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INDONESIA
IKRA-ITH Informatika : Jurnal Komputer dan Informatika
Published by Universitas Persada Indonesia YAI
ISSN : 25804316     EISSN : 26548054     DOI : -
Core Subject : Science,
Computer Science & IT
Arjuna Subject : -
Articles 504 Documents
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Klasifikasi Risiko Dropout Mahasiswa Menggunakan Algoritma Random Forest pada Dataset Predict Students' Dropout and Academic Success Novita Sari Siagian; Monica Sari Batubara; Leony Sinaga
IKRA-ITH Informatika : Jurnal Komputer dan Informatika Vol. 10 No. 2 (2026): IKRAITH-INFORMATIKA Vol 10 No 2 Juli 2026
Publisher : Fakultas Teknik Universitas Persada Indonesia YAI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37817/ikraith-informatika.v10i2.7102

Abstract

The high rate of student dropout remains a significant challenge for higher education institutions because it affects graduate quality, educational effectiveness, and student retention. Therefore, an effective approach is required to identify students at risk of dropping out at an early stage based on available academic data. This study aims to classify student dropout risk using the Random Forest algorithm and the Predict Students’ Dropout and Academic Success dataset obtained from the UCI Machine Learning Repository. A quantitative approach was employed through several stages, including dataset collection, data cleaning, binary target transformation (Dropout and Non-Dropout), data partitioning, Random Forest model development, and model evaluation using 10-Fold Cross Validation. The dataset consists of 4,424 student records with 35 attributes, including 34 predictor attributes and one target attribute. Model performance was evaluated using Accuracy, Precision, Recall, F1-Score, Area Under the Curve (AUC), and Matthews Correlation Coefficient (MCC). The experimental results achieved an Accuracy of 86.1%, Precision of 85.8%, Recall of 86.1%, F1-Score of 85.8%, AUC of 0.902, and MCC of 0.673. The AUC value indicates excellent classification capability in distinguishing students at risk of dropping out from those who are not, while the MCC value demonstrates good predictive quality despite the class imbalance in the dataset. These findings indicate that the Random Forest algorithm provides strong classification performance and has the potential to be implemented as a decision support system for the early identification of students at risk of dropping out, enabling universities to provide timely academic interventions. Keywords: Random Forest, machine learning, student dropout, classification, academic data.
Prediksi Jumlah Perjalanan Wisatawan Nusantara Berdasarkan Data Historis Menggunakan Artificial Neural Network Serenita Gisela Silalahi; Irene Lestaria sinaga; Stefani Silalahi
IKRA-ITH Informatika : Jurnal Komputer dan Informatika Vol. 10 No. 2 (2026): IKRAITH-INFORMATIKA Vol 10 No 2 Juli 2026
Publisher : Fakultas Teknik Universitas Persada Indonesia YAI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37817/ikraith-informatika.v10i2.7109

Abstract

Domestic tourist travel is an important indicator of the development of Indonesia's domestic tourism sector. Predicting the number of domestic tourist trips is essential to support data-driven planning and decision-making. This study aims to develop a prediction model for domestic tourist travel based on historical data using an Artificial Neural Network (ANN). Secondary data obtained from Statistics Indonesia (BPS) were processed through preprocessing, Min-Max normalization, sliding window pattern formation, and ANN training using the Backpropagation algorithm. Model performance was evaluated using MAE, MSE, RMSE, MAPE, and R². The results obtained MAE of 0.0582, MSE of 0.0066, RMSE of 0.0813, MAPE of 9.44%, and R² of 0.3665, indicating that the proposed ANN model is capable of providing reasonably accurate predictions of domestic tourist travel based on historical data.
Penerapan Machine Learning dalam Klasifikasi Wajah dan Benda Menggunakan Teachable Machine Berbasis Lovable Stefani Silalahi; Serenita Gisela Silalahi; Irene Lestaria Sinaga
IKRA-ITH Informatika : Jurnal Komputer dan Informatika Vol. 10 No. 2 (2026): IKRAITH-INFORMATIKA Vol 10 No 2 Juli 2026
Publisher : Fakultas Teknik Universitas Persada Indonesia YAI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37817/ikraith-informatika.v10i2.7111

Abstract

The rapid advancement of Artificial Intelligence (AI), particularly in the fields of Machine Learning and Computer Vision, has accelerated the development of image classification systems capable of recognizing various objects automatically. One of the platforms that facilitates the development of machine learning models without requiring advanced programming skills is Google Teachable Machine, while Lovable is an AI-based application development platform that simplifies and accelerates web application development. This study aims to implement machine learning in developing a face and object classification system using Google Teachable Machine integrated into a Lovable-based application. The research methodology consists of dataset collection involving face and object images, data preprocessing, model training using Google Teachable Machine, model export in TensorFlow.js format, integration of the trained model into the Lovable application, and system testing using both image and video inputs in real time. The evaluation was conducted to assess the system's ability to classify trained face and object categories under various testing scenarios. The outcome of this research is a web-based application capable of classifying faces and objects through image and video inputs with fast and accurate performance. This study is expected to provide an alternative approach for developing machine learning-based applications that are easy to implement while supporting the broader application of Artificial Intelligence technologies in various fields.
Implementasi ANN untuk Prediksi Angkutan Barang Kereta Api Berbasis Data Historis Irene Sinaga; Marcel Alezandro Sihombing; Serenita Silalahi; Stefani Silalahi
IKRA-ITH Informatika : Jurnal Komputer dan Informatika Vol. 10 No. 2 (2026): IKRAITH-INFORMATIKA Vol 10 No 2 Juli 2026
Publisher : Fakultas Teknik Universitas Persada Indonesia YAI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37817/ikraith-informatika.v10i2.7112

Abstract

Railway transportation plays an important role in supporting goods distribution in Indonesia, particularly on the islands of Java and Sumatra. Changes in freight transportation volume over time form historical patterns that can be utilized for forecasting purposes. This study aims to implement an Artificial Neural Network (ANN) to predict railway freight transportation volume using historical data published by Statistics Indonesia (BPS) for the 2019–2026 period. The data were processed through preprocessing stages, including data cleaning, sliding window construction, Min-Max normalization, and splitting into training and testing datasets. The ANN model was developed using the Backpropagation algorithm and implemented in Google Colab utilizing TensorFlow and Keras libraries. The implementation results indicate that the model successfully learned historical data patterns, as demonstrated by the decreasing training loss during training and prediction results that closely followed the actual data trend. The findings indicate that ANN can be effectively applied as a time series forecasting approach for predicting railway freight transportation volume.
Pemodelan Sistem Manajemen Arsip Berbasis Workflow Untuk Mendukung Tata Kelola Dokumen Cici Melisma; Armansyah Armansyah
IKRA-ITH Informatika : Jurnal Komputer dan Informatika Vol. 10 No. 2 (2026): IKRAITH-INFORMATIKA Vol 10 No 2 Juli 2026
Publisher : Fakultas Teknik Universitas Persada Indonesia YAI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37817/ikraith-informatika.v10i2.7113

Abstract

Records management at the North Sumatra Provincial Regional Revenue Agency faces various challenges, such as a lack of integration in document recording processes, manual records management workflows, inefficient document retrieval, and the risk of record loss or damage. These issues highlight the need for a records management system capable of enhancing document management efficiency and supporting structured records governance. This study aims to develop a workflow-based records management system model to serve as a reference for developing a digital records management system. Research data were collected through observation, interviews, and a review of existing records management processes. The study employed the Prototyping development method, encompassing requirements analysis, business process modeling using Business Process Model and Notation (BPMN), system modeling using Unified Modeling Language (UML), database design using Entity Relationship Diagrams (ERD), and interface prototype development using Figma. The study produced a system model, database design, and interface prototype, all of which were validated for alignment with business processes and user requirements. Validation results indicate that all functional requirements are accommodated within the proposed model, enabling it to systematically represent the records management process and serve as a viable reference for developing a digital records management system.
Analisis Perkembangan Machine Learning di Indonesia Berdasarkan Publikasi Ilmiah Oktav Kornelius Hutagaol; Judea Tirta Jordan Simamora; Alex Septama Sihite; Marcel Alezandro Sihombing
IKRA-ITH Informatika : Jurnal Komputer dan Informatika Vol. 10 No. 2 (2026): IKRAITH-INFORMATIKA Vol 10 No 2 Juli 2026
Publisher : Fakultas Teknik Universitas Persada Indonesia YAI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37817/ikraith-informatika.v10i2.7118

Abstract

Machine Learning is one of the fastest-growing branches of Artificial Intelligence and has been widely applied in various sectors, including education, healthcare, agriculture, industry, finance, and government. The increasing number of scientific publications indicates that this technology is increasingly utilized as a data-driven decision-making solution. This study aims to analyze the development of Machine Learning research in Indonesia based on scientific publications published in national and international journals. The research employs the Systematic Literature Review (SLR) method by identifying, selecting, evaluating, and synthesizing relevant scientific articles. The literature search was conducted through several academic databases, including Google Scholar, Garuda, SINTA, IEEE Xplore, and Scopus, using predetermined inclusion and exclusion criteria. The findings reveal that the number of Machine Learning publications in Indonesia has consistently increased over recent years. Education, healthcare, and industry are the dominant application areas, while the most frequently used algorithms include Decision Tree, Random Forest, Naïve Bayes, Support Vector Machine, and K-Nearest Neighbor. Despite its significant potential to support digital transformation, the development of Machine Learning in Indonesia still faces several challenges, including limited datasets, data quality, computational infrastructure, and the availability of skilled human resources. This study is expected to provide valuable references for researchers and practitioners in understanding the research trends and future directions of Machine Learning development in Indonesia. Keyword : Machine Learning, Artificial Intelligence, Systematic Literature Review, Scientific Publication, Indonesia.
Analisis Metode K-Means Clustering pada Data Penjualan Barang NDT PT. Sandya Lestari Mega Permata Sapani; Yossy Veiebrian
IKRA-ITH Informatika : Jurnal Komputer dan Informatika Vol. 10 No. 2 (2026): IKRAITH-INFORMATIKA Vol 10 No 2 Juli 2026
Publisher : Fakultas Teknik Universitas Persada Indonesia YAI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37817/ikraith-informatika.v10i2.7120

Abstract

NDT (Non-Destructive Testing) is a material testing technique that does not damage the object being tested, with its main products including Liquid Penetrant Testing and Magnetic Particle Testing. The accumulation of unsold stock at PT. Sandya Lestari has become an important consideration in formulating marketing and sales strategies. This study aims to analyze NDT sales data using the K-Means Clustering method to group items based on their sales level. The data used consists of 28 items with the attributes of Initial Stock (SA), Sold Stock (ST), and Final Stock (SAK) over a given period. The clustering process was carried out both manually using Euclidean distance and with the help of RapidMiner Studio software, using 3 clusters. The results show that the cluster centers converged at the third iteration, with the final result being Cluster 0 (C0) containing 25 slow-moving items, Cluster 1 (C1) containing 2 moderately-selling items, and Cluster 2 (C2) containing 1 best-selling item. The cluster validity value measured using Cluster Distance Performance (Average Within Centroid Distance) was 21,073.928. The manual calculation and the software processing produced consistent results, indicating that most items (25 of 28) are classified as slow-moving, contributing to stock accumulation. These findings can serve as a basis for developing more effective marketing strategies and inventory management for the company. Keywords: K-Means, Clustering, Sales Data, NDT Products, PT. Sandya Lestari
Implementasi Arsitektur MERN Stack Pada Sistem Informasi Penjualan UMKM Dapur Si Mbok Dengan Tinjauan Performa Terhadap Native Programming Maheswara Abhista Hamdan Hafiz; Oman Komarudin; Intan Purnamasari
IKRA-ITH Informatika : Jurnal Komputer dan Informatika Vol. 10 No. 2 (2026): IKRAITH-INFORMATIKA Vol 10 No 2 Juli 2026
Publisher : Fakultas Teknik Universitas Persada Indonesia YAI

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Dapur Si Mbok is a home-based micro-enterprise selling traditional culinary products in Purwasari District, Karawang Regency, facing challenges such as a conventional sales system, limited promotion, and manual transaction recording. Digitalization through third-party platforms (GoFood, GrabFood) proved less effective, as high service fees reduced the competitiveness of product pricing. This study aims to design a web-based information system to support online sales for Dapur Si Mbok using the Prototype method with MERN Stack technology (MongoDB, Express.js, React.js, Node.js). The system includes authentication, product management, shopping cart, order processing, Midtrans Snap payment integration, online/offline transaction recording, sales reports, and order notifications, deployed on Railway with MongoDB Atlas as the database. Testing consisted of Black-box Testing (44 scenarios, all valid), White-box Testing (Cyclomatic Complexity of 5 for createOrder() and 7 for handlePaymentNotification()), User Acceptance Testing (a score of 94.00%, categorized as Very Good), and performance testing showing that the MERN Stack architecture achieved higher throughput and greater stability compared to native programming. The results indicate that the Prototype method is effective for developing web-based information systems for MSMEs (Micro, Small, and Medium Enterprises), and that the resulting system successfully addresses the operational issues faced by Dapur Si Mbok.
Perbandingan K-Means dan Agglomerative Hierarchical Clustering dalam Pengelompokan Provinsi di Indonesia Berdasarkan Tingkat Kepemilikan JKN Maulidina Cahaya Rani; Desmulyati Desmulyati
IKRA-ITH Informatika : Jurnal Komputer dan Informatika Vol. 10 No. 2 (2026): IKRAITH-INFORMATIKA Vol 10 No 2 Juli 2026
Publisher : Fakultas Teknik Universitas Persada Indonesia YAI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37817/ikraith-informatika.v10i2.7135

Abstract

The National Health Insurance (JKN) is a government program designed to achieve Universal Health Coverage; however, its ownership rate varies across provinces in Indonesia. This study aims to cluster the 38 provinces based on JKN ownership levels during 2023–2025 and compare the performance of the K-Means and Agglomerative Hierarchical Clustering (AHC) methods using the Ward Linkage approach. Data were obtained from Statistics Indonesia (BPS) and processed using Python in Google Colab through preprocessing, optimal cluster determination, clustering, and evaluation using the Silhouette Coefficient and Davies-Bouldin Index (DBI). The results indicate that the optimal number of clusters is three, representing low, medium, and high categories. K-Means achieved a Silhouette Coefficient of 0.595773 and a DBI of 0.453632, while AHC obtained a Silhouette Coefficient of 0.587456 and a DBI of 0.433335. Based on the higher Silhouette Coefficient, K-Means is recommended as the more effective method to support policy evaluation and the equitable distribution of JKN participation across Indonesia.
Analisa Kompetensi Teknologi Informasi dan Komunikasi (TIK) Siswa SMP Negeri 4 Cibitung Menggunakan Algoritma Naive Bayes Salsabila Oktavia Jusuf; Arafat Febriandirza
IKRA-ITH Informatika : Jurnal Komputer dan Informatika Vol. 10 No. 2 (2026): IKRAITH-INFORMATIKA Vol 10 No 2 Juli 2026
Publisher : Fakultas Teknik Universitas Persada Indonesia YAI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37817/ikraith-informatika.v10i2.7138

Abstract

The rapid development of Information and Communication Technology (ICT) requires students to possess adequate competencies to support learning activities in the digital era. However, students demonstrate varying levels of ICT competence, making an objective classification method necessary. This study aims to classify students' ICT competencies using the Naïve Bayes algorithm. The research data were collected through questionnaires distributed to 101 students of SMP Negeri 4 Cibitung. Before the classification process, the data were labeled using the quartile method, resulting in four competency categories: Not Proficient, Less Proficient, Moderately Proficient, and Proficient. The classification process was carried out using RapidMiner, while the model performance was evaluated using a Confusion Matrix. The results showed that 21 students (20.79%) were categorized as Not Proficient, 20 students (19.80%) as Less Proficient, 33 students (32.67%) as Moderately Proficient, and 27 students (26.73%) as Proficient. The proposed classification model achieved an accuracy of 97.03%, indicating that the Naïve Bayes algorithm performed very well in classifying students' ICT competencies. The findings are expected to support schools in evaluating and improving students' ICT competencies. Keywords : ICT Competency, Naïve Bayes, Data Mining, Classification, RapidMiner.

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