Article Per Year (5 Year)
p-Index From 2021 - 2026
4.615
P-Index
This Author published in this journals
All Journal Sinkron : Jurnal dan Penelitian Teknik Informatika Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) JURNAL MEDIA INFORMATIKA BUDIDARMA JITK (Jurnal Ilmu Pengetahuan dan Komputer) Journal Information System Development JURNAL TEKNOLOGI DAN ILMU KOMPUTER PRIMA (JUTIKOMP) METIK JURNAL Building of Informatics, Technology and Science REMIK : Riset dan E-Jurnal Manajemen Informatika Komputer Jurnal Pengabdian kepada Masyarakat Nusantara DEVICE Jurnal Sains Teknologi dan Sistem Informasi 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 International Journal of Multidisciplinary Approach Research and Science
Pangaribuan, Jefri Junifer
Universitas Pelita Harapan
Aerospace Engineering Agriculture, Biological Sciences & Forestry Arts Humanities Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Immunology & microbiology Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mathematics Mechanical Engineering Medicine & Pharmacology Nursing Public Health Social Sciences Transportation Veterinary Other

Published : 38 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 38 Documents
Search

 1   2   3   4 
Penerapan Metode Collaborative Filtering Dan Knowledge Item Based Terhadap Sistem Rekomendasi Kamera DSLR Romindo; Jefri Junifer Pangaribuan; Okky Putra Barus; Jusin
SATIN - Sains dan Teknologi Informasi Vol 8 No 2 (2022): SATIN - Sains dan Teknologi Informasi
Publisher : STMIK Amik Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (502.054 KB) | DOI: 10.33372/stn.v8i2.883

Abstract

Sistem rekomendasi adalah sistem yang dibuat dengan tujuan untuk membantu pengguna dalam mengetahui item yang diminati oleh mereka. Sistem rekomendasi banyak diimplementasikan di marketplace, sosial media dan untuk tujuan lainnya. Salah satu proses yang membutuhkan sistem rekomendasi adalah pada proses pemilihan kamera. Pemilihan kamera untuk fotografer yang belum berpengalaman menggunakan kamera menjadi salah satu permasalahan yang cukup penting dikarenakan banyaknya kamera yang bermunculan hingga saat ini. Proses pemilihan kamera biasanya dilakukan dengan bertanya kepada fotografer senior yang sudah terjun lama dalam bidang fotografi agar diberikan rekomendasi terkait kamera yang sesuai dengan kriteria. Proses konvensional tersebut tentunya akan memakan waktu yang sangat lama. Oleh karena permasalahan tersebut, maka perlu dilakukan penelitian untuk sebuah sistem informasi rekomendasi pada proses pemilihan kamera. Pada penelitian ini akan diterapkan 2 metode rekomendasi yaitu metode Collaborative Filtering dan Knowledge Item Based. Hasil penelitian menunjukkan bahwa sistem informasi rekomendasi kamera DSLR yang dibangun menerapkan metode Collaborative Filtering dan Knowledge Item Based dalam memberikan rekomendasi prediksi pilihan kamera berdasarkan pola rating dari user lainnya.
Classification of Hearing Loss Degrees with Naive Bayes Algorithm Okky Putra Barus; Romindo; Jefri Junifer Pangaribuan
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 7 No 4 (2023): August 2023
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v7i4.4683

Abstract

According to the World Health Organization (WHO), hearing loss is one of the fourth leading causes of disability. The number of people with hearing loss continues to increase yearly. This increase occurred due to delays in recognizing hearing loss, leading to delays in providing treatment. To solve this problem, one solution to deal with this is early identification to detect the degree of hearing loss. This research will use machine learning to classify the degree of hearing loss. The algorithm implemented in this study is naive Bayes. This study uses a data set from the Zenodo open access repository with 3105 raw data and 19 features. This study evaluates the performance of overall accuracy, precision, recall, and f1-score and classified four classes: mild, moderate, moderately severe, and severe. The methodology classification stages in this study include data preprocessing, data training, data testing, and evaluation. From evaluating the performance of the Naive Bayes algorithm, the classification results obtained the highest impacts in the form of 94% overall accuracy, 100% precision, 100% recall and 97% f1-score in classifying the degree of hearing loss.
IMPLEMENTASI ALGORITMA TF-IDF DAN SUPPORT VECTOR MACHINE TERHADAP ANALISIS PENDETEKSI KOMENTAR CYBERBULLYING DI MEDIA SOSIAL TIKTOK Romindo Romindo; Jefri Junifer Pangaribuan; Okky Putra Barus
Device Vol 13 No 1 (2023): Mei
Publisher : Fakultas Teknik dan Ilmu Komputer (FASTIKOM) UNSIQ

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32699/device.v13i1.5260

Abstract

Cyberbullying is the act of sending text, images, or videos using the internet, mobile phones, or other devices with the aim of hurting and shaming other people. Cyberbullying is often done through several social media platforms, one of which is through comments on the TikTok application. According to a report by We Are Social, TikTok has 1.4 billion monthly active users aged 18 and above globally. Indonesia currently ranks second in the world in terms of active TikTok users. As a result, the potential for cyberbullying instances will grow as the number of users grows. By using data mining, the public can create a detection system, which can perform analysis on comments in the TikTok application. The method used is Term Frequency-Inverse Document Frequency (TF-IDF) and Support Vector Machine (SVM). The stages passed are to collect comments that are labelled manually. Then, text preprocessing, tokenizing, and weighting were carried out with TF-IDF. Then, implement the Support Vector Machine algorithm to detect cyberbullying comments. This study uses 80% training data and 20% testing data. From the performance results of the Support Vector Machine algorithm, 88% overall accuracy, 88% precision, 96% recall, and 92% f1-score were obtained in detecting cyberbullying comments on social media TikTok.
Pengembangan Sistem Informasi Pemesanan Makanan Berbasis Web pada Rumah Makan Mie Hokkien Akheng Jaclyn Tjuarsa; Jusin Jusin; Ade Maulana; Jefri Junifer Pangaribuan
PaKMas: Jurnal Pengabdian Kepada Masyarakat Vol 3 No 1 (2023): Mei 2023
Publisher : Yayasan Pendidikan Penelitian Pengabdian Algero

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54259/pakmas.v3i1.1758

Abstract

This activity aims to provide broader knowledge, understanding and insight to young people, especially students and employees related to accounting. This activity aims to enable participants to understand the definition of accounting, the history of accounting, the purpose of accounting itself, and have a good understanding of the contents of financial statements so that this knowledge can become a provision in the future when these young people enter the world of work or the world of investment or even become an expert in accounting later. This activity is carried out online through the Google Meet application. The target of this activity is young people aged 17 to 20 years who are in Pontianak, West Kalimantan. The number of participants in this activity was 66 participants. This activity took place on Saturday, March 4 2023 and went very well due to good cooperation and coordination from the various parties involved. This activity is expected to be able to contribute and have a positive impact on increasing the knowledge and understanding of participants regarding accounting and can provide broader views and insights for participants
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.
Perancangan Aplikasi Pencarian Fasilitas Kesehatan ‘Find Medical’ dengan Menggunakan Metode Haversine dan Algoritma Dijkstra Elrico Tanto Jaya; Ade Maulana; Jefri Junifer Pangaribuan
SATESI: Jurnal Sains Teknologi dan Sistem Informasi Vol. 3 No. 2 (2023): Oktober 2023
Publisher : Yayasan Pendidikan Penelitian Pengabdian ALGERO

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54259/satesi.v3i2.2365

Abstract

Currently, technology is rapidly advancing, enabling communication to be done with anyone, anywhere. The sophistication of this technology can be utilized to address issues in various fields, particularly in the healthcare sector, as healthcare is one of the most influential aspects of human life. One common problem in the healthcare sector is the lack of evenly distributed information about healthcare services, leading to difficulties for individuals in finding suitable healthcare facilities. Therefore, this research aims to develop an application called "Find Medical" as a solution to facilitate the public in searching for healthcare services that meet their needs. In this study, the Haversine method and Dijkstra's algorithm are employed to recommend the nearest healthcare services based on the user's location. The research objectives are to design the "Find Medical" application and analyze the accuracy of the employed methods. The "Find Medical" application is developed using the Laravel framework. Based on the testing results, the majority of respondents agree that the application is effective in addressing the inefficient healthcare information retrieval system. Additionally, the Haversine formula and Dijkstra's algorithm implemented in the application provide accurate recommendations with a precision rate of 90%. Therefore, the "Find Medical" application can be an effective and efficient solution for individuals seeking appropriate healthcare services.
Blood Donation Classification with Decision Tree Method using C4.5 Algorithm Pangaribuan, Jefri Junifer; Putra, Alexander
International Journal of Multidisciplinary Approach Research and Science Том 2 № 03 (2024): International Journal of Multidisciplinary Approach Research and Science
Publisher : PT. Riset Press International

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59653/ijmars.v2i03.961

Abstract

Donating blood is an altruistic act driven by concern for others and personal commitment to health. It is crucial for patients needing transfusions due to excessive bleeding. However, there has been a decline in blood donations globally. To address this, the medical community needs a method to predict whether a donor will donate again, enabling proactive measures to ensure an adequate blood supply. This study utilizes data from the University of California, Irvine (UCI) Machine Learning Repository, specifically the Blood Transfusion Service Data Set, employing the Decision Tree method with the C4.5 algorithm. C4.5, an improvement over Iterative Dichotomiser 3 (ID3), can handle missing values, pruning, and continuous data. The aim is to classify blood donor eligibility accurately. The aim of this study is to explore how the utilization of the C4.5 algorithm in decision tree classification can predict whether an individual will donate blood again or not. The analysis identifies five key attributes—Recency, Frequency, Monetary, Time (Months), and Decision—as determinants of repeat donation likelihood. Using a confusion matrix to assess accuracy, the C4.5 algorithm achieved a 77.68% accuracy, with an error rate of 22.32%, a sensitivity of 30.19%, and a specificity of 92.40%.
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%.
UNLEASHING THE POWER OF SVM AND KNN: ENHANCED EARLY DETECTION OF HEART DISEASE Jefri Junifer Pangaribuan; Ade Maulana; Romindo Romindo
JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) Vol. 10 No. 2 (2024): JITK Issue November 2024
Publisher : LPPM Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/jitk.v10i2.5719

Abstract

Heart disease is a fatal illness responsible for approximately 36% of deaths in 2020. Therefore, it is important to pay attention to and better anticipate the risk of heart disease. One technological contribution that can be made is through information related to the risk of heart disease. Classification techniques in data mining can be used to diagnose and identify the risk of heart disease earlier by processing medical data and making predictions. This study compares the effectiveness of two classification algorithms, Support Vector Machine (SVM) and K-Nearest Neighbor (KNN), in predicting the risk of heart disease using a Kaggle dataset consisting of 303 records with 14 attribute columns. The data is divided into 70% for training and 30% for testing. The software used in this study is Orange Data Mining to build the SVM and KNN models. The results show that the SVM accuracy is 85.6%, while KNN achieves 81.1%. Based on the confusion matrix, the SVM algorithm has a lower error rate compared to KNN. In conclusion, the SVM algorithm is superior to KNN in predicting the risk of heart disease. These findings indicate that SVM has a better potential in identifying individuals at high risk of experiencing a heart attack. This research can contribute to the development of a more accurate medical decision support system for early detection of heart disease.
Co-Authors Ade Maulana Ade Maulana Ali Akbar Lubis Anggara, Alfin Angkasa, Vincent Aulia Rizky Muhammad Hendrik Noor Asegaff Barus, Okky Putra Calista Chandra Chandra, Wenripin Chilwin Chilwin Chilwin Elrico Tanto Jaya Erick Suliegna Erlanie Sufarnap Faisal Nadjar Ferawaty, Ferawaty Frans Mikael Sinaga Henry Tanjaya Huzaifah, Ade Sarah Ilhami, Mirza Iskandar Muda Jaclyn Tjuarsa Jepronel Saragih Jovita Zesty Jusin Jusin Jusin Kenichi Kenichi Kenichi, Kenichi Kevin Bastian Sirait Leonie, Valerie Lestari, Megawaty Megawaty Lestari Mirza Ilhami Mirza Ilhami Muhammad Thafa Kurniawan Okky Barus Putra, Alexander Reynaldo Fang Riche Riche Ricky Ricky Ricky Robin - Romindo Romindo Romindo Romindo Sabrina Jovanka Sabrina Jovanka Shendy Dennison Shendy Dennison Sherin Karisa Tanjaya, Henry Triandes Sinaga Vincent Angkasa Wiliem, Evelyn William William Yudhistira A. Pratama Yudhistira Adhitya Pratama Yudhistira Adhitya Pratama Yugopuspito, Pujianto