Garuda - Garba Rujukan Digital

Implementasi Metode Case-Based Reasoning (CBR) dalam Sistem Pakar untuk Mendapatkan Diagnosis Anxiety Disorders Gunung, Tar Muhammad Raja; Lubis, Siti Sahara; Siregar, Manutur; Simanjuntak, Peter Jaya Negara; Jinan, Abwabul
Jurnal Teknologi Terpadu Vol 10 No 2 (2024): Desember, 2024
Publisher : LPPM STT Terpadu Nurul Fikri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54914/jtt.v10i2.1480

This research aims to develop an expert system based on the case-based reasoning method for diagnosing anxiety disorders. Anxiety Disorder is a mental health disorder that is often experienced by the public but is often not detected correctly. The case-based reasoning method was chosen because of its ability to utilise previous cases to solve new problems that have similarities. Case-based reasoning uses four main stages: retrieval, reuse, revise, and retain. The case-based reasoning method is implemented using case data obtained from psychology clinics and interviews with mental health experts. Testing the case-based reasoning method shows a high level of accuracy in diagnosing various types of Anxiety Disorders, such as Generalised Anxiety Disorder, Panic Disorder, and Specific Phobias. The results of this study show that the case-based reasoning method can be an effective tool in helping mental health professionals diagnose Anxiety Disorders more quickly and accurately. After searching using the symptoms obtained, the percentage of each type of disease is the percentage of Generalised Anxiety Disorder 35.7%, the percentage of Panic Disorder 30.7%, and the percentage of Specific Phobias 65%.

Use of Machine Learning in Power Consumption Optimization of Computing Devices Rivalri Kristianto Hondro; Hendro Sutomo Ginting; Peter Jaya Negara Simanjuntak; Hanna Tresia Silalahi; Sarwandi
Indonesian Journal of Data and Science Vol. 6 No. 1 (2025): Indonesian Journal of Data and Science
Publisher : yocto brain

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56705/ijodas.v6i1.231

Introduction: The high-power consumption of computing devices poses both economic and environmental challenges in the digital era. This study aims to optimize power usage using machine learning to maintain device performance while reducing energy costs and carbon emissions. Methods: The Random Forest algorithm was selected for its robustness in handling non-linear interactions among features. A dataset containing historical power consumption, workload metrics, environmental conditions, and hardware configurations was collected from sensors and logs. Data pre-processing included cleaning, normalization, and feature selection. The model was trained and evaluated using accuracy, precision, recall, F1-score, MAE, and RMSE metrics. Hyperparameter tuning via grid search, random search, and Bayesian optimization was applied to enhance model performance. The model was deployed on real devices to test energy optimization under varied workloads. Results: The Random Forest model achieved 92% accuracy and an RMSE of 0.15. Tuning reduced RMSE by 10% and improved F1-score from 0.875 to 0.905. Implementation on computing devices led to average power savings of 15–20% across workload scenarios without notable performance degradation (<5%). The model also projected annual carbon emission reductions of up to 5 tons of CO₂ and operational savings of $50,000 when scaled to 1,000 servers. Conclusions: Machine learning, particularly Random Forest, proves effective in optimizing power consumption on computing devices. The proposed approach not only ensures computational efficiency but also promotes environmental sustainability. These findings support further exploration of ML-based solutions for green technology initiatives in IT infrastructure.

Peningkatan Literasi Digital Siswa SMK dalam Menghadapi Bahaya Judi Online Sinaga, Mikha; Simanjuntak, Peter; Tambunan, Mutiha; Hia, Charistian
Publikasi Pengabdian Masyarakat Vol 5 No 1 (2025): PUBLIDIMAS Vol. 5 No. 1 MEI 2025
Publisher : LPPM Universitas Potensi Utama

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

The increasing use of digital technology among vocational school students has had a positive impact in terms of access to information and skills development. However, this phenomenon has also opened up a gap for the emergence of online gambling threats, which are increasingly widespread among teenagers. Online gambling often targets young age groups through games and applications that seem harmless, which can lead to addiction and financial loss. On the other hand, the level of digital literacy among vocational school students is still relatively low. Although they are skilled in using digital devices, many do not understand the risks involved, such as the dangers of online gambling. This lack of understanding is exacerbated by limited education on digital ethics and wise internet use in schools, as well as a lack of awareness among parents and teachers regarding these potential risks. The importance of digital literacy is a solution to this problem, because with a good understanding, students can recognize and avoid the dangers of online gambling. However, educational programs on digital literacy and online gambling prevention are still limited, and many schools lack the resources to provide adequate training. As a solution, an empowerment program is needed for vocational school students through digital literacy training that aims to increase their awareness of the risks of online gambling, as well as teach them the responsible use of technology. This program also requires support from schools, parents, and the community to create a safe and healthy digital environment for students. The target output of this activity is scientific papers published in national journals with ISSN. The activities that will be carried out start from giving pretests to students to see students' knowledge of online gambling. After that, counseling is carried out on the negative impacts of online gambling. Then a posttest is carried out to see students' understanding of the material that has been presented.

Analisis Kesehatan Tanaman Sawi (Brassica juncea L) Menggunakan Algoritma Random Forest Simanjuntak, Peter; Mikha Dayan Sinaga; Akbar Idaman; Muhammad Imam Zarkasyi
Jurnal SAINTIKOM (Jurnal Sains Manajemen Informatika dan Komputer) Vol 24 No 2 (2025): Agustus 2025
Publisher : PRPM STMIK TRIGUNA DHARMA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53513/jis.v24i2.12131

Penelitian ini membahas analisis kesehatan tanaman sawi (Brassica juncea L) menggunakan algoritma Random Forest. Data yang digunakan meliputi suhu, kelembapan tanah, dan intensitas cahaya, yang dikumpulkan secara periodik. Label status tanaman ditentukan berdasarkan ambang batas tertentu: “Sehat” jika kelembapan ≥ 60%, suhu antara 28–34°C, dan intensitas cahaya ≥ 700 lux; “Stres” jika kelembapan < 45% atau cahaya < 700 lux; serta “Perlu Disiram” untuk kondisi lainnya. Model Random Forest digunakan untuk mempelajari hubungan antara parameter lingkungan dan status tanaman. Hasil evaluasi menunjukkan tingkat akurasi yang tinggi, mengindikasikan bahwa algoritma ini efektif dalam mengklasifikasikan kondisi tanaman. Pendekatan ini dapat membantu petani dalam pengambilan keputusan berbasis data, sehingga meningkatkan efisiensi perawatan tanaman sawi.

ANALISIS PERBANDINGAN RANDOM FOREST DAN KNN PADA KLASIFIKASI PENERIMA MANFAAT PROGRAM MAKAN BERGIZI GRATIS Zarkasyi, Muhammad Imam; Simanjuntak, Peter Jaya Negara; Nababan, Junerdi
JOURNAL OF SCIENCE AND SOCIAL RESEARCH Vol 9, No 1 (2026): February 2026
Publisher : Smart Education

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54314/jssr.v9i1.5753

Abstract: The Free Nutritional Meal Program (PMBG) is a government initiative to improve students' nutrition and school attendance. This study evaluates and compares the performance of Random Forest (RF) and K-Nearest Neighbors (KNN) algorithms in classifying students most eligible for PMBG based on socio-economic criteria. The dataset comprises 205 public elementary school students in Medan City, collected via questionnaires. Features include parental income, number of dependents, housing status, asset ownership, and participation in other social aid programs. The data was clustered into three priority groups using K-Means. To address class imbalance, the Synthetic Minority Over-sampling Technique (SMOTE) was applied. Modeling used three data split scenarios (70:30, 80:20, 90:10) and was evaluated with accuracy, precision, recall, F1-score, and cross-validation. Results show that RF consistently outperformed KNN across all scenarios. After SMOTE, both models improved, with Balanced-RF achieving the highest accuracy and F1-score (94%) in the 70:30 split. The combination of RF and SMOTE proves effective for building an objective and accurate priority classification system for PMBG beneficiaries. Keyword: Free Nutritious Meal Program; Random Forest; K-Nearest Neighbors; SMOTE. Abstrak: Program Makan Bergizi Gratis (PMBG) merupakan inisiatif pemerintah yang bertujuan meningkatkan asupan gizi dan mendorong kehadiran siswa di sekolah. Penelitian ini bertujuan untuk mengevaluasi dan membandingkan kinerja dua algoritma machine learning, yaitu Random Forest dan K-Nearest Neighbors, dalam mengklasifikasikan siswa yang paling berhak menerima manfaat PMBG berdasarkan kriteria sosial-ekonomi. Dataset yang digunakan terdiri dari 205 siswa Sekolah Dasar Negeri di Kota Medan yang dikumpulkan melalui kuesioner. Fitur yang digunakan meliputi pendapatan orang tua, jumlah tanggungan, status tempat tinggal, kepemilikan aset, dan partisipasi dalam program bantuan sosial lainnya. Dataset yang telah dikumpulkan kemudian dikelompokkan menggunakan algoritma K-Means menjadi tiga klaster prioritas. Untuk mengatasi ketidakseimbangan distribusi data, digunakan metode Synthetic Minority Over-sampling Technique (SMOTE). Pemodelan dilakukan dalam tiga skenario pembagian data (70:30, 80:20, 90:10) dan dievaluasi menggunakan metrik akurasi, presisi, recall, f1-score, dan cross-validation. Hasil penelitian menunjukkan bahwa algoritma Random Forest secara konsisten memberikan kinerja yang lebih unggul dibandingkan KNN pada semua skenario. Setelah penerapan SMOTE, kedua algoritma mengalami peningkatan performa, dengan Random Forest-Balanced mencatat akurasi dan f1-score tertinggi sebesar 94% pada skenario 70:30. Temuan ini menunjukkan bahwa kombinasi Random Forest dan SMOTE merupakan pendekatan yang efektif dan efisien untuk membangun sistem klasifikasi prioritas penerima manfaat PMBG yang objektif dan akurat. Kata kunci: Program Makan Bergizi Gratis; Random Forest; K-Nearest Neighbors; SMOTE

Analisis Peningkatan Literasi Digital Siswa melalui Edukasi Interaktif Berbasis Tools Digital dan Pre-Post Test Khairani Puspita; Nita Sari Br Sembiring; Peter Jaya Negara Simanjuntak; Nia Mardiah; Armayse Rolando Piherta
Yumary: Jurnal Pengabdian kepada Masyarakat Vol 6 No 4 (2026): Juni
Publisher : Penerbit Goodwood

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35912/yumary.v6i4.6409

Purpose: This study aims to improve digital literacy understanding and raise awareness among Grade XII students of Sekolah Menengah Kejuruan Swasta (SMKS) Budi Agung Medan in using technology productively to support digital economic activities. Methodology: The study used a pre-test and post-test design involving 25 students through digital literacy outreach and education. The materials included digital literacy concepts, the use of tools (Canva, CapCut), and digital economic activities such as content creation and affiliate programs. Results: The average score increased from 94.0 to 98.2, with the minimum score improving from 80 to 90. The number of students achieving perfect scores rose from 18 to 21, indicating improved understanding and awareness. Conclusions: Digital literacy outreach effectively improves students’ knowledge, awareness, and responsible use of technology in the digital economy. The program also encourages students to use digital tools more critically, ethically, and productively. Limitations: This study involved only 25 students from one school, limiting the generalizability of the findings. It also focused on short-term outcomes without measuring long-term behavioral changes. Contributions: This study shows that digital literacy programs can improve students’ digital competencies and provides a useful reference for schools, educators, and policymakers in developing technology-based educational initiatives.

Design of an IoT-Based Heart Rate and Room Temperature Monitoring System Using ESP32 Peter Simanjuntak; Muhammad Imam Zarkasyi
Journal of Intelligent Computing and Advanced Data Science Vol. 1 No. 2 (2026): July 2026
Publisher : Universitas Satya Terra Bhinneka

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

Introduction/Main Objectives: Continuous monitoring of heart rate and environmental conditions is essential for supporting remote healthcare services and real-time observation. This study aims to design and implement an Internet of Things (IoT)-based monitoring system using ESP32 for simultaneous monitoring of heart rate, room temperature, and humidity. Background Problems: Conventional monitoring systems generally require separate devices for physiological and environmental measurements and provide limited remote monitoring capabilities. Therefore, there is a need for an integrated, low-cost, and real-time IoT-based monitoring system. Novelty: The proposed system integrates a pulse heart rate sensor and a DHT11 temperature-humidity sensor into a single ESP32-based IoT platform. The system incorporates Wi-Fi communication, cloud-based visualization using the Blynk platform, and a finger-detection mechanism to minimize false heart rate readings caused by sensor noise. Research Methods: The system was developed using an ESP32 microcontroller, a pulse heart rate sensor, and a DHT11 sensor. Sensor data were acquired, processed, and transmitted through a Wi-Fi network to the Blynk cloud platform for real-time visualization. Experimental evaluation was conducted in a laboratory environment under three operating conditions: Danger, Normal, and Not Detected. Finding/Results: The experimental results demonstrate that the proposed system successfully monitored heart rate, room temperature, and humidity in real time. The system accurately classified monitoring conditions into Danger, Normal, and Not Detected states. Furthermore, the implemented finger-detection mechanism effectively prevented false heart rate measurements when no finger was placed on the sensor, while maintaining stable wireless communication and continuous cloud-based monitoring. Conclusion: The proposed ESP32-based IoT monitoring system provides a practical, low-cost, and reliable solution for integrated physiological and environmental monitoring. The successful implementation demonstrates the feasibility of using ESP32 as an IoT gateway for real-time health monitoring applications, particularly for educational purposes, laboratory experiments, remote monitoring, and prototype smart healthcare systems.

ANALISIS PERBANDINGAN RANDOM FOREST DAN KNN PADA KLASIFIKASI PENERIMA MANFAAT PROGRAM MAKAN BERGIZI GRATIS Muhammad Imam Zarkasyi; Peter Jaya Negara Simanjuntak; Junerdi Nababan
JOURNAL OF SCIENCE AND SOCIAL RESEARCH Vol. 9 No. 1 (2026): February 2026
Publisher : Smart Education

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54314/jssr.v9i1.5753

Abstract: The Free Nutritional Meal Program (PMBG) is a government initiative to improve students' nutrition and school attendance. This study evaluates and compares the performance of Random Forest (RF) and K-Nearest Neighbors (KNN) algorithms in classifying students most eligible for PMBG based on socio-economic criteria. The dataset comprises 205 public elementary school students in Medan City, collected via questionnaires. Features include parental income, number of dependents, housing status, asset ownership, and participation in other social aid programs. The data was clustered into three priority groups using K-Means. To address class imbalance, the Synthetic Minority Over-sampling Technique (SMOTE) was applied. Modeling used three data split scenarios (70:30, 80:20, 90:10) and was evaluated with accuracy, precision, recall, F1-score, and cross-validation. Results show that RF consistently outperformed KNN across all scenarios. After SMOTE, both models improved, with Balanced-RF achieving the highest accuracy and F1-score (94%) in the 70:30 split. The combination of RF and SMOTE proves effective for building an objective and accurate priority classification system for PMBG beneficiaries. Keyword: Free Nutritious Meal Program; Random Forest; K-Nearest Neighbors; SMOTE. Abstrak: Program Makan Bergizi Gratis (PMBG) merupakan inisiatif pemerintah yang bertujuan meningkatkan asupan gizi dan mendorong kehadiran siswa di sekolah. Penelitian ini bertujuan untuk mengevaluasi dan membandingkan kinerja dua algoritma machine learning, yaitu Random Forest dan K-Nearest Neighbors, dalam mengklasifikasikan siswa yang paling berhak menerima manfaat PMBG berdasarkan kriteria sosial-ekonomi. Dataset yang digunakan terdiri dari 205 siswa Sekolah Dasar Negeri di Kota Medan yang dikumpulkan melalui kuesioner. Fitur yang digunakan meliputi pendapatan orang tua, jumlah tanggungan, status tempat tinggal, kepemilikan aset, dan partisipasi dalam program bantuan sosial lainnya. Dataset yang telah dikumpulkan kemudian dikelompokkan menggunakan algoritma K-Means menjadi tiga klaster prioritas. Untuk mengatasi ketidakseimbangan distribusi data, digunakan metode Synthetic Minority Over-sampling Technique (SMOTE). Pemodelan dilakukan dalam tiga skenario pembagian data (70:30, 80:20, 90:10) dan dievaluasi menggunakan metrik akurasi, presisi, recall, f1-score, dan cross-validation. Hasil penelitian menunjukkan bahwa algoritma Random Forest secara konsisten memberikan kinerja yang lebih unggul dibandingkan KNN pada semua skenario. Setelah penerapan SMOTE, kedua algoritma mengalami peningkatan performa, dengan Random Forest-Balanced mencatat akurasi dan f1-score tertinggi sebesar 94% pada skenario 70:30. Temuan ini menunjukkan bahwa kombinasi Random Forest dan SMOTE merupakan pendekatan yang efektif dan efisien untuk membangun sistem klasifikasi prioritas penerima manfaat PMBG yang objektif dan akurat. Kata kunci: Program Makan Bergizi Gratis; Random Forest; K-Nearest Neighbors; SMOTE

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