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All Journal Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) JURNAL MEDIA INFORMATIKA BUDIDARMA Journal Information System Development Building of Informatics, Technology and Science Jurnal Mantik Jurnal Pengabdian kepada Masyarakat Nusantara DEVICE IICS Jurnal Pengabdian Kepada Masyarakat Prosiding Konferensi Nasional PKM-CSR Jurnal Sains dan Teknologi Jurnal Pengabdian Masyarakat Bidang Sains dan Teknologi SATIN - Sains dan Teknologi Informasi Engagement: Jurnal Pengabdian Kepada Masyarakat
Barus, Okky Putra
Universitas Pelita Harapan
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Peningkatan Produksi Dan Pemasaran Melalui Smart Greenhouse Dan Content Marketing Strategy Untuk Urban Farmer Hidroponik Ade Maulana; Okky Putra Barus; Haryati; Aditya Kristanto; Derick Chainatra; Winar Joko Alexander
Jurnal Pengabdian kepada Masyarakat Nusantara Vol. 4 No. 4 (2023): Jurnal Pengabdian kepada Masyarakat Nusantara (JPkMN)
Publisher : Lembaga Dongan Dosen

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

Abstract

Syifa Hidroponik, sebuah Usaha Mikro, Kecil, dan Menengah (UMKM) yang bergerak dalam budidaya hidroponik, didirikan oleh Ir. Suardi Raden pada tahun 2014, menghadapi tantangan dalam manajemen produksi dan pemasaran. Pendekatan Pengabdian kepada Masyarakat telah diambil untuk mengatasi masalah-masalah ini. Di bidang produksi, solusi telah diterapkan untuk meningkatkan pengelolaan kualitas air, pemberian nutrisi yang tepat, perlindungan tanaman dari kontaminasi dan hama, serta pengendalian suhu dan kelembapan. Hasilnya, kualitas air telah ditingkatkan, pemahaman tentang nutrisi tanaman meningkat, perlindungan tanaman diperkuat, dan suhu serta kelembapan terkontrol secara efisien. Di bidang pemasaran, strategi pemasaran konten yang lebih tepat telah diterapkan dengan mengidentifikasi target pasar yang spesifik, memberikan pelatihan, dan memanfaatkan berbagai platform media sosial. Hasilnya adalah peningkatan lalu lintas media sosial, pengunjung situs web, dan penggunaan iklan digital. Implementasi ini telah memberikan manfaat signifikan bagi Syifa Hidroponik dengan meningkatkan penjualan dan daya saing di pasar. Secara keseluruhan, pendekatan Pengabdian kepada Masyarakat telah membantu Syifa Hidroponik mengatasi masalah produksi dan meningkatkan strategi pemasaran, dengan dampak positif pada kualitas produksi dan pertumbuhan bisnis.
Implementation of the Naive Bayes Algorithm to Predict the Safety of Heart Failure Patients Okky Putra Barus; Kevil Lauwren; Jefri Junifer Pangaribuan; Romindo
IAIC International Conference Series Vol. 4 No. 1 (2023): SEMNASTIK 2023
Publisher : IAIC Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34306/conferenceseries.v4i1.651

Abstract

Heart disease stands as a prominent contributor to global mortality, as indicated by data released by the World Health Organization (WHO). In 2019 alone, an estimated 17.9 million individuals succumbed to cardiovascular disease, accounting for 32% of all worldwide deaths. Of these fatalities, 85% were attributed to heart disease and stroke. Individuals harboring the potential for heart failure often persist in unhealthy lifestyles, regardless of their awareness of underlying heart conditions. To address this issue, the research explores the application of machine learning to identify an optimal method for classifying heart failure patients, employing the Naive Bayes technique. This algorithm has found extensive use in the health sector, demonstrating success in classifying various conditions such as hepatitis, stroke, respiratory infections, and more. The Naive Bayes algorithm, applied in this study, exhibited notable accuracy, precision, sensitivity, and overall classification efficacy. Specifically, the classification accuracy for heart failure patients reached 74.58%, the precision level was 97.67%, sensitivity achieved 75%, and the AUC (Area Under ROC Curve) stood at 0.857, indicating excellent classification within the 0.80 to 0.90 range. These findings can serve as an early warning system for individuals at risk of heart failure.
OPTIMALISASI ALGORITMA C4.5 TERHADAP METODE DECISION TREE DALAM MENENTUKAN PLAFON KREDIT NASABAH Romindo Romindo; Okky Putra Barus; Jefri Junifer Pangaribuan
Device Vol 14 No 1 (2024): Mei
Publisher : Fakultas Teknik dan Ilmu Komputer (FASTIKOM) UNSIQ

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32699/device.v14i1.6877

Abstract

The most basic banking activity is collecting money and buying money from the whole society. Then sell the collected money by directing it to the community through credit or credit. However, it is often found that customers are unable to pay their receivables based on the amount of receivables which often exceeds the specified payment period. Therefore, banking companies must know the ability to pay customers by providing credit limits to avoid losses. The purpose of this study was to analyze the data using the Decision Tree method with the C4.5 Algorithm on the report data of BPR Pijer Podi Kekelengen receivables in order to determine the customer's credit ceiling. From the data obtained from the accounts receivable report, the company produces 5 attributes, namely Payments, Receivables, Transactions, Recommendations, and Ceiling where the decision label is Ceiling. After testing the report data at BPR Pijer Podi Kekelengen using the Decision Tree method with the C4.5 Algorithm, it is concluded that if the ceiling is large, the payment is not good.
Implementasi Algoritma Support Vector Machine Terhadap Klasifikasi Pose Balet Romindo, Romindo; Barus, Okky Putra; Pangaribuan, Jefri Junifer; Pratama, Yudhistira Adhitya; Wiliem, Evelyn
Building of Informatics, Technology and Science (BITS) Vol 4 No 3 (2022): December 2022
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v4i3.2647

Abstract

Ballet is considered as one of the most difficult dance due to its technical posture demanded. If performed without guidance it may cause bad posture to ballerina and some serious injuries. A model in identifying different ballet poses is developed with artificial intelligence in order to tear down this barrier. The main purpose of this paper is to demonstrate a methodology that simplified Ballet Pose Recognition using an opensource framework called MediaPipe and a machine learning algorithm called Support Vector Machine. How the model work is it will pass through two stages: first, it extracts data points from an image dataset using the MediaPipe Pose Estimation library, and then it preprocesses the data, trains, validates, and tests it using the Support Vector Machine algorithm to do some pose classification. The model is trained in seven distinct ballet poses, including First Position, Second Position, Third Position, Fourth Position, Fifth Position, Tendu Devant, and Tendu Derrière. This is purposely done in order to assess the competence of the classification model. An accuracy score of 87% is achieved from the ballet pose classification model and is developed to work on images and live videos.
Implementasi Metode Naive Bayes Classifier Terhadap Klasifikasi Topik Kemacetan Lalu Lintas Indonesia Melalui Tweet Romindo, Romindo; Barus, Okky Putra; Pangaribuan, Jefri Junifer
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 8, No 2 (2024): April 2024
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v8i2.7470

Abstract

The causes of traffic congestion in Indonesia include traffic accidents, poor road infrastructure, and the increasing number of motor vehicles. In 2023, the number of vehicles reached 152.6 million, exceeding half of Indonesia's population of 276 million, according to the Indonesian Traffic Police Corps data. Twitter has a user base of approximately 4.23% of the total global population, which amounts to 436 million user and Indonesia is one of the countries with the largest number of Twitter users. Twitter data will be used to determine the sentiment level of traffic congestion in Indonesia using the Naïve Bayes Classifier method to evaluate overall accuracy performance, precision, recall, and f1-score. The research classified two groups, negative and positive. Classification is carried out through several stages, including data pre-processing, data training, data testing, and evaluation. After evaluating the Naive Bayes algorithm, the highest results achieved an overall accuracy of 77%, precision of 86%, recall of 82%, and f1-score of 84%.
Penerapan Smart Indoor Farming dan Clean Energy Technology untuk Peningkatan Kualitas Produksi Hidroponik Stephanie, Stephanie; Jovin Kendrico; Vanesia Roselin; Winar Joko Alexander; Ziven Louis; Okky Putra Barus
PaKMas: Jurnal Pengabdian Kepada Masyarakat Vol 4 No 2 (2024): November 2024
Publisher : Yayasan Pendidikan Penelitian Pengabdian Algero

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54259/pakmas.v4i2.2953

Abstract

The Global Food Security Index publication notes that Indonesia's food security condition is below the global average. One of the main factors contributing to this issue is climate change and extreme weather. To address this problem, communities can create self-sufficient food sources through home hydroponic farming. Syifa Hidroponik Satu, an MSME in North Sumatra, has been a pioneer in hydroponic plant education and cultivation. However, Syifa Hidroponik Satu faces several production issues, including uncontrolled pests, unpredictable sunlight intensity, high electricity consumption, and inefficient farm monitoring. To overcome these problems, the "Future Farmers" team from Universitas Pelita Harapan implemented smart indoor farming technology and clean energy technology. This technology allows the regulation of temperature, nutrients, pH, and light through an IoT system, and the use of solar panels for energy efficiency. This implementation has successfully improved the quality and productivity of hydroponic farming at Syifa Hidroponik Satu. With these follow-up actions, it is hoped that the quality and productivity of hydroponic farming will continue to increase, making a significant contribution to local food security and supporting greener and more sustainable agriculture.
Sosialisasi Peran Virtual Reality terhadap Pembelajaran dan Edukasi Kevin Bastian Sirait; Jefri Junifer Pangaribuan; Okky Putra Barus; Triandes Sinaga; Romindo, Romindo
ABDIKAN: Jurnal Pengabdian Masyarakat Bidang Sains dan Teknologi Vol. 4 No. 2 (2025): Mei 2025
Publisher : Yayasan Literasi Sains Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55123/abdikan.v4i2.5022

Abstract

Education orients on the process of transferring and acquiring knowledge and skills from learning activities. With the use of Virtual Reality (VR) in education, it can help students to experiment with the learned concepts and assess their implications within the virtual environment. The idea and implementation of VR in education are crucial since they enhance the student’s learning experience and process to understand various concepts and implement them to solve problems. Therefore, this socialization aims to provide deeper insights to the students of SMA Chandra Kumala Medan on how VR can help them improve their learning experience and performance. At this event, the socialization is conducted by following three sessions: (1) material presentation, (2) questions and answers session, and (3) VR demonstration where the students can take part. The results show that the students are highly engaged in all three sessions. It is found in the questions asked by the students, from how to create a virtual environment to the roles and impact of VR in real life (e.g., business). These findings indicate that the students are interested in how VR can improve their learning experience by understanding and testing new ideas or concepts within the virtual environment.
Analisis Kualitas Wine Menggunakan Machine Learning dengan Pendekatan SMOTE dan Seleksi Fitur Triandes Sinaga; Kevin Bastian Sirait; Pangaribuan, Jefri Junifer; Barus, Okky Putra; 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.
OPTIMALISASI ALGORITMA C4.5 TERHADAP METODE DECISION TREE DALAM MENENTUKAN PLAFON KREDIT NASABAH Romindo, Romindo; Barus, Okky Putra; Pangaribuan, Jefri Junifer
Device Vol 14 No 1 (2024): Mei
Publisher : Fakultas Teknik dan Ilmu Komputer (FASTIKOM) UNSIQ

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32699/device.v14i1.6877

Abstract

The most basic banking activity is collecting money and buying money from the whole society. Then sell the collected money by directing it to the community through credit or credit. However, it is often found that customers are unable to pay their receivables based on the amount of receivables which often exceeds the specified payment period. Therefore, banking companies must know the ability to pay customers by providing credit limits to avoid losses. The purpose of this study was to analyze the data using the Decision Tree method with the C4.5 Algorithm on the report data of BPR Pijer Podi Kekelengen receivables in order to determine the customer's credit ceiling. From the data obtained from the accounts receivable report, the company produces 5 attributes, namely Payments, Receivables, Transactions, Recommendations, and Ceiling where the decision label is Ceiling. After testing the report data at BPR Pijer Podi Kekelengen using the Decision Tree method with the C4.5 Algorithm, it is concluded that if the ceiling is large, the payment is not good.
Penyuluhan Mengenai Artificial Intelligence Untuk Siswa-Siswi SMP dan SMA Sekolah Lentera Harapan Medan Barus, Okky Putra; Pangaribuan, Jefri Junifer; Romindo, Romindo; Anggara, Alfin; William, William
ABDIKAN: Jurnal Pengabdian Masyarakat Bidang Sains dan Teknologi Vol. 2 No. 4 (2023): November 2023
Publisher : Yayasan Literasi Sains Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55123/abdikan.v2i4.2281

Abstract

Artificial Intelligence (Artificial Intelligence or AI) is a field that is growing rapidly and has great potential in influencing various aspects of human life. AI education is important for the younger generation in facing future challenges. This study aims to teach the basics of AI to junior high school (SMP) students at Sekolah Lentera Harapan (SLH). Learning sessions are carried out using interactive methods and actively involve students in discussions. The material includes an introduction to AI, the history and purpose of AI, how it works, the types of AI, and the advantages and disadvantages of AI. AI needs data to come up with appropriate answers, and with time, it will learn and improve. There are three types of AI, namely Artificial Narrow Intelligence (limited AI), Artificial General Intelligence (general AI), and Artificial Superintelligence (super AI). The advantages of AI include fast data processing, job efficiency and handling of dangerous tasks. However, there are also drawbacks such as reliance on big data, limitations to the specific capabilities of AI, and security concerns. This research provides insight into AI and promotes optimal use of AI in society.
Co-Authors Ade Maulana Ade Maulana Aditya Kristanto Anggara, Alfin Anton Tehja Calista Chandra Chilwin Chilwin Chilwin Derick Chainatra Elvina Winarto Faisal Nadjar Gultom, Diana Astria Haryati Iskandar Muda Jefri Junifer Pangaribuan Jefri Junifer Pangaribuan Jesselyn Verina William Jovin Kendrico Jusin Kevil Lauwren Kevin Bastian Sirait Pangaribuan, Jefri Junifer Ricky Ricky Ricky Robin Robin Romindo Romindo Romindo Romindo Romindo Sabrina Jovanka Sabrina Jovanka Shendy Dennison Shendy Dennison Sherin Karisa Stephanie Stephanie Tehja, Anton Triandes Sinaga Triandes Sinaga Vanesia Roselin Wiliem, Evelyn William William Winar Joko Alexander Yudhistira A. Pratama Yudhistira Adhitya Pratama Yudhistira Adhitya Pratama Yugopuspito, Pujianto Ziven Louis