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All Journal Jurnal Sains Teknologi dan Sistem Informasi Jurnal Sains dan Teknologi Jurnal Pengabdian Masyarakat Bidang Sains dan Teknologi
Triandes Sinaga
Universitas Pelita Harapan
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Klasifikasi Tingkat Kelunturan Warna Kain Menggunakan KNN, SVM, dan Random Forest Romindo Romindo; Triandes Sinaga; Kevin Bastian Sirait; Arosochi Yosua Daeli; Jepronel Saragih
INSOLOGI: Jurnal Sains dan Teknologi Vol. 5 No. 3 (2026): Juni 2026
Publisher : Yayasan Literasi Sains Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55123/insologi.v5i3.8721

Abstract

The laundry industry faces challenges in maintaining service quality, particularly regarding fabric color fading after washing. Assessments that are still performed manually tend to be subjective and inconsistent, so a more objective automated classification system is required. This study aims to apply and compare three algorithms, namely KNN, SVM, and Random Forest, to classify the level of fabric color fading based on digital images. The features used comprise color in the RGB and HSV spaces as well as shape in the form of area and shape ratio, all extracted automatically. A total of 300 images were divided into 250 training data and 50 testing data, then mapped into three categories, namely not faded, fairly faded, and faded. The testing results show that Random Forest delivers the best performance with an accuracy of 0.96, followed by SVM at 0.94 and KNN at 0.88. All models faced difficulties in recognizing the minority class due to data imbalance. This study proves that the machine learning approach, particularly Random Forest, is able to assess color fading levels more accurately and consistently than manual evaluation, while supporting quality control in the laundry industry.
Pemanfaatan Teknologi IoT dan Aplikasi Android untuk Pengendalian Kadar Amonia pada Peternakan Unggas Aditya Kristianto; Kevin Sirait; Triandes Sinaga
ABDIKAN: Jurnal Pengabdian Masyarakat Bidang Sains dan Teknologi Vol. 4 No. 4 (2025): November 2025
Publisher : Yayasan Literasi Sains Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55123/abdikan.v4i4.6910

Abstract

This community service activity aims to implement an Internet of Things (IoT)–based system integrated with an Android application to monitor ammonia gas levels in poultry farms. The activity took place at Ziven Chicken Farm in Stabat, North Sumatra, which faces challenges related to ammonia accumulation from livestock waste. The methodology follows four phases: (1) initiation, involving initial observation and interviews with farm’s owner and workers concerning problems identifications and needs; (2) planning, including the design of the IoT solution and mobile application; (3) implementation, consisting of assembling the IoT device using the MQ-137 and DHT-22 sensors, integrating them with the ESP32 microcontroller, and developing the application connected to a cloud server; and (4) monitoring and control, which includes system performance monitoring, data validation, and evaluation of system use by the partner. The system provides real-time visualization of ammonia, temperature, and humidity levels through tables and graphs. Results show an upward trend of ammonia concentration, reaching 21 ppm on the third day, with humidity positively affecting ammonia levels. A notable spike occurred between 20:00 and 21:00 WIB, indicating the need for improved ventilation and cleaning routines. Overall, the system assists farmers in determining optimal maintenance schedules and supports the adoption of digital technologies in poultry farm management.
Segmentasi Investor Cryptocurrency Menggunakan Metode K-Means: Studi terhadap Faktor-Faktor yang Mendorong Keputusan Investasi Arosochi Yosua Daeli; Vicky Darmana; Kevin Bastian Sirait; Triandes Sinaga
SATESI: Jurnal Sains Teknologi dan Sistem Informasi Vol. 5 No. 2 (2025): Oktober 2025
Publisher : Yayasan Pendidikan Penelitian Pengabdian ALGERO

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54259/satesi.v5i2.7405

Abstract

The growth of cryptocurrency investors is very rapid with diverse characteristics and various investment driving factors. This study aims to analyze and form investor segmentation in cryptocurrency based on investment driving factors using the K-Means Clustering algorithm. A quantitative approach was applied through online questionnaires to 300 respondents who are cryptocurrency investors, with 289 valid data meeting the research criteria. The variables studied include four driving factors: Fear of Missing Out (FOMO), social media influence, high profit potential, and interest in the investment world. Data were processed through Min-Max normalization, Principal Component Analysis (PCA), and K-Means clustering using Orange Data Mining. The optimal number of clusters was determined using the Silhouette Score, while cluster validation used K-Nearest Neighbors (KNN). ANOVA and Games-Howell tests confirmed significant differences between clusters. The results identified four clusters: Cluster 1 (Emotional Investors, n=37), Cluster 2 (Ambitious Investors, n=156), Cluster 3 (Rational Investors, n=50), and Cluster 4 (Passive Investors, n=46). Cluster 3 is the most optimal in investment decision-making with a profit rate of 90% and zero loss (0%). These findings confirm that optimal investment decisions are driven by rational analysis and logical consideration without excessive emotional influence.
Analisis Kualitas Wine Menggunakan Machine Learning dengan Pendekatan SMOTE dan Seleksi Fitur Triandes Sinaga; Kevin Bastian Sirait; Jefri Junifer Pangaribuan; Okky Putra Barus; Romindo Romindo
INSOLOGI: Jurnal Sains dan Teknologi Vol. 4 No. 3 (2025): Juni 2025
Publisher : Yayasan Literasi Sains Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55123/insologi.v4i3.5436

Abstract

Conventional wine quality assessment remains reliant on subjective expert judgment, which introduces potential bias and inconsistency in quality control processes. This study aims to develop an objective and automated machine learning-based classification model to enhance the accuracy of wine quality prediction. To address the issue of class imbalance, the Synthetic Minority Over-sampling Technique (SMOTE) was applied, along with ANOVA F-test-based feature selection to optimize model performance. The White Wine Quality dataset from the UCI Machine Learning Repository (4,898 samples, 11 numerical features) was utilized to evaluate five classification algorithms: Naïve Bayes, Decision Tree, Random Forest, Support Vector Machine (SVM), and K-Nearest Neighbors (KNN). Before SMOTE application, the Random Forest model achieved an accuracy of only 67.55%. After implementing SMOTE and parameter tuning, the Random Forest (Tuned) model demonstrated the best performance with 90.29% accuracy, 89.99% precision, 90.29% recall, and 89,97%.  % F1-score. Additionally, Decision Tree and KNN algorithms also exhibited notable improvements. SMOTE effectively balanced extreme minority class representations (quality levels 3 and 9). The most influential features in quality classification were alcohol content, density, and chlorides. These findings indicate that the proposed framework offers a reliable, objective, and scalable solution for automated wine quality control in industrial production environments.
Evaluasi Robustness dan Deployment Readiness Model XGBoost untuk Prediksi Risiko Gagal Jantung di Indonesia Triandes Sinaga; Ayumi Ayumi; Jefri Junifer Pangaribuan
INSOLOGI: Jurnal Sains dan Teknologi Vol. 4 No. 6 (2025): Desember 2025
Publisher : Yayasan Literasi Sains Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55123/insologi.v4i6.7087

Abstract

Cardiovascular diseases, particularly heart failure, remain a leading cause of mortality in Indonesia, affecting an estimated 2.78 million individuals. This study aims to develop a heart failure risk prediction model using the XGBoost algorithm and to evaluate its performance through a comparative validation approach across two datasets with distinct characteristics. The primary model was trained on a large-scale Indonesian population dataset (N = 158,355; 28 features) representing the complexity of real-world clinical data, while the UCI Heart Disease dataset (N = 918; 12 features) was used as a benchmark under more controlled conditions. Experimental results show that the Indonesian model achieved a testing accuracy of 73.50% with a very small training–testing performance gap of 0.53% and an AUC-ROC value of 0.814, indicating strong stability and generalization capability. In contrast, the model trained on the UCI dataset obtained a higher accuracy of 88.59% but exhibited moderate overfitting, reflected by a larger performance gap of 4.60%. Feature importance analysis consistently identified a history of heart disease, hypertension, and smoking behavior as the most influential predictors across both datasets. These findings highlight that model stability and generalization on real-world data are more critical than raw accuracy derived from small, idealized datasets when assessing the clinical deployment readiness of medical artificial intelligence systems in Indonesia.
Co-Authors Aditya Kristianto Arosochi Yosua Daeli Ayumi Ayumi Barus, Okky Putra Jefri Junifer Pangaribuan Jepronel Saragih Kevin Bastian Sirait Kevin Sirait Pangaribuan, Jefri Junifer Romindo Romindo Vicky Darmana