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All Journal CCIT (Creative Communication and Innovative Technology) Journal G-Tech : Jurnal Teknologi Terapan
Nur Azizah
Universitas Raharja, Indonesia
Computer Science & IT Decision Sciences, Operations Research & Management Energy Engineering

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Design of an Expert System for Early Detection of Domestic Violence Using Keyword Matching, Sentiment Analysis and Forward Chaining Sekar Ayu Wijaya Kusumaningrum; Oleh Soleh; Nur Azizah
G-Tech: Jurnal Teknologi Terapan Vol 10 No 1 (2026): G-Tech, Vol. 10 No. 1 January 2026
Publisher : Universitas Islam Raden Rahmat, Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70609/g-tech.v10i1.8977

Abstract

Domestic Violence (KDRT) is a critical humanitarian issue where victims often under-report due to fear, dependence, and stigma. Consequently, many victims turn to social media to express distress implicitly using vague language, rendering existing passive reporting systems and manual detection ineffective against unstructured narratives. This research aims to design a Hybrid Expert System architecture that integrates Keyword Matching and Sentiment Analysis with Forward Chaining to objectively detect indications of KDRT in Indonesian text, specifically targeting implicit venting that lacks explicit violence keywords. The study employs a systematic development method involving knowledge acquisition from psychological (cycle of abuse) and legal  domains to construct a robust knowledge base. The technical architecture combines sentiment analysis to gauge emotional intensity with Forward Chaining inference logic. This logic utilizes dynamic frequency parameters to validate findings through case tracing simulations. The results demonstrate that the proposed architecture successfully classifies various violence types, including physical, verbal, economic, and multi-type violence. The simulation confirms the system’s capability to distinguish between common household conflicts and specific abuse patterns by applying zero-tolerance thresholds for acute violence and repetition filters for chronic psychological abuse. Consequently, this system functions as a robust decision support tool, providing measurable risk assessments and appropriate intervention recommendations for early detection.
Optimization of New Student Admission Process through a Web-Based System: Enhancing Speed, Accuracy, and Real-Time Monitoring Meri Mayang Sari; Mohamad Rakhmansyah; Nur Azizah
CCIT Journal Vol 19 No 2 (2026): CCIT JOURNAL
Publisher : UNIVERSITAS RAHARJA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33050/ccit.v19i2.3258

Abstract

Education is the key to national progress; therefore, an effective system is required to support theadmission process of new students. Manual and semi-digital registration systems that are stillused in several schools often cause problems such as delays in data processing and the risk ofinaccurate information. This study aims to design and develop a web-based student admissionsystem that improves efficiency, speed, and accuracy in processing registration data. Thedevelopment method applies an object-oriented approach with stages of requirements analysis,system design, implementation, and testing. Data were collected through observation, interviews,and documentation studies at the selected school. The implementation results show that thedeveloped application can process applicants’ data faster than the previous method and minimizeinput errors. In addition, the system facilitates admission committees in monitoring data in realtime. Thus, the web-based student admission system is expected to become an innovative solutionto support the digitalization of education in Indonesia.
Implementation of the KNearest Neighbor Algorithm for Classifying Immigration Residence Permit Applicants at the Class I Special Immigration Office TPI SoekarnoHatta Nur Azizah; Henderi Henderi; Berisno Hendro Pardamean Manik Raja
CCIT Journal Vol 18 No 2 (2025): CCIT JOURNAL
Publisher : UNIVERSITAS RAHARJA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33050/ccit.v18i2.3577

Abstract

This study aims to apply the K-Nearest Neighbor (KNN) algorithm in classifying immigration residence permit applicants at the Class I Special Immigration Office TPI Soekarno-Hatta, focusing on the algorithm's effectiveness and accuracy in categorizing residence permit applicants based on the types of residence permits: Visit Stay Permit (ITK), Limited Stay Permit (ITAS), and Permanent Stay Permit (ITAP). This study employs a quantitative, experiment-based approach utilizing a dataset of 17,212 residence permit applicant records consisting of 11 key attributes, such as nationality, visa type, residence permit type, gender, and age group.The research process began with data preprocessing stages, including data cleaning, normalization, and dataset splitting into training and testing sets with 80:20 and 70:30 partitioning scenarios. The KNN algorithm was implemented using a parameter of k=5k = 5k=5, chosen based on experimentation to achieve optimal performance. The model's performance evaluation was conducted using accuracy, precision, and recall metrics derived from a confusion matrix. The findings reveal that the KNN algorithm successfully classifies data with the highest accuracy of 96.95% in the 80:20 dataset partition scenario and 96.84% in the 70:30 scenario. The Visit Stay Permit (ITK) class demonstrated the best performance with a precision of 97.46% and a recall of 99.97%, whereas the Permanent Stay Permit (ITAP) class showed the lowest performance with a recall of 59.79%, indicating challenges in recognizing patterns for this class. This study also identifies the advantages of the KNN algorithm, including its simplicity of implementation, flexibility in handling multiclass data, and effectiveness for low-dimensional datasets. However, the algorithm has limitations, such as sensitivity to imbalanced data distributions and high computational time for large datasets.
Electrical Data Classification with LVQ to Optimize Power Efficiency and Cost Indrianto Indrianto; Sarifuddin Madenda; Prihandoko Prihandoko; Nur Azizah
CCIT Journal Vol 19 No 2 (2026): CCIT JOURNAL
Publisher : UNIVERSITAS RAHARJA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33050/ccit.v19i2.3748

Abstract

Rapid population development causes such a high energy demand among the community in Indonesia, especially those which concern with electrical energy. The use of electrical energy must be supported by good technology in the provision to distribution of electricity. With energy arrangements, energy can be saved and financing will be lighter. Energy saving can be done by making an energy management application based on incoming data from customers. The LVQ method in this study is used to classify customer data that enters the server. The data comes in low, medium, and high usage categories. The findings of this study can be further processed to enhance the classification results obtained from the LVQ method, allowing for a more optimal classification that maximizes energy efficiency. The results indicate that the LVQ algorithm achieved an accuracy rate of 91.66%. The highest electricity consumption occurred between 5:30 PM and 10:00 PM, while the lowest consumption was recorded between 12:30 AM and 8:00 AM, as well as from 11:00 PM to 12:00 AM. Meanwhile, medium electricity loads were observed between 8:30 AM and 5:00 PM. This is crucial for managing energy resources to ensure efficiency and optimization.
Co-Authors Berisno Hendro Pardamean Manik Raja Henderi Henderi Indrianto Indrianto Meri Mayang Sari Oleh Soleh Prihandoko Prihandoko Rakhmansyah, Mohamad Sarifuddin Madenda Sekar Ayu Wijaya Kusumaningrum