kriteria melalui perbandingan berpasangan, dan Teknik untuk Urutan Preferensi berdasarkan Kesamaan dengan Solusi Ideal (TOPSIS) untuk memberi peringkat berdasarkan kedekatan dengan solusi ideal. Sistem yang dikembangkan adalah aplikasi web berbasis framework Laravel, yang dievaluasi menggunakan teknik black-box dan MAPE. Evaluasi menghasilkan nilai MAPE sebesar 0,131%, menunjukkan akurasi sistem yang luar biasa tinggi. Temuan ini menegaskan bahwa sistem ini mampu meningkatkan transparansi dan objektivitas distribusi zakat, dan berpotensi untuk diimplementasikan oleh lembaga amil zakat lainnya.