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All Journal Scientific Journal of Informatics Jurnal Bisnis, Manajemen, dan Informatika JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING JURNAL MEDIA INFORMATIKA BUDIDARMA Jurnal Elektro dan Telkomunikasi INFOKUM Jurnal Scientia Budapest International Research and Critics Institute-Journal (BIRCI-Journal): Humanities and Social Sciences JPM: JURNAL PENGABDIAN MASYARAKAT International Journal of Engineering, Science and Information Technology Journal of Informatics Management and Information Technology INCODING: Journal of Informatics and Computer Science Engineering Bulletin of Information Technology (BIT) EXPLORER Prosiding Snastikom Journal of Computer Science and Informatics Engineering Jurnal Matematika Dan Ilmu Pengetahuan Alam LLDikti Wilayah 1 (JUMPA) Journal of Computing and Informatics Research Journal Of Artificial Intelligence And Software Engineering Jurnal Ilmiah Teknik Informatika & Elektro (JITEK) Prosiding Seminar Nasional Ilmu Komputer, Sosial Sains, Teknik Dan Multi-disiplin Ilmu
Andre Hasudungan Lubis
Universitas Medan Area
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Clustering Culinary Locations Using the DBSCAN Algorithm Halawa, Anestin; Lubis, Andre Hasudungan
Journal of Artificial Intelligence and Software Engineering Vol 5, No 3 (2025): September
Publisher : Politeknik Negeri Lhokseumawe

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30811/jaise.v5i3.7512

Abstract

The culinary industry plays a vital role in driving creative economic growth and local tourism while also being an integral part of urban lifestyle. Given the high number and diversity of culinary locations, clustering techniques are needed to group them based on marketing characteristics, enabling more efficient decision-making for both consumers and businesses. This study aims to cluster culinary locations based on marketing-related attributes using the DBSCAN (Density-Based Spatial Clustering of Applications with Noise) algorithm. Secondary data was obtained from Kaggle, consisting of restaurant information in Semarang City, with attributes such as rating, number of reviews, and operating hours. After preprocessing and exploratory analysis, DBSCAN was applied with adjusted parameters to generate optimal clusters. The results produced 41 clusters with diverse characteristics, including several outliers detected as noise. Performance evaluation using Silhouette Score and Davies-Bouldin Index showed that DBSCAN achieved more compact and well-separated clusters compared to K-Means. These findings demonstrate that DBSCAN is more adaptive for non-uniform culinary data with varying densities and is suitable for segmentation and strategic decision-making in the culinary industry.
Klasifikasi Produk Iphone dengan Menggunakan Algoritma XGBoost Sihombing, Stevi Freshia; Pakpahan, Josua Prayuda; Lubis, Andre Hasudungan
Journal of Informatics Management and Information Technology Vol. 5 No. 3 (2025): July 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/jimat.v5i3.649

Abstract

The iPhone has become a symbol of advanced technology and a modern lifestyle that is highly sought after by the global community, including in Indonesia. Known for its stable and exclusive iOS operating system, this product offers seamless cross-device integration, consistent system updates, and high performance through the support of the latest generation of processors. The iPhone also has a visual appeal through a minimalist and elegant design, as well as superior features such as professional camera quality, high-level data security, and power efficiency. The high popularity of the iPhone makes it one of the most competitive products in the smartphone market. However, the diversity of models, features, and prices of each iPhone series causes user preferences to be diverse and complex. In an effort to understand these preferences, an accurate classification method is needed to group products according to consumer appeal. This study adopts the XGBoost algorithm which is known to be effective in handling complex and large data. By utilizing iPhone product sales transaction data in the Indonesian market, this model is designed to identify purchasing patterns and user segmentation. The classification results are expected to provide deeper insights for manufacturers and marketers in formulating more targeted data-based marketing strategies.
Prediksi Produksi Tanaman Padi di Indonesia dengan Menggunakan Algoritma Random Forest Regressor Manurung, Dinikxon; Zealtiel, Billiam; Lubis, Andre Hasudungan
Journal of Computing and Informatics Research Vol 4 No 3 (2025): July 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/comforch.v4i3.2125

Abstract

Produksi padi merupakan komponen utama dalam menjaga ketahanan pangan nasional di Indonesia, mengingat beras adalah makanan pokok mayoritas penduduk. Namun, kestabilan produksi padi sering kali terganggu oleh berbagai faktor, terutama kondisi agronomis dan variabilitas iklim yang sulit diprediksi. Oleh karena itu, diperlukan pendekatan berbasis data yang mampu memodelkan kompleksitas faktor-faktor tersebut secara akurat. Penelitian ini bertujuan untuk membangun model prediksi produksi padi menggunakan algoritma Random Forest Regressor, sebuah metode pembelajaran mesin yang dikenal andal dalam menangani data non-linear dan kompleks. Dataset yang digunakan mencakup parameter pertanian seperti luas panen dan produktivitas, serta data iklim meliputi suhu, kelembaban udara, dan curah hujan, yang dikumpulkan dari sumber terbuka seperti Kaggle dan Badan Meteorologi Klimatologi dan Geofisika (BMKG) untuk rentang tahun 2018 hingga 2024. Metodologi yang diterapkan dalam penelitian ini terdiri dari beberapa tahapan, yaitu prapemrosesan data (penanganan nilai hilang dan normalisasi), analisis data eksploratif untuk memahami pola dan korelasi antar variabel, pelatihan model prediksi, serta evaluasi performa model menggunakan metrik Mean Squared Error (MSE) dan R-squared (R²). Hasil penelitian menunjukkan bahwa konfigurasi terbaik diperoleh saat data dibagi dengan rasio pelatihan dan pengujian sebesar 90:10, serta penggunaan 200 decision tree dalam model. Konfigurasi ini menghasilkan nilai MSE sebesar 0.0004 dan R² sebesar 0.9918, yang mengindikasikan tingkat akurasi prediksi yang sangat tinggi serta kemampuan model dalam merepresentasikan hubungan antar variabel dengan baik. Penelitian ini menunjukkan bahwa Random Forest Regressor efektif dalam memprediksi produksi padi dan berpotensi menjadi alat bantu pengambilan keputusan strategis bagi pemangku kepentingan di sektor pertanian.
Penerapan Mesin Pemberi Pakan Ikan Otomatis dan Pelatihan Konsep IoT Sayuti Rahman; Asmah Indrawati; Andre Hasudungan Lubis
JPM: Jurnal Pengabdian Masyarakat Vol. 6 No. 4 (2026): April 2026
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/jpm.v6i4.2962

Abstract

Marindal II Village has considerable potential in the freshwater aquaculture sector; however, it still faces various challenges related to feeding efficiency, high operational costs, and limited adoption of technology. Manual feeding practices often lead to feed waste, irregular feeding schedules, and water pollution in fish ponds. This Community Service Program (PKM) aims to enhance the efficiency and productivity of fish farming through the development and implementation of an automatic fish feeding machine as an initial step toward an Internet of Things (IoT)-based system. The PKM activities were carried out in Marindal II Village in collaboration with the Karang Taruna Youth Group and the PKK community group as partners. The implemented automatic fish feeder is capable of regulating feeding time and dosage in a scheduled manner without manual intervention, thereby reducing feed waste and improving labor efficiency. Although the system has not yet been directly integrated with IoT technology, the design approach and training activities were oriented toward preparing partners for future IoT-based development, such as remote monitoring and sensor integration. Evaluation results indicate improvements in partners’ understanding and skills in managing fish farming activities, feeding time efficiency, and feed waste reduction. This program serves as an important foundation for the sustainable development of IoT-based automatic fish feeding systems in Marindal II Village.
Analisis Sentimen Produk Berdasarkan Review Pelanggan Shopee Menggunakan KNN Fira Irwannia; Andre Hasudungan Lubis
INCODING: Journal of Informatics and Computer Science Engineering Vol 5, No 2 (2025): INCODING OKTOBER
Publisher : Mahesa Research Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34007/incoding.v5i2.865

Abstract

This study aims to conduct sentiment analysis on customer reviews of mukena products available on the Shopee application using the K-Nearest Neighbors (KNN) algorithm. The data used is primary data consisting of 200 reviews collected manually. The analysis process begins with data preprocessing such as case folding, tokenization, stopword removal, and stemming, followed by feature extraction using the TF-IDF method, and classification using the KNN algorithm. The model's performance is evaluated using a confusion matrix. The results show that the proportion of training data and the n_neighbors parameter significantly affect the model's accuracy. A 90% training and 10% testing proportion produced the highest accuracy of 90%. However, with n_neighbors = 3, the best performance was achieved with a 70:30 data split, reaching 81.67% accuracy. This study demonstrates that KNN is an effective method for sentiment analysis on product reviews.
Analisis Sentimen Komentar Pengunjung Terhadap Tempat Wisata Tjong A Fie Mansion Menggunakan Metode Naïve Bayes Classifier Erlina Siregar; Andre Hasudungan Lubis
INCODING: Journal of Informatics and Computer Science Engineering Vol 5, No 2 (2025): INCODING OKTOBER
Publisher : Mahesa Research Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34007/incoding.v5i2.848

Abstract

This study aims to analyze the sentiment of visitor comments on the Tjong A Fie Mansion tourist attraction in Medan City using the Naïve Bayes Classifier method. A total of 100 comments were manually collected from Google Maps and underwent preprocessing stages, including case folding, tokenization, stopword removal, and stemming. Feature extraction was then performed using the TF-IDF method, followed by classification using the Multinomial Naïve Bayes algorithm. Model performance was evaluated using a confusion matrix. The test results showed that a data split of 80% for training and 20% for testing yielded the highest accuracy, reaching 80%, with a sentiment classification result of 100% positive. These findings indicate that the Naïve Bayes method can effectively and efficiently classify text-based comments. The sentiment analysis results are expected to provide input for tourism managers to improve service quality and serve as a reference for the development of user opinion-based decision support systems.
Predicting Burnout in Start-Up Environments: A Multivariate Risk Scoring Approach for Early Managerial Intervention Nos Sutrisno; Maricha Elveny; Andre Hasudungan Lubis; Rahmad Syah; Hartono Hartono; Sabina Krisdayanti
International Journal of Engineering, Science and Information Technology Vol 5, No 4 (2025)
Publisher : Malikussaleh University, Aceh, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52088/ijesty.v5i4.1663

Abstract

Start-up organisations operate under fast timelines, lean staffing, and constantly shifting priorities, exposing employees to chronic workload pressure and emotional strain. Unmanaged burnout in these settings threatens individual well-being, talent retention, and long-term execution capacity. This study proposes a multivariate burnout risk scoring approach that aims to identify and prioritise employees at elevated risk before full deterioration occurs, enabling early managerial intervention rather than reactive recovery. The proposed pipeline integrates principal component analysis (PCA), Random Forest, and Support Vector Machine (SVM). PCA is first applied to reduce redundancy across workplace indicators, yielding five principal components (PC1–PC5) that together explain 88% of the total variance in self-reported stress level, job satisfaction, emotional exhaustion, work-life balance, performance, and social interaction. These components are then used as predictors in two supervised classification models, Random Forest and SVM, to estimate the likelihood that each employee belongs to a high-burnout-risk class. The Random Forest model achieved an accuracy of 88%, and the SVM model achieved an accuracy of 86%, demonstrating strong predictive capability in distinguishing higher-risk employees from lower-risk employees. The resulting predicted probability is interpreted as an individualised burnout risk score, which can be mapped to action categories such as workload redistribution, role clarification, targeted supervisory check-ins, or temporary protection from critical-path tasks. In this way, the framework operationalises burnout prediction not only as a detection task but also as an actionable decision-support signal for leaders. The study therefore offers both a quantitative method for forecasting burnout in start-up environments and a practical structure for translating prediction into preventive intervention.
Implementation of Federated Learning for Disease Classification with Imbalanced Dataset Syarifah Yusnaini Putri; Nia Ramadani; Andre Hasudungan Lubis
Prosiding Seminar Nasional Ilmu Komputer, Sosial Sains, Teknik dan Multi-Disiplin Ilmu Vol. 2 (2026)
Publisher : Raskha Media Group

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64803/ikosstemi.v2.148

Abstract

Imbalanced data is one of the main challenges in applying machine learning to stroke disease classification, where the number of stroke patient data is significantly smaller than non-stroke patient data. This condition causes classification models to become biased toward the majority class, resulting in poor detection of stroke cases. In addition, centralized learning approaches raise privacy and security concerns due to the sensitive nature of patient data. Therefore, this study proposes a Federated Learning Random Forest (FL-RF) approach to improve classification performance on imbalanced data while preserving data privacy. The study uses the Cerebral Stroke Prediction – Imbalanced Dataset obtained from Kaggle. The research stages include data preprocessing, semi non-IID data distribution across multiple clients, local training using Balanced Random Forest, and model aggregation within a federated learning environment. Model evaluation was conducted using accuracy, precision, recall, F1-score, confusion matrix, and ROC curve metrics. The results show that the conventional Random Forest achieved an accuracy of 0.9817 but failed to detect the minority class. Meanwhile, the proposed FL-RF model obtained an accuracy of 0.7976 with an improved recall of 0.56 and an F1-score of 0.0910 for the stroke class. These findings indicate that the FL-RF approach is more effective in improving sensitivity toward minority classes compared to conventional Random Forest.  
The Expert System of Determining the Type of Malaria by using Dempster-Shafer Method Ronal Maruli Marusaha; Dian Noviandri; Andre Hasudungan Lubis
Bulletin of Information Technology (BIT) Vol 4 No 3: September 2023
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bit.v4i3.887

Abstract

Malaria is the most dominant disease in Asia and Africa and may become a life-threatening disease for it suffers. The types of malaria such as Plasmodium Vivax, Plasmodium Ovale, Plasmodium Malariae, and Plasmodium Falciparum are mostly infected people around the world. These types of malaria have certain symptoms that drives difficulties for some patients to confirm which malaria that their infected. A clinical testing and medical diagnostic assessments may be performed to determine the types of malaria, but utilizing a system also brings some benefits for rural areas which lack of medical facilities. The study develops a system by implementing the Dempster Shafer method to determine types of malaria. We collected the knowledge from the experts including 18 possible symptoms along with the density value. This paper present 5 cases of sufferers and provide the system result with the possibilities of malaria types. The result pointed out a various percentage of malaria types that may infected to the patients.
A Comparative Analysis of ResNet50, ConvNeXtTiny, and Vision Transformer for Rice Leaf Disease Classification susilawati susilawati; Stephanus Priyowidodo; Andre Hasudungan Lubis
JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING Vol. 10 No. 1 (2026): Issues July 2026
Publisher : Universitas Medan Area

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31289/jite.v10i1.18397

Abstract

Rice leaf diseases can significantly reduce crop productivity and grain quality, making rapid, objective, and accurate identification essential. This study compares the performance of three deep learning architectures, namely ResNet50, ConvNeXtTiny, and Vision Transformer (ViT-B16), for rice leaf disease classification. The dataset consists of 5,932 images representing four disease classes: Bacterial Blight, Blast, Brown Spot, and Tungro. The data were divided into 3,559 training images, 1,186 validation images, and 1,186 testing images using the same random seed. All models were trained using transfer learning with ImageNet pretrained weights, data augmentation applied only to the training set, dropout regularization, and a two-stage training strategy involving feature extraction and fine-tuning. Performance was evaluated using validation loss, accuracy, precision, recall, F1-score, training curves, and confusion matrices. The results show that ResNet50 achieved the best performance, with a validation accuracy of 99.92% and a validation loss of 0.00326, followed by ViT-B16 (99.16%) and ConvNeXtTiny (98.99%). All models classified the Tungro class with high accuracy, while minor misclassifications occurred among the Bacterial Blight, Blast, and Brown Spot classes due to their similar visual characteristics. Overall, ResNet50 proved to be the most effective model for rice leaf disease classification, although validation using real-world field images is still required before practical deployment
Co-Authors Alex Simanungkalit Asmah Indrawati Bukit, M Iqbal Fahilla Dian Noviandri Dinda Rizky Aprillya Elimiana Berutu Erlina Siregar Farida, Juni Irsan Fauziah Rahman Fira Irwannia Gulo, Senang Hati Halawa, Anestin Hartono Hartono Hesti Sabrina Idrus, Syed Zulkarnain Syed Indah Sari Gaurifa Juanda Hakim Lubis Juanda Hakim Lubis Madani, Fadillah Manurung, Dinikxon Maricha Elveny Moh Hafiz Raja Pratama , Munthe Muhammad Fikri Riandra Muhathir, Muhathir Nia Ramadani Nos Sutrisno Nurul Khairina Pakpahan, Josua Prayuda Pariyandani, Ayu Rahmad B.Y Syah Ronal Maruli Marusaha Sabina Krisdayanti Sayuti Rahman Sianipar, Carmenita Sihombing, Stevi Freshia Stephanus Priyowidodo Sucitra Dewi Susilawati Susilawati SUTRISNO Syarifah Yusnaini Putri Utami, Widya Rizki Wan Rizca Amela Wanti, Eka Pirdia zalukhu, Jenius Zealtiel, Billiam Zulfikar Sembiring Zulhikmah Marpaung